Search Results (70)

This study estimates a willingness-to-pay (WTP) space mixed logit model to evaluate user valuations of travel time, safety, and comfort attributes associated with common access modes in Bangkok, including walking, motorcycle taxis, and localized minibuses. The model accounts for preference heterogeneity by specifying random parameters for travel time. Results indicate that users—exhibiting substantial variation in preferences—place higher value on reducing motorcycle taxi travel time, particularly in time-constrained contexts such as peak-hour commuting, whereas walking is more acceptable in less pressured settings. Safety and comfort attributes—such as helmet availability, smooth pavement, and seating—significantly influence access mode choice. Notably, the WTP for helmet availability is estimated at THB 8.04 per trip, equivalent to approximately 40% of the typical fare for station access, underscoring the importance of safety provision. Women exhibit stronger preferences for motorized access modes, reflecting heightened sensitivity to environmental and social conditions. This study represents one of the first applications of WTP-space modeling for valuing informal station access transport in Southeast Asia, offering context-specific and segment-level estimates. These findings support targeted interventions—including differentiated pricing, safety regulations, and service quality enhancements—to strengthen first-/last-mile connectivity. The results provide policy-relevant evidence to advance equitable and sustainable transport, particularly in rapidly urbanizing contexts aligned with SDG 11.2. Full article

Risk perception and travel behavior under extreme weather have attracted increasing scholarly attention due to their implications for sustainable transport. This study investigates how perceived risks influence commuters’ travel mode choices during heavy rainfall in Qingdao, China, using data from a pilot survey and a stated choice experiment. A Range-varying Generalized Weberian Regret–Rejoice Model (RGWRRM) is developed to capture nonlinear perceptual sensitivities and decision-making under uncertainty. Results indicate that safety and reliability risks significantly shape travel behavior, with commuters showing heightened loss aversion and increased willingness to pay for safer and more dependable modes. The RGWRRM outperforms traditional utility- and regret-based models, offering deeper behavioral insights. By elucidating the mechanisms linking risk perception to mode shifts, this study contributes to the design of resilient and sustainable urban transport strategies in the face of climate-induced disruptions. Full article

Traffic congestion is becoming an increasing problem due to the rapid growth of the population. In the current situation, the mode choice of the people has a direct impact on traffic density. For this reason, many studies have been carried out by researchers and planners to reduce the number of vehicles on the road. Various strategies have been proposed, such as incentives for public transport, parking restrictions, parking pricing and car sharing. It is very important that these strategies are implemented by the institutions in order to reduce traffic during the commuting hours, which coincide with the rush hour. Especially in areas such as shipyards and industrial zones, which are far from the city center and relatively difficult to reach but which provide employment opportunities for thousands of people, a shuttle service is one of the most preferred strategies to discourage employees from using private cars. However, in companies with thousands of employees, this situation generates costs that cannot be ignored. The examined case study similarly needs to optimize and reduce operational costs related to fuel consumption, maintenance and tax expenses by optimizing the number of two different types of service vehicles required for employee transportation at the Yalova Shipyard. For this aim, a dynamic linear programming (DLP) model was used to achieve a cost-effective, sustainable and demand-responsive shuttle service. According to the analysis results, it was concluded that the annual fuel cost of the vehicles will be reduced by 33.9%, the maintenance cost by 35.2% and the annual tax cost by 49.3% by disposing of the unneeded vehicles (27%) in the studied Yalova Shipyard. Taking all these positive improvements into account, it is clear that the optimization study significantly reduces the costs incurred by the service. Full article

The motorization of school commutes reduces the physical activity of children and causes a series of urban traffic and social problems, such as traffic congestion in school districts and parents becoming necessary for transportation. To alleviate traffic jams and related social problems, as well as to encourage physical activity amongst students, we advocate non-motorized travel modes for students, such as walking and cycling. Based on a case study of the Wuhan East Lake High-Tech Development Zone, we use a multiple linear regression model to analyze the relationship between influence factors and student travel mode choices. The results show that built environment factors (the built environment factors are divided into density, diversity, accessibility, and destination) have a significant impact on school travel mode choices, especially accessibility and diversity. Furthermore, the study highlights the pivotal role of travel perceptions, particularly perceptions of safety, comfort, and convenience. Through a questionnaire survey, we collect students’ travel perceptions and their actual school travel modes, which offer valuable insights for urban planners and policymakers. The findings indicate the complex interplay between student commuting and the built environment. Additionally, these findings can be valuable, both in academia and for policymakers. We provide strategies that could be beneficial for reducing motor vehicle activities (especially driving). Full article

