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Analysing bus travel time is essential for providing valuable information to users for effective journey planning. This study aimed to analyse bus travel time in Klang Valley, Malaysia, which includes the urban centre of Kuala Lumpur as well as the surrounding suburban areas. A two-part study is presented here. The findings on superiority of proposed mixture model, namely the Burr XII mixture model as a new statistical distribution to fit travel time distribution based on day-to-day variability under spatial (number of signalized intersections, link length, route type) and temporal aggregation (15 minutes aggregated, peak/non-peak period) are validated in the previous publication. Based on the results, the study was deepened to analyse the effects of some factors on travel time variability. As an extension, factors and indicators influencing multimodality travel time patterns on all the bus sections are examined in this paper. This research explores the impact of several factors outlining the usage of Burr XII mixture regression model, particularly the quantile regression analysis on three distinct routes, each exhibiting unique characteristics, originating from both suburban and urban areas. The results have demonstrated that the factors vary depending on the time of day and coverage area. By gaining an understanding of and effectively addressing the crucial elements that influence travel time, the overall effectiveness of bus systems can be enhanced. [ABSTRACT FROM AUTHOR]

Roads in mesoscopic traffic simulation models are often parametrized by a free speed attribute, which describes the speed at which a vehicle can traverse a road in the absence of congestion. Unlike microscopic models, mesoscopic models do not typically simulate traffic lights, individual driver behavior or acceleration and deceleration of vehicles. In consequence, the free speed parameter of each road must account for all the aforementioned effects. Modeling free speed accurately is important, as it determines the travel time, which in turn affects the decision-making of agents in the simulation. This paper introduces an approach combining microscopic simulation with machine learning models to estimate road segment free speeds. Using the microscopic simulator SUMO, we generate training data for a model search employing Bayesian optimization. Subsequently, these models undergo fine-tuning via gradient-based optimization using real-world point-to-point travel times. A significant advantage is the adaptability of this approach to diverse road networks, including those from OpenStreetMap, and the ability to incorporate routing data from various online providers independently. Our evaluation illustrates a notable decrease in prediction error between 30% and 60% compared to baseline models that assume a uniform free speed reduction for all urban roads. The fine-tuned models are able to generalize well to unseen regions and are therefore applicable to case studies where data for new or altered roads is unavailable. [ABSTRACT FROM AUTHOR]

This paper studies the effectiveness of the bus priority lane (BPL) for public transport buses in the city of Bengaluru in India. We use the travel times on the BPL corridor as a measure of the effectiveness of the BPL. We find that there is a significant improvement in the travel times after the introduction of the BPL; for the worst 10% of the travel times, we find an improvement between 4% and 28%. Our methodology involves extracting trips on the BPL and computing the travel times for these trips from a time series of GPS information. Our methodology is scalable and can be used to compute the travel times between any two given points in other similar studies. We supplement our results with a novel test (called the D-test) for comparing the levels of stressful driving in the following scenarios: (a) morning peak hours (IST 07:00 h to 11:00 h) versus evening peak hours (IST 17:00 h to 21:00 h), and (b) northward trips versus southward trips on the BPL. We are able to infer that the drivers are generally more stressed during the morning peak hours and during the southward trips on the BPL. Partitioning the BPL into segments, we show that a majority of the segments exhibit similar effectiveness and driver stress trends as the full BPL stretch. We anticipate that corrective measures for the betterment of travel times and driver stress levels (e.g., introducing additional buses subject to vehicle re-balancing constraints, carefully planning the bus schedules to regulate bus traffic throughout the day, etc.) in some segments can lead to further improvements in travel times and reduction in driver stress levels. • Implementation of the Bus Priority Lane can reduce travel times. • Segmented analysis helps identify traffic bottlenecks for targeted intervention. • Driver-stress hot-spots can be identified for targeted intervention. • A novel statistical test compares driving stress levels across a pair of routes. [ABSTRACT FROM AUTHOR]

The efficiency of the planning process in public transport is represented through different measures, which are practically impossible to optimize simultaneously. This study defines a bi-objective optimization problem for the transit network design to analyze the trade-off between minimization of travel times and reducing monetary costs for passengers (which was not addressed in the literature) while considering a hard constraint for operational costs. Indeed, the minimization of monetary costs for passengers is relevant in transport systems without a complete integrated fare system, where passengers may pay for each trip-leg; thus, modeling monetary costs for users is essential when referring to the system's accessibility and route choice. To achieve our goal, we implement an epsilon-constraint algorithm capable of obtaining high-quality approximations of the Pareto front for benchmark instances in hours of computational time, which is reasonable for strategic planning problems. Numerical results show that the conflict between both objectives is evident, and it is possible to identify the more useful lines to optimize each objective, leading to relevant information for the decision-making process. Finally, we perform a sensitivity analysis on the budget parameter of our optimization problem, showing the classic trade-off between the operational costs and the level of service in terms of travel time and monetary cost. • Our study defines a bi-objective problem for the transit network design. • This study analyzes the trade-off in reducing travel times and monetary costs. • We identify the lines that have a more significant impact on each objective. • Our model is a flexible tool to obtain information about transport systems. [ABSTRACT FROM AUTHOR]

Increasing congestion levels and their elusive impact warrants the development of congestion mitigation strategies. Quantifying congestion and analyzing the spatiotemporal variations is imperative to achieve this target. Travel time is being explored as a measure of congestion with the advent of Intelligent Transportation Systems (ITS) and the deployment of related technologies. Researchers identified congested conditions when the average travel time exceeds 1.33 or 1.66 times free-flow travel time (FFTT). It is well known that travel time under congested conditions (T c) is more sensitive to land use, road geometry, and traffic control characteristics than the FFTT. Therefore, the extension of FFTT to derive T c may not be appropriate. This study focuses on developing a hazard-based model to derive T c. Travel time is modeled using a parametric accelerated failure time (AFT) model. The applicability of the proposed methodology is justified using empirical and simulated datasets. The T c derived from the AFT model is close to the travel time for the level of service (LOS F). Based on the T c , a new measure of congestion, termed congestion index (CI), is proposed. The proposed index can quantify the frequency and intensity of congestion on a link or network. The traffic states identified based on CI were mapped on the fundamental diagram (FD) and the macroscopic fundamental diagram (MFD). It was concluded that if travel times are uncertain and unstable under low-density conditions, then the ascending leg of the FD or MFD can be marked congested. Uncertain and unstable travel times indicate that traffic flow is unstable, and therefore, it can be concluded that traffic instabilities significantly affect congestion. • A hazard-based duration model is applied to model travel times to derive travel time under congested conditions. • Travel time corresponding to the inflection point of hazard function was considered as travel time under congested conditions. • Instabilities in travel times under low-density conditions mark the ascending leg of the fundamental diagram as congested. • The application of the study is demonstrated by evaluating the effect of traffic signal control. [ABSTRACT FROM AUTHOR]

