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The demands of on-orbit transporting large structures via multiple space robots are emerging in space applications to increase the limited transportation capability of single space robots. However, the high level of system coupling arising from the increased number of individuals and physical interaction between structures and robots brings great challenges to the consequent control. This paper focuses on the motion planning of collaboratively transporting a large space structure using rigidly connected space robots. First, the system model is established at a kinematic level with non-conserved momenta due to the effects of thrusters on the bases of robots. Then, a cooperative control model is formulated to minimize the fuel consumption by balancing the thrusts and torques respectively acting on the bases and redundant arm joints of space robots. The physical limitations of space robots and mission related constraints such as joint angle limits, control input saturation and collision avoidance for multiple space obstacles are also taken into account in the optimization. Afterwards, the effectiveness of the proposed control scheme is validated and demonstrated by numerical case studies. [ABSTRACT FROM AUTHOR]

Ports are severely challenged by the uncertainty of the transportation environment. To evaluate the stability of maritime transportation network in Northeast Asia under port disruption, a network stability analysis model based on node failure rate that considers static and structural, dynamic and functional is proposed in this paper. In this model, the stability of the maritime transportation network is redefined. Based on complex network theory, the statistical topological parameters and evolution of the network are quantitatively analyzed. The active response and passive adaptation process of the maritime transportation network to the failure rate of the port are analyzed. The evaluation results illustrate that the initial stability of the port is proportional to the non-redundant connections and independence of the port. Network location advantages profoundly affect the probability of port failures. The diffusion of port failures in maritime transportation is phased, and the network is adaptive to port disruptions and has high stability. These results contribute to better planning of maritime transportation routes and strategies for port calls by strategic planners of shipping companies. • Node pressure is introduced to quantify the probability of node failure. • A network stability model that combines static and structural, dynamic and functional. • The number of non-redundant links in the port determines its initial stability. • The port's network location advantage is correlated with the port's failure probability. • The impact of failures on marine transportation is phased [ABSTRACT FROM AUTHOR]

Mobility as a Service (MaaS) has become an emerging trend in transportation planning and management due to its potential to overcome the challenges of urban mobility toward the achievement of sustainable mobility goals. This requires people to be willing to use MaaS apps. However, given their relative novelty, there is still a lack of research on the factors that influence the use of these apps, as well as the possible role of gender in such use. In this context, this study aims to explore the complex relationships between a set of relevant factors and their influence on the intention to adopt MaaS apps, with special attention to the role of gender as a moderating variable. For this purpose, a survey was conducted in the Madrid Metropolitan Area (Spain), comprising 8358 respondents (3627 men and 4731 women), and a four-step methodological procedure was adopted: (i) Exploratory Factor Analysis (EFA), (ii) Confirmatory Factor Analysis (CFA), (iii) Structural Equation Model (SEM) on the total sample (iv) Gender-sensitive analysis, including a Multi-group analysis – SEM. The results demonstrate that technophilia, perceived usefulness and perceived ease of use are determining factors. Green values and attitudes toward shared modes have a notable total positive effect. However, contrary to expected, attitudes toward public transport, car and bicycle hardly explain the intention to adopt MaaS apps. Our analysis also reveals significant gender differences in the causal relationships hypothesized in the model. These findings allow us to outline the policy initiatives that could favor the use of MaaS apps, with a gender-sensitive approach. • Development and validation of a structural model on the intention to use MaaS apps. • Technophilia, perceived usefulness and perceived ease of use are crucial factors. • Green values and attitudes toward shared modes have a notable total positive effect. • Attitudes toward public transport, car and bicycle hardly explain MaaS apps usage. • Gender plays a moderating role in the structural model proposed. [ABSTRACT FROM AUTHOR]

Traditional transportation planning emphasized the direct economic benefits of transportation infrastructure. While there is a growing awareness of the environmental consequences, social impacts are frequently overlooked due to methodological complexities and political neglect. Although existing literature acknowledges the existence of transportation-related social exclusion, it primarily concentrates on quantifiable and immediate short-term social outcomes arising from improved accessibility and affordability, which facilitate place-to-place interactions and activities. There has been limited exploration of the more intricate and indirect societal impacts associated with transportation. This paper argues that new transportation infrastructure not only affects mobility and travel experiences but also has deeper social impacts on the sense of place. These effects may extend to broader benefits, including academic performance, by influencing place identity, attachment, and dependency, which in turn affect daily activities unrelated to travel. Our study focuses on a university student community (N = 323), a pivotal future workforce, and investigates how the introduction of a new metro line passing through the campus reduces commuting time, subsequently enhancing academic productivity. Employing structural equation modeling method, our analysis reveals that sense of place acts as a mediator between travel experience and student engagement under the influence of transportation mode choice. These findings may have broad applicability in identifying indirect social impacts of transportation infrastructure, thus providing insights for investment decisions. • Place-centered approach for assessing social impact of transport infrastructure. • Focus on a student community as non-working population but future workforce. • Investigate deeper/wider impact to/from sense of place on academic productivity. • Case study of a recent metro extension project, Shatin to Central Link, in Hong Kong. • Switching from bus to train enhances SoP and academic performance. [ABSTRACT FROM AUTHOR]

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality. • A traffic sensor location and path flow estimation joint optimization model is formulated. • A data-driven path flow measurement method based on Wasserstein metric is proposed. • A customized greedy algorithm by combining a search strategy is developed. • Numerical experiments are conducted based on the real-word transportation network. [ABSTRACT FROM AUTHOR]

Local authorities have a strategic role in mitigating the environmental impacts of the transport sector. However, they struggle to integrate environmental issues into their decision-making processes, especially planning. In the European context of the Sustainable Urban Mobility Plan approach and Strategic Environmental Assessments (SEAs), this paper scrutinises three French localities to determine the current best practices and limitations for designing mobility plans and integrating environmental issues. Several limitations are identified: (1) limited expertise in defining and characterising actions and objectives, which complexifies plans' design, understanding, and monitoring; (2) a lack of a framework to conduct long-term quantitative environmental assessments and to use the results to influence decision effectively; and (3) monitoring processes are barely described in the documents, and the planning horizon where objectives are defined is not in sync with the indicators' mandatory evaluation period. This French case study thus reveals that European planning practices must be further analysed and improved to deal with the rising environmental concerns, e.g. through an operational framework to design mobility plans with effective integration of environmental issues. • Planning supports local authorities in moving towards a more sustainable mobility. • There is no robust approach to defining and characterising actions and objectives. • Robust long-term quantitative assessments are lacking to support planning. • Monitoring modalities are scarcely described in plans and SEAs. • Improving planning frameworks would help the integration of environmental issues. [ABSTRACT FROM AUTHOR]

Motivated by a transit line planning problem in transportation systems, we investigate the following capacitated assignment problem under a budget constraint. Our model involves L bins and P items. Each bin l has a utilization cost c l and an n l -dimensional capacity vector. Each item p has an n l -dimensional binary weight vector r l p , where the 1s in r l p (if any) appear in consecutive positions, and its assignment to bin l yields a reward v l p. The objective is to maximize total rewards through an assignment that satisfies three constraints: (i) the total weights of assigned items do not violate any bin's capacity; (ii) each item is assigned to at most one open bin; and (iii) the overall utilization costs remain within a total budget B. We propose the first randomized rounding algorithm with a constant approximation ratio for this problem. We then apply our framework to the motivating transit line planning problem, presenting corresponding models and conducting numerical experiments using real-world data. Our results demonstrate significant improvements over previous approaches in addressing this critical transportation challenge. [ABSTRACT FROM AUTHOR]