While extensive research has examined how major life events affect travel habits, less attention has been paid to the impact of minor environmental changes on commuting behavior, particularly regarding shared autonomous vehicles (SAVs). This study investigated how daily disruptions and incremental environmental changes influence commuter behavior patterns and SAV adoption in Shanghai, applying the theory of interpersonal behavior framework. The study surveyed 517 Shanghai residents, examining travel satisfaction, commuting habits, psychological factors (such as habit strength and satisfaction), and attitudes towards SAVs. Structural equation modeling was employed to test hypotheses about psychological factors influencing SAV adoption, while logistic regression analyzed how these factors affected mode choice across different disruption contexts. Analysis revealed that psychological factors, particularly habit and satisfaction, were stronger predictors of SAV adoption than attitude-based factors. Route obstructions and workplace relocations significantly increased SAV consideration. Even minor, recurring disruptions, such as construction zones, showed strong effects on commuting behavior, supporting the habit discontinuity hypothesis and emphasizing the importance of minor disruptions in driving behavioral change. The study extends the theory of interpersonal behavior by integrating habit discontinuity theory to explain how minor disruptions drive SAV adoption. This research provides actionable insights for urban planners and policymakers, recommending that SAV trials and targeted interventions be implemented during infrastructure changes or other commuting disruptions to promote SAV adoption and foster more sustainable transportation systems. Full article

Using survey data from three dwelling types in Jakarta, we examine how dwelling type, socioeconomic identity, and commuting distance affect women’s travel-mode choices and motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) model with two machine-learning classifiers, random forest (RF) and XGBoost, using Shapley additive explanations (SHAP) for interpretation. The models’ efficacy varies across different datasets, with XGBoost mostly outperforming other models. The women’s preferred commuting modes varied by dwelling type and trip purpose, but their motives for choosing the nearest activity were similar. Over half of the women rely on private motorized vehicles, with women living in the gated community heavily relying on private cars. For nearby shopping trips, low income and young age discourage women in urban villages (kampungs) and apartment complexes from walking. Women living in gated communities often choose private cars to fulfill household responsibilities, enabling them to access distant options. For nearby leisure, longer commutes discourage walking except for residents of apartment complexes. Car ownership and household responsibilities increase private car use for distant options. SHAP analysis offers practitioners insights into identifying key variables affecting travel-mode choice to design effective targeted interventions that address women’s mobility needs. Full article

Carbon emission reduction strategies are being implemented in the transportation sector by encouraging the adoption of eco-friendly vehicles and introducing demand management policies such as Mobility as a Service (MaaS). Nevertheless, the efficacy of MaaS in reducing carbon emissions remains uncertain. This study introduces Sustainable Public Transit (SPT) as a public transit alternative consisting of only green modes to promote sustainability. We explore the preferences of SPT in a commuting context, incorporating individual preference heterogeneity in a discrete choice model. We systematically identify the relationship between choice behaviors and individual heterogeneity in alternative attributes and psychological factors stemming from socio-demographic characteristics. The integrated choice and latent variable (ICLV) model with a mixed logit form is adopted, and the key findings can be summarized as follows: Preference heterogeneity is observed in the travel cost variable, which can be explained by characteristics such as the presence of a preschooler, household size, and income. CO₂ emissions do not have a statistically significant impact on choices. Furthermore, psychological factors are also explained through socio-demographic characteristics, and it is found that low-carbon knowledge positively influences low-carbon habits. Psychological factors significantly affect choices. Respondents who dislike transfers and prioritize punctuality are less likely to choose SPT, while those who have positive low-carbon attitudes are more likely to do so. Finally, scenario analysis is conducted to forecast mode share based on improvements in SPT alternative attributes and variations in attribute levels. Policy implications are then provided to enhance the acceptability of SPT. Full article

To address the limitations of existing studies on the built environment and commute mode choice, which primarily focus on the objective and residential built environment, this study investigates how commuters’ perceptions of the built environment at their residences and workplaces influence their choice of commuting mode. First, six latent variables are proposed to characterize the perceived built environment. Then, commuters’ socio-economic and commuting characteristics are treated as exogenous variables. Subsequently, the influence of the perceived built environment on commute mode choice is analyzed using both a Multinomial Logit (MNL) model without latent variables and a Hybrid Choice Model (HCM) incorporating variables related to the perceived built environment. Finally, a case study conducted in Shanghai reveals that the goodness-of-fit value of the HCM improves by approximately 27.4% compared to that of the MNL, indicating that the perceived built environment plays a significant role in explaining commute mode choice. Furthermore, commuters’ socio-economic profiles, commuting characteristics, and perceptions of the built environment all significantly influence their commute mode choices. The perceived built environment at residences has a stronger impact on commute mode choice than that at workplaces. Among the various commute modes of driving, cycling, walking, and public transit, the perceived built environment most significantly influences public transit usage. Based on these findings, several policy implications are offered, providing decision-making support for urban planning and traffic management authorities. Full article

