Search

The UC Davis Campus Travel Survey is an annual survey led by Transportation Services (TS)—formerly known as Transportation and Parking Services (TAPS)—and the National Center for Sustainable Transportation (NCST), part of the Institute of Transportation Studies (ITS) at UC Davis. It collects a rich set of data about travel to the UC Davis campus, demographics, and attitudes toward travel.The 2023-24 survey collected data from 4,774 people affiliated with UC Davis about their travel to campus during a single week in October and November 2023. It used a stratified random sampling method with the intent to gather a representative sample of the campus population. About 14 percent of those invited responded to this year’s survey. For the statistics presented throughout this report, we weight the responses by campus role (freshman, sophomore, junior, senior, Master’s, PhD, faculty, and staff) and gender so that the proportion of respondents in each group reflects their proportion in the campus population.

The State of California has increasingly urged construction of affordable housing development in transit-rich areas (California Department of Housing and Community Development 2024) but so far transit-oriented development has generally not reduced vehicle miles traveled for low-income renters (Chatman et al. 2019; Lund, Cervero, and Willson 2004). This report quantifies the cost of daily travel needs for affordable housing residents in San Diego, California, especially seniors aged 62 and older, in two ways. First, it analyzes their trip travel time for the entire San Diego region using activity-based model (ABM) data. Second, it summarizes results from surveys of residents in six affordable housing buildings, three of which provide supportive housing to seniors. Overall, it finds that affordable housing residents use public transit more often than those who have access to a car. But traveling by public transit takes much longer on public transit than traveling by personal vehicle. Survey respondents under age 62 expressed greater dissatisfaction with the costs of public transit ridership, compared to seniors, and were also more likely to express dissatisfaction if they were working. Seniors were more likely to express dissatisfaction with the conditions of public transit stops.

The COVID-19 pandemic has had a significant impact on public transit ridership in the United States, especially for rail transit. Land use, development density, and the pedestrian environment are strongly associated with station-level transit ridership. This study examines how these characteristics affect transit ridership pre- and post-COVID and how they differ across station types based on longitudinal data for 242 rail stations belonging to Bay Area Rapid Transit, San Diego Metropolitan Transit System, Sacramento Regional Transit, and LA Metro between 2019 and 2021. We found overall a 72% decrease in station-level ridership, but changes were not uniform. Station areas with a higher number of low-income workers and more retail or entertainment jobs tend to have lower ridership declines, while areas with a large number of high-income workers, high-wage jobs, and higher job accessibility by transit had more ridership losses. When comparing station area ridership and activity changes based on mobile phone user data, ridership declined more drastically than activity across all four rail systems, which implies that rail transit riders switched to other modes of transportation when accessing the station areas. Given these findings, it is likely that rail transit services oriented toward commute travel, especially core station areas with jobs for higher income workers, will continue to have an uneven recovery, posing critical implications for transit resilience planning and equity in the post-pandemic era. Considering sources of funding other than passenger fares to sustain rail transit, strategizing to reinvent and reinforce downtowns as destinations, and shifting rail transit services to appeal to non-commute travel can be promising strategies to support rail transit.

Drawing on the California Household Travel Survey, we demonstrate strong associations between choosing to drive and having free parking at work or home. We find that the median household vehicle in California spends 22 hours a day parked, and that households with parking included in the rent or purchase price of their homes are more likely to drive,and less likely to use transit. We further find that employees with free parking at work are more likely to drive for their commutes. We estimate regressions that analyze the decision to work from home. Largely for data reasons, these regressions are less conclusive.

Los Angeles County's 15-year initiative to overhaul its transportation infrastructure aims to reduce car reliance and promote public transit, walking, and cycling through dense, transit-oriented development (TOD) near stations. Despite significant investment, numerous challenges remain including bureaucratic obstacles, declining per capita ridership, and concerns about displacement and gentrification. The study evaluated LA Metro rail stations' impact on nearby communities, using ACS data from 2009 to 2022 to analyze demographic, housing, and mode choice changes within a half-mile radius of stations built between 2010 and 2020.Findings indicated increased population density, racial diversification, and higher education levels near stations. Median household income and housing costs surged, signaling economic growth and gentrification. Urban renewal was evident with increased construction activity, rising rent and home values, and rise in remote work. Decline in public transportation usage was less significant near stations compared to city-wide. To enhance Los Angeles' existing TOD policies, several recommendations were proposed. Encouraging diverse housing types near transit stations through mixed-use zoning, multi-family homes, and ADUs can increase density without major disruption. Innovative housing models like community land trusts and modular housing can improve affordability. Implementing robust anti-displacement measures, such as extending rent control, can safeguard low-income residents from gentrification. Promoting economic development near transit stations through local hire policies can reduce unemployment and foster economic stability. Lastly, expanded monitoring of TOD areas are necessary to better understand residential and travel behaviors, refining strategies to promote sustainable development and ensure inclusive, resilient, and thriving transit-oriented communities in Los Angeles.