The effect of pre-slaughter risk factors was evaluated on the behavior of 2,124 batches of steers and 1,937 batches of females. The factors that caused excitable behavior in the batches were sex, handling practices, vehicle load density, travel time and carcass fat. Sex had the greatest influence on behavior, with the female batches being 70.7% more likely to display excitable behavior. In the overall analysis, the condition of the installations and the handling practices influenced the appearance of excitable behavior in the female batches, which increased by 31.1% and 38.8% when the conditions were regular and inappropriate, respectively, compared to when the handling was appropriate; behavior in the batches of steers was also affected. There was a greater chance of excitable behavior appearing in batches where the journey lasted longer than 360 min, when the batches were analyzed together or when separated by sex. Increases in load density (kg/m2) caused a greater chance of excitable behavior regardless of the sex of the cattle. In animals with a calmer behavior, the fat cover on the carcass was thicker. Appropriate handling on the farms, and care with animal welfare when loading and transporting the cattle, determine less-reactive behavior. • Travel time and load density modify cattle behavior. • Pre-slaughter behavior of beef cattle varies according to sex and environment. • Adaptations on farms and at slaughterhouses determine fewer losses in the beef production chain. [ABSTRACT FROM AUTHOR]

Closing delivery units increases travel time for some women. Whether increased travel time is associated with maternal outcomes is important for understanding the consequences of such closures. Previous studies are limited in measuring travel time and restricted to the outcome of caesarean section. Our population-based cohort includes data from the Swedish Pregnancy Register for women giving birth between 2014 and 2017 (N = 364,630). We estimated travel time from home to the delivery ward using coordinate pairs of actual addresses. The association between travel time and onset of labour was modelled using multinomial logistic regression, and logistic regression was used for the outcomes postpartum haemorrhage (PPH) and obstetric anal sphincter injury (OASIS). Over three-quarters of women had ≤30 min travel time (median 13.9 min). Women who travelled ≥60 min arrived to care sooner and laboured there longer. Women with further to travel had increased adjusted odds ratio (aOR) of having an elective caesarean section (31–59 min aOR 1.11; 95% confidence interval [CI] 1.07–1.16; ≥60 min aOR 1.25; 95% CI 1.16–1.36) than spontaneous onset of labour. Women (at full term with spontaneous onset) living ≥60 min away had reduced odds of having a PPH (aOR 0.84; 95% CI 0.76–0.94) or OASIS (aOR 0.79; 95% CI 0.66–0.94). Longer travel time increased the odds of elective caesarean section. Women with furthest to travel arrived sooner and spent more time in care; although they had a lower risk of PPH or OASIS, they also tended to be younger, have a higher body mass index and were Nordic born. [Display omitted] [ABSTRACT FROM AUTHOR]

This study proposes an anisotropic concentration diffusivity model in the Reynolds-averaged Navier-Stokes equations (RANS) and the Eulerian dispersion model. The proposed model combines models to consider the turbulent anisotropic and near-source limited diffusivity based on the generalized gradient-diffusion hypothesis and travel time. The proposed model and conventional isotropic models were applied to predict the pollutant dispersion in an atmospheric boundary layer from elevated and ground-level sources. The predicted concentration profile and plume half-width were validated with a previous wind tunnel experiment in the literature. Both the proposed and isotropic models using the diffusivity limiter accurately predicted the mean concentration profiles at the central vertical plane. The isotropic models did not accurately predict the horizontal and vertical plume widths of the ground-level source while the proposed model successfully predicted those. The equivalent turbulent Schmidt numbers in the proposed model differed in each direction. The proposed model predicted counter-gradient turbulent diffusion in the streamwise direction. In addition, accurate Reynolds stress was found to be crucial for reproducing the anisotropic concentration diffusivity in the proposed model. [ABSTRACT FROM AUTHOR]

The emphasis on environmentally friendly solutions is steadily increasing in the transport sector. The topic of this article is a discussion of the long-term European initiative shift to rail. This initiative is confronted with historical facts, using the rail connection between Prague and Pilsen in Czechia. Numerous modernisation activities have been carried out on this line and have led to a qualitative change in transport, albeit on a conventional railway line and not high-speed rail (HSR). The paper evaluates whether a significant rail infrastructure upgrade leads to a change in transport and residential behaviour. The evaluation relies on identifying relevant upgrade projects and assessing the costs of upgrading this conventional railway. According to the results showing a significant increase in ridership, passengers consider changes, frequency, and travel time to be the crucial factors of the fundamental change in service quality caused by the modernisation of the line. These changes are confirmed and emphasised by 29 in-depth interviews with new or more frequent rail passengers using the train connection between Prague and Pilsen, identifying individual preferences and motives for changing travel behaviour. The results show that an upgrade of a conventional rail line (comparable in cost to the construction of HSR in Spain) lead to significant savings in travel time of 17 % and motivated operators to increase frequency by almost 50 %, increasing ridership to more than double. Passengers' interviews revealed more topics, such as services, comfort, and the difficult parking situation in Prague possible determinants for relocation and daily commuting. • A significant rail infrastructure upgrade leads to a change in travel behaviour. • 17 % savings in travel time and higher frequency increased ridership to more than double. • Services on board and comfortable journey are great motivators to travel by train. • The interviewees recognise the 'positive utility' of travel. • Good train connection can contribute to moving from Prague to Pilsen. [ABSTRACT FROM AUTHOR]

Urban expressways are one of the largest infrastructure investment projects in cities. Nevertheless, the impact of expressway construction on alleviating travel time has long been controversial. The conventional approach calculates travel time utilizing origin-destination (OD) flow from taxi trajectory data. However, OD flow typically overlooks data scarcity and individual choices, resulting in biased travel time estimates. This study proposes a novel method for travel time estimation based on path flow that considers the real space-time paths and driving directions. Subsequently, we employ the difference-in-differences approach to assess the effect of expressway construction on travel time. Experiments performed in Wuhan, China, indicate that expressway construction has a substantial impact on reducing travel time, exhibiting considerable spatiotemporal heterogeneity in this effect. Moreover, the built environment notably influences travel time. These findings offer valuable insights for urban planners and transportation policymakers to aid in the development of pertinent policies. • We propose a novel method to estimate travel time based on a path flow perspective. • We use a DID approach to assess the impact of expressway construction on travel time. • We examine the role of the built environment on travel time. • Expressway construction has a substantial impact on reducing travel time. • The impact of expressway construction on travel time shows a ring-layer effect. [ABSTRACT FROM AUTHOR]