Decarbonizing the transportation sector is one of the key challenges to reduce greenhouse gas (GHG) emissions and meet EU Green deal 2030 targets. Road transport decarbonization requires a holistic approach encompassing various strategies and interventions aimed at fostering the sustainable transition of this sector. This challenge is crucial given that road is responsible for over 90% of the overall transport GHG emissions. Within this framework, this study aims, for the Italian case, to: a) estimate the national passenger and freight road travel demand with respect to three different time periods: 2005 (reference year for EU " fit for 55 " target); 2019 (last consolidated pre-covid year); 2022 (base-line year); b) define possible road transport scenarios to 2030 following the Avoid-Shift-Improve (ASI) approach; c) estimate the GHG and energy consumption inventory related to national road transport for the 2005, 2019; 2022 and 2030 scenarios. Due to the current "deep uncertainty" regarding key economic and social variables in this period, two forecasting scenarios – named "moderate decarbo" and "high decarbo" – were designed and based on different and somewhat opposite hypotheses on exogenous variables (e. g., GDP) and penetration rates of measures and policies promoting sustainable transportation (e. g., subsidies for electric vehicles) already in place. Although the current European legislation refers only to Tank-to-Wheel (TtW) objectives, this study included also the Well-to-Wheel (WtW) evaluation, which is more accurate in assessing the GHG impact of different policies, energy carriers, and technologies. As for the past, results suggest that GHG reductions over the period 2005–2022 are mostly due to crises and stagnation of the Italian economy. "Moderate decarbo" forecasts for 2030 lead to a 12% reduction of TtW (and 15% for WtW) GHG emissions compared to 2005 levels, with ASI policies compensating for the assumed economic growth. In any case these values are far from the EU "Fit for 55" goal setting a 43.7% reduction of TtW emissions. Even under very favourable hypotheses ("high decarbo" scenario), GHG emissions might be reduced by only 28% for TtW and 33% for WtW, still well below EU objectives. Furthermore, passengers and freight show very different behaviour, with the latter contributing to emissions more than proportionally, and "shift-to-rail" policies, although very useful in some market segments, contribute moderately at the overall national level. The main conclusion of this study is that the pathway towards road transport decarbonization in Italy (and possibly in other EU Countries) is very uncertain, and the ambitious 2030 target is unlikely to be met with currently planned policies alone. The transportation system needs to be constantly monitored, and new policies have to be implemented to reach higher emissions reduction goals. • Road transport is widely acknowledged as a hard-to-abate sector, contributing over 90% of total transport GHG emissions. • The study estimates Italian road transport demand (2005, 2019, 2022 and 2030) and evaluates GHG emissions. • Following the Avoid-Shift-Improve (ASI) approach, two forecasting scenarios were designed. • Even under very favourable hypotheses, WtW GHG emissions might be reduced by only 33%, still well below EU objectives. • Constant monitoring and new policies are required to meet higher emissions reduction targets. [ABSTRACT FROM AUTHOR]

Auto ownership behavior is driven by complex relationships that can vary dramatically across different traveler groups and communities. Differences in auto ownership among racial groups have been of particular interest, given ongoing efforts to advance equity in transportation outcomes. There are a number of studies documenting racial disparities in auto ownership associated with racial and ethnic residential clustering, termed "automobile mismatch." Yet, these differences in auto ownership behavior by race and residential location are virtually never considered in models of travel behavior, despite calls for the consideration for race in transportation planning and decision making. This study aims to bridge the gap between understandings of the connections between race and space and transportation outcomes, using Los Angeles County as a case study. A series of auto ownership model specifications are used to investigate statistical connections between the racial and ethnic categories of residents, and neighborhoods, revealing systematic variations across racial and spatial dimensions. The composite model, which includes racial and spatial indicators, outperforms the base model, suggesting that the inclusion of race and space explains significantly more information on variations in auto ownership and provides a superior fit to the data. Our findings also suggest that the exclusion of racial and spatial indicators may lead to overestimation of certain effects, and may completely misrepresent the importance of certain household, individual-level, and built environment effects in explaining auto ownership preferences. Given the increasing attention to equity and representation in transportation outcomes, models that exclude considerations for race and space may be poorly positioned to support meaningful transportation equity analyses. • Current travel models of auto ownership lack consideration for race and space in shaping auto ownership outcomes. • We systematically compare auto ownership model specifications, that include racial and spatial indicatiors. • The results show that models with such indicators offer better statistical fit and explain more about auto ownership. • Excluding these indicators may bias certain effects and misrepresent important household, individual, and built environment effects. • Interactions between race and space also matter and can siginificantly shape auto ownership outcomes. [ABSTRACT FROM AUTHOR]

• The impact of recognizability of CAV platoons on drivers' behavior and safety was investigated. • Standard deviation of speed and acceleration decreased significantly during merging in the presence of recognizable CAV platoon compared to the presence of non-recognizable CAV platoon. • Time-to-collision (TTC) during merging was higher in the presence of recognizable CAV platoon. • Male drivers showed safer driving behavior during diverging in the presence of recognizable CAV platoon. Platoons of connected and automated Vehicles (CAVs) comprising both passenger cars and trucks are expected to appear on highways in the coming years. Though prior studies explored driver behaviors in the presence of CAV platoons, it remains unknown whether it is necessary for a traditional vehicle's driver to recognize CAV platoons in a mixed traffic environment for safe maneuvering. This study aims to test the hypothesis that drivers behave differently when interacting with recognizable CAV platoons compared to interacting with non-recognizable CAV platoons. A driving simulator experiment was conducted featuring four merging and diverging scenarios. Participants were informed beforehand that blue-colored vehicles driving with short gaps would be CAV platoons. However, they also encountered CAV platoons consisting of several vehicles with different colors (not blue). The blue-colored platoon was considered as recognizable CAV platoons and the latter as non-recognizable CAV platoons. Performance indicators including speed, maximum acceleration and deceleration, standard deviation (SD) of speed and acceleration, and time-to-collision (TTC) were considered to examine drivers' behavior. The generalized estimating equation (GEE) approach was applied to evaluate the effect of recognizability and demographic characteristics on driver performance. The GEE results revealed significant improvement in drivers' behavior in the presence of recognizable CAV platoons. The merging SD of speed and acceleration were around 32% and 29% lower, respectively in the presence of recognizable CAV platoons. The merging TTC was around 20% higher in the presence of recognizable CAV platoons. Although no significant main effect of recognizability was found for diverging maneuvers, a noteworthy result was that male drivers had around 37% higher diverging TTC than female drivers in the presence of recognizable CAV platoons. The findings also suggested significant effects of gender, age, driving experience, and education on driver performance while merging, as well as effect of gender and prior crash involvement on driver performance while diverging. These findings provide valuable insights to transportation planning authorities in determining the potential need for making CAV platoons recognizable and developing associated guidelines for safer traffic movements in mixed traffic environments. [ABSTRACT FROM AUTHOR]

This study presents the computational architecture of the integrated transport, land-use, and emission (iTLE) modeling system. It incorporates the effects of physical-virtual activity-space interactions within the microsimulation framework, making it a comprehensive tool for predicting travel demand and evaluating the impacts of evolving activity-travel behavior on transport and land-use systems. The model systematically integrates households' and individuals' long-term, medium-term, and short-term travel related decisions, and traffic assignment allowing for more reliable estimates of travel demand. Application of the iTLE prototype in the Halifax Regional Municipality (HRM), Canada reveals notable trends, including a reduction in travel time for work activities, highlighting the potential of teleworking as a pragmatic strategy for traffic management. The model's flexibility is demonstrated through its ability to capture the trade-offs between physical and virtual activity environments, aligning with the intricacies of modern digitalized societies. Integration of iTLE with the traffic assignment system enhances its applicability across diverse urban contexts, streamlining the process of setting origin-destination trip matrices and significantly improving computational efficiency. By systematically coupling the micro-behavioral decision processes, iTLE generates robust estimates of traffic flow, emissions, and energy use, providing reliable insights for sustainable and efficient transportation planning. This study contributes to advancing integrated urban modeling, offering a valuable tool that can be replicable for multiple cities in Canada and beyond. [ABSTRACT FROM AUTHOR]

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems. [ABSTRACT FROM AUTHOR]

Although modal shift from private motorized vehicles to public and active transport is regarded as crucially important for a sustainable society, such a shift has not been successful in Japan. One reason may be that measures have not satisfied the latent factors underlying people's transportation mode choices. Moreover, these factors may have changed during the COVID-19 pandemic. This study is aimed at ascertaining what workers have emphasized in transportation mode choice, and how those emphases have affected their actual mode choice. We also examined how changes in individuals' emphases during the pandemic have affected their actual mode choices. We used a four-time panel survey with 1377 respondents from Japan that asked participants about important items related to their transportation mode choice and activity patterns at four time points before and during the COVID-19 pandemic. After conducting a factor analysis to ascertain the latent factors behind mode choice, we applied a logistic regression model showing whether or not the respondents used each transportation mode at each time point. A conditional logistic regression model with individual fixed effect was applied to the relationship between an individual's change in emphasis and actual mode choice. Factor analysis results indicated four latent factors: usability and reliability, personal time–space, attitude toward the environment and health, and ease of use and convenience. A higher emphasis on personal time–space encouraged car use and discouraged public transport use at any time points. This trend became stronger during the pandemic. Changes in the emphasis level of this factor for an individual also affected the actual mode choice. A higher emphasis on usability and reliability also discouraged public transport use. Ease of use and convenience encouraged public transport use, and attitude toward the environment and health neither encouraged nor discouraged actual mode choice at any time point. Emphases related to transportation mode choice changed over time. However, changes in individuals' emphasis altered the actual mode choice at some time points but not at others. These results should be considered in transportation planning in order to prompt desired modal shifts by transportation users. • Latent factors of transportation mode choice have changed during COVID-19 era. • Greater emphasis on personal time-space discourages public transport use. • COVID-19 pandemic increased the importance of personal time-space in mode choice. • Emphasis on attention to the environment and health does not affect mode choice. [ABSTRACT FROM AUTHOR]