This study aims to comprehensively identify the contributing factors affecting the adoption of flex route transit. A survey was conducted in 2019, and a multiple indicators multiple causes (MIMIC) model was utilized to capture individuals' preferences toward car, fixed route, and flex route transit. Subsequently, the multinomial logit model with attitudinal features is employed to identify the key factors influencing the shift from car or fixed route to flex route transit. Results indicate that several factors contribute to flex route transit adoption, with the top three being car restriction, travel cost, and age groups. Car restriction is positively associated with flex route transit adoption (marginal effects 11.67%). Travel costs (≥1.49 US$) and elderly groups also contribute to an increase in flex route transit adoption, with marginal effects of 9.18% and 8.01%, respectively. These findings provide insightful implications for decision-makers in identifying potential adopters and improving flex route transit adoption. [ABSTRACT FROM AUTHOR]

Passenger comfort significantly influences commuter satisfaction and mode preferences in urban transportation systems. To maintain competitiveness and attract more passengers, conducting continuous performance evaluations based on key performance indicators is commendable. Therefore, this study is aimed at conducting a mode‐wide comfort performance evaluation and comparisons to identify the strengths and weaknesses of respective mass transport alternatives found in Addis Ababa, Ethiopia. The study employs a comprehensive approach by integrating both subjective surveys and objective measurements on key comfort determinants, including in‐vehicle passenger loading, travel time, vibration, and sound, across five transport modes: minibus taxis, midi buses, PSETSE, Anbesa City Bus Service Enterprise (ACBE), and Sheger Mass Transport Enterprise (SMTE). Analysis of variance (ANOVA) reveals variations among transport modes, with minibus taxis emerging as the most comfortable, featuring a travel time of 1.7 min/km, 2.6 min/km during off‐peak and peak times of the day, respectively, and an average of 1.13 passengers per seat in both periods of the day. In contrast, midi buses are identified as the least comfortable, characterized by elevated noise production (77.46 and 80 dB) and an average vibration rating of 3.9 and 4.2 at peak and off‐peak periods of the day. Following midi buses, the ACBE exhibits the highest passenger loading and travel time (2.113 and 1.548 passengers per seat and takes 4.2 and 3.34 min) to cover 1 km during peak and off‐peak periods, respectively. The holistic evaluation demonstrates an average correlation of 81.75%, indicating a strong alignment between peak‐hour objective and subjective assessments. This could help commuters make relevant mode choice decisions and transport companies make market‐oriented decisions in this competitive environment. [ABSTRACT FROM AUTHOR]

Electric mobility hubs (eHUBS) represent an innovative approach to providing diverse shared electric transportation options, aimed at curbing private car use, and mitigating associated environmental impacts. Assessing the impact of eHUBS on travel choices across different cities requires significant resource and time investment due to the need for localized data collection and model development. This paper proposes a potential solution to this challenge by investigating the transferability of mode choice models originally developed for eHUBS in Amsterdam to predict behaviour towards eHUBS in Manchester. Multinomial Logit (MNL) and mixed logit models were transferred using four different procedures, and their effectiveness was evaluated using three assessment measures. The findings indicate that a scaled mixed logit model with an updated Alternative Specific Constant (ASC) outperforms other models in terms of its transfer effectiveness, both for disaggregate and aggregate assessment measures. The interplay between transfer procedures and assessment measures also was examined, with results indicating enhancements in disaggregate transferability measures with the 'scaling' transfer procedure, while 'updating the Alternative Specific Constants (ASCs)' improved predictions of aggregate mode shares. Following the analysis, the paper presents an in-depth discussion to provide a nuanced understanding of transferability and thus offers valuable insights for researchers planning future studies and practical considerations for policymakers. • The paper examines spatial transferability of mode choice models between eHUBS cities. • Model scaling resulted in improvement of disaggregate measures of transferability. • Updating ASC improves aggregate mode share predictions of transferred model. • Scaled mixed logit model with updated ASC explain 75% of overall behaviour. • Transferability of mixed logit model gives better results as compared to MNL model. [ABSTRACT FROM AUTHOR]

Tourism is a major source of income for many regions; however, its impacts on residents' daily lives are significant, especially regarding their travel behavior. Local transportation infrastructure is challenged by large tourist flows, and thus residents need to adapt to the new environment by changing their usual habits, such as travel mode, frequency, destination etc. In this study, a multinomial logistic regression mode choice model is developed to capture tourism impacts on residents' travel behavior, using data from the island of Rhodes, Greece. According to study findings, tourism has in fact an impact on residents' travel mode preferences, as they tend to opt for more agile modes such as motorcycles, instead of cars, and adopt defensive driving during tourist seasons. This study can facilitate public transport operators, planners, and municipalities in tourist regions, to apply effective policies to mitigate negative impacts of tourism in local traffic conditions. [ABSTRACT FROM AUTHOR]

Implementing the concept of sustainability in tourism needs a significant reduction of vacationists' ecological footprint at destination level. A key aspect there is tourists' travel mode choice on‐site. Based on an online‐panel based survey in Italy and Germany and applying ordinal regression, this study analyzes for the destination South Tyrol, the impact of five blocks of potential factors on public transportation (PT) usage during a vacation: sociodemographics, everyday mobility at residence, attitudes toward climate change and environment, preferences for cars, and PT services at the destination, including the availability of a guest card for free PT use. The results of an ordinal regression analysis show that the availability of a guest card and the level of daily use of PT at home significantly increase PT usage. This is countered by daily use of the car at home and a positive affinity toward cars, having a negative effect on PT use. [ABSTRACT FROM AUTHOR]