• Route usage patterns of metro passengers is studied using smart card data. • Considering the dynamic characteristics of travel time in the metro system. • Travel time between OD pairs is portrayed with a mixture log-normal distribution. • Employed the Hidden Markov Chain to realize adaptive parameter learning. Due to the dynamic changes in timetables, passenger demand, and passenger composition, the distribution of passengers within a metro system becomes quite complex. Many studies divide a day into intervals to account for the dynamics of travel time. However, the intervals used in these studies are insufficient to capture the gradual and fine-grained changes in passenger travel patterns. This study proposes an adaptive dynamic route inference model (ADRIM) that overcomes these limitations. In the ADRIM, we introduce a constrained Expectation Maximization algorithm (CEM) by confining the parameters of the mixture log-normal distribution model (MLND) within confidence intervals, thereby reducing anomalous estimations. We use the concept of Hidden Markov Models (HMMs) to achieve a parameter-adaptive characterization for the dynamics of route choice and travel time distributions for MLND through an iterative process. For a Nanjing metro case study, the proposed model exhibits superior performance in fitting the actual distribution of travel times and accurately captures the dynamic trends in route travel times. Besides, it is revealed that the maximum difference in expected travel times among multiple valid routes for the same origin–destination (OD) pair primarily falls within the interval [5 min, 15 min], and the distribution range of the maximum ratio is mainly between [1.1, 1.6]. The high consistency in passenger route choice proportions observed for two consecutive weeks, along with an analysis of route choice patterns under dynamic conditions, serves as strong evidence supporting the reliability and practical utility of the dynamic route inference model in understanding and managing metro passenger flows. [ABSTRACT FROM AUTHOR]

Accurate vessel travel time estimation is essential for operational efficiency and route optimization. Despite the prevalent use of the automatic identification system (AIS) in garnering multifaceted real-time data, ambiguities and inaccuracies in travel time predictions persist. It leads to planning uncertainties, inefficient resource allocations, and heightened operational costs in maritime logistics. To addresses these issues, this paper proposed a nuanced method to enhance the precision and reliability of vessel travel time estimations. Firstly, a directed maritime network is constructed by extracting essential information from AIS-based historical vessel trajectories. This lays the foundation for the subsequent analytical processes. Secondly, the non-parametric kernel density estimation (KDE) is applied to this constructed network, enabling the estimation of vessel travel time distributions across various network links. The non-parametric KDE is used in combination with AIS data, which improves the specificity and accuracy of the travel time estimations at the link level. Finally, this paper employs cellular automata (CA) simulations to validate the accuracy of the KDE-based estimations. Comparison between the simulation results and real-world data reveals a high degree of accuracy in the proposed method, confirming its applicability and effectiveness in estimating vessel travel times. • Develops an algorithm for building a directed shipping network from vessel trajectory data. • Utilizes KDE for precise estimation of vessel travel time distributions within the network. • Exhibits superior performance in travel time estimation compared to traditional models. [ABSTRACT FROM AUTHOR]

• A metro station resilience model for addressing passengers and infrastructures is developed. • Impacts of passenger queuing delay on station resilience are examined. • Station resilience varies dominantly with the facilities types and queuing delays. • Failures of auto-ticket gates would have the highest impact on station resilience. • Transfer stations would be more important once more than half of infrastructures fail. Metro stations are important infrastructures ensuring people's daily commuting with tremendous travels every day, but susceptible to disruptions posing challenges in the resilience of the metro system. Previous studies mainly contributed to the resilience of the whole metro network, neglecting the resilience methodology and analysis of important stations. Integrating the infrastructure characteristics and the evolvement of passengers, a metro station resilience analysis methodology is developed. The approach is demonstrated with the stations of the metro system of Xi'an, China. Results show that station resilience varies differently across stations dominated by infrastructure types and queuing delay. Based on the comparison analysis under different infrastructure failures, failures of auto-ticket gates would have the highest impact on station resilience. When incidents on one infrastructure last for a period of time over 15 min, the resilience of station would be reduced by 9.8%, and dramatic resilience reductions could be observed if there are short-time incidents within 5 min on more than five infrastructures of a station. The findings would have practical significance for the resilience improvement of metro stations in infrastructure planning and passenger flow control against metro incidents. [ABSTRACT FROM AUTHOR]

• We conducted two discrete choice experiments for farmers and consumers in SFSCs. • Consumers are less sensitive to outlet types than farmers. • Relational proximity is valued by consumers, but only if logistics are manageable for farmers. • Farmers value mutual aid, reducing prices when collaborating with peers. • Both consumers and farmers prefer short travel times, but farmers accept longer ones. In France, Short Food Supply Chains (SFSCs) operate under specific regulatory frameworks, involving either direct sales or transactions with no more than one intermediary. SFSCs are of particular interest to local communities as they offer a potential pathway to enhancing food security through the local provisioning of urban areas. This study investigates both consumers' and farmers' preferences for different SFSC outlets. Harmonized discrete choice experiments (DCEs) were conducted in 2022 in France, involving 1,021 consumers and 154 market gardeners, focusing on the consumption and sale of fresh vegetables. The objective is to examine the consistency and divergence between farmers' and consumers' choices among different outlets and attributes. The results indicate that consumers are less sensitive to the type of outlet than farmers. For consumers, relational proximity—direct interaction with farmers—emerges as a key factor, whereas farmers value this proximity only when it does not entail additional logistical burdens. Furthermore, mutual aid among farmers is highly appreciated, with farmers willing to lower prices when collaborating on tasks such as delivery. The findings suggest that travel time and outlet accessibility are critical, with longer travel times negatively affecting consumers more than farmers. These insights provide valuable guidance for enhancing SFSCs development, suggesting that policymakers should prioritize improving outlet accessibility, promoting farmer collaboration, and addressing logistical costs to better align supply with demand. [ABSTRACT FROM AUTHOR]

Travel time to health facilities is one of the most important factors in evaluating health disparity. Previous extensive research has primarily leveraged the driving time to the nearest health facility to gauge travel time. However, such ideal travel time (ITT) may not accurately represent real individual travel time to health services and is often underestimated. This study aims to systematically understand such gaps by comparing ITT to actual travel time (ATT) derived from smartphone-based human mobility data and further identifying how various population groups across regions are most likely to be affected. This study takes mental health as an example and compares ATT with ITT to mental health facilities. Results indicate that ITT and ATT demonstrate significant disparities between urban and rural areas. ITT is consistently underestimated across the contiguous US. We compare travel times among diverse sociodemographic groups across eight geographical regions. The findings suggest that different age groups have similar travel times to mental health facilities. However, racial groups exhibit varied travel times. Hispanics have a larger percentage of the population experiencing longer ATT than ITT. We also employed spatial and non-spatial regression models, such as Ordinary Least Squares, Spatial Lag Model, and Spatial Error Model, to quantify the correlation between travel times and socioeconomic status. The results revealed that the proportion of older adults and high school dropouts positively correlates with travel times in most regions. Areas with more non-Hispanics show positive correlations with both travel times. Overall, this study reveals pronounced discrepancies between ITT and ATT, underscoring the importance of using smartphone-derived ATT to measure health accessibility. • Utilize smartphone mobility data to determine actual travel times to mental health facilities across the United States. • Examine geographic and demographic variations in travel times to mental health facilities nationwide. • Reveal limitations of conventional access metrics by identifying disparities between ideal and actual travel times. [ABSTRACT FROM AUTHOR]

