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Mixed-model assembly systems are widely used to guarantee higher flexibility, variety and throughput. In such assembly systems, fixed worker (FW) and walking worker (WW) workforce strategies are mainly adopted, influencing tactical and operational decisions with different and sometimes contrasting strengths and weaknesses. Thus, several aspects require a joint investigation to evaluate in which context a WW workforce strategy is preferable to the FW one. In this paper, the effect of assembly and station time variability on WW systems performance is investigated and compared to FW systems through a simulative study. Further, the number of workers and workstations and the effect of the travel time are also analysed. Results show that time variability has an impact on the throughput of both systems, in a different way. Starting from the target throughput and the given time variability among tasks, results derived from data elaboration allow us to propose a decision tree and a selection procedure for choosing between FW and WW systems, highlighting their most proper application conditions. According to the results, the time variability among tasks and the ratio between the number of workers and workstations are the two main drivers influencing the choice between WW and FW strategies. [ABSTRACT FROM AUTHOR]

The paper presents an analytical model for travel time evaluation in double-deep, triple-deep, quadruple-deep and fivefold-deep tier-captive shuttle-based storage and retrieval system (SBS/RS). Under the assumption of the deepest location rule for the storage process and the nearest neighbour rule for the retrieval process, the expressions for single-command and dual-command cycle times of the shuttle vehicle and elevator are developed, from which the SBS/RS performance can be estimated. The model based on the probability theory enables the evaluation of all individual times that make up the cycle times and assessment of the average number of totes relocated during a retrieval process. Special attention is devoted to the modelling of the relocation of totes which block access to the requested tote during the retrieval process. The validity of the analytical model is examined via the developed simulation model. The given case study shows that the proposed analytical travel time model results match the results of the simulation model. The presented modelling strategy could be applied to bigger system racking depths following the same modelling logic. [ABSTRACT FROM AUTHOR]

Developing an effective emergency collaborative distribution system is critical to improve on-time response performance, especially considering secondary disasters. To address the challenge, this paper investigates a bi-objective robust optimisation model on relief collaborative distribution among three echelons of authorities, the province, the municipality, and the county, which aims to minimise the total travel time and the total humanitarian logistics cost simultaneously. The optimal location of relief supply facilities and the relief distribution schemes will be determined by the optimisation model, which considers uncertain demand and travel time. Moreover, two robust optimisation methods are utilised to deduce the robust counterparts of the proposed model. An epsilon-constraint-based approach is used to solve the bi-objective optimisation model. A real-world case study based on an earthquake and aftershocks with different magnitudes in Yunnan Province is provided. The results show that incorporating secondary disaster scenarios contributes to reducing the total travel time and cost. For making full use of emergency resources and preventing situations from worsening, the centralised decision scheme is more effective than the decentralised one. The uncertainty of demand of primary disaster relief has a higher impact on the optimal solution than that of travel time. [ABSTRACT FROM AUTHOR]

The purpose of this study was to further explore the relationship between travel time and frequency of visits to collegiate recreation facilities. After surveying all students using collegiate recreation facilities at a large public southeastern US university, the local address of the 559 respondents was converted into travel time to their preferred facility using their preferred transportation method. This travel time was compared with swipe data on visits to the facility over a semester. A multinomial logistic regression was conducted to analyze the relationship between collegiate recreation facility usage level and travel time. Results support past research and ecological models of health behaviors demonstrating that proximity of recreation facilities is positively associated with higher usage by college students. [ABSTRACT FROM AUTHOR]

Traffic forecasting is crucial for a variety of applications, including route optimization, signal management, and travel time estimation. However, many existing prediction models struggle to accurately capture the spatiotemporal patterns in traffic data due to its inherent nonlinearity, high dimensionality, and complex dependencies. To address these challenges, a short-term traffic forecasting model, Trafficformer, is proposed based on the Transformer framework. The model first uses a multilayer perceptron to extract features from historical traffic data, then enhances spatial interactions through Transformer-based encoding. By incorporating road network topology, a spatial mask filters out noise and irrelevant interactions, improving prediction accuracy. Finally, traffic speed is predicted using another multilayer perceptron. In the experiments, Trafficformer is evaluated on the Seattle Loop Detector dataset. It is compared with six baseline methods, with Mean Absolute Error, Mean Absolute Percentage Error, and Root Mean Square Error used as metrics. The results show that Trafficformer not only has higher prediction accuracy, but also can effectively identify key sections, and has great potential in intelligent traffic control optimization and refined traffic resource allocation. [ABSTRACT FROM AUTHOR]

Travel time estimation (TTE) is a critical function in intelligent driving systems. Current research and applications related to TTE primarily focus on urban environments. The objective of this study is to develop TTE methods that are applicable to wilderness areas characterized by plateau and mountainous topography. We selected Transformer, which has greater robustness in capturing long-distance dependencies than LSTM, to develop a Transformer-based model. The model simultaneously integrates positional encoding and multi-head self-attention mechanisms with the objective of enhancing the accuracy of travel time predictions based on a substantial number of trajectory points in wilderness settings. A meta-learning strategy was employed to improve the model's generalization ability, thereby ensuring its applicability for accurate travel time estimation across a range of challenging environments. Two datasets were constructed based on measurements from two selected areas in eligible plateau and mountainous regions of western China. For each dataset, two categories of features were defined: terrain-weather features and spatio-temporal features. These categories were established in accordance with the influence of seven specific features on traffic conditions in both urban and wilderness areas. Experiments were conducted on both datasets utilizing terrain-weather features. When evaluated alongside the five models that are most commonly utilized in urban settings, the mean absolute percentage error (MAPE) of our model exhibited a 14.89% improvement in plateau environments and a 12.20% improvement in mountainous environments in comparison with the most effective model, namely MetaTTE-GRU. These findings substantiate the assertion that the proposed model is an effective means of estimating travel times in complex environments, and that it exhibits superior accuracy compared to existing LSTM-based estimation models. [ABSTRACT FROM AUTHOR]