Smart working has been the subject of extensive research over the past years, but it has become a hot topic following the Covid19 due to its impact not only on human resources and management, but also on mobility patterns. The pandemic highlighted the critical role of entrepreneurship in adapting to new work modalities, pushing organizations to innovate and reevaluate their smart working strategies and its implications on mobility. Where initial studies suggested that smart working could enhance mobility by reducing commute trips, recent studies are concluding that smart working has a limited impact on mobility improvement for various reasons, including an increase of usage of private car over public transport. To provide further insights over this topic, this empirical research aims to evaluate if smart workers are willing to spend more resources (monetary and temporal) in its mode of transport decision-making and, to identify factors that may moderate the smart worker willingness to dedicate more resources. To address this research targets, a conceptual model has been developed and has been tested in a sample of 338 smart workers in Madrid through Structural Equation Modelling. The research findings confirm that (i) smart workers tend to use private car for commuting for non- smart working days, (ii) that smart workers are willing to spend more money and time trip, especially more money, but (iii) to a certain extent. These findings provide new insights into this field, revealing significant entrepreneurship implications as enterprises must develop strategies and policies fostering the new social and mobility context. [ABSTRACT FROM AUTHOR]

Global trade and social relationships are greatly facilitated by transportation. However, the majority of nations, including Bahrain, face substantial challenges with their transportation systems. For the development of technical solutions that can promote the progress of these transport systems, it is now crucial to have a complete understanding of travel demands and driver's characteristics. This paper aims to explore the influential factors concerning travel mode choice in Bahrain and utilize mode choice models to forecast the probable utilities of various future public transport modes. The study utilizes diverse, 3864 data records extracted from previous surveys as well as a recent one conducted within this research. Subsequently, using Minitab software, two types of mode choice models were built, namely the logit model and the classification tree model, focusing on modelling the future transportation system, considering potential public transport modes (Public Bus, Metro, and Tram). The analysis of the data identified trip cost as the top predictor, moreover, direct, and quick travel, accessibility, and convenience were also found to significantly influence the choice of travel mode in Bahrain. Additionally, the findings indicate that the metro is the preferred choice for future public transport, with a strong preference observed for a combination of metro and tram. The research also suggests, in terms of model performance, that when capturing more complex patterns, as in this study, the classification tree outperforms the multinomial Logit model. Overall, the research provides valuable insights into mode choice in Bahrain and highlights the important factors influencing commuting decisions. The results of this study can support the development of an efficient public transportation system that would satisfy the needs and preferences of commuters in Bahrain and ultimately lead to a sustainable and accessible transportation infrastructure in the country.

This paper develops a dynamic procedural model to understand the day-to-day evolution of road traffic and risky mode choice behaviour. We embed a modified Bureau of Public Roads function and a queueing model into a dynamic traffic flow model to estimate information on travel time and in-vehicle crowding. Then, the estimated information is fed into a risky mode choice model, with the joint consideration of intra- and inter-individual risk attitude heterogeneity. In order to reflect what may be happening in the real market, we establish a close loop with individuals' mode choice behaviours shaped by their travel experiences and historical information, the induced impact on traffic flow, and more importantly, the feedback and interplay between them. Our model delivers realistic and robust results, in which risk attitudes play the most prominent role. [ABSTRACT FROM AUTHOR]

Since young adults (i.e., aged between 18 and 30) are generally more flexible and conscious of economic and environmental concerns, it is essential to assess their travel perceptions and tendency to shift towards sustainable transportation modes. Therefore, this research aims to evaluate the acceptance of carpooling (CP) in the younger population to alleviate traffic congestion, fuel demand, and pollution. This study was conducted in Islamabad, a fast-growing city in Pakistan with a high percentage of young residents, to evaluate travelers' perceptions, mode choice decisions, and potential acceptance of CP. An online questionnaire-based survey was developed and distributed to collect a total of 656 responses from the young adult population. Various factors influencing travel perceptions, mode choice, and tendency to carpool were categorized and analyzed using confirmatory factor analysis, stepwise, and mediated multiple regression analysis. Thereafter, the tangible impacts of CP, including reduced traffic congestion, fuel consumption, and emissions resulting from the potential adoption of CP by the younger population, were quantified. The findings imply that young travelers' mode choice mediates the relationship between travelers' perception and the tendency to carpool. The outcomes also affirm the impact of demographic variables, including gender and education levels, on the willingness of the young adult population to shift towards carpooling. Furthermore, results predicted that adopting CP, especially by younger adults, may reduce by about 33.6% the number of private vehicles in Islamabad. The findings of this study could offer helpful insights for transport planners, commercial carpool service operators, environmentalists, and policymakers to promote carpool systems, especially among young adults. [ABSTRACT FROM AUTHOR]

[Display omitted] • A structural equation model for understanding how negative encounters influence the satisfaction of shared auto-rickshaw usage as public transport. • Availability is the most significant factor in predicting the adoption of shared auto-rickshaws. • Negative encounters in shared auto-rickshaws substantially negatively influence users' satisfaction. • Provide interventions and policy proposals on transparent fare structures and digital payment options to address concerns about unapproved fares. Emerging research has highlighted the significant role auto-rickshaws play in travel services, and yet, compared to other transport modes, they have received relatively less attention in research. No study has investigated travel behaviour and negative encounters in shared auto-rickshaws. The assumption that negative encounters deter users from utilising shared auto-rickshaws has not yet been validated. Moreover, previous studies have focused on adopting auto-rickshaws and travel behaviour, neglecting the specific dynamics of shared services within the public transport landscape. Here, we investigate the determinants influencing shared auto-rickshaw usage as a distinct mode in Ghana. We also identify negative attributes by drawing the link between negative user encounters and their impact on satisfaction, loyalty, and openness to policy propositions. The study deployed structural equation modelling, which is generally preferred in transport research. We discovered that 'Availability' is the most significant factor in predicting the adoption of shared auto-rickshaws. The estimated model established that negative encounters substantially impact users' satisfaction, and users' satisfaction was impacted by discomfort with seating arrangement, driver conduct, fleecing of users, and long travel time, among others. Older commuters reported higher levels of satisfaction than younger commuters, whereas commuters with higher levels of education tend to report lower levels of satisfaction. This research expands the limited understanding of shared auto-rickshaw usage by advancing our theoretical grasp of how negative encounters influence satisfaction and loyalty from a user-centric perspective. These insights can aid policymakers, service providers, and transport authorities in crafting legislation that prioritises user needs, enhances service quality, and creates a positive commuting experience. Furthermore, this study discusses managerial implications for transport authorities and policymakers. [ABSTRACT FROM AUTHOR]