• There are major cross-provincial differences in child vaccination coverage in PNG. • Travel time to health facilities lowers vaccination coverage among children in PNG. • Travel time to health facilities disproportionately affects vaccination among poor. • Cross-provincial coverage differences exist even after accounting for travel time. With infant and child mortality rates that are among the highest in the Pacific region, and basic vaccination coverage rates that are 39% among children 12–23 months, increased coverage of vaccines is a high priority investment for Papua New Guinea (PNG). Using recently gathered household survey data for PNG, this paper contributes to the evidence-base for enhancing investments in frontline facilities by examining the implications of travel time to health facilities for basic vaccination coverage among children in PNG. We find that vaccination coverage rates among children 12–23 months old in PNG are decreasing in distance to healthcare facilities; and this holds whether the outcome is receipt of basic vaccinations (BCG; 3 dose pentavalent; OPV3; Measles), or basic vaccinations-plus (basic vaccinations + Hepatitis B + PCV3). We also find that travel time to health facilities lowers vaccination rates among children 12–23 months old in poor households to a greater extent than for children from richer households. Thus, enhanced geographical access to and resourcing of frontline facilities is likely to expand not only immunization coverage, lower mortality and increase aggregate economic gains, but also improve the distribution of immunization coverage in PNG across socioeconomic groups. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model to optimize zonal demand responsive transit (DRT) considering heterogeneous environment (i.e., community boundary, land use, demand distribution, line-haul travel time, etc.) under the advent of Mobility-as-a-Service (MaaS). Since most previous models over-simplified conditions of the DRT service area, we propose a new modeling approach to formulate the operator and user costs. Passengers with varied expectations of vehicle arrival time at a drop-off location are considered. The average cost is minimized through optimizing service zone areas and associated headways subject to practical constraints (i.e., policy headway and vehicle capacity). A real-world region in the City of Calgary, Canada, is applied to demonstrate the applicability of the model. The impact of real-time vehicle arrival information to the optimal solution is assessed. The relationship between system parameters (i.e., line-haul travel time, demand density, vehicle capacity, and passenger composition, etc.) and the optimized solutions (i.e., zone area, headway, and costs) is explored through the sensitivity analysis. [ABSTRACT FROM AUTHOR]

When it comes to the competitiveness of companies, and of nations, the key role of the transportation infrastructure to increase logistics value of supply chains is clearly acknowledged. For example, the lack and/or inadequate transportation infrastructure is often mentioned as the most important challenge to companies located in emerging countries to successfully participate in global supply chains. In fact, many comparative indicators that measure countries' competitiveness consider infrastructure of transportation as a key element. Nonetheless, most part of them give more credit either to the infrastructure density (km of roads per km2 of territory) or to the amount of resources invested in its development (millions of dollars) than to the its logistics effectiveness and resilience that may bring to the supply chain operations. Based on six indicators: i) Travel time (95 PCTL); ii) Average travel time; iii) Projected travel time; iv) Freight rates; v) Risk of accident; and vi) Carbon footprint, the objective of this paper is twofold. First, to propose a standardized measurement approach of logistics value provided by the transportation infrastructure to supply chains as a unified perspective. Second, to expose a proof of concept of an interactive digital map that is useful for public and private sectors when making decisions. The results obtained are useful for academics as well as for public and private decision makers. It delivers insights about the potential of data when looking to increase logistics performance of companies and regions. Finally, a proposal for future research is presented. • Offers insights about the significance of measuring the logistics value provided by the transportation infrastructure. • Proposes a measurement approach and a proof of concept of a digital map to monitor the transportation infrastructure. • Provides analysis to understand and take actions when looking for improving freight fluidity. • Delivers practical insights for a a more sustainable transportation infrastructure management. [ABSTRACT FROM AUTHOR]

This study evaluated the effectiveness of the first Bus Rapid Transit (BRT) system in Dar es Salaam. A Bayesian Hierarchical modeling approach and Bayesian hypothesis test were used to compare the operational performance of BRT against the non-BRT buses and private automobiles. The study also examined factors affecting the travel times of the three modes of transportation. The study corridor, which is 10.2 kilometers long, is an arterial segment along Morogoro Road. The predicted travel time for BRT was lowest at the 95% Bayesian credible interval, with a mean value of 16 minutes. Non-BRT bus trips had the highest travel time of 28 minutes, followed by personal automobile trips with an average travel time of 18 minutes, indicating that BRT buses improve travel time along the study corridor. Factors such as delay, time of day, dwelling time, number of stops, and trip direction significantly influenced travel time variability along the study routes. [ABSTRACT FROM AUTHOR]

We present a method oriented to determine indicators representing different dimensions of users' travel time, such as in-vehicle time and its variability. We focus the service level measurement on users, but not their subjective perception, since we build the indicators from objective data. We apply the method to six Latin American cities, which shows the method's flexibility to fit different available information collected from sources such as in-field measurements or electronic control systems for bus operations. The method's core is the selection of a representative sample of trips. We estimate the service level based on different variables (e.g., speed, frequency, travel time, and waiting time) for every sampled trip. The contribution of this work is the design and implementation of a methodology for service level evaluation. As a byproduct, we compare public transportation systems' levels of service in the studied cities. [ABSTRACT FROM AUTHOR]

The rural-urban divide has traditionally been used to characterize key features of regions, including features of economic geography like density and connectivity that determine the prospects for economic growth and other dimensions of territorial development. In this paper, we develop a methodology that identifies areas along the urban-rural continuum according to their key features of population density and distance to large urban settlements at a more-detailed scale than the smallest administrative areas, taking Chile as a case study, a country that has experienced a rapid socio-economic transition over the last three decades. Information derived from the application of this methodology allows the identification of geographic categories according to observable density and distance. We also present geographic definitions of urban, rural, and mixed areas, which allow a practical, policy-relevant taxonomy of the rural-urban landscape, consistent with the 3-tier approach taken in most OECD countries. We demonstrate the relevance and practical importance of this smaller-scale approach by showing that, in the case of Chile, there is a significant mismatch between the official, cruder definitions, based on political subdivisions and currently in use by the government, and the realities of the country's present rural-urban landscape. Our results demonstrate that a focus on the scale at which information can be interpreted and summarized is not merely an academic exercise, but a way of correctly identifying, within the policymaking framework, communities according to the realities of their needs and capabilities. • Density and connectivity are used to create an urban/rural continuum. • Connectivity, measured as travel time, is derived using modern GIS tools. • The urban/rural continuum can be aggregated in a familiar binary taxonomy. • The scale at which density is estimated is key to describe geographic units. • Urban/rural categories relevant for practical policy analysis are presented. [ABSTRACT FROM AUTHOR]