Background: Methadone is an effective treatment for opioid use disorder; however, its provision in the US is limited to federally-regulated opioid treatment programs (OTP). Expansion of methadone treatment into non-OTP substance use disorder (SUD) treatment programs ('expanded methadone treatment access') is a promising intervention to increase access. Methods: We performed a cross-sectional geospatial analysis of public transit times to OTPs, expanded methadone treatment access, and other healthcare facilities as of March, 2024 in New York City (NYC). We estimated one-way public transit travel time and compared travel times using population weighted paired t-tests. Results: For OTPs, 38.2% (95% CI: 38.0, 38.4) of the NYC population was within 15 minutes and 79.7% (95% CI: 79.5, 79.9) was within 30 minutes. For expanded methadone treatment access, 72.1% (95% CI: 71.9, 72.2) of the NYC population was within 15 minutes and 97.5% (95% CI: 97.5, 97.6) was within 30 minutes. The mean travel time was 20.4 minutes (SD: 10.9) for OTPs and 12.1 minutes (SD: 7.1) for expanded methadone treatment access (difference: -8.3 minutes [95% CI: -8.5, -8.1]; P < 0.001). The mean travel time for expanded methadone treatment access was slightly longer than the mean travel time for dialysis facilities (difference: 0.22 minutes [95% CI: 0.06, 0.39]; P = 0.009]), not significantly different than Federally Qualified Health Centers (difference: -0.06 minutes [95% CI: -0.22, 0.11]; P = 0.51), and significantly shorter than the mean travel time to ambulatory surgical centers (difference: -6.3 [95% CI: -6.5, -6.0]; P < 0.001) and hospitals (difference: -8.1 [95% CI: -8.3, -7.9]; P < 0.001). Conclusion: Efforts to increase access to methadone treatment in the US should promote expansion to additional non-OTP outpatient SUD treatment programs. Such integration is anticipated to increase spatial accessibility of methadone treatment substantially, greatly enhancing the potential for patient access. [ABSTRACT FROM AUTHOR]

The article discusses a new method for processing tunnel seismic data to predict geological structures ahead of tunnel faces. The method, named CUGTSP, encompasses data preprocessing, compensation, bandpass filtering, reflected wave extraction, P-S wave separation, predictive deconvolution, and imaging. The study conducted in the Dananshan Tunnel in China validated the method's effectiveness in identifying discontinuities and faults. The article highlights the importance of removing noise from seismic records and suggests improvements in data collection and interpretation for better results. [Extracted from the article]

Shared autonomous vehicles (SAVs) are a promising first-mile mode to access public transportation. This study focuses on the mode choice analysis assuming that SAVs are introduced in Bangkok, Thailand. Stated preference experiments based on efficient design were conducted. The rescaled logit model was obtained from the nested logit trick on the combined revealed-preference and stated-preference data. The results showed that with the introduction of SAVs, the shares of existing taxi, motorcycle taxi (MC), and light public transit (LPT) modes will decrease by approximately 28.24%, 27.50% and 21.93%, respectively. These indicate that people who are accustomed to using taxi and MC as their first-mile services are respectively most likely and second-most likely to change their choices to SAVs. The values of times for income groups were estimated. Service providers should consider reducing travel cost to attract more lower-income users, and reducing travel time to attract more higher-income users. The average elasticities and simulation results imply that the improvement of the travel cost of SAVs should be a priority over travel time in order to effectively attract more individuals to the SAVs mode, and the improvement of travel time of SAVs leads to the greatest shift from taxi and MC. [ABSTRACT FROM AUTHOR]

Developing an integrated transportation system has required strategies to improve mobility in public transportation networks. This study aims to construct a route choice model incorporating heterogeneous travel behaviors and suggest policy improvements considering travel behavioral characteristics for the elderly in transportation planning. A stated preference survey has been established to explore the travel behaviors on public transit. The behavioral differences of elderly travelers are compared with young adult travelers by the parameters of travel time attributes. Travel time multipliers are developed to compare the time impedance for the travels on public transit using the concept of willingness to pay. Travelers are reluctant to increase the transfer time for all types, resulting in multipliers ranging from 1.91 to 2.11. The results provide the implications of transport policy for the elderly considering a convenient transfer on the integrated transit system in an aging society. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) travel planning is a complex task that involves optimizing both the routes and the charging sessions for EVs. Existing algorithms rely on single-objective optimization, which limits their ability to consider EV users' multiple, often conflicting objectives. In this paper, we introduce a new, genuinely multi-objective approach to EV travel planning, which can find Pareto sets containing multiple EV travel plans optimized simultaneously for multiple objectives. We focus on the bi-objective optimization for travel time and cost. To our knowledge, our algorithm is the first to perform such a genuine multi-objective optimization on realistically large country-scale problem instances involving 12,000 charging stations. We implemented our approach into a fully operational prototype application and extensively evaluated it on real-world data. Our results show that our approach can achieve practically usable planning times with only a minor loss of solution quality despite the very high computational complexity of the problem. [ABSTRACT FROM AUTHOR]

Authorities often adopt rehabilitation or redevelopment strategies to enhance the built environment (BE) of urban villages to address issues like traffic congestion, pollution, and public security. There is a lack of investigation into the specific impacts of the built environment on bus usage in urban villages with different renewal strategies. We utilized data from the 2018 Zhuhai resident survey and employed the XGB-SHAP model to examine the non-linear relationship and threshold effects of the BE on transit usage in urban villages undergoing rehabilitation or redevelopment. The finding indicated that travel time and distance are the most influential factors, while the density of bus stops and employment density have the greatest impact among the BE variables. The BE variables exhibit distinct nonlinear characteristics and display threshold effects, with significant difference in their performance. Our empirical evidence and policy implications provide valuable insights for promoting public transit usage in urban villages. [ABSTRACT FROM AUTHOR]

Public transport inherently involves encounters with other people. For women, negotiating everyday overcrowded, unsafe, and unreliable conditions is a major barrier to accessing public transport mobility that triggers emotions. Using qualitative research methods – in-depth interviews and visual surveys – this study delves beyond understanding the barriers and looks at the affective realm to comprehend how affects and emotions shape accessibility, acceptability, and affordability of public transport for women in Kolkata. The disruptive affects of overcrowded, unsafe, and unreliable conditions produce emotional ordeals, increase travel time and costs, and restrict mobility. The sense of despair that emerges compels women to adjust, accept, and even opt out of overcrowded, unsafe, and unreliable public transport more often than not. This paper argues that affects, emotions, reactions, and consequences are entangled and impact the accessibility, acceptability, and affordability of public transport. The contribution of this paper lies in bringing to the fore the need for feminist inquiries into gendered mobility inequalities and the role of affects and emotions therein. [ABSTRACT FROM AUTHOR]