This study at the University of Engineering and Technology (UET), Lahore, examined students’ travel behavior, mode choice and constraints. Data from 1,449 students were analyzed using various statistical methods, including descriptive analysis, latent class analysis (LCA), multinomial logistic regression (MNL), correlation, and linear regression. The model’s accuracy was evaluated by calculating the percent error. Walking was the most popular mode of transportation within the campus, preferred by 37% of students. Most students traveled 1-15 km, taking 10-15 minutes, with off-campus students typically making three trips to their destination. Safety concerns significantly influenced 37% of students’ travel behavior due to built-in constraints. [ABSTRACT FROM AUTHOR]

This article leverages a five-treatment response mode experiment (paper-only, web-only, sequential web-mail, choice, and choice-plus [choice with a promised incentive for responding online]) that was conducted within a nationally representative survey. Because this survey's sample was drawn from respondents to another nationally representative survey, we have rich frame data that includes multiple indicators of comfort using the internet for our sample members and we can compare their response behavior across two surveys. We find that the paper-only treatment yielded a lower response rate than most of the other treatments, but there were not significant differences between the response rates for the other treatments. Among our mixed-mode treatments, the sequential web-mail treatment had the highest percentage of response by web and the lowest cost per response. When focusing on the subgroups that we expected to be the least—and the most—comfortable with the internet, we found that the paper-only treatment generally performed worse than the others, even among subgroups expected not to be comfortable with the internet. We generally did not find significant differences in the effect of response mode treatment on the response rate or percentage of response by web between the subgroups who were the most and least comfortable with the internet. In terms of the consistency of response mode choice over time, our results suggest that some people respond consistently—but also that response mode preferences are weak enough that they can be influenced by the way in which the modes are offered. We ultimately recommend using a sequential web-mail design to minimize costs while still providing people who cannot or will not respond by web with another response mode option. We also find evidence that there may be a growing lack of interest in responding by paper; more research is needed in this area. [ABSTRACT FROM AUTHOR]

The availability and utilization of ride-sourcing services have the potential to transform how people travel. While these services could improve mobility and accessibility, they could also attract users away from active modes and public transit and increase congestion and emissions. Understanding the impacts of transportation network companies (TNCs) on the transportation system is critical to ensure that the benefits of ride-sourcing are captured, and its negative externalities are minimized. This study uses web-based survey data administered to Metro Vancouver residents to explore the characteristics of ride-sourcing trips and the early impacts of ride-sourcing use on mode choice, given that TNCs are new to the study area. Additionally, this study utilizes stated preference experiments and error-components mixed logit models to examine the influence of sociodemographic characteristics and attitudinal factors on mode choice decisions for commuting and non-commuting trips. The results offer insights into the relationship between ride-sourcing and private vehicles, local and regional transit, taxi, and active modes (such as walking and cycling). Furthermore, model results highlight the heterogeneity in mode substitution behavior across population segments. This study can help planners and agencies capitalize on the advantages of TNCs and better integrate ride-sourcing into the transportation system. [ABSTRACT FROM AUTHOR]

E-scooters have emerged as a popular micromobility option for short trips, with many cities embracing shared e-scooters to enhance convenience for travelers and reduce reliance on automobiles. Despite their rising popularity, there is a lack of clear understanding of how user preferences and adoption practices vary by location. This study aims to explore user and non-user attitudes towards e-scooter use in diverse urban settings. A meta-analysis of data from three surveys (N = 1197) conducted in Washington, D.C., Miami, FL, and Los Angeles, CA, was performed to compare e-scooter users and non-user profiles, mode choice factors, and attitudes and preferences towards e-scooter use. Additionally, machine learning (ML) and SHAP (SHapley Additive exPlanations) analysis were utilized to identify influential factors in predicting e-scooter use in each city. The results reveal that the majority of e-scooter users are 25 to 39 of age, male, with higher income and a bachelor's degree, and 92% possess a driver's license. Significant differences in attitudes between e-scooter users and non-users highlight the complexity of perceptions towards e-scooter usage. The ML model indicates that employment status negatively impacts the prediction of e-scooter users, while factors such as living without a car and using non-motorized modes positively influence e-scooter use. Educational background is a significant e-scooter mode choice factor in Washington, D.C. and Miami, whereas attitudinal questions on car and technology usage are influential in Los Angeles. These findings provide valuable insights into the factors shaping e-scooter adoption, informing urban transportation planning and policymaking and enhancing understanding of shared micromobility and its impact on urban mobility. [ABSTRACT FROM AUTHOR]

The UC Davis Campus Travel Survey is an annual survey led by Transportation Services (TS)—formerly known as Transportation and Parking Services (TAPS)—and the National Center for Sustainable Transportation (NCST), part of the Institute of Transportation Studies (ITS) at UC Davis. It collects a rich set of data about travel to the UC Davis campus, demographics, and attitudes toward travel. The 2022-23 survey collected data from 3,228 people affiliated with UC Davis about their travel to campus during a single week in October 2022. It used a stratified random sampling method with the intent to gather a representative sample of the campus population. About 18 percent of those invited responded to this year’s survey. For the statistics presented throughout this report, we weight the responses by campus role (freshman, sophomore, junior, senior, Master’s, PhD, faculty, and staff) and gender so that the proportion of respondents in each group reflects their proportion in the campus population.