For the production of renewable hydrogen in Egypt and its transport to growing hydrogen demand centers such as Germany, relatively low costs are given in the literature. These low costs are attracting investors. However, due to the scope of the respective studies, not all techno-economic aspects of the supply-chain that influence levelized costs of hydrogen are considered. This work represents a systematical analysis of the levelized costs of hydrogen in Egypt by using wind and solar power for producing 40 kt per year of green hydrogen. A maximal investment volume of four billion euros, and an available area of 37 km2 in central Egypt is assumed. The techno-economic evaluation of the system, from the production site to the selling location, is conducted by a mixed-integer linear optimization model. Depending on the system configuration and production site, the study reveals energy losses of 52–61% of the harvested energy and a levelized costs of hydrogen of 7.17–12.31 €/kg. [Display omitted] • Eleven hydrogen supply chain configurations from Egypt to Germany are analyzed. • The levelized costs of hydrogen, including the transport, are 7.17–12.31 €/kg. • Energy losses of the harvested energy are between 52 and 61%. • Wheeling scheme increases system flexibility and decreases the costs significantly. • Low costs found in the literature can be achieved in highly flexible systems. [ABSTRACT FROM AUTHOR]

To address the growing uncertainties in the future transportation industry, planners and engineers have identified and implemented scenario planning, a multi-step and strategic approach, in long-range transportation planning. A comprehensive examination of how equity is envisioned, planned, and analyzed during this process remains absent, despite several key guidelines of scenario planning exercises. This paper conducts a systematic literature review focusing on how equity has been considered and evaluated in scenario planning. The resources include academic papers, agency reports, and materials from the recent scenario planning conference, the 2nd Conference on Scenario Planning in Transportation hosted by the Transportation Research Board. This paper examines equity in scenario planning in three important ways: 1) the ways equity has been integrated across steps; 2) how equity analysis has evolved; and 3) when it is necessary to consider equity. This work adds value to the existing body of knowledge by presenting the policy implications based on the review of the current scenario planning research and practices. Early policy recommendations include the need for more vertical equity evaluations, the construction of robustness and justice indicators, and the adoption of a flexible mindset of planning for rather than being fixated on a single scenario. • A systematic literature review focusing on equity analysis in scenario planning. • It is vital to conduct policy robustness analyses incorporating equity. • More vertical equity research needs to be incorporated. • Having the mindset of equity planning for in more than one scenario is critical. [ABSTRACT FROM AUTHOR]

The usefulness of transportation project evaluation depends on the completeness of its benefit measures. Since transportation networks are intrinsically spatial, transportation improvement projects have spatial access and location characteristics that can lead to a variety of non-user economic benefits. Recent research has enabled us to better understand how spatial context and spatial heterogeneity play further roles in generating efficiency gains for non-users, in the form of productivity, income, and cost savings for both private and public sectors of the economy. This paper draws upon that body of research to expand our understanding of the means by which transportation projects can generate economic efficiency gains, and approaches needed to measure them. It covers topics beyond those captured by current definitions of "wider economic benefits," including additional sources of scale economies associated with freight distribution and connectivity, and further public and private sector economic gains enabled by environmental and social inclusion improvement. It points to ways that non-user economic benefits can be more comprehensively defined and better measured by recognizing their spatial scale, context, and threshold effects. It also identifies ways that current benefit measurement methods introduce unintended bias into transportation investment decision-making through omission and mismeasurement. The result is a case for a refresh of thinking about how we classify and recognize non-user economic benefits in transportation evaluation, and how we apply transportation planning and economic models to support their measurement. • Transport decisions biased when non-user benefits are ignored or mis-measured. • Non-user economic benefits depend on spatial access, proximity, or connectivity. • Spatial scale, context, and heterogeneity affect measurement of non-user benefit. • Benefits from freight density, virtual density, effective density, non-user cost. • Rigid travel modeling standards hold back non-user benefits. [ABSTRACT FROM AUTHOR]

A historic urban district is a quarter built many years ago and contains valuable memories over a limited area with narrow streets for pedestrians, primitive transportation, and outdoor activities. Over the years, many districts have been growing randomly, and motorized transport has enlarged significantly and created severe problems. Improving the situation needs heritage protection and sustainable mobility development on its streets. The desired mobility focuses on managing travel demands, accessibility for all street users, and enhancing the environment. This research presents a proposed approach with an application framework for sustainable mobility development on historic roads according to its current conditions at the strategic level. It includes sequential phases to define desired goals, formulate and evaluate planning scenarios containing different action measures based on sustainability indicators and application of micro-simulation. In case of inappropriate evaluation results, approach phases allow the modification and re-evaluation of additional measures until deciding an acceptable solution. The application is limited to Ras El-Teen Palace Street, prominent in the ancient El Gomrok district, Alexandria and restricted to a Do-Nothing solution and a planning scenario. The application proves the approach's practicality and feasibility by improving traffic behaviour. Furthermore, the planning approach can indicate technical recommendations for further improvement. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AVs) present enormous uncertainties and challenges for future urban transport and mobility. While urban and transportation planning have significant roles to play in shaping these futures, a critical challenge is identifying and reconciling divergent values and competing visions in relation to this potentially disruptive transport technology and the associated mobility services. In this paper, we demonstrate the use of a participatory multi-criteria visioning and appraisal framework and methodology to enable stakeholders to envision, identify and interrogate essential tensions between imagined AV futures and long-term transport and mobility imperatives. Based on workshops with stakeholders at the forefront of policy and practice, and academia in Greater Manchester (UK) and Melbourne (Australia), we reveal several insights. Regarding the prospects of AVs, our participants are neither 'opponents' nor 'evangelists', but instead, manifest the contrasting attitudes and perspectives of excitement, optimism, ambivalence, scepticism and uncertainty all at the same time. In the visions outlined and appraised, our stakeholders identify AVs prospects in various use cases, such as public transport, personal and shared-use and urban freight and delivery applications, while at the same time recognising the inherent contradictions between automated driving futures outlined and imperatives such as reversing auto-mobility and creating safe and inclusive urban environments. Finally, the study brings to the fore the significant role of governance in mediating the politics and resolving contestations in critical areas including data management and privacy, cybersecurity and implementing viable business models and ownership arrangements. • Develop and apply participatory multi-criteria framework to envision and appraise automated driving futures. • Identify and interrogate essential tensions between imagined AV futures and long-term transport and mobility imperatives. • Identify AVs prospects in various use cases, such as public transport, personal and shared-use. • Inherent contradictions between automated driving, reversing auto-mobility and creating safe urban environments. [ABSTRACT FROM AUTHOR]

This research aimed to address a problem of the Transit-NDP with an upper level that optimizes the vehicles' Network Travel Time (NTT) subject to a budget constraint, and the lower level estimates vehicles' NTT based on a travel demand model (TDM), which involves identifying the optimal transit projects for reducing the NTT. The study objective is to explore the impact of a naïve model of the more complex Transit-NDP, where stakeholders suggest a group of potential transit projects. A complete evaluation of the enumeration and sampling search strategy is conducted for the two test networks. A sensitivity analysis is conducted on the impact of a demand increase for each test network. The Halle Network exhibits an almost constant NTT reduction ranging between 47.64% and 50.79% while the Karlsruhe Network exhibits a different behaviour with an NTT reduction ranging between 39.70% and 52.40%. In both networks, a set of candidate transit projects were members of the best solution for all test runs. The sensitivity analysis demonstrated that some candidate transit projects selected at lower budget levels, demand levels, or both were not necessarily selected as the best solutions at upper budget, demand levels, or both. In addition, the resulting dataset of the enumeration and sampling was utilized to develop a Random Forest regression model that produces estimates of the NTT with a success rate of 90%. [ABSTRACT FROM AUTHOR]