This paper aims to predict compressional and shear sonic travel time logs using conventional logs that are easier and less expensive to acquire during the development cycle. The study uses a gradient boosting algorithm and incorporates a novel method of using statistical feature engineering with wavelet transform to improve prediction accuracy across thin beds in hydrocarbon reservoirs. The approach uses a discrete wavelet transform over the neutron log, a prominent feature, to identify thin layers and enhance prediction accuracy. The detailed coefficients are analyzed using Daubechies and Haar wavelets to reconstruct newly transformed data with an enhanced thin-layer signal. The Haar wavelet with three levels is the most optimum wavelet and decomposition levels. The algorithm for the reconstructed log shows an increased thin layer resolution, with an accuracy improvement of 6.8% for the prediction. The proposed approach significantly contributes to geologists, geophysicists, and reservoir technologists for reservoir characterization and safe drilling operations. [ABSTRACT FROM AUTHOR]

With the increased service quality standards from the demand side and competitions from the supply side, bus transit is facing great challenges. With exclusive lanes and specialized platforms, bus rapid transit (BRT) brings opportunities to comprehensively upgrade ground transportation services from dimensions of reliability, efficiency, and smoothness. To collectively minimize its punctuality deviations at stops, delays at intersections, and speed deviations compared with design speed along segments, a multiobjective goal programming model and its linearization is established to optimize speed and holding time for BRT operation trajectory. Fengpu Express line in Shanghai, China, is used as a case study to validate the performance of the proposed model. Results show that the average punctuality deviation, intersection delay, speed deviation, and passenger travel time for a single bus trip are reduced by 97.64%, 99.86%, 73.84%, and 13.22%, respectively. The average travel time of buses is shortened by 5.56%. Passenger waiting time and in-vehicle time are decreased by 29.49% and 11.21%. The performance of the joint method improves with the increase of the green ratio, the decrease of passenger boarding and alighting time, and the increase of maximum speed limits. Under the green ratio higher than present, passengers boarding and alighting time within −4 to 8 s increase of current stops, and 55 km/h and higher maximum speed limit, primary goals of on-time performance and low intersection delays could be best achieved. [ABSTRACT FROM AUTHOR]

Accurately analyzing the level of service (LOS) of signalized intersections is vital for evaluating signal control strategies. Currently, most research on LOS focuses on the factors that influence commuters' perceptions. However, these methods lack immediacy and universality, making them challenging to apply to dynamic and real-time scenarios. This study proposes an innovative framework for dynamically analyzing the LOS at signalized intersections using license plate recognition data (LPR data). This framework integrates free-flow speed estimation, average vehicle delay calculation from LPR data, unreasonable delay correction within windows, and dynamic LOS evaluation at signalized intersections according to Highway Capacity Manual (HCM). Specifically, Gaussian mixture models are employed for estimating free travel time at signalized intersections and calculating average vehicle delays using LPR data. The day is segmented into 144 10-min windows for LOS assessment, with each window's average vehicle delay determining the intersection's LOS according to HCM. The Local Outlier Factor algorithm is used to detect unreasonable abrupt changes in delays, then smoothing with Loess regression, ultimately achieving dynamic intersection delays calculation and LOS evaluation throughout the day. The method's efficacy and universality are validated using simulation data from SUMO and LPR data from three signalized intersections with different traffic patterns in Xiaoshan District, Hangzhou, China. The method's effectiveness in dynamically calculating intersection delays and evaluating service levels throughout the day underscores its potential for widespread use in traffic management and control strategies. Practical Applications: Level of service is an important indicator for measuring the traffic performance of a signalized intersection. This paper, based on license plate recognition data, achieves a dynamic evaluation of the level of service of an intersection. We used license plate recognition data to perform travel time calculation, data cleaning, free travel time fitting, and delay calculation. Then, using the standards from Highway Capacity Manual, we achieved a dynamic evaluation of the all-day service level for the intersection. We validated the efficacy and universality of our method through simulation and real-world data. Our method can be directly applied to intersections equipped with license plate recognition devices, allowing for dynamic evaluation of traffic performance throughout the day. Other practical applications include identifying peak hours, evaluating signal strategies, and depicting the traffic evolution process, etc. If real-time license plate recognition data can be obtained, our method can also calculate delays and evaluate service level in real time, demonstrating potential for widespread use in traffic management and evaluation. [ABSTRACT FROM AUTHOR]

Left-turn maneuvers pose significant challenges for connected and automated vehicles (CAVs) regarding both safety and traffic efficiency, particularly at mixed traffic unsignalized intersections. This paper addresses these challenges by proposing a multiple turning exit lanes (MTEL) approach. Inspired by behaviors of human drivers observed at unsignalized intersections, the MTEL approach aimed to optimize the turning paths of left-turn vehicles by using multiple available exit lanes. Given the uncertain intentions of surrounding human-driven vehicles (HVs), the decision-making processes for left-turn CAVs were formulated as a partially observable Markov decision process (POMDP) and addressed using an adaptive belief tree (ABT) algorithm. The results demonstrate that the MTEL approach provides an effective turning strategy for left-turn CAVs. Compared to conventional fixed-path approaches, the MTEL method achieved significant improvements, including a reduction in travel time by 10.9% and stops by 49.5% across all experimental scenarios. The proposed method proved effective in identifying feasible turning paths amid mixed traffic conditions, thereby enhancing the efficiency of left-turn CAVs while maintaining safety. [ABSTRACT FROM AUTHOR]

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. This study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide. [ABSTRACT FROM AUTHOR]

In adults, there is a link between socioeconomic status (SES) and cancer prognosis, notably due to increased time to diagnosis (TTD) in deprived population leading to the dissemination of the disease. In children, such an association has not been clearly reported. The objective of our study was to assess the impact of SES on TTD of childhood cancer and its potential consequences on cancer prognosis. We carried out a multicenter retrospective study based on the LOGAFTER multi centric database. We studied SES at the individual level (parental professions, family structure) and the ecological level (EDI score, travel time by car). We assessed the factors potentially associated with an increased TTD with a Cox regression model, and we illustrated TTD by categories by using Kaplan–Meier curves. 854 children were included. The median time to diagnosis was 28 days [12;64]. TTD differed significantly according to the type of tumor. An usual care pathway did not impact TTD. However, an initial management by professionals not usually involved in the specific childhood cancer context increased TTD. None of the SES ecological variables were strictly associated with an impact on TTD, and a trend was noted for single-parent families (increased TTD, p = 0.057). In our cohort, TTD did not impact on the vital and relapse status. In this study, the impact of SES on TTD in children on both the individual and ecological levels was not clear. However, we noted some keys at the individual scale that require further investigation to explain potential associations. [ABSTRACT FROM AUTHOR]