This report presents the views expressed by UC Davis commuters related to various issues, challenges, and concerns that they have with the transportation facilities and services in the community in an open-ended question on the 2022-23 UC Davis Campus Travel Survey. The authors analyzed the contents of comments provided by survey respondents and identified common themes to group the comments. Based on this content analysis, they have grouped the views of students and employees across the themes of safety, parking facilities, transportation for people with disabilities, transit issues, biking, challenges with micro-mobility, and EV charging facilities. Only selected comments are included. They present the comments unedited and anonymously, indicating only whether the comment was provided by an Undergraduate Student (U), Graduate Student (G), Faculty (F), Post-Doc (P), or Staff (S).

Every year the UC Davis Campus Travel Survey includes questions related to travel to campus during different days in the reference week. Respondents from all role groups indicate how often they travel to the campus during the reference week for school or work. They also indicate the reasons for not coming to work in-person during the same time period. Travel to campus was highly impacted by the COVID-19 pandemic in the 2020-2021 school year as campus switched to remote instruction. In 2021-22, the campus returned to in-person working and instruction which led to increased physical travel. Travel to campus further increased in 2022-23 as more students and staff returned to campus for in-person learning and working. This report discusses some of the key results from the survey on questions related to physical travel to campus and reasons for no travel to campus before and after COVID-19 pandemic. It presents a comparative analysis of physical travel to campus during the reference week across three years, 2019-20, 2021-22, and 2022-23. The report also provides details on how telecommuting patterns changed before and after the pandemic among both students and employees.

This report presents some of the key results from the two new blocks of questions included in the 2022-23 Campus Travel Survey. The first block includes questions to assess the safety of pedestrians, bicyclists, and other micro-mobility users. The second block ask respondents questions about incidents related to bike theft and vandalism on campus. To assess pedestrian safety on campus respondents were asked if they had been hit while walking on campus since the beginning of fall quarter 2022 and if so, to select the mode they were hit by. The set of questions related to fall and crash of bike and micro-mobility users was shown only to respondents who indicated that they were associated with UC Davis during the 2021-22 academic year. The report presents results of questions related to safety of bike and micro-mobility users, including questions on type of fall or crash related incidents, and reasons for their fall or crash while using a particular mode. Respondents were also asked to indicate the location of fall or crash on the campus. The block on bike theft asked respondents if they were the victims of bike theft or vandalism during the year 2021-22. The questions were designed to better understand the situations under which the bike was stolen, whether it was locked indoors or outdoors, type of object the bike was locked to, and the type of lock used.

Decision on travel choices in dynamic multimodal transportation networks is non-trivial. In this paper, we tackle this problem by proposing a new joint travel mode and departure time choice (JTMDTC) model based on deep reinforcement learning (DRL). The objective of the model is to maximize individuals travel utilities across multiple days, which is accomplished by establishing a problem-specific Markov decision process to characterize the multi-day JTMDTC, and developing a customized Deep Q-Network as the resolution scheme. To render the approach applicable to many individuals with travel decision-making requests, a clustering method is integrated with DRL to obtain representative individuals for model training, thus resulting in an elegant and computationally efficient approach. Extensive numerical experiments based on multimodal microscopic traffic simulation are conducted in a real-world network of Suzhou, China to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed approach is able to make (near-)optimal JTMDTC for different individuals in complex traffic environments, that it consistently yields higher travel utilities compared with other alternatives, and that it is robust to different model parameter changes.

This paper focuses on modeling transportation mode choice for commuting to work and school. The study employs a Multinomial Logit (MNL) model to examine how individuals choose their modes of transportation for work and school trips in Hawassa city. Additionally, the study aims to predict both the current and future distribution of transportation modes. To construct the model, surveys were distributed across the city's seven sub-cities, involving inspections of workplace and school travel. Primary data were collected through site visits to key transportation hubs, and travel cost data were obtained from the city's Road and Transportation Bureau. The data used for the MNL model describe the travel behaviors of employees and students, and these behaviors are integrated into the statistical analysis to formulate utility functions. The choice of transportation mode for a trip is treated as the dependent variable, while independent variables include factors like out-of-vehicle travel time, in-vehicle travel time, and travel cost. The model's validity and accuracy were assessed by examining the direction of parameter signs and comparing them to the fundamental properties of the MNL model. The study revealed that factors such as average monthly income, in-vehicle travel time, out-of-vehicle travel time, total travel cost, and comfort during the journey significantly influence individuals' choices of transportation modes. The results from the travel behavior forecast, which examines how employees choose their transportation modes, highlight the pressing need for implementing effective policy measures to incentivize the adoption of more sustainable transportation modes and promote a modal shift. Without such measures, employees are likely to increasingly favor motorcycles as their preferred mode of transportation, potentially exacerbating issues related to fuel consumption and congestion. It's evident that students tend to favor public transportation over motorcycles when selecting their mode of travel. The findings of this study offer valuable insights to decision-makers and transportation planners, shedding light on the critical factors influencing travel patterns, as well as providing estimates of existing and future market shares. These findings can serve as a foundation for crafting targeted policy adjustments to encourage sustainable transportation choices in a comprehensive manner.