Natural ventilation offers significant advantages, especially in terms of energy conservation. However, most published articles have focused on the ventilation flow rate to determine the average contaminant concentration, while few have examined local ventilation distributions. Therefore, isothermal steady-state Reynolds-averaged Navier–Stokes simulations were conducted to analyze the airflow and scalar concentration fields of a cross-ventilation model sheltered by buildings. Based on these fields, we generated the spatial distributions of ventilation indices, namely, the net escape velocity (NEV), point-to-point indices transfer probability (TP), and travel time (TT), to discuss the dispersion of scalars emitted from local points in a room with natural ventilation. The NEV, which indicates the direction of the scalar discharge from each cell, exhibited a distribution distinct from the advection velocity because of concentration gradient effect. Higher TPs were observed when evaluated from a contaminant source to a target point following the NEV streamlines. Meanwhile, lower TTs were observed when evaluated in a point near the source and also from a contaminant source to a target point following the NEV streamlines. Although TP and TT are independent ventilation indices, a negative correlation between them was observed. Instead of the ventilation flow rate, the detailed structure of scalar dispersion can now be described by simultaneously analyzing the ventilation efficiency indices, which provide information on the general contaminant transport direction, and the probability and duration of transfer from a source to a target point. This potentially helps in the design of ventilation system especially in room lay-out to avoid cross-contamination. • Simultaneous indoor/outdoor simulations using RANS. • Net escape velocity streamlines as the true scalar transfer path. • Ventilation indices distributions in cross-ventilation. • Comprehensive point-to-point ventilation quantification of scalar transport. • Inverse correlation of transfer probability and travel time. [ABSTRACT FROM AUTHOR]

Quantifying flow pathways within a larger catchment can help improve diffuse pollution management strategies across subcatchments. But, spatial quantification of flow pathway contributions to catchment stream flow is very limited, since it is challenging to physically separate water from different paths and very expensive to measure, especially for larger areas. To overcome this problem, a novel, combined data and modelling approach was employed to partition stream flow in the Piako catchment, New Zealand, which is a predominantly agricultural catchment with medium to high groundwater recharge potential. The approach comprised a digital filtering technique to separate baseflow from total stream flow, machine learning to predict a baseflow index (BFI) for all streams with Strahler 1st order and higher, and hydrological modelling to partition the flow into five flow components: surface runoff, interflow, tile drainage, shallow groundwater, and deep groundwater. The baseflow index scores corroborated the spatial distributions of the flow pathways modelled in 1st order catchments. Average depth to groundwater data matched well with BFI and Hydrological Predictions for the Environment (HYPE) modeled flow pathway partitioning results, with deeper water tables in areas of the catchment predicted to have greater baseflow or shallow and deep groundwater contributions to stream flow. Since direct quantification of flow pathways at catchment-scale is scarce, it is recommended to use soft data and expert knowledge to inform model parameterization and to constrain the model results. The approach developed here is applicable as a screening method in ungauged catchments. [ABSTRACT FROM AUTHOR]

Background: Individuals with congenital heart defects (CHDs) are recommended to receive all inpatient cardiac and noncardiac care at facilities that can offer specialized care. We describe geographic accessibility to such centers in New York State and determine several factors associated with receiving care there.Methods: We used inpatient hospitalization data from the Statewide Planning and Research Cooperative System (SPARCS) in New York State 2008-2013. In the absence of specific adult CHD care center designations during our study period, we identified pediatric/adult and adult-only cardiac surgery centers through the Cardiac Surgery Reporting System to estimate age-based specialized care. We calculated one-way drive and public transit time (in minutes) from residential address to centers using R gmapsdistance package and the Google Maps Distance Application Programming Interface (API). We calculated prevalence ratios using modified Poisson regression with model-based standard errors, fit with generalized estimating equations clustered at the hospital level and subclustered at the individual level.Results: Individuals with CHDs were more likely to seek care at pediatric/adult or adult-only cardiac surgery centers if they had severe CHDs, private health insurance, higher severity of illness at encounter, a surgical procedure, cardiac encounter, and shorter drive time. These findings can be used to increase care receipt (especially for noncardiac care) at pediatric/adult or adult-only cardiac surgery centers, identify areas with limited access, and reduce disparities in access to specialized care among this high-risk population. [ABSTRACT FROM AUTHOR]

• Travel time and cost difference between modes impact commute mode choice. • Commuters significantly prefer driving when transfers are required more than once. • Longer walking distances during transfer and egress affect mode choice. • Commuters from different income and age groups react distinctively. • Policy implications for auto-to-transit modal shifts are elaborated. It is well known that faster and less-expensive transit systems with shorter walking distances, as well as less transfers, are associated with transit use. In this regard, recent studies have focused on estimating transit's competitiveness and further suggested possible policy interventions. However, there were not much attention on measuring travel time and cost differences between transit and auto by considering the temporal variability. In addition, only a limited study have focused on the impact of either walking during transfers on mode choice or the stage of walking that most affects mode choice. This paper provides additional insights into the impact of travel time, cost, and transit burdens on commute mode choice based on the analysis in a transit-oriented mega city, Seoul, Korea. Together with the 2016 household travel survey data, we utilize the web-based route guide information to measure time, cost, and transit burdens for both chosen and non-chosen modes. The results reveal that commuters are significantly affected by the travel time and cost difference between modes. Additionally, the results indicate that the odds of driving increases by 4.7% and 24.3% when commuters have to transfer once or more than once, respectively. Also, commuters were mostly disinclined to walk longer distances during transfer and egress. The effect of time, cost, and transit burdens on mode choice also varied by income and age groups, further implying the group of people that should be focused when devising policies to encourage transit use. To conclude, this paper suggests potential levers for transportation policies to trigger modal shifts toward sustainable modes by using advanced data sources that better estimate the travel characteristics by departure time based on a point-to-point travel survey data. [ABSTRACT FROM AUTHOR]

• We survey 1,297 people about perceived distance and walk time to nearby destinations. • People tend to overestimate walk times and distance, which in turn reduces walking. • Familiarity with the area lower estimates while fear of getting lost increases them. • Wayfinding materials can correct overestimates about distance and increase walking. • Student samples differ from the general population. Use student data with caution. In this paper we use an intercept survey of 1,297 people at seven locations in New Jersey to answer three questions about perceived distances and walk times to nearby destinations. First, we seek to clarify conflicting results from the literature by asking: what factors are associated with perceived distance and walk times? Like other studies, we find that people overestimate both walk times and distances. We find that characteristics of the route matter. People are more likely to overestimate in car-dependent locations, along routes with many turns or barriers, or for destinations that are relatively closer. In terms of personal characteristics, experience walking and familiarity with the area both lead to lower estimates, while concerns about crime, getting lost, or carrying something heavy increase estimates. Next, we explore whether overestimating walk times and distances reduces the likelihood of walking. We find that people who overestimate are less likely to walk, everything else equal. Together, these results suggest that wayfinding campaigns that clarify the travel time to nearby destinations and ease the fear of getting lost are likely to improve perceptions and increase walking. We make an additional contribution by highlighting important differences between our university and town center samples, which raises questions about the reliance on student samples in research. [ABSTRACT FROM AUTHOR]