Many cities plan to grow cycling as a prominent mode to improve accessibility and environmental and financial sustainability. However, relatively few cities have made meaningful headway in this direction. Policymakers would be more inclined to implement the necessary interventions when they have certainty about potential demand, especially knowing where it is located in space. This paper introduces an approach to estimating potential cycling demand using agent-based modelling to determine who may benefit from switching from their current modes to cycling. People benefit when they obtain a similar or higher travel utility score when cycling between their daily activities than when using their existing modes. The model is based on individual mode selection, that all activities in the trip chain are included and can include detailed road and cycle network elements. The co-evolutionary mechanisms within the agent-based simulation allow us to test the potential for cycling relative to the performance of other modes on the network. The case for Cape Town, South Africa, shows that about 32% of those that travel would benefit from cycling based on their utility score. Understanding that travel time benefits are not the only criteria for mode selection, we apply a rejection sampling algorithm based on demographic factors to demonstrate that a more realistic, or pragmatic, cycling potential for Cape Town is in the region of 8%. The results also show that more than 46% of the observed pragmatic demand for cycling is concentrated in an area covering less than 7% of the study area. This has practical implications for policymakers to target interventions both in space and towards specific demographic market segments. [ABSTRACT FROM AUTHOR]

Insights into tourist travel behaviours are crucial for easing traffic congestions and creating a sustainable tourism industry. However, a significant portion of the literature analysed tourist travel behaviour by predefined tourist trip chains which result in the loss of more representative classification. Using tourist travel survey data from Nanjing, China, this paper presents an innovative methodology that combines the tourist trip chain identification and the trip chain discrete choice model to comprehensively analyse the travel behaviour of tourists. The discretized trip chains of tourists are clustered using the ordering points to identify the clustering structure (OPTICS) clustering algorithm to identify typical tourist trip chains, which will then be considered as the dependent variable in the nested logit model to estimate the significant explanatory variables. The clustering results show that there are two main categories, namely single and multiple attraction trip chains, and seven subcategories, which were named according to the characteristics of trip chains. The clustering result is analysed and three main trip chain patterns are derived. Departure city, travel cost, travel time, and travel mode show significant influence on the choice between single and multiple attraction trip chains. The urban attraction trip chain is more favoured by tourists with children, and the typical trip chain shows stronger dependence on travel intention. Visiting Lishui for the first time only affects the choice of the multiple suburban attraction trip chain. These findings are valuable for optimising tourist public transport infrastructure, promoting travel by public transport and better tourism management. [ABSTRACT FROM AUTHOR]

Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a novel approach to partition road networks through network modularity to produce data-driven static traffic assignment models from loop detector data on large road systems. The use of partitioning allows the estimation of the key model input of Origin–Destination demand matrices from flow counts alone. Previous network tomography-based demand estimation techniques have been limited by the network size. The amount of partitioning changes the Origin–Destination estimation optimisation problems to different levels of computational difficulty. Different approaches to utilising the partitioning were tested, one which degenerated the road network to the scale of the partitions and others which left the network intact. Applied to a subnetwork of England's Strategic Road Network and other test networks, our results for the degenerate case showed flow and travel time errors are reasonable with a small amount of degeneration. The results for the non-degenerate cases showed that similar errors in model prediction with lower computation requirements can be obtained when using large partitions compared with the non-partitioned case. This work could be used to improve the effectiveness of national road systems planning and infrastructure models. [ABSTRACT FROM AUTHOR]

In the context of last-mile logistics, the vehicle-drone delivery system increasingly snone a light on its commercial potential in logistics. It offered advantage of dispatching drones which were not influenced by road congestion, and even the parcels were transported from various origins to different destinations. Efficiently determining the appropriate synchronized vehicle-drone routes and times for pickup-and-delivery pairs in a traffic congestion scenario presented a significant challenge. We first presented a mixed-integer linear programming model and designed a tailored heuristic. Then, an improvement based on deep reinforcement learning (DRL) was proposed through learning a policy of selecting requests to remove and choose operators for the optimization of combined routes. Computational results showed the efficiency of the improved heuristic in computational time and solution quality. We have also applied the DRL-based improvement in realistic road congestion scenarios in Beijing and estimated potential annual cost savings of 10.49 billion CNY through the implementation of a vehicle-drone delivery system. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation and reduction of energy consumption during railway train movement on a straight track section with reduced freight wagon mass. A theoretical model was developed to simulate energy consumption based on input parameters, including train speed, track gradient, section length, travel time, and train mass. The results indicate that energy consumption increases by 18.9% as speed rises to 90 km/h and as gradients increase to 2.0‰, while energy consumption decreases by 14.5% on a descending gradient of 1.5‰, which corresponds to the expected dynamics of railway trains. These results are supported by experiments showing that the MAPE error does not exceed 1.9%, which can confirm the accuracy of the developed model. A comprehensive analysis of the potential reduction in energy consumption with reduced freight wagon mass was also conducted. Using a freight wagon design with a reduced mass of 2.3% allows for a reduction in energy consumption by 8–89 kW·h, depending on the length of the section and the speed of movement. [ABSTRACT FROM AUTHOR]

Autonomous driving has many positive impacts, such as improving driver and passenger safety, comfort, and traffic efficiency, but all these advantages are based on people's trust and acceptance of this mode of driving. Anthropomorphic driving can enhance the trust and comfort of drivers and passengers and is seen as a feasible measure to increase people's acceptance of autonomous driving. This paper reports the microscopic traffic simulation of three scenarios around a frequently congested intersection, using non-automated vehicles, autonomous driving vehicles, and anthropomorphic driving vehicles to explore their impact on traffic efficiency. The result shows that, compared to non-automated vehicles, both autonomous vehicles and anthropomorphic driving vehicles can improve traffic efficiency during congested periods, increase traffic volume per unit time, reduce the total travel time and time loss, and have a higher average speed. Compared to autonomous vehicles, anthropomorphic driving has a shorter total travel time and a similar time loss. In terms of average speed, anthropomorphic driving performed better than autonomous driving in terms of both congested and non-congested times. In summary, compared to non-automated vehicles, autonomous driving vehicles have a positive effect on improving traffic efficiency, while, compared to autonomous driving, anthropomorphic driving has more advantages in increasing traffic efficiency and reducing traffic congestion. [ABSTRACT FROM AUTHOR]