We examine pre-COVID declines in transit ridership, using Southern California as a case study. We first illustrate Southern California's unique position in the transit landscape: it is a large transit market that demographically resembles a small one. We then draw on administrative data, travel diaries, rider surveys, accessibility indices, and Census microdata for Southern California, and demonstrate a strong association between rising private vehicle access, particularly among the populations most likely to ride transit, and falling transit use. Because we cannot control quantitatively for the endogeneity between vehicle acquisition and transit use, our results are not causal. Nevertheless, the results strongly suggest that increasing private vehicle access helped depress transit ridership. Given Southern California's similarity to most US transit markets, we conclude that vehicle access may have played a role in transit losses across the US since 2000.

People with disabilities often encounter more and different problems with transportation compared to their socioeconomic peers without disabilities, but their desires for transportation mode choices, usage frequencies, activity frequencies, and neighborhood features have been poorly understood. The authors have begun to rectify those deficiencies with this study, developed in close cooperation with disability advocacy organizations (DAOs). The authors conducted a focus group in 2021 November involving 20 adults with various disabilities across California, including rural, suburban, and urban parts of the major coastal metropolitan areas as well as areas in the interior of the state. Focus group participants’ comments evinced a broad theme of problems for people with disabilities arising from car-oriented land use patterns, as they asked for more street lighting, seating, and shade, more frequent public transit service with more geographic coverage, and similar support for infrequent yet critical longer-distance trips. Based on focus group participants’ suggestions and pre-testing as well as feedback from DAOs, the authors developed a survey of adults across California to capture how disability affects the choices and desires that people make for transportation mode frequencies, activity frequencies, and neighborhood features. The survey collected nearly 2,000 cleaned responses, reflecting the diversity in disability, geography, and socioeconomic conditions in California.

Planned special events (PSEs) can significantly increase traffic demand, posing greater challenges than daily operations. In response, host cities often invest heavily in transportation upgrades. This study proposes a model integrating cumulative prospect theory (CPT) and multiple-attribute decision-making to understand mode choice behavior of PSE attendees. This integration enables direct extraction of traveler preference information, offering a more suitable description of the decision-making process. The decision matrix considers mode and attribute heterogeneity. The reference point for CPT is derived from previous PSE trips or commutes. Furthermore, this paper assesses asymmetric nonlinear sensitivity and distorted perceptions of different attributes. Analysis of mode-choice behavior for PSE participation, using survey data from Tianjin, China, validates the approach. It better characterizes PSE choice behaviors and demonstrates varying sensitivity to gains and losses, dependent on different attributes and distances. Thus, event planners should prioritize improving travel time and convenience to increase public transport mode share. [ABSTRACT FROM AUTHOR]

Electric micromobility, both as a private option and as a shared service, can represent an alternative to cars, particularly for given user groups (market segments) and specific classes of travel distance. The paper explores the potential for shifting from cars to electric micromobility (specifically, e-bikes and e-scooters) for commuting trips, investigated through floating car data (FCD). The methodology combines the calibration of random utility models (RUMs) and the subsequent simulation through the adoption of FCD spanning the entire city of Rome (Italy). The data used for the calibration of RUM models have been sourced from an online revealed preferences and stated preferences survey carried out between November 2020 and January 2021. Socioeconomic factors, along with transport features (travel time, access time, monetary costs, and perceived safety levels), enter into the definition of the mode choice probability. The first results showed that in Rome, the potential demand for electric micromobility could range between 14% of the FCD sample in the best case (low cost, high accessibility, and road infrastructures with a high perceived level of safety) and about 2% in the worst case (high cost, low accessibility, and a low perceived level of safety). [ABSTRACT FROM AUTHOR]

High-occupancy vehicle (HOV) lanes, which exclusively allow vehicles with two or more travelers on board to enter, has been recognized as a promising and practical way to motivate travelers to carpool. However, in the presence of ride-sourcing services, ride-sourcing vehicles with one driver and one (or more) passenger on board also meet the vehicle requirements of HOV lanes. So instead of encouraging travelers to carpool, HOV lanes may benefit ride-sourcing users and stimulate ride-sourcing demand. In this paper, in a single-corridor network with both HOV lanes and general purpose (GP) lanes, we model travelers' mode choices among transit, driving alone, ride-sourcing, and carpool, and delineate travelers' lane choices at user equilibrium, with the travel time of GP/HOV lanes and the carpool price being endogenously determined. With numerical examples, we show that the occupation of HOV lanes by ride-sourcing vehicles will greatly reduce the time saving on HOV lanes, thus lower the attractiveness of carpool, and increase the total travel time cost on the corridor. To solve this problem, we propose to implement an additional tolling scheme on HOV lanes, so that carpool users can split the toll evenly, while ride-sourcing riders have to pay the toll alone. As shown by numerical examples, the optimal tolling scheme can effectively reduce the occupancy of ride-sourcing vehicles on the HOV lane and encourage users to carpool. And the impacts of the ride-sourcing price, driving car cost and the HOV lane capacity ratio on the effectiveness of the proposed hybrid toll and HOV strategies are examined through sensitivity analysis. • Model the impacts of HOV lane on ride-sourcing and carpool demand. • Reveal the phenomena that HOV lanes may stimulate ride-sourcing demand. • Reveal how ride-sourcing vehicles on the HOV lane discourage carpool demand. • Propose tolling and tradable credit schemes to improve the HOV-lane efficacy. [ABSTRACT FROM AUTHOR]