• The teleportation test is one way to measure an affinity for travel. • People with longer commutes were more likely to prefer to teleport. • Walk and bicycle commuters would rather travel than teleport. • Some people find commuting to be productive and enjoyable. • Teleportation preference does not imply wasted travel or zero ideal travel time. The "teleportation test" is a unique question—used in some travel behavior research since the 2000s—to examine individuals' perceptions towards travel time and affinities for travel. It gives people a hypothetical choice between teleporting or spending some time traveling. All studies (using different methods, including focus groups, interviews, and questionnaires) have consistently reported high teleportation preferences (about 70%). Yet, few correlates of teleportation preferences (especially sociodemographic and perceptual characteristics) have been investigated. Using data from an online questionnaire of 648 commuters in Portland, Oregon, this study investigated factors affecting individuals' teleportation preferences. While generally representative of the area's commuters, the sample did have higher shares of high-income workers and people using active modes or transit. Overall, 62% of respondents preferred to teleport than to spend some time commuting. Results from binary logit models identified actual commute duration, typical mode of travel, and "ideal" travel time (but no demographic attributes) as major predictors of preferences to teleport. As expected, people with longer commute durations had a greater desire to teleport, whereas bicyclists and pedestrians more often preferred to commute. People with longer ideal travel times had a greater affinity to commute and were less keen to teleport. Analysis of follow-up questions exploring reasons for respondents' answers suggested that most "teleporters" were motivated by saving time to do other things, while most "commuters" found parts of their commutes to be enjoyable or productive. Findings from this study are consistent with related studies in implying that a teleportation preference does not necessarily represent zero ideal travel time or viewing travel time as wasted. Finally, this study discusses several travel behavior policy implications of the teleportation test, along with limitations and areas for future research. [ABSTRACT FROM AUTHOR]

• We derive the value of travel time information in very general setting. • Value increases with information quality. • Predictable travel time variability may be costly. • Examples show analytic calculation of value of travel time information. • Results facilitate design and analysis of information policies. In this paper, we derive the value of a signal obtained by a traveller prior to the choice of departure time. The signal does not have to be a perfect prediction of the travel time. It is sufficient that it carries information about the travel time. The traveller may then consider the distribution of travel time conditional on the signal to increase her expected utility by making a better informed choice of departure time. We show that a signal always increases the expected utility compared to the situation without any signal. For a broad class of signals, the expected utility is monotone with respect to signal strength. Furthermore, we demonstrate that even perfect travel time information does not necessarily eliminate the cost of travel time variability and we establish necessary and sufficient conditions for when it does. We find that the predictable part of travel time variability may or may not be costly, depending on the shape of the traveller's scheduling utility at the origin of the trip. Using estimates of scheduling preferences from the literature, we show that the cost of predictable travel time variability may constitute a substantial part of the total cost of travel time variability. In a particular case of scheduling preferences, travel time distribution and noise distribution, we establish an analytic relationship between the strength of the signal and the expected utility of the trip and evaluate the marginal cost of signal weakness. This knowledge may facilitate design and cost-benefit analysis of traveller information systems and policies decreasing travel time variability. [ABSTRACT FROM AUTHOR]

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network. [ABSTRACT FROM AUTHOR]

Passenger transit hub is a quintessential complex system, characterized by intricate interactions among humans, facilities, and the surrounding environment. External disturbances often precipitate crowd congestion and safety risks. Modeling the spatial–temporal distribution of passenger flow within the hub is an important element in operational management. Prevailing research predominantly focuses on static models or monitoring data to assess the spatial–temporal characteristics of passenger flow throughout the hub. Nevertheless, few studies have been found in modeling passenger flow distribution for passenger transit hub from the vantage point of traveler behavior in the intricate 'human-facility-environment' complex network. Thus, this paper proposes a computational model for the passenger flow spatial–temporal distribution based on traveler behavior. First, combining consideration of critical spatial facilities and passenger flow streamlines, a passenger flow network is established. Second, the instantaneous travel times of link and node are defined while considering queuing and congestion at crucial facilities in the hub. Then, a passenger flow spatial–temporal distribution model for the passenger transit hub is constructed, which consists of dynamic route choice model and dynamic passenger flow loading model. A solution algorithm is simultaneously designed. Finally, the effectiveness of the model and algorithm are verified by a numerical example. The results show that the proposed model can effectively capture real-time congestion and the dynamic distribution of passenger flow in the hub. Therefore, this study contributes to the safety management and layout optimization of the hub, holding significant importance for improving hub operational efficiency and service levels. • A network is established based on the layout and streamlines within the hub. • Congestion at passage and service facilities is described using travel time models. • The spatial-temporal distribution model dynamically updates passenger distribution. • The model ensures fast computation, and is not influenced by the passenger scale. [ABSTRACT FROM AUTHOR]

This study introduces a novel method for assessing spatial access to supermarkets in the United States, addressing limitations of the US Department of Agriculture Food Access Research Atlas definition. Our method eliminates the food desert poverty requirement and derives empirical time-based thresholds that reflect a nuanced urban/rural spectrum and regional differences in normative drive times. Using these thresholds, we identify more than 66 million individuals experiencing suboptimal food access, distributed across 30,011 census tracts. Travel time thresholds ranged from 2.93 to 7.17 min (sd = 1.09) in urban blocks, 5.86 to 19.21 min (sd = 1.93) in suburban blocks, 2.68 to 12.98 min (sd = 1.81) in large rural blocks, and 9.74 to 28.13 min (sd = 3.39) in small rural blocks. The shortest thresholds were found in urban centers around New York City, Chicago, and Los Angeles, while the longest were in sparsely populated regions of Northern California, the Southwest, and the Great Plains. Our approach represents a significant methodological advancement in food access research and can be applied to measure variation in geographical access to other community resources and services. • New method for defining travel time thresholds for optimal supermarket access. • Thresholds vary regionally by population distribution, urbanity, and commuting norms. • 66 million individuals with suboptimal supermarket access. • More robustly distributed across suburban and rural settings than previous studies. [ABSTRACT FROM AUTHOR]

The precise estimation of macroscopic traffic parameters, such as travel time and fuel consumption, is essential for the optimization of traffic management systems. Despite its importance, the comprehensive acquisition of vehicle trajectory data for the calculation of these macroscopic measures presents a challenge. To bridge this gap, this study aims to calibrate car-following models capable of predicting both microscopic measures and macroscopic measures. We conduct a numerical analysis to trace the cumulative process of model prediction errors across various measurements, and our findings indicate that macroscopic measures encapsulate the accumulation of model errors. By incorporating macroscopic measures into vehicle model calibration, we can mitigate the impact of noise on microscopic data measurements. We compare three car-following model calibration methods: MiC (using microscopic measurements), MaC (using macroscopic measurements), and BiC (using both microscopic and macroscopic measurements)—utilizing real-world trajectory data. The BiC method emerges as the most successful in reconstructing vehicle trajectories and accurately estimating travel time and fuel consumption, whereas the MiC method leads to overfitting and inaccurate macro-measurement predictions. This study underscores the importance of bi-scale calibration for precise traffic and energy consumption predictions, laying the groundwork for future research aimed at enhancing traffic management strategies. [ABSTRACT FROM AUTHOR]