This paper deals with minimisation of fuel consumption under real driving conditions using a vehicle speed advisor. The aim is to explore the potential of speed profile optimisation in real driving conditions while assessing the suitability of an application which recommends the driver the optimal vehicle speed sequence that minimises the fuel consumption on a particular route. The speed advisor is based on solving the Optimal Control problem of covering a particular route with minimum fuel consumption with a defined time constraint. The approach presented was applied to and implemented on a real passenger vehicle to obtain a trade-off between fuel consumption and travel time for several trips on the route. Experimental results are presented with and without advisory. With speed advisor, the results approach the pareto front with lesser dispersion. On the other hand, without advisory, the dispersion is higher and largely above the pareto front. [ABSTRACT FROM AUTHOR]

The shape characteristics of flow hydrographs hold essential information for understanding, monitoring and assessing changes in flow and flood hydrology at reach and catchment scales. However, the analysis of individual hydrographs is time consuming, making the analysis of hundreds or thousands of them unachievable. A method or protocol is needed to ensure that the datasets being generated, and the metrics produced, have been consistently derived and validated. In this lab protocol, we present workflows in Python for extracting flow hydrographs with any available temporal resolution from any Open Access or publicly available gauging station records. The workflow identifies morphologically-defined flow and flood types (i.e. in-channel fresh, high flow and overbank flood) and uses them to classify hydrographs. It then calculates several at-a-station and upstream-to-downstream hydrograph shape metrics including kurtosis, skewness, peak hydrograph stage, peak arrival time, rate-of-rise, peak-to-peak travel time, flood wave celerity, flood peak attenuation, and flood wave attenuation index. Some metrics require GIS-derived data, such as catchment area and upstream-to-downstream channel distance between gauges. The output dataset provides quantified hydrograph shape metrics which can be used to track changes in flow and flood hydrographs over time, or to characterise the flow and flood hydrology of catchments and regions. The workflows are flexible enough to allow for additional hydrograph shape indicators to be added or swapped out, or to use a different hydrograph classification method that suits local conditions. The protocol could be considered a change detection tool to identify where changes in hydrology are occurring and where to target more sophisticated modelling exercises to explain the changes detected. We demonstrate the workflow using 117 Open Access gauging station records that are available for coastal rivers of New South Wales (NSW), Australia. [ABSTRACT FROM AUTHOR]

Urban planning and mobility analysis have traditionally been studied through observation or questionnaires, which can be time consuming and costly. However, the rapid advancement of technology has enabled tracking devices to be installed in individual vehicles, allowing the measurement of various values, particularly global positioning system (GPS) signals. The location data collected is accurate, regularly updated, and can offer valuable insights into people's movements and behavior. Because the amount of trajectory data is substantial and continues to increase over time, specialized platforms and skills are needed for its analysis. In this study, we developed large-scale analysis toolkits to extract insights, including trip statistics, origin--destination analysis, and hotspot identification from vehicle-based GPS trajectories. The toolkits are specifically designed to handle large-scale datasets using Apache Spark, an analytics engine capable of processing large volumes of data by distributing tasks across a Hadoop cluster for efficient processing. Algorithms for the analytics model were created to reconstruct trips based on their type of mobility, and trip locations were mapped using open data such as administrative boundaries and points of interest. We then verified our approach using real-world taxi data from Bangkok, Thailand. The results revealed that taxis had more vacant trips than busy trips, and the travel time and distance taken to search for passengers was longer than those taken to pick them up and drop them off. Taxi activity was concentrated in the city center and nearby areas, particularly those within the vicinity of transport-connecting hubs. Taxi stay hotspots were mainly areas near tourist attractions and parking hubs. Furthermore, we found that the processing performance of the proposed approach increased with the number of executor cores. This study comprehensively presented information on taxi travel patterns, service availability, hotspots, and processing performance using the developed trip analysis toolkits. [ABSTRACT FROM AUTHOR]

In the age of e-commerce, logistics companies often operate within extensive road networks without accurate knowledge of travel times for their specific fleet of vehicles. Moreover, millions of dollars are spent on routing services that fail to accurately capture the unique characteristics of the drivers and vehicles of the companies. In this work, we address the challenge faced by a logistic operator with limited travel time information, aiming to find the optimal expected shortest path between origin-destination pairs. We model this problem as an online shortest path problem, common to many last-mile routing settings; given a graph whose arcs' travel times are stochastic and follow an unknown distribution, the objective is to find a vehicle route of minimum travel time from an origin to a destination. The planner progressively collects travel condition data as drivers complete their routes. Inspired by the combinatorial multiarmed bandit and kriging literature, we propose three methods with distinct features to effectively learn the optimal shortest path, highlighting the practical advantages of incorporating spatial correlation in the learning process. Our approach balances exploration (improving estimates for unexplored arcs) and exploitation (executing the minimum expected time path) using the Thompson sampling algorithm. In each iteration, our algorithm executes the path that minimizes the expected travel time based on data from a posterior distribution of the speeds of the arcs. We conduct a computational study comprising two settings: a set of four artificial instances and a real-life case study. The case study uses empirical data of taxis in the 17-km-radius area of the center of Beijing, encompassing Beijing's "5th Ring Road." In both settings, our algorithms demonstrate efficient and effective balancing of the exploration-exploitation trade-off. [ABSTRACT FROM AUTHOR]