Road freight transport has the negative externalities of traffic congestion, traffic accidents, energy consumption, and environmental pollution. Therefore, understanding the factors of road freight transport mode choice is important for urban transport policy analysis. This study aims to analyze the influence of behavior patterns on mode choices in the logistics sector in Bali. Data have been collected by interviewing road freight transport operators by using questionnaires. Models have been constructed by using discrete choice analysis. The study results show that vehicle ownership, type of goods transported, and fuel costs significantly influence all freight mode choices. The estimated elasticity of choice analysis indicates that all modes are not sensitive to changes in all of that vehicle ownership, type of goods transported, and fuel costs. These may indicate the outcome of the expected success of policy measures. This study suggests that a time limit for the movement of goods should be proposed in order to ensure that freight vehicles only run at certain times, i.e. at night time or not during peak hours. In order to support this policy, further research on Origin-to-Destination (OD) analysis of freight transport is needed to analyze the pattern of road freight traffic over a given period and its impact on freight and fuel costs. [ABSTRACT FROM AUTHOR]

• This paper reviews factors influencing shift to public transport. • The focus is on developing countries. • The findings highlight a modal shift from motorcycles to public transport. • The findings highlight a theoretical framework for the modal shift. Most studies on commuter and public transport mode choice rely on theoretical frameworks that draw boundaries between the utility function, social-ecological system, demographic and socio-economic attributes, and psychological theory. While models predicting the shift towards public transport are commonly applied in developed countries, there is a lack of similar studies that construct these models in developing countries. In addition, in some developing countries, the motorcycle is one of the main private motorised travel modes. Therefore, the modal shift from motorcycles to public transport in Southeast Asia to reduce private mobility is one of the important topics to examine. This paper presents a systematic literature review, utilising a comprehensive search strategy across electronic databases and reputable sources to identify directed acyclic graphs (DAGs), revealing factors influencing the choice and intention to shift to public transport. Data synthesis from selected studies highlights the intrinsic and extrinsic variables influencing public transport use, motorcycle use, and the modal shift to public transport. The study also proposes a theoretical framework for the modal shift in usage from motorcycles to public transport. [ABSTRACT FROM AUTHOR]

This study aims to identify the impacts of the COVID-19 pandemic on school trips using a survey of 1120 students and their households in Cairo, Egypt. We found that students' age, distance to school, and school type (public or private) influence the mode used to school, the majority of parents would prefer to wait for one month or more after the lockdown before sending their children to school, and the pandemic has a significant impact on parents' evaluation of the various schooling options. Based on the responses and using different scenarios, we predicted that the original vehicle-kilometers-traveled would increase by 17% to 34%, with most of the expected modal shift occurring from large occupancy vehicles to smaller vehicles. This shift was attributed to; the distance to school, the father's occupation and education level, parents waiting more time to send children to school, and parents expecting reduced class capacity. [ABSTRACT FROM AUTHOR]

While some researchers consider the traditional product-sharing model in which the platform only focuses on its profits, this study considers a green-sharing mode, where the platform focuses on its profits and provides green services to consumers. We analyze the optimization problems on the pricing scheme under the traditional mode and on the pricing scheme and effort for offering green services under the green mode. We investigate how the green service implementation affects stakeholders and find that providers are always better off under the green mode. When consumers prefer the product-sharing model, the green mode can generate higher profits given that 1) the lower difference and larger cost coefficient or 2) the higher difference and smaller cost coefficient, and the traditional mode increases consumer surplus if the difference is sufficiently high. When consumers prefer the baseline model, whether the green mode brings higher profits depends on the relationship between the sensitivity degree of consumers and discount ratio, and consumers are better off under the traditional mode if the difference and discount ratio are higher. Furthermore, we establish a contract in which all stakeholders are better off under the green mode when consumers have green preferences. [ABSTRACT FROM AUTHOR]

Congestion pricing and License Plate Rationing (LPR) are classical transportation policies targeting travel demand. Travelers are constrained to reduce their cars' usage to improve traffic conditions or decrease exhaust gases emissions. They have already been proven effective in the field but have also received some criticism for being unfair or badly perceived by users. On the other side, Tradable Credit and Permit Schemes (TCS/TPS) have been investigated in the literature for more than a decade but have never been implemented in practice. In this paper, we present a large-scale dynamic simulation study, corresponding to the morning peak hour for the Lyon city (France) to benchmark pricing, LPR, TCS, and TPS in terms of modal shift from personal cars to Public Transportation (PT), over a horizon of several days and with different charging schemes. Congestion dynamic in the transportation system is reproduced using a trip-based Macroscopic Fundamental Diagram (MFD) framework. We compute the modal assignment at equilibrium by iteratively solving a Quadratic Problem (QP). The initial formulation, presented in previous work for a single day and a uniform TCS, is extended to account for different days, validity cycles, charging schemes, and TPS. The benchmarking shows that the TCS outperforms LPR in terms of both social cost and carbon emissions. Making credit valid over multiple days reduces the price variability while keeping the same system performances and gain distributions. [ABSTRACT FROM AUTHOR]