Integrating renewable energy into planning and operation of transportation infrastructures can help to promote the various sector collaborative decarbonization. For the highway service area micro-energy grid (HSAMEG), its optimization lacks the source-load-storage cooperation and the modeling that considers both accuracy and complexity, and is hard to balance reliability and flexibility due to uncertainties in renewable energy and charging-demand. For these issues, a novel dispatch is proposed to balance the dispatch reliability and flexibility, and the model accuracy and complexity by combining advantages of robust and stochastic optimizations and applying the multi-granularity modeling. First, the source-load-storage configuration is established. Then the multi-granularity model is developed by fine-grained model based on the operating characteristics and coarse-grained model based on the equivalent energy storage characteristics. Finally, based on distribution characteristics of online-optimization forecast-errors, a multi-granularity source-load-storage cooperative dispatch combining robust optimization and stochastic optimization is proposed. The simulation results show that the source-load-storage collaboration increases the self-contained objective by 10%. Compared with robust optimization, the proposed strategy enhances the economic objective by 17% and the self-contained objective by 16.2%. Compared with stochastic optimization, the proposed strategy improves the computation efficiency by over 5 times and the self-contained objective by 8.8% without constraint violations. [ABSTRACT FROM AUTHOR]

The inclusion of human behavioral factors in travel demand analysis is a new trend in transportation planning. Perceived security (PS) is one of the crucial behavioral factors governing urban travelers' choices and demand analysis, exclusively for active modes of transportation like bicycles and vulnerable road users like women. However, no scientific attempt has been made to explain and quantify PS for women cyclists. We estimate women cyclists' PS by considering individual, social, and built environment features from the data collected in Tehran, Iran. Fifty-two women were recruited as the test participants in a VR-based bicycle experimental system (VRBES). For analysis, we focused on the tree-based machine learning (ML) methods besides an econometric model. In the end, the random forest (RF) algorithm is chosen and improved for the best performance (with test-R2=0.69) on the dataset, and feature importance showed population, a social feature, is the most important. [ABSTRACT FROM AUTHOR]

In the cost–benefit analysis of transportation investment, although the benefit estimation method due to the effect of travel time saving has been widely used, several limitations with the method have been pointed out in a number of studies. As an alternative, a logsum-based benefit calculation has been widely adopted, with most of the research focused on urban transportation. Therefore, it is rarely applied to interregional transportation. In this study, we applied a logsum-based approach to the calculation of benefits for expressway road projects in Korea's long-term transportation plan. To calculate the logsum, this study used input data from both the mode choice and destination choice models, finding that an analysis of the feasibility of inter-regional road investment was possible. Various studies have calculated the benefits based on one of the two models. The two methods have similar formulas; however, varying results were derived due to differences in the application of formulas for parameter estimation and benefit estimation. In conclusion, the proposed methods are expected to enable a more comprehensive evaluation of the transport system. Looking at more detailed research results, the logsum based on the mode choice model had great benefits in the Seoul metropolitan area where demand was high; however, the logsum of the destination-based model showed a large difference in distribution of benefits evenly by various metropolitan areas. On the other hand, it was found that the traditional reduction of travel time was much less by region than the logsum methods, but the effect was large in the metropolitan area. From the perspective of the theoretical and practical consumer surplus analysis, we suggest open discussions regarding which method to choose when calculating benefits from the mode choice logsum model, the destination logsum model and travel time savings method. [ABSTRACT FROM AUTHOR]

In common transportation planning practice and research, disability is largely mistreated and oversimplified. This review essay identifies four shortcomings in U.S. transportation planning that perpetuate oversight, exclusion, and inadequate transportation systems for disabled people. Compliance-based agendas are used by public agencies to implement accessibility accommodations based on minimum requirements, failing to consider the everyday experiences of disabled people. Data gaps and access to opportunity assumptions reveal how transportation planning and analysis approaches are inadequate when considering the disability community. Lastly, the narrow focus on mobility disability in the transportation field ignores the diversity of human conditions. These existing challenges are defined in detail with examples of how disability justice ideas and inclusive public engagement practices can address each shortcoming. Meaningful public engagement with the disability community would improve how transportation planning, policy, and practice address the needs and lived experiences of this critical population group. • Disabled people face complex barriers to travel. • We identify four shortcomings that affect how travel barriers are addressed. • Meaningful public engagement integrates lived experiences into planning. • A transportation justice approach improves accessibility. [ABSTRACT FROM AUTHOR]

• Pedestrian accidents based on traffic analysis zones have been analyzed. • A macro-level model considering spatial correlation was developed for pedestrian accidents in Novi Sad. • Traffic and infrastructural factors for pedestrian accidents are modeled by applying GWR techniques, where the GWPR model had the best goodness of fit. • State roads, unclassified streets, bus stops, parking spaces, and object units are positively correlated to pedestrian accidents. • Road network and secondary roads are negatively correlated to pedestrain accidents. Introduction: Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies. Method: This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance. Results: This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area. Conclusion: These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces. Practical Applications: The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity. [ABSTRACT FROM AUTHOR]

Compared with men, women have different travel behavior. Various factors may affect women's transportation-related decisions, especially in a developing country like Iran with a patriarchal culture. Moreover, distinct characteristics of some transportation systems in Iran, such as rounding and fixed-route shared taxis, whose properties are different from the conventional taxis in western countries, may lead to further differences in Iranian women's transportation mode choice behavior. Studies investigating women's travel behavior and transportation mode choice have often focused on quantitative modeling and mainly highlight the gender differences rather than identifying the underlying factors contributing to women's different behavior. To fulfill this gap, this study attempts to explore the factors and motivations affecting women's shared mobility choices, specifically, shared taxis, in a less developed country with specific socio-cultural structures. Such exploratory research may necessitate a qualitative study that can clarify the cause of the behaviors. Thus, in this study, the conventional content analysis with an inductive approach, in both the latent and manifest levels, was performed. In-depth semi-structured interviews with 15 students and working women living in Tehran (the capital of Iran) were conducted. According to the latent content analysis results, a complex set of factors contributes to women's intention to use shared taxis that can be classified into seven main themes with several sub-themes. The study showed that the fear of abuse and imaginary harassment strongly influence women's transportation-related decisions. Also, women may face direct and indirect restrictions from the family and society. The gender social halo and family norms , more prevalent in traditional and developing societies, may also challenge women's everyday mobility. Moreover, the impact of individual values and beliefs , as well as personality factors on women's travel behavior, cannot be ignored. Indirect social learning is another important factor identified as influential in using shared taxis. Manifest content analysis also resulted in three main categories, including environment-specific factors , trip-vehicle-specific factors , and individual-specific factors. The study attempted to provide more profound insight into women's transportation-related behaviors for policymakers in order to reconsider transportation planning. Also, findings highlighted the role of collaborative efforts of sociologists, psychologists, and transportation planners in tackling women's transportation-related issues in less developed countries with different socio-cultural norms from Western societies. • A qualitative exploration of factors influencing women's intention to use shared taxis in a developing country with a patriarchal culture is provided. • Content analysis, in both the latent and manifest levels, is performed. • Fear of abuse, imaginary harassment, gender social halo, and family norms are found to be influential on Iranian women's transportation-related decisions. • Indirect social learning, individual values and beliefs, as well as personality factors are other important factors. • Environment, trip-vehicle, and individual-specific factors are the results of manifest analysis. [ABSTRACT FROM AUTHOR]

Urban public transportation is crucial for achieving low-carbon cities but faces challenges due to urban planning and transportation coordination issues. This study introduces a joint decision model for multiple public transportation options, grounded in the cross-nested logit (CNL) framework, integrating departure time and travel mode dimensions. It combines three key variables—travel scheme, built environment, and carbon emissions—to assess transportation choices comprehensively. CO 2 emissions are evaluated with the COPERT (COmputer Programme to calculate Emissions from Road Transport) model for accurate environmental impact assessments. Utilizing multi-source data from Shenzhen, including smart card, vehicle trajectory, and order data, the study evaluates factors influencing public transportation choices and compares emission reduction potentials. Results indicate that Shenzhen travelers are more sensitive to traffic and built environment service levels when choosing departure times rather than travel modes. The findings highlight the significant role of metros and shared bicycles in reducing emissions and suggest built environment adjustments to promote these low-carbon travel modes. Additionally, elasticity analysis and Monte Carlo simulations provide insights into future strategies under different emission reduction scenarios. These findings offer a theoretical basis and practical guidance for improving transportation structure and built environment, supporting the development of sustainable and low-carbon cities. • A multi-dimensional choice model considering built-environment factors is proposed. • The emission reduction capacity of various public transportation choices is explored. • An elastic analysis is conducted between travel emissions and multiple trip options. • The proportions of alternatives are simulated in varied emission reduction scenarios. [ABSTRACT FROM AUTHOR]