The General Transit Feed Specification (GTFS) is an open standard format for recording transit information, utilized by thousands of transit agencies worldwide. In this study, a new tool named GTFS2STN for converting GTFS data into the spatiotemporal networks is introduced. To analyze the travel time variability, it is important to transform a transit network to a spatiotemporal network to enable a comprehensive analysis of transit system accessibility. GTFS2STN is a new tool that converts General Transit Feed Specification (GTFS) data into spatiotemporal networks, addressing the lack of open-source solutions for transit analysis. The tool includes a web application that generates isochrone maps and calculates travel time variability between locations. Validation against Google Maps APIs shows that journey time (i.e., the summation of the transit time, walking time, and waiting time) differences in the Mean Absolute Percentage Error are typically within 12%. A before–after analysis shows that for the transit journey time in 2024 in Nashville, Tennessee, 8 out of 10 pivotal bus stops showed a significantly decreased journey time compared with the case of 2019. A further set of before–after analyses shows that although journey time between transit sites significantly dropped on May 2020 during COVID-19 emergencies, the journey time almost totally recovered to the before-COVID-19 level by November 2020. By supporting any valid GTFS schedule, GTFS2STN enables the analysis of historical and planned transit systems, making it valuable for long-term accessibility assessment and travel time variability studies. [ABSTRACT FROM AUTHOR]

This study presents a new approach to optimize the dynamic evacuation process through a dynamic traffic assignment model formulated using mixed-integer linear programming (MILP). The model approximates the travel time for evacuee groups with a piecewise linear function that accounts for variations in travel time due to load-dependent factors. Significant delays are transferred to subsequent groups to simulate delay propagation. The primary objective is to minimize the network clearance time—the total time required for the last group of evacuees to reach safety from the start of the evacuation. Given the model's computational intensity, a simplified version is introduced for comparison. Both the original and simplified models are tested on small networks and benchmarked against the Cell Transmission Model, a well-regarded method in dynamic traffic assignment literature. Additional objectives, including average travel time and average evacuation time, are explored. A sensitivity analysis is conducted to assess how varying the number of evacuee groups impacts model outcomes. [ABSTRACT FROM AUTHOR]

We observed high-quality waves from a repeatable airgun seismic source recorded by a linear ultra-dense seismic array across the Xiliushui fault zone, one of the inactive faults in the Qilian Mountain, on the northeastern margin of the Tibetan Plateau, China. We used Snell's law of seismic ray propagation to determine a simplified ambient velocity model. Based on the flexible and precise spectral element method, we computed broadband synthetic seismograms for a shallow low-velocity fault zone (FZ) to model the direct P-wave travel time delay and incident angle of the wavefield near the FZ. The FZ extent range and boundaries were inverted by apparent travel time delays and amplification patterns across the fault. According to prior information on the properties of the direct P-waves, we could constrain the inverse modeling and conduct a grid search for the fault parameters. The velocity reduction between the FZ and host rock, along with the dip angle of the FZ, were also constrained by the P-wave travel time delay systematic analysis and incoming angle of the P-waves. We found that the Xiliushui fault has a 70~80 m-wide low-velocity fault damage zone in which the P-wave velocity is reduced to ~40% with respect to the host rock. The fault damage zone dips ~35°southwest and extends to ~165 m in depth. The repeatability and environment protection characteristics of the airgun seismic survey and the economic benefits of a limited number of instruments setting are prominent. [ABSTRACT FROM AUTHOR]

With the development of smart cities and intelligent transportation systems, path planning in multi-scenario urban mobility has become increasingly complex. Traditional path-planning approaches typically focus on a single optimization objective, limiting their applicability in complex urban traffic systems. This paper proposes a multi-objective vehicle path-planning approach tailored for diverse scenarios, addressing multi-objective optimization challenges within complex road networks. The proposed method simultaneously considers multiple objectives, including total distance, congestion distance, travel time, energy consumption, and safety, and incorporates a dynamic weight-adjustment mechanism. This allows the algorithm to provide optimal route choices across four application scenarios: urban commuting; energy-efficient driving; holiday travel; and nighttime travel. Experimental results indicate that the proposed multi-objective planning algorithm outperforms traditional single-objective algorithms by effectively meeting user demands in various scenarios, offering an efficient solution to multi-objective optimization challenges in diverse environments. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasing research on EV route optimization, personalized path planning that caters to individual user needs remains underexplored. To bridge this gap, we propose the electric vehicle charging route planning based on user requirements (EVCRP-UR) problem, which aims to integrate user preferences and multiple constraints. Our approach utilizes topology optimization to reduce computational complexity and improve path planning efficiency. Furthermore, we introduce an improved ant colony optimization (IACO) algorithm incorporating novel heuristic functions and refined probability distribution models to select optimal paths and charging stations. To further enhance charging strategies, we develop a discrete electricity dynamic programming (DE-DP) algorithm to determine charging times at efficiently chosen stations. By combining these methods, the proposed IACO algorithm leverages the strengths of each approach, overcoming their individual limitations and delivering superior performance in EV routing and charging optimization. [ABSTRACT FROM AUTHOR]

This paper proposes the capacity flexibility models of transit networks to describe the ability of urban transit networks to accommodate the changes in passenger demands. In order to keep the variations of the average passenger travel time in a reasonable way, the concept of coefficient of variation (CoV) is introduced to measure and limit the deviation from a baseline time of the average passenger travel time. The CoV could transform the capacity flexibility values to non-dimensional form in order to compare the capacity flexibility characteristics of different scale transit networks on an equal basis. The genetic algorithm with deep search (GA-DS) for this problem is proposed to find the approximating optimal solution of the capacity flexibility models more effectively. The algorithm has been tested with benchmark problems reported in the existing literature. The optimal solution of the flexibility models shows that the flexibility values are further improved by reducing the number of stops for transit routes under different transit operation conditions. At last, the influence factors of the capacity flexibility of transit systems are discussed to address reliable transit services. [ABSTRACT FROM AUTHOR]

In this paper we analyze the traversability of static and evolving Schwarzschild-Anti-de Sitter wormholes. The wormhole metric under consideration is not asymptotically flat. Hence one can only embed this metric into the Euclidean space for a limited radius r max . For r > r max , an exterior vacuum spacetime should be matched to the wormhole spacetime. In the framework of f(T) gravities, we discuss the null energy condition that the matter supporting the wormhole should satisfy and find that the nontrivial form of f(T) is necessary. For the wormholes to be suitable for human to traverse, we consider the tidal force that a traveler would have felt during his trip. This leads to an upper bound of the traveler's velocity. Utilizing the velocity allowed, we will estimate the travel time through the wormhole. In the evolving cases, the wormhole should not be expanding too fast, otherwise the traveler may not be able to arrive at the other side of the wormhole. Besides this, for static wormholes, we briefly discuss the geodesics in the plane θ = π / 2 . [ABSTRACT FROM AUTHOR]