Departure time choice is a key component of travel behavior that directly influences the spatial and temporal distribution of travel demand. This paper tries to develop a modeling framework for choosing the departure time that minimizes travel costs. In this regard, a modeling framework for generating departure time recommendations is proposed and applied to real commuting trips. The methodology is an extension of the departure time choice model with unreliable travel time. Two cases are considered. The first calculates the optimal time of departure when the mean of the travel time varies by time of day but the variance is constant. An exact solution to the departure time choice problem is provided for this case. In the second case, both the mean and variance vary with the time of day. A numerical solution is proposed; it is proved that the sequence of the numerical solution is contractive with a unique fixed point obtainable for any initial guess. We apply both to the departure time planning problem for a transportation operator that offers repetitive mandatory trips on a dense network. The case study offers two insights into departure time choice analyses. First, the assumption that the travel time variance at peak hours is constant induces biases for the optimal departure time. However, this assumption provides plausible results for the off-peak period. Second, travelers relying on personal judgment may have significantly different costs of travel than passengers making their decisions based on the system's recommendations. • A methodological framework for a departure time guidance system is proposed. • The method considers the time-varying nature of the dense urban networks. • A real departure time guidance is applied to commuting trips of service personnel with a fixed work start time. • Results reflect two caveats as far as departure time choice analyses are concerned. [ABSTRACT FROM AUTHOR]

This paper systematically reviewed the slipping operation, which is a train separation at cruising speed. For this, we describe the historical and operational background of the operation scenario practiced for over 100 years. Based on the concept of slipping, we discuss the holistic potential to improve current railway operations, considering travel time saving, energy saving, the increase of capacity utilization, station topology, driver requirements, and vehicle usage. Finally, a simulation of a theoretical urban railway line with several scenarios quantifies the magnitudes of the improvements. Based on the slipping test cases, one parameter can improve enormously, e.g., up to −65 % energy saving, −33 % capacity usage, and travel time reductions. Otherwise, slipping can slightly improve several parameters simultaneously. • Systematic review of the historically slipping operation (train separation at cruising speed). • Analyzing potential improvement of slipping by a simulation of a test case on an urban railway line. • Slipping allows simultaneous improvement in energy-saving, capacity usage and travel time. • Slipping allows enormous improvement by focusing on a single issue, e.g., up to −65 % energy saving or −33 % capacity usage. [ABSTRACT FROM AUTHOR]

The relationship between the built environment and transportation mode choice is well-studied, but less attention has been spent on the way that urban environments influence the relative travel costs for different modes. This paper uses a 'cost-distance' framework to assess the tradeoffs between transportation modes for commuters in the Dublin metropolitan area and employs random forest models to investigate non-linearities in the relationships between relative mobility by mode and built environment characteristics. The results suggest that more 'walkable' built environments increase the efficiency of active transport modes; however, we also find that these environments are negatively related to cost efficiency for public transport, likely due to congestion effects for buses. Beyond these theoretical insights, the results also provide a spatially targeted set of priorities for policymakers looking to improve the efficiency of sustainable transportation modes. These methods could be applied to any global region with access to the requisite data. • A flexible travel cost-distance framework can be used to assess modal tradeoffs. • Built environment features are the most important predictors of cost efficiency. • More walkable built environments increase the efficiency of active transport modes. • Public transport suffers from congestion effects in dense areas. • Relationship between built environment and cost efficiency is often nonlinear. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-objective eco-routing strategy aimed at optimizing driving routes by minimizing both energy consumption and travel time. First, the vehicle and powertrain dynamics are modelled. The energy consumption of three powertrain types, including hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV), are calculated based on a white-box model. Second, this paper models the traffic network by considering the intensity of each road. The energy consumption associated with different intensity levels is also modelled using a typical driving cycle. The dual-objective routing problem, considering both energy consumption and travel time, is solved by an Iterative Dijkstra's algorithm. Finally, several simulation results demonstrate that the proposed dual-objective eco-routing strategy achieves a favorable balance between energy consumption and travel time. Specifically, for HEV, the dual-objective eco-routing strategy resulted in a 5.3% reduction in travel time with a marginal increase of only 0.4% in fuel consumption compared to the single-objective eco-route. In addition, the eco-routing strategy exhibits distinct patterns across different vehicle powertrains. HEV and BEV tend to utilize heavy traffic conditions more frequently compared to the CV. • Build the energy consumption models of hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV). • Energy consumption associated with different intensity levels is modelled. • Formulate the dual-objective routing problem, considering both energy consumption and travel time. • Eco-routing strategy exhibits distinct patterns across different vehicle powertrains and traffic conditions. [ABSTRACT FROM AUTHOR]

• Determine the minimum fire apparatus requirement for petrol station. • Propose an evaluation model of fire apparatus requirements for petrol stations. • Use 38 fire stations and 458 petrol stations as case studies. • Petrol stations of bad and worst levels are mainly distributed in rural areas. • Traffic congestion has a negative impact on firefighting in urban areas. The safety of petrol stations is critical because it stores flammable and explosive gasoline and diesel. Therefore, appropriate fire apparatuses should be deployed to petrol stations in case of a fire emergency. In this work, we proposed a regional evaluation model to predict the fire apparatus requirements of petrol stations based on travel times extracted from online maps. The travel time of the minimum fire apparatus requirements (1 water truck and 1 foam truck) are determined to be within 4 min. To validate the model, 37 fire stations and 458 petrol stations in Changsha, China were considered as case studies leading to 144 evaluation scenarios. The results showed the fire apparatus requirement scores of 144 scenarios were ranged from 47.69 to 56.69 (less than 60) out of 100, which were in bad levels. Furthermore, results from visualization showed that the worst part of scores were petrol stations in rural areas. Therefore, these petrol stations in rural areas should be equipped with more fire extinguishers and foam extinguishing systems. [ABSTRACT FROM AUTHOR]