Transportation systems are growingly subject to extreme weather events induced by climate change. Yet, delays in operating public transportation systems due to adverse weather conditions are not well understood. In this study, we quantify the impact of temperature shocks on school bus delays in New York City during 2015–2019. We find that low temperatures lead to significant school bus delays regarding the frequency and delay time. Our heterogeneity analysis further shows that increased school bus delay time in cold weather is primarily driven by Curb-to-School buses (for students with special education and needs) and school bus operations in the mornings. Our findings provide new evidence on how temperature shocks affect transportation systems and highlight the need for adaptive strategies and planning that can aid school buses in better responding to adverse weather conditions. [ABSTRACT FROM AUTHOR]

Washington, District of Columbia (DC), like many cities around the world has responded to the UN's call to help national governments deliver on their 2030 Nationally Determined Contribution (NDC) covered under the UN's sustainable development goal #13, Climate Action. DC's contributions to the United States NDC include making its transportation sector sustainable. DC is part of the Washington Metro region which includes jurisdictions in Maryland and Virginia and during the week commuters from both states increase DC's population by almost 80 % exacerbating traffic congestion. Each jurisdiction in the region is responsible for its financial, transportation, and land use policies. To determine whether this system of fragmented governance impacts DC's transportation goals, this study used Banister's sustainable mobility framework to analyze transportation projects constructed or implemented in jurisdictions in the region. The results reveal that fragmented governance has led to jurisdictions in Maryland and Virginia placing greater emphasis on transportation projects that widen, extend, or create new roads and this tends to meet neighborhood or county needs instead of regional targets which would facilitate DC's efforts to reach its sustainable development goals. The study also finds regional agencies such as the Metropolitan Washington Council of Governments, the National Capital Region Transportation Planning Board, and the Transportation Land-Use Connections Program do not have regulatory powers and hence cannot enforce transportation and land use policies nor fund any projects in the region instead each jurisdiction is responsible for its transportation and land use policies. The study also reveals that these regional agencies can be manipulated by local politicians and recommends the creation of a regional governing body that will make decisions on finance, land-use, housing, and transportation and be accountable to constituents and thus avoid interference from local politicians. • Integrating land use and transport promotes sustainable urban development. • To achieve sustainable transportation, cities need metropolitan governance. • Fragmented governance can lead to uncoordinated and contradicting actions. [ABSTRACT FROM AUTHOR]

The urgency to decarbonize the transportation sector has accelerated the adoption of micro-mobility solutions, with cycling network development witnessing remarkable growth. Robust and quantitative evaluation frameworks are needed to evaluate the quality of such developments. While a plethora of bike network evaluation approaches exist, their diversity creates issues of interpretability and comparability due to varying metrics and domain-specific terms. We present three contributions to address these challenges. First, we construct a formal ontology, VeloNEMO, that captures key attributes of evaluation metrics for harmonizing bike network evaluation metrics. Second, we generate a machine-readable knowledge base containing these metrics, enabling meta-analyses and resolving some of the existing terminological discrepancies. Third, we propose recommendations for transparent and comparable metric descriptions across various evaluation approaches, illustrated by exploratory metric selection scenarios for a forthcoming bike network evaluation tool. In summary, our research addresses the need for a structured and shared vocabulary for bike network evaluations. This ontology-based approach aims to improve the coherence of evaluation methods as the field of bike network planning continues to evolve, ultimately supporting decision-making for sustainable transportation planning. • We propose VeloNEMO, an ontology designed to unify and standardize metrics for bike network evaluations. • We created a Knowledge Graph featuring a wide array of machine-readable evaluation metrics. • We provided guidelines for adopting machine-readable metric standards to enhance bike network planning. • The results illustrate how ontologies can help to evaluate multi-criteria metric selection strategies. [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicles (EVs) and renewable distributed generators (DGs), which support net-zero emissions, poses technical challenges to the planning of transportation (TN) and distribution (DN) networks. This work proposes a two-stage strategy for the optimal planning of EV charging stations (EVCS) and DGs in a coupled TN-DN. In the first stage, a fuzzy max–min framework embedded particle swarm optimization and an EV waiting time constraint-encapsulated M/M/S queuing theory are used to obtain the optimal locations and sizes of EVCS. The objectives considered in this stage are maximizing EVCS operator profit and minimizing the costs of EV users and DN operator (DNO). In the second stage, a distribution system integrated with EVCS is used to find suitable sites and capacities for renewable DGs. A sensitivity analysis is carried out to identify the ideal locations of renewable DGs, and an exhaustive search-based analytical method is used to determine their sizes. Moreover, in this stage, a cost–benefit analysis is performed to assess the economic viability of planning renewable DGs. To address the uncertainties associated with EVs and renewable sources, Hong's 2m+1 point estimation method is used. The efficacy of the suggested approach is tested on a coupled 28-node TN and a 69-node grid-connected DN. The findings reveal that the EVCS and DN operators will achieve an average incremental profit of 21.80% and 42.03%, respectively, while meeting all the system constraints. Further, the DNO gains an additional revenue of 20.17% by adopting distribution network reconfiguration. [Display omitted] • Stochastic planning of EV charging stations and DGs is done for a coupled network. • Uncertainties of both EVs and renewable DGs are considered. • A fuzzy max–min framework is used to solve the multi-objective planning problem. • Economic feasibility of DG placement is evaluated using a cost–benefit analysis. • Network reconfiguration is done to enhance distribution network operator profit. [ABSTRACT FROM AUTHOR]

This paper analyses Cork's trajectory towards a sustainable, innovative, and globally connected urban centre. On Ireland's south coast, Cork serves as an exemplar of how second-tier cities can undergo transformation and structural change, emerging as an exemplar of economic success. The paper explores the synergies between policymaking, labour markets, and transportation improvements, specifically in the pharmaceutical and IT sectors, noting their clustering and success in international markets. It explores challenges and opportunities presented by demographic shifts alongside an analysis of Cork's transport system, its port capabilities, and connectivity. The paper links to the UN SDGs and suggests strategies to promote sustainable growth and boost innovation. These enhance Cork's technological capacities, educational attainment, sustainable urban planning, and transport systems. It offers insights into urban development, focusing on place-based, future-oriented solutions and strategic directions for Cork. This study extends beyond Cork, offering a roadmap for other second-tier cities grappling with similar challenges and opportunities. It emphasises the need for comprehensive unified approaches to urban planning, aligning with global sustainability objectives, innovative standards, and best practices in city management. Cork not only pioneers its path towards resilience and innovation but also serves as a model for global second-tier cities, showcasing the growth and transformation potential of urban centres. • Cork's resilience and innovation model second-tier city transformation • Strategic FDI in Cork sets a benchmark for economic growth in similar cities. • Cork's education-labour model guides workforce development in second-tier cities. • Cork's infrastructure upgrades boost global connectivity for second-tier cities. • Inclusivity in Cork boosts economic participation and diversity in similar cities. [ABSTRACT FROM AUTHOR]

Freight transport is crucial in fostering economic growth and enhancing societal well-being, but it also poses challenges for traffic management and environmental sustainability. For freight planning and policy formulation, it is important to measure and evaluate regional freight accessibility. This paper proposes a novel framework for analyzing regional freight accessibility based on truck trajectory data. The framework is structured around two principal components: extracting freight trip information and constructing a freight accessibility model. It introduces precise and effective methodologies for identifying truck parking zones based on trajectory data, thereby facilitating the extraction of complete freight trip information. Afterward, the framework integrates a distance-weighted topological analysis with multiple relevant indicators to comprehensively assess the freight accessibility of a region. Additionally, it incorporates spatial lag modeling to examine the factors influencing the spatial distribution of freight accessibility within a region. Applied to a case study in Hunan Province, China, the framework demonstrates its efficacy. The analysis reveals that accessibility in Changsha and two other cities stands out significantly higher than in other cities. The study offers valuable insights for strategic freight planning and policy formulation. [ABSTRACT FROM AUTHOR]