Queue length is one of the indicators of the state of traffic and is often used to measure the operational state of signalized intersections. Many studies have proposed estimating queue length from vehicle trajectory data (e.g., floating car GPS data); however, its sparse spatio-temporal distribution and low sampling frequency present substantial challenges in practice. In some jurisdictions, the widespread deployment of automatic vehicle identification (AVI) technologies presents the opportunity to improve queue length estimation at signalized intersections by combining AVI and trajectory data from floating (probe) vehicles. The method proposed in this paper is applicable for both under and oversaturated traffic conditions, is evaluated using field data [Next Generation Simulation (NGSIM) data set] and simulation data, and is compared to ground truth and the method proposed by the author Tan. The results from the field data evaluation indicate that the method provides a good estimation of the queue size (mean average error less than three vehicles for a floating vehicle penetration rate of 5% and a GPS sampling interval of 10 s). The simulation data evaluation indicated that the proposed method performs better than the Tan's method. [ABSTRACT FROM AUTHOR]

This study presents a novel method for assessing systemwide resilience of highway transportation systems by considering the likelihood of disruptions and the significance of critical links in the network while relying only on data available to state transportation agencies. This work expands on corridor-level resilience and vulnerability studies by expanding the methods to network-level analyses and incorporating both data-driven estimates and stakeholder inputs. Six criteria are used to estimate the criticality of roadway segments: annual average daily traffic, roadway classification, freight value, tourism value, social vulnerability index, and redundancy. To compute redundancy, this study adopts a Network Robustness Index (NRI) method, which computes the total system travel time under a closed link scenario. Individual criticality metrics are combined using a ranked weighting approach that incorporates stakeholder feedback through the Analytical Hierarchy Process (AHP). Vulnerability is assessed for floods, landslides, and earthquakes. The criticality and vulnerability of roadway segments are combined and scored as low, moderate, and high. The methodology is applied to the Arkansas state-maintained roadway network with approximately 32,186 km segmented into 70,000 links. Comparisons are shown across scoring and weighting approaches to identify top-ranked sites. The key contribution of this work is a network expanded approach to data-driven criticality and vulnerability assessment balanced with stakeholder insights with an emphasis on commonly available data. [ABSTRACT FROM AUTHOR]

We consider a hybrid ride-hailing platform, such as Uber, Lyft, and Didi, offering passengers the choice between a ride-sourcing service and a ride-pooling service. A passenger who chooses the ride-sourcing service can enjoy the trip alone, while passengers who pool can benefit from a lower trip fare; however, the latter may take more travel time (i.e., waiting time and in-vehicle time) and may require passengers to share their ride with strangers. When a passenger is matched to a distant driver, the ride-sourcing or ride-pooling service may get stuck in an inefficient wild-goose-chase (WGC) regime, wherein the driver spends a lot of time picking up passengers. To explore the non-WGC regime of ride-sourcing and ride-pooling services in the hybrid ride-hailing market, we build an equilibrium model that maximizes the profit of the platform, obtain the optimal trip fares of the two services, derive the non-WGC conditions for both services, and then compare them to those in the single market. We find that the interaction between the two services not only contributes to demand shifting effect, which adjusts fares and promotes differentiated pricing strategies, but also alters the non-WGC conditions of each service in the hybrid market, deepening their interdependence so that the waiting time of one service has a non-monotonic effect on the non-WGC regime of the alternative service. Moreover, benefiting from the coordinated complementarity of the two services with each other, passenger demand is more evenly distributed, which enhances real capacity and turnover, reduces the waiting time threshold (WTT) for each service to fall into the WGC regime under the mild condition, and contributes to travel efficiency overall. Such advantages of market diversity lay a solid foundation for ride-hailing market platforms to be able to offer more diverse service options. • Proposes a model to characterize the equilibrium in a hybrid ride-hailing market. • Determine the conditions under which each service in a hybrid market is in the non-WGC regime. • The complex interaction between the two services contributes to the demand transfer effect. • The diversification advantage of a hybrid market reduces the WTT of each service under mild conditions. [ABSTRACT FROM AUTHOR]

All-pair shortest path routing within stochastic road networks is often more complicated and computationally challenging than routing in deterministic networks because uncertainties in travel time are introduced. We develop and test a GPU-enabled approach to resolve this computational challenge. This approach is based on a simulation framework that includes four steps: simulation of road networks impedances, computation of candidate paths using an all-pair shortest path algorithm, travel time estimation across candidate paths, and all-pair optimal path calculations. We developed GPU algorithms to accelerate the second and third steps, which are computationally demanding for large road networks. We used the road network of Wuhan, China as a case study. Our experiments indicate that the GPU approach leads to thousands of times in improvement of acceleration, which makes all-pair shortest path routing within stochastic road networks computationally feasible. [ABSTRACT FROM AUTHOR]

Seismic inversion, a crucial process in reservoir characterization, gains prominence in overcoming challenges associated with traditional methods, particularly in exploring deeper reservoirs. In this present study, we propose an inversion approach based on modern techniques like sparse layer reflectivity and particle swarm optimization to obtain inverted impedance. The proposed sparse layer reflectivity and particle swarm optimization techniques effectively minimize the error between recorded seismic reflection data and synthetic seismic data. This reduction in error facilitates accurate prediction of subsurface parameters, enabling comprehensive reservoir characterization. The inverted impedance obtained from both methods serves as a foundation for predicting porosity, utilizing a radial basis function neural network across the entire seismic volume. The study identifies a significant porosity zone (>20%) with a lower acoustic impedance of 6000–8500 m/s g cm3, interpreted as a sand channel or reservoir zone. This anomaly, between 1045 and 1065 ms two‐way travel time, provides high‐resolution insights into the subsurface. The particle swarm optimization algorithm shows higher correlation results, with 0.98 for impedance and 0.73 for porosity, compared to sparse layer reflectivity's 0.81 for impedance and 0.65 for porosity at well locations. Additionally, particle swarm optimization provides high‐resolution subsurface insights near well location and across a broader spatial range. This suggests particle swarm optimization's superior potential for delivering higher resolution outcomes compared to sparse layer reflectivity. [ABSTRACT FROM AUTHOR]