Public transport strikes comprise a form of traffic disruption affecting "normal" traveller behaviour, and result in the increase of traffic congestion, traffic accidents and air pollution. Travellers modify their trip characteristics in anticipation of strike effects, while their behaviour depends on various factors including individual characteristics, trip flexibility, available transport modes and attitudes towards them. This study explores the effect of public transport strikes on traffic conditions utilising loop detector data and, in particular, examining the impacts on traffic flow, mean speed and travel time. The city of Athens is used as a reference case. The analysis employs descriptive statistics, as well as the design of generalised linear models to capture the impact of public transport strikes on traffic conditions, and to define the contributing spatio-temporal factors affecting the impacts. Study results highlighted the increase of congestion as expressed through all explored indicators, as a result of the strikes. In particular, increased traffic flow (up to 30%), reduced mean speed (up to 27%) and increased travel times (up to 25%) were observed at the majority of the explored sites. Public transport strike coverage and time of day were found to be the dominant factors. Other contributing factors were found to be day of the week and site-specific characteristics including: the existence of tolls; site location relative to the centre of Athens; and the existence of traffic lights on the explored links. The results of this study can form the basis for the design and implementation of targeted countermeasures that mitigate congestion effects, as well as measures promoting green transport resulting in travellers adopting more sustainable trip routines in the city of Athens. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the most notorious troubles, one that undermines social costs in urbanized areas in the world. Because of the congestion problem, drivers are trying to find the shortest and most reliable paths. Regarding the reliability of travel time, there already exist a few measures, such as Standard Deviation, Buffer Time, and Buffer Index. However, they have fundamental limitations in calculation or in application. Therefore, we tried to apply the Gini Coefficient, which is a well-known measure of statistical dispersion, which describes the inequality of income or wealth distribution among a nation's residents, as a new measure for evaluating travel time reliability. In order to verify the new measure, we calculated Gini coefficients of five different expressway sections on AADT day and Chuseok and reviewed the potential advantage of the alternative by comparing the result with those from existing measures. In the analysis, the new measure showed us reasonable and reliable results and proved its applicability of appraising travel time reliability. [ABSTRACT FROM AUTHOR]

Even though air travel often provides the fastest transport option, it also has the highest climate impact. Especially on long-haul trips, an aircraft usually represents the only feasible option. Nevertheless, aircraft are more often used on short-haul routes as well. It is the short-haul flights that produce the highest emissions per passenger. These are also the ones that could be replaced the most easily by land-based transportation modes. This study investigates the greenhouse gas emissions reduction potential of replacing short-haul flights with train, coach and car travel within Finland while also taking into account real travel times from door to door. Our results showed that replacing short-haul flights could significantly reduce a country's climate impact. Furthermore, we found that existing land-based transportation modes can keep up with the travel times of aircrafts on routes up to 400 km. • Domestic flights are the least efficient but the easiest replaceable by other modes. • Substituting all domestic flights in Finland with land-based modes was studied. • Carbon dioxide emissions and door to door travel time were taken into account. • Replacing domestic flights could reduce a country's climate impact significantly. • Train, car and coach can keep up with air travel times on routes up to 400 km. [ABSTRACT FROM AUTHOR]

Equal access to opportunities has emerged in public transport planning as a social objective that many transport agencies are trying to achieve. Yet in practice, not all public transport agencies are currently providing urban residents with comparable levels of service due to physical barriers in the public transport network that can significantly hinder the ability of individuals with physical disabilities to access opportunities. In countries without a strong federal accessibility act and/or with major financial constraints, some public transport agencies fall behind in applying universal access design principles, making it even harder for people with a physical disability to access opportunities. The objective of this study is to develop a methodology that can be used by public transport agencies or disability advocates to clearly highlight and quantify the performance of the public transport network in a region, in terms of providing transit services to people in a wheelchair and compare that to the service offered to an individual not in a wheelchair. In this study we use accessibility, the ease of reaching destinations, by public transport as the key performance measure in two major Canadian Cities (Montreal and Toronto). Furthermore, we focus on job accessibility in the most socially vulnerable census tracts in both cities, to evaluate levels of job accessibility for wheelchair users residing in socially vulnerable areas. The findings from our study show striking contrasts between the numbers of accessible jobs by public transport for wheelchair users compared to the general population. On average, wheelchair users in Toronto have access to 75% of jobs that are accessible to users that are not in a wheelchair, whilst their counterparts in Montreal have access to only 46% of the jobs accessible to other users. This research is expected to highlight for public transport engineers, planners, policy makers and advocates for those with disabilities, the importance of universal access in a region, especially along public transport networks, using a widely used land use and transport performance measure. [ABSTRACT FROM AUTHOR]

In this paper, we apply a two-lane car-following model to explore each vehicle's two trip costs and the corresponding total costs in one two-lane traffic corridor, where one is the travel cost, and the other includes the travel cost and fuel cost. The numerical results show that each trip cost and the total cost in the two-lane corridor are both less than those in the corresponding single-lane corridor, but the lateral distance in the two-lane car-following model enhances each vehicle's trip cost and the corresponding total cost. • A two-lane car-following model is used to explore each vehicle's operational cost and the total cost. • The impacts of the number of lanes on the operational cost and the total cost are studied. • The impacts of the lateral distance on the operational cost and the total cost are studied. [ABSTRACT FROM AUTHOR]

• The aim of this study is achieving higher on-time arrival reliability for customers. • The reliability is improved with reliable path method and historical traffic information. • A system-beneficial path guidance is proposed for mitigating road network congestion. • Total travel time of the road network is reduced by the system-beneficial path guidance. Dynamic shared autonomous taxi (SAT) systems are regarded as a promising means of improving travel flexibility. With no human drivers, SATs urgently require precise traffic information in order to plan accurate paths independently; in addition, on-time arrival is an essential service quality in SAT systems. In this study, taxis are assumed to be replaced with ride-sharing autonomous vehicles. To improve the probability of on-time arrival, the reliable path concept and collected travel time information are used to facilitate path finding for SATs, and the potential benefits are examined. Two simulation scenarios—one based on historical traffic information and the other based on real-time traffic information—are executed to evaluate the information's usefulness in reliable path finding. In simulation results, reliable path scenarios showed a higher on-time arrival ratio than shortest path scenarios, in which the shortest path algorithm is used in path finding for SATs, and the historical information-based scenarios showed a higher on-time arrival ratio than the real-time information-based scenarios. A system-beneficial path finding method is proposed and is verified to be effective for mitigating road network congestion. [ABSTRACT FROM AUTHOR]

• Free-floating car sharing (FFCS) is studied through booking data in 12 cities. • Rental times are compared with travel times for alternative modes. • Rental times are often longer than biking times. • Public transport is often slower but the time gain with FFCS is not always that large. • Usage patterns differ between electric and conventional vehicles, but the spread is large. Free-floating car-sharing (FFCS) allows users to book a vehicle through their phone, use it and return it anywhere within a designated area in the city. FFCS has the potential to contribute to a transition to low-carbon mobility if the vehicles are electric, and if the usage does not displace active travel or public transport use. The aim of this paper is to study what travel time and usage patterns of the vehicles among the early adopters of the service reveal about these two issues. We base our analysis on a dataset containing rentals from 2014 to 2017, for 12 cities in Europe and the United States. For seven of these cities, we have collected travel times for equivalent trips with walking, biking, public transport and private car. FFCS services are mainly used for shorter trips with a median rental time of 27 min and actual driving time closer to 15 min. When comparing FFCS with other transport modes, we find that rental times are generally shorter than the equivalent walking time but longer than cycling. For public transport, the picture is mixed: for some trips there is no major time gain from taking FFCS, for others it could be up to 30 min. For electric FFCS vehicles rental time is shorter and the number of rentals per car and day are slightly fewer compared to conventional vehicles. Still, evidence from cities with an only electric fleet show that these services can be electrified and reach high levels of utilization. [ABSTRACT FROM AUTHOR]