The State of Temperature (SOT) plays a crucial role in ensuring the safety and reliability of lithium-ion batteries, as well as the stability of electric vehicles (Evs). Recently, data-driven methods for lithium-ion battery temperature estimation have often obtained short-term estimation information in a single scenario. To alleviate the above issues, this paper proposes a method for estimating the surface temperature of lithium-ion batteries based on Local and Global Trend (LGT) data augmentation and Long Short-Term Memory (LSTM) model transfer. Initially, voltage and current data are processed using two types of filtering methods to reduce noise while preserving the fluctuating characteristics of the original data. Differential features are subsequently extracted from the polynomially filtered curves. Then all the data, including the filtered data and the features are fed into the LGT data augmentation algorithm, followed by the use of an LSTM transferable model to estimate the surface temperature of the target battery. Furthermore, the method is tested on both laboratory datasets and datasets with real driving conditions, covering a wide range of environmental temperatures and driving scenarios typical of EVs. The experimental results demonstrate that retraining with only the first 30 % of the target battery's data yields effective SOT estimation results. • Utilizing voltage and current to capture the temperature characteristics. • LGT augmentation enhances data diversity and robustness in SOT prediction models. • Transferable LSTM model predicts surface temperature with limited early-stage data. • The method's performance is validated on lab and real-world driving datasets. [ABSTRACT FROM AUTHOR]

Traffic forecasting technology has widespread applications in various domains, such as urban traffic planning and intelligent transportation systems. Traffic forecasting encounters challenges in effectively capturing the intricate spatial–temporal correlations in traffic data. While the latest methods have achieved satisfactory performance, they still suffer from two limitations: (i) Most methods overlook the memory of valuable traffic patterns at each traffic node, thus making it struggle to reveal dynamic spatial–temporal correlations using their inherent periodicity and trend characteristics from a broader perspective. (ii) As the research progresses, recently proposed models become increasingly complex and massive. To address these issues, we propose a Spatial-Temporal Aggregation Memory Transformer (STAMT) for traffic forecasting. Specifically, we propose a memory bank to enhance vanilla spatial attention and cache the traffic patterns of historical input. By querying these traffic patterns, which contain rich spatial–temporal semantic information, the model can optimize prediction performance by extracting trends and regularities across various periods. To reduce the computational costs, we introduce a temporal module to capture temporal correlations while reducing temporal dimension information. In addition, we leverage the random feature map and matrix multiplication associativity property, which reduce the quadratic complexity of spatial attention to linearity with regard to the number of nodes. Ultimately, our theoretical analysis concludes that a single-layer spatial attention network is sufficient to capture spatial–temporal correlations deeply without stacking. Extensive experiments on nine real-world datasets demonstrate that STAMT outperforms state-of-the-art baselines in regular, long-range, and large-scale traffic forecasting tasks while significantly reducing the computational costs. Codes are available at https://github.com/LiuAoyu1998/STAMT. • An efficient Transformer is proposed for various traffic forecasting tasks. • A spatial-temporal memory is proposed to cache and query evolving traffic patterns. • Our model significantly reduces the computational cost of spatial-temporal modeling. [ABSTRACT FROM AUTHOR]

• Propose a multi-task deep learning model to predict real-time and sparse travel demand. • Propose an algorithm converting repeated calls into travel demand. • Employ zero-inflated loss function to handle data sparsity issue. • Apply the proposed model to demand responsive transit data in South Korea. • Multi-task structure with zero-inflated loss can mitigate the data sparsity problem. Demand Responsive Transport (DRT) is a flexible transportation service that adjusts its routes and schedules in real time based on user requests. Real-time demand forecasting has become a timely issue since the resolution of transportation planning has increased to provide flexible services to users. Although many previous studies have been conducted to design the demand forecasting model using the mobility service historical data, there are more considerations when predicting real-time demand for on-demand mobility services. Therefore, this study aims to develop a demand forecasting model for DRT services addressing two issues. First, repeated calls for service and abandoned requests are converted into the unit of demand to capture served and potential demand. Second, given the sparsity of DRT historical data, a multi-task deep learning model is proposed to predict both the probability and volume of demand simultaneously to mitigate the data sparsity problem. A zero-inflated loss function was introduced to consider the skewed demand distribution. To attain our goals, the historical dataset of the I-MOD service trial in Yeongjongdo, Inchon, Korea was used. The performance of the proposed model is compared against several baseline models, demonstrating its superiority in forecasting demand for mobility services. The results show that a multi-task structure can alleviate the data sparsity problem by excluding where demand is not generated from the forecasting problem. The proposed models can provide insights into real-time demand forecasting with sparse demand. [ABSTRACT FROM AUTHOR]

Traffic flow modeling has become increasingly important in today's society due to the growing number of vehicles on the roads, leading to congestion and traffic incidents. Mathematical modeling can aid in the planning and optimization of transportation systems, enhancing overall efficiency and safety. The main result of this study is a mathematical model that describes the dynamics of multiple vehicles, taking into account the prediction of the leading vehicle's behavior. The model is formulated as a system of delay differential equations. A computer program has been developed based on this model, which simulates traffic flow in various road scenarios. The simulation results are consistent with observed data from real traffic flows. • A novel mathematical model for traffic flow is presented, which incorporates the prediction of the leading vehicle's dynamics. • The novel model is numerically investigated using specially developed software, demonstrating its applicability in various traffic scenarios. • The novel model shows significant improvements in traffic dynamics compared to the original model and closely aligns with real traffic flow observations. • The novel model reduces delay times, resulting in increased traffic throughput and efficiency. • The novel model aligns well with observed data, indicating its potential use in automated driving systems. [ABSTRACT FROM AUTHOR]

With the rapid increase in the percentage of the world's population living in cities, the design of existing transportation infrastructure requires serious consideration. Current road networks, especially in large cities, face acute pressures due to increased demand for vehicles, cyclists, and pedestrians. Although much attention has been given to improve traffic management and accommodate the increased demand via coordinating and optimizing traffic signals, research focused on adapting the static allocation of street spaces and right-of-way dynamically based on mixed traffic flow is still scarce. This paper proposes a multi-agent reinforcement learning (RL) agent approach that cooperatively adapts the individual lane widths and right-of-way access permissions based on real-world mixed traffic flow. In particular, multiple cooperative agents are trained with mixed temporal data that learn to decide suitable lane widths for motorized vehicles, bicycles, and pedestrians, along with whether co-sharing space between pedestrians and cyclists is safe. Using a microscopic traffic simulator model of a four-legged intersection, we trained our RL agent on synthetic data, and tested it on realistic multi-modal traffic data. The proposed approach reduces the overall average waiting time and queue length by 48.9% and 37.7%, respectively, compared to the Static (baseline) street design. Additionally, we observe CALM's ability to gradually adjust lane widths, contrasting with the Heuristic implementation's erratic lane adjustments, which pose potential safety concerns. Notably, the model learns to adaptively toggle the co-sharing of street space between cyclists and pedestrians as one co-shared lane, ensuring comfort and maintaining the level of service according to the designer's policy. Finally, we demonstrate CALM's scalability on a simulated large-scale traffic network. • We propose a decentralized MARL algorithm suitable for large-scale networks. • Our method is robust under extreme traffic surges and generalizes to new scenarios. • Our approach fairly adapts street space to dynamically changing mixed traffic flow densities. [ABSTRACT FROM AUTHOR]