Multimodal freight transport allows switching among various modes of transportation to efficiently utilize transport facilities. A multimodal transport system incorporates geographical scales from global to local. Travel time estimation in a multi‐modal cargo transportation network is essential for enhancing supply chain (SC) and logistics operations. Accurate travel time prediction is of great importance for cargo transportation, as it enables SC participants to increase logistics efficiency and quality. It requires adequate input data, which can be generated. In recent times, the machine learning (ML) algorithm has been well‐suited to resolve complex and nonlinear relationships in the collected tracking data. This study designs a deep learning‐powered travel time estimation in multimodal freight transportation networks (DLTTE‐MFTN) technique. The goal of the DLTTE‐MFTN technique is to estimate the travel time using a hyperparameter‐tuned ensemble learning approach. To achieve this, the DLTTE‐MFTN method initially undergoes data pre‐processing to convert the input raw data into a useful format. In addition, the singular value decomposition (SVD) model can be applied for feature dimensionality reduction in multimodal transport data, considerably improving travel time prediction. Besides, the DLTTE‐MFTN method estimates travel time using an ensemble of three DL approaches including one‐dimensional convolutional neural network (1D‐CNN), stacked autoencoder (SAE) attention, and recurrent neural network (RNN). Finally, the hyperparameter tuning of the DL models takes place using the whale optimization algorithm (WOA). The performance analysis of the DLTTE‐MFTN method takes place using the Kaggle dataset. The experimental results stated that the DLTTE‐MFTN technique attains superior performance over other ML and DL models. [ABSTRACT FROM AUTHOR]

An autonomous vehicle can sense its environment and operate without human involvement. Its adequate management in an intelligent transportation system could significantly reduce traffic congestion and overall travel time in a network. Adaptive traffic signal controller (ATSC) based on multi‐agent systems using state‐action‐reward‐state‐action (SARSA (λ)) are well‐known state‐of‐the‐art models to manage autonomous vehicles within urban areas. However, this study found inefficient weights updating mechanisms of the conventional SARSA (λ) models. Therefore, it proposes a Gaussian function to regulate the eligibility trace vector's decay mechanism effectively. On the other hand, an efficient understanding of the state of the traffic environment is crucial for an agent to take optimal actions. The conventional models feed the state values to the agents through the MinMax normalization technique, which sometimes shows less efficiency and robustness. So, this study suggests the MaxAbs scaled state values instead of MinMax to address the problem. Furthermore, the combination of the A‐star routing algorithm and proposed model demonstrated a good increase in performance relatively to the conventional SARSA (λ)‐based routing algorithms. The proposed model and the baselines were implemented in a microscopic traffic simulation environment using the SUMO package over a complex real‐world‐like 21‐intersections network to evaluate their performance. The results showed a reduction of the vehicle's average total waiting time and total stops by a mean value of 59.9% and 17.55% compared to the considered baselines. Also, the A‐star combined with the proposed controller outperformed the conventional approaches by increasing the vehicle's average trip speed by 3.4%. [ABSTRACT FROM AUTHOR]

Tourism trails connect destinations, points of interest, and travel-related businesses. By enhancing connectivity, these trails reduce travel time, allowing tourists to maximize their exploration of sites, leading to more efficient and satisfying travel experiences. The rising popularity of e-scooters in urban areas has highlighted the need to identify safe and accessible routes, particularly in cities where safety concerns have led to restrictions. Multi-Criteria Decision Analysis (MCDA) and Geographic Information System (GIS) network analysis can be employed to determine optimal paths by considering multiple criteria. This study focuses on finding an optimal street art trail for e-scooters in Bukit Bintang using Fuzzy-AHP (FAHP) and GIS. The objectives of this study are to identify the criteria of optimal pathway conditions for e-scooters and to develop a framework to find potential trails for e-scooters by using FAHP and GIS. FAHP was used to compute criterion weights based on expert input, revealing Path Facility as the most critical factor (weight: 0.495), followed by Slope (0.194), Traffic Speed (0.172), and Obstruction (0.139). These weights were integrated into a GIS-based network model, with the Travelling Salesperson Problem (TSP) method applied to identify the optimal e-scooter route. The resulting map showcases the ideal e-scooter trail connecting street art locations, alongside details on path conditions, street art sites, nearby train stations, and e-scooter rental stations. This methodology effectively combines FAHP and GIS to determine optimal routes, aiding authorities in improving e-scooter infrastructure. Additionally, the map can be integrated into e-scooter-sharing applications or displayed at key transportation hubs to guide riders. [ABSTRACT FROM AUTHOR]

The effects of Connected and Autonomous Vehicles (CAVs) on interchanges such as full-cloverleaf interchanges have not yet been examined in prior studies. This study aims to explore the impacts of CAVs on traffic operation and safety at full-cloverleaf interchanges. It also assesses the influence of various Market Penetration Rates (MPRs) of CAVs on travel time, queues lengths, and conflict points considering various weaving lengths using VISSIM. The model calibration and validation were conducted considering different measures of effectiveness (throughput, speed, and travel time). The finding showed that higher MPRs of CAVs led to consistent travel time reductions across on-ramps, off-ramps, and weaving segments. Specifically, moving from the base scenario to 100% MPRs, travel times decreased by 46%, 41%, and 32% at the three interchanges under investigation, respectively. Additionally, safety evaluations utilizing the Surrogate Safety Assessment Model demonstrated considerable declines in the numbers of vehicle conflicts with increases in MPRs, averaging reductions of 20%, 32%, and 26% at the three interchanges from the base scenario to full MPRs, respectively. Also, it was found that increasing the weaving lengths enhanced traffic operation and safety. Overall, the findings highlight the promising benefits of integrating CAVs into the transportation system at full-cloverleaf interchanges. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles (AVs) is poised to transform urban landscapes, notably by altering perceptions of travel time value, reshaping parking structures, and introducing additional travel demand through empty-cruising and self-parking capabilities. These changes are anticipated to influence both residential and parking location choices. This study delves into the complexities of these decisions by integrating insights from 28 modelling studies on residential and/or parking location choices in the context of AVs and by proposing an evaluation framework for future modelling efforts. A critical gap identified is the oversight of empty-cruising and parking costs in the context of residential relocation choice for owners of privately-owned AVs. Furthermore, no study has adopted a joint-choice modelling approach for residential and parking location choices. By offering guidelines for the incorporation of these elements, this study contributes to the development of a holistic residential and parking location choice model with AVs, thus providing a valuable roadmap for future research in urban planning and transportation economics. [ABSTRACT FROM AUTHOR]