The importance of public transportation service quality research is significantly increasing in recent years, it is the key to understanding and analyzing passengers' preferences. Different approaches are utilized to explore users' preferences however, dominantly these apply merely subjective scoring of the attributes and alternatives of the mobility. In this paper, we design a specific model for public transportation mode choice which is capable of integrating subjective scoring with scoring by objective measures such as distance or time. Owing to this purpose, we combine the outranking Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) as a method to evaluate passengers' preferences for tangible and intangible criteria with the fuzzy theory, and the Graphical Analysis for Interactive Aid (GAIA) plane to visualize the interactions between attributes as well as to test the robustness of the results via sensitivity analysis. The contribution of this paper is the constructed integrative method that is less subjective than the well-known models but also keeps the freedom of individual evaluators in expressing their preferences. Moreover, another significant issue of mode choice analysis is the group consideration, which is also refined in the new methodology by taking into account not only the mean of group preferences but also their range. A common characteristic of public surveys, the possible vague responses of the layman pattern is solved with the fuzzy approach to reduce the risk of uncertain scoring. The proposed model acts as a great base for the fuzzy inference system that can facilitate mode choice for passengers within a changing environment. The efficiency of the new methodology is demonstrated through a real-world case study of Budapest city, the obtained results are supporting underground mode service quality and highlighting its impact on citizens' behavior in favor of public transport. [ABSTRACT FROM AUTHOR]

COVID-19 prompted a significant number of employees to shift to remote work for the first time, potentially reshaping future work patterns. This study examines the post-COVID impacts on telecommuting, commuting behaviour, travel activities, and lifestyles in the city of Adelaide, South Australia. A multivariate nominal logistic regression analysis of 305 post-restriction survey responses revealed that home distance from the CBD, industry, occupation, and income significantly influence post-COVID telecommuting trends. Individuals living over 20 kilometres from the CBD, those in professional or managerial roles, and higher-income earners (>125k) are more prone to regular telecommuting, highlighting the impact of commute lengths, job flexibility, and financial resources on the ability to work remotely. The study revealed a higher adoption of telecommuting post-COVID, with more individuals working from home and telecommuting more often each week. This led to reduced usage of private cars and public transport, indicating a decrease in overall travel frequency. Respondents also adopted flexible work schedules, resulting in fewer peak-hour commutes, which would have resulted in lower congestion and emissions and led to more sustainable travel practices. The study also investigated future telecommuting perspectives, revealing a preference for remote work 3–4 days a week. Some respondents who initially could not telecommute have since considered it feasible and want to adopt it. Notably, about 25% of respondents would even change jobs for flexible, home-based work arrangements. The study's results suggest that remote work frequency may influence individuals' future house location preferences. These findings offer valuable insights for sustainable transport and urban planning considerations in the post-COVID era. [ABSTRACT FROM AUTHOR]

Global trade and social relationships are greatly facilitated by transportation. However, the majority of nations, including Bahrain, face substantial challenges with their transportation systems. For the development of technical solutions that can promote the progress of these transport systems, it is now crucial to have a complete understanding of travel demands and driver's characteristics. This paper aims to explore the influential factors concerning travel mode choice in Bahrain and utilize mode choice models to forecast the probable utilities of various future public transport modes. The study utilizes diverse, 3864 data records extracted from previous surveys as well as a recent one conducted within this research. Subsequently, using Minitab software, two types of mode choice models were built, namely the logit model and the classification tree model, focusing on modelling the future transportation system, considering potential public transport modes (Public Bus, Metro, and Tram). The analysis of the data identified trip cost as the top predictor, moreover, direct, and quick travel, accessibility, and convenience were also found to significantly influence the choice of travel mode in Bahrain. Additionally, the findings indicate that the metro is the preferred choice for future public transport, with a strong preference observed for a combination of metro and tram. The research also suggests, in terms of model performance, that when capturing more complex patterns, as in this study, the classification tree outperforms the multinomial Logit model. Overall, the research provides valuable insights into mode choice in Bahrain and highlights the important factors influencing commuting decisions. The results of this study can support the development of an efficient public transportation system that would satisfy the needs and preferences of commuters in Bahrain and ultimately lead to a sustainable and accessible transportation infrastructure in the country. [ABSTRACT FROM AUTHOR]

Commuting is an important part of primary school students' travel behavior, which has been concerned for a long time. We found that the commute-mode choice behavior of primary school students in the context of regional segmentation shows strong characteristics in space, but has not yet been considered in traditional research. To fill this gap, this study summarizes the commute-mode choices of primary school students with different characteristics based on the Beijing School Commute Survey. And the geographically weighted regression (GWR) model is built to analyze the zonal heterogeneity of the impact of personal characteristics, family factors and school factors on the primary school students' commute-mode choices from a low-carbon perspective. The results demonstrate that the possibility of primary school students choosing low-carbon commuting modes is positively correlated with the grade, commuting time, commuting escort type and housing category, but is inversely related with the commuting distance and the distance from the school to the city center. The coefficient estimates of explanatory variables vary across regions. Finally, we put forward policy suggestions regarding promoting the low-carbon commuting structure, such as developing the urban slow traffic system, which serve as a reference for policymakers. [ABSTRACT FROM AUTHOR]