• We present an experiment investigating pedestrians' responses to knife-based attacks. • The experiment uses financial incentives and an 'aggressor' to recreate a knife-based terrorist attack. • The results show that participants were stressed and reported fear during the experiment. • Both social influences and demographics had an impact on participants' response. Terrorist attacks have become both more frequent and deadlier in recent decades. Knife attacks by terrorists in particular are both high impact and relatively frequent. Understanding how individuals move when confronted by an attacker can save lives by informing the planning of transportation hubs, entertainment venues and other spaces where large numbers of people congregate. Using pedestrian dynamics to predict responses to such attacks has the potential to further improve the survival of those involved by providing insights to emergency service responders when a terrorist situation has occurred in a crowded venue. However, given the lack of appropriate data, it is currently impossible to accurately predict pedestrian movement responses to terrorist attacks. This paper describes a practical study that developed a methodology and implemented a set of experiments examining responses to knife-based attacks by an unexpected and hostile individual. The experiments used financial incentives and an 'aggressor' to recreate a knife-based terrorist attack in an ethically viable setting. The participants of this study (n = 80) were tracked using ultra wide-band sensors to provide temporal and spatial positional data in relation to the attacker and to each other. Participants subsequently completed a questionnaire to report their psychological response during the experiment, thus allowing psychological perception and movement responses to be compared. The results show that participants were stressed and reported fear during the experiment, indicating that the experimental approach is a potentially valid proxy for a real-life attack. The analysis highlights that participants' decisions to stay or run away were strongly influenced by the actions of other nearby participants, while participants' continuous movement response (movement speed) was highly dependent on relative position to the aggressor. Participant demographics were a factor with female participants less likely to move and slower moving, while older participants were also slower moving. From these results we have been able to construct a model of how pedestrians may react when faced with a sudden attack. We discuss our findings and their potential to contribute to emergency planning and response, finally we comment on opportunities for further study. [ABSTRACT FROM AUTHOR]

[Display omitted] • Evaluation of energy consumption of a high-speed transportation plan by dynamically simulating the entire railway system. • A non-negligible part of the energy consumption is due to the loss of energy in the electrification system. • Energy loss is different for each scenario, as it depends on the concurrency of trains within the electrification sections. • Through proper scheduling of services, it is possible to operate in a minimum energy loss scenario. The need to reduce overall energy consumption motivates the search for ways to improve energy use by transportation. Electrification has helped to significantly reduce the energy intensity of rail transportation. On today's high-speed lines, power flows between trains and traction transformers are generally transmitted through the 2x25 electrification system. Although most of the energy provided to the railway system is consumed by the moving trains, a part of the energy is lost in the electrification system itself. In this study, dynamic computer simulations of realistic high-speed rail operating scenarios are developed and the energy losses in the electrification system are evaluated. The results show that power transmission losses are significant, and their value depends on the operation scenario. Finally, a method for selecting the minimum transmission loss operating scenarios is proposed, without modifying the transportation capacity offered or altering the resources required for the operation of a high-speed rail. [ABSTRACT FROM AUTHOR]

• Adopt multi-row side transplanting method to achieve dense transplanting of seedlings. • Positions of transplanting and seedling conveying devices are matched and adjusted. • Based on the Arduino control system to achieve precise control of the machine. • Coincidence of the soil entry point and exit point greatly reduces damage to the film. At present, China is the leading country in sweet potato cultivation worldwide. However, there are still several problems such as high labor intensity, low efficiency of manual transplanting, and lack of machinery for side transplanting for sweet potato seedlings on mulch film. Therefore, a side dense transplanting machine for sweet potato seedlings on mulch film is designed, using a multi-row side transplanting method. It is mainly composed of a transplanting device, a seedling conveying device, a traveling device, and a control system. Based on the agronomic requirements for sweet potato transplanting, the study focuses on the structure and working principle of the complete machine. It involves the detailed structural design and motion analysis of the crank-rocker and cam of the transplanting device. Based on the sweet potato seedlings transportation plan, parameters such as the vertical height of the seedling conveying device, the size of the support frame, and the relative relationship between position of the transplanting device are determined. To ensure the smooth transplantation of sweet potato seedlings, the motor parameters required for each device are calculated and determined. An Arduino control system is developed to manage the coordinated movement of each device. Positioning components are integrated to ensure accurate coordination between the transplanting device and the seedling conveying device. This setup enables the seedling claws to pick sweet potato seedlings and perform the side transplanting on the mulch film. Field test results show the missed rate of seedlings, the qualified rate of transplanting and the qualified rate of transplanting depth are 4.13 %, 91.62 % and 93.86 % respectively. The transplant spacing coefficient of variation and the average spacing between seedlings are 1.90 and 177.06 mm respectively. All indicators of the transplanting machine meet the agronomic requirements for side dense transplanting for sweet potato seedlings on mulch film. The transplanting machine is integrated with sweet potato seedling transplanting agronomy, achieving mechanized transplanting of sweet potato seedlings, reducing labor intensity, and promoting the quality and efficiency of sweet potato production. This research provides a reference for the development of sweet potato seedlings transplanting equipment. [ABSTRACT FROM AUTHOR]

The long-distance cold chain transportation of fresh products from the place of origin to sale is costly due to the high probability of spoilage and high carbon emissions. The potential factors for the high cost of long-distance cold chain transportation are the spoilage of fresh goods caused by transportation vibration and the carbon emission caused by refrigerant use. How to minimize the impact of these factors through optimal transportation route planning is the current problem to be solved. Firstly, a longdistance cold chain logistics route planning model including goods spoilage cost and carbon emission cost is developed, considering the goods spoilage cost caused by transportation vibration and the carbon emission cost generated by refrigerant as well as several other factors. Secondly, an improved ant colony algorithm is used to solve the optimal solution of the model, and the algorithm is redesigned for the characteristics of the model. Thirdly, the pheromone update mechanism in the ant colony algorithm is improved by adding pheromone coefficients to accelerate the convergence speed of the algorithm. Finally, the validity of the model is verified by an arithmetic example. The model and method proposed in this paper help to reduce the cost in the process of long-distance cold chain transportation and bring practical application value. [ABSTRACT FROM AUTHOR]

[Display omitted] Monitoring transportation for planning, management, and security purposes has become a growing interest for various stakeholders. A methodology for detecting moving vehicles is based on the acquisition time gap between the pushbroom detector sub-arrays. However, this technique requires overcoming differences in ground sampling distance and/or spectral features of the sensor's bands used for change detection. The current work demonstrates a proof of concept for the VENµS satellite's capability to detect moving vehicles in a single pass with a relatively low spatial resolution. The VENµS Super-Spectral Camera has a unique stereoscopic capability because the two spectral bands, with the same central wavelength and width, are positioned at the extreme ends of the camera's focal plane. This design enables a 2.7-sec difference in observation time. The normalized difference moving object index (NDMOI) has been designed to detect moving vehicles using these bands without image preprocessing for dimensionality reduction or geometric corrections, as other sensors require. Results show the successful detection of small- to medium-sized moving vehicles. Especially interesting is the detection of private cars that are, on average, 2–3 m smaller than the VENµS ground sampling distance. Vehicle movement was effectively detected in different backgrounds/environments, e.g., on asphalt and unpaved roads, as well as over bare soil and plowed fields. Furthermore, a multitemporal analysis of moving vehicles during the Covid-19 pandemic in 2020 shows the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Traffic analysis zone (TAZ) is the basic unit of urban transportation planning. The appropriateness of TAZ delineation will affect the rationality of transportation planning and the accuracy of transportation analysis, and thus affect the final planning decision-making. However, in the big data era, there is still a lack of methods to integrate multi-source data for TAZ delineation. How to effectively fuse multi-source heterogeneous data in the TAZ delineation process is still an unsolved technical difficulty. To fill this gap, this paper designs a multi-source data-driven stepwise strategy to solve the TAZ delineation problem by creating a zoning system with zones showing homogeneous mobility behaviors and containing homogeneous land use characteristics. Firstly, mining the spatial-temporal travel features and land use information of transit stations and parcels from multi-source data, including transit smart card data, ride-hailing data, bike-sharing trip data, and point-of-interest (POI) data. Then, a core parcel determination algorithm which mainly consists of Fuzzy C-Means station clustering and a constructed parcel core degree function is proposed to generate the core parcels of each potential TAZ. After that, parcels are clustered around the core ones into TAZs through a multi-feature driven clustering algorithm, in the process of which the homogeneity within TAZs is guaranteed. Finally, the optimal zoning system is obtained by comparing the information loss calculation result of multiple zoning schemes. Taking Beijing as a case study area, 624 TAZs are obtained by applying the proposed method. The delineation result is comparatively analyzed with TAZs obtained by the traditional delineation method and a baseline method by applying multi-indicator measurement. The result reveals that the presented approach can promote the rationality of TAZ delineation. • A stepwise delineation model is proposed, which can fuse multi-source heterogeneous data and generate TAZs by data-driven. • The method ensures the optimal homogeneous zoning results in terms of mobility behaviours and land use within each zone. • The effectiveness of the delineation method is verified by taking Beijing as a case study area. • This paper provides innovative ideas on how to integrate multi-source big data to improve transportation planning. [ABSTRACT FROM AUTHOR]

Many travel surveys used in transportation planning still collect information over a single weekday while mobility and activity patterns fluctuate from day to day. However, the emergence of new continuous data sources has led to the generation of large amounts of longitudinal data. Therefore, this paper aims to value these two data types to cross-analyze the temporal variability of aggregated travel behaviors. A weighting method based on large-scale single-day survey samples was first developed to extend the representativeness of the daily trip diaries available in the three most recent Origin-Destination household travel surveys of Montreal, Canada, to the entire data collection period (four months in the fall). This method was then validated using passive data streams (smart card and count data) for three modes: car, subway, and bicycle. The variations in the daily use of these modes measured independently by the surveys and passive data were compared over several months of the fall period and throughout an average week. Indicators were also proposed to evaluate whether the two data sources capture the same trend and seasonality, especially the weekly rhythms. The results show that it is possible to accurately infer day-to-day variability in travel behavior from a large-scale cross-sectional single-day survey. Furthermore, this paper demonstrates the potential for complementarity between traditional surveys and emerging data (at least over the survey period), as well as the possibility to combine them since there are large similarities in the aggregated travel patterns they allow monitoring. [ABSTRACT FROM AUTHOR]

• Optimal protection against sea level rise can decrease transportation equity. • The model system incorporates hydrodynamic and transportation simulations. • A case study is based on the Communities of Concern in the SF Bay Area. • Accessibility and mobility of disadvantaged communities are studied. Transportation equity is an essential aspect of urban transportation planning. With climate change becoming inevitable, coastal cities are considering the mitigation of the impact of sea level rise on infrastructure. Transportation equity and sea level rise adaptation are usually considered separately. However, research pointed out that these two challenges could have considerable overlap and interaction. The present paper discusses transportation equity issues resulting from the impact of sea level rise and associated protection strategies. A case study of the San Francisco Bay Area points out cases where transportation equity can be negatively impacted when the optimal protection strategy against sea level rise is implemented. An integrated hydrodynamic and transportation model system is used in the present paper to demonstrate several scenarios where the most efficient protection strategies for the whole region increase the inequity that exists between the disadvantaged communities and other communities. Nevertheless, this impact can be mitigated with a relatively small addition to the protection strategy. The paper suggests that transportation equity cannot be overlooked in planning climate adaptation, as even the protection plan that maximizes benefits for the region may negatively impact the most vulnerable communities. [ABSTRACT FROM AUTHOR]

Transportation equity research and assessments undertaken by academics and practitioners stretches back decades. Despite the abundance of work, questions still remain about how decisions are made in the transportation planning process, including how routes are analyzed and prioritized. To understand this, we examine the potential equity outcomes of bus rapid transit routes that were neither funded nor constructed in three Canadian case studies (Ottawa, ON; Winnipeg, MB; and York, ON). Through a mixed-methods approach including spatial analysis, content analysis, and interviews, we analyze the equity implications of both built and unbuilt route options, and transparency in the planning processes. Relying primarily on publicly-available data may have limitations for understanding decision-making, yet it also provides insight into the transparency of processes. While in some cases, built BRT routes serve a larger proportion overall of high-need census tracts than proposed options, several unbuilt route options would have likely provided greater improvements for equity-seeking groups in these cities. Despite the push towards increased public engagement in transportation planning, we find a lack of explicit or systematic documentation as to why routes were discarded. With multiple priorities shaping transportation planning and decision-making, planners and policy-makers should be explicit in both the selection criteria used and how they are weighted. • Analysis of equity impacts of both built and proposed (unfunded/not built) BRT routes. • Built BRT generally serves areas with higher unemployment, immigration, and lower incomes. • Unfunded routes likely provide more significant improvements for equity-seeking groups. • Lack of explicit documentation on why route options were discarded. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an integral part of an intelligent transportation system (ITS) for proactive transportation planning and management in public transit network systems. However, due to the influence of passengers' travel modes and the difficulty of data collection, some traffic flow datasets are not only nonstationary in temporal distribution but also insufficient in data size, leading to inaccurate prediction results. Therefore, a transfer learning-based method is proposed to predict nonstationary traffic flow with inadequate data in the public transit system. In the proposed model, a novel approach named AdaRNN-DCORAL is presented to achieve accurate traffic flow prediction in the temporal dimension. Specifically, AdaRNN is an adaptive prediction network for nonstationary traffic flow data, while DCORAL performs domain adaptation between the source and target domain in the public transit system. First, two domains of traffic flow datasets in two public transit modes are established, which are the target domain with insufficient data and the source domain with sufficient data. Subsequently, the traffic flow datasets are divided into weekday and holiday time series based on their distribution characteristics. On this basis, AdaRNN-DCORAL is trained on the constructed datasets, thus achieving the precise nonstationary prediction of data scarcity in the target domain. Finally, the experiments demonstrate the improved performance of the proposed method over that of state-of-the-art models in terms of prediction accuracy and computation time. [ABSTRACT FROM AUTHOR]

Existing accessibility measures mainly focus on the physical limitations of travel and ignore travelers' perceptions, behavior, and socio-economic differences. By integrating approaches in time geography and travel behavior, this study introduces a bottom-up inclusive accessibility concept that aggregates individual-level travel perceptions across socio-economic groups to evaluate their multimodal access to opportunities. We classify accessibility constraints into hard constraints (physical space-time limitations to travel) and soft constraints (perceptual factors influencing travel, such as safety perceptions, comfort, and willingness to travel). We categorize travelers into 12 mutually exclusive socio-economic groups from a mobility survey dataset of 477 travelers. We apply a support vector regressor-based ensemble algorithm to estimate network-level walking perception scores as soft constraints for each social group. We derive group-specific inclusive accessibility measures that consider space-time limitations from transit and sidewalk networks as hard constraints and minimize the group-specific soft constraint to a certain threshold. Finally, we demonstrate the effectiveness of group-specific inclusive accessibility by comparing it with the classic access measure. Our study provides scientific evidence on how people of varying socio-economic statuses perceive the same travel environment differently. We find that socio-economically disadvantaged communities experience higher mobility barriers and lower accessibility while walking and using transit in Columbus, OH. Our study demonstrates a transition from person- to place-based accessibility measures by sequentially quantifying mobility perceptions for individual travelers and aggregating them by social groups for a large geographic scale, making this approach suitable for equity-oriented need-specific transportation planning. • Inclusive access considers both physical and perceptual barriers to travel. • Perceptions of safety and comfort vary with people's socio-economic statuses. • Existing infrastructure and services yield unequal access across communities. • High-income white populations experience transportation privileges over others. • Low-income communities with greater risk exposures perceive higher mobility barriers. [ABSTRACT FROM AUTHOR]

Although many cities are incentivizing non-auto modes of transportation in response to the climate crisis, their sustainable mobility transition efforts are being challenged by the rising intensity and frequency of heatwaves. Pedestrians are exposed to high levels of heat stress on hot days, which may reduce their willingness to walk. It is thus important to understand how heat affects pedestrian behavior and accessibility, so that climate mitigation strategies can be better targeted to support walking as a mode of transport but also as a first-/last-mile connection to public transit. In this study, we used a dataset of pedestrian trips undertaken during the summer of 2014 in Boston, MA. Along with several route attributes (such as length, turns, sidewalk width, amenities, Normalized Difference Vegetation Index, and Sky View Factor), we also included a measure of heat stress (Universal Thermal Climate Index - UTCI) to explain pedestrian route choice behavior. Using path-size logistic regression models, we found evidence to suggest that heat stress has a considerable and statistically significant effect on the perceived walking distance. We also found that the effect was non-uniform and possibly exponential. Additionally, we illustrated the extent to which heat stress can reduce pedestrian accessibility to important destinations (such as public transit). This reduction was significant on a typical summer day, with an even sharper reduction on the hottest summer day. Non-White residents were observed to have lower accessibility levels compared to all pedestrians, likely because of disparities in urban heat exposure. Our findings highlight the importance of incorporating heat exposure into transportation planning and urban design frameworks, especially with an equity lens to address unequal consequences. • Sustainable mobility transition efforts are being challenged by climate change. • We investigated the effect of heat on pedestrian route choice in Boston, MA. • Heat stress has a significant and non-uniform effect on perceived walking distance. • We showed how heat stress can reduce pedestrian access to important destinations. • Non-White residents were found to be affected more, raising equity concerns. [ABSTRACT FROM AUTHOR]