The predictors of urban trip mode choice and one of its important components, public transit ridership, have still not been thoroughly investigated using case studies in Central Europe. Therefore, this study attempts to clarify the correlates of mode choices for commute travel and shopping, and entertainment travel to distant places, as well as the frequencies of public transit use of university students, using a wide range of explanatory variables covering individual, household, and socio-economic attributes as well as their perceptions, mobility, and the nearby built environment. The correlation hypothesis of these factors, especially the role of the street network, was tested by collecting the data from 1288 university students in Krakow and developing Binary Logistic and Ordinal Probit models. The results show that gender, age, car ownership, main daily activity, possession of a driving license, gross monthly income, duration of living in the current home, daily shopping area, sense of belonging to the neighborhood, quality of social/recreational facilities of the neighborhood, and commuting distance can predict commute and non-commute mode choices, while gender, daily activity, financial dependence from the family, entertainment place, quality of social/recreational facilities, residential self-selection, number of commute trips, time living in the current home, and street connectivity around home are significantly correlated with public transit use. Some of these findings are somewhat different from those regarding university students in Western Europe or other high-income countries. These results can be used for policy making to reduce students’ personal and household car use and increase sustainable modal share in Poland and similar neighboring countries. Full article

The travel behavior of urban residents has gradually changed in response to the widespread adoption of ride-hailing services. This paper explores the travel mode choices made by individuals utilizing multiple forms of ride-hailing services. Eight scenarios were established, which considered combinations of activity types (commute or recreation), travel periods (peak or off-peak), and price levels (discounted or normal rates for ride-hailing). Moreover, socio-psychological variables such as perceived value, behavioral intention, and subjective norm were integrated into the analysis. The findings reveal that consumers of ride-hailing services generally exhibit characteristics such as being younger in age, having higher income, lack of car ownership, and having greater experience in using ride-hailing services. Furthermore, the inclusion of socio-psychological variables significantly improved the model’s fitness. Travelers exhibit a preference for ride-hailing services in scenarios involving recreational activities, normal travel periods, and discounted ride-hailing prices. In conclusion, this study sheds light on the evolving travel behavior of urban residents in light of the widespread availability of ride-hailing services. The incorporation of socio-psychological factors is essential in comprehending and predicting travel mode choices. The insights derived from this research contribute to a nuanced understanding of the factors influencing the adoption of and preference for ride-hailing services among urban commuters. Full article

Public transport (PT) inequality is evidenced to have adverse consequences on various social–urban–economic aspects of urban residents’ lives; however, the impact of this inequality on PT itself, particularly its utilization, is a less explored area of study. This paper examines the association between PT inequality and PT utilization patterns in Melbourne, Australia, using journey-to-work data in a multivariate regression model. By analyzing commuting and socioeconomic factors, we investigate how PT inequalities affect the travel choices of the residents. Our findings indicate that regions with lower PT inequality demonstrate higher PT usage for daily commuting, emphasizing the importance of the equitable distribution of resources. This finding is consistent across different PT modes of trains, trams, and buses, and using different inequality measures of the Gini index and the 90/10 ratio. Spatial variations and factors like income levels, education, home ownership, and age are also found to influence PT usage. The findings offer valuable insights into the potential impact of incorporating equity considerations during the planning stages of PT projects. Furthermore, they could justify targeted interventions aimed at enhancing the equity of PT services. Full article

Mobility as a Service (MaaS) is a new mobility solution that brings together different modes of transportation, such as car-sharing, public transport, taxis, and bicycles, to create personalized service packages for commuters. The present study aims to identify key factors affecting the adoption of a Mobility as a Service system, and to explore the extent to which a local community is ready to accept the implementation of MaaS. The case study investigates the city of Thessaloniki, which is the second largest urban agglomeration in Greece. This study applies a triangulation approach by combining quantitative and qualitative analysis, providing a comprehensive understanding of the opportunities and the challenges arising with the implementation of a MaaS system in the city of Thessaloniki. Furthermore, the utilization of MaaS as a tool for vulnerable people, a crucial aspect that has not been analyzed properly in the existing literature, is examined. A quantitative survey analysis was conducted, inferential statistics were applied, and a binary logistic regression model was developed to determine the significant characteristics that most affect citizens’ willingness to use a MaaS system. In addition, stakeholders were interviewed to examine their readiness to promote and collaborate for the development of a MaaS system. Results showed that age, driving license, daily time spent on urban trips, the frequency of commuting as car passenger or by public transport (PT), previous usage of a MaaS system, and the number of family members seem to be the most influential factors of citizens’ choice to adopt MaaS. For stakeholders, the quality of service provided, and the user friendliness of the system are necessary prerequisites. The findings reveal that the views of residents and stakeholders provide some positive foundations for the development of a MaaS system in the city. Full article

Park and Ride (P&R) as a demand management tool has the effect of reducing traffic congestion in urban centers, saving energy and reducing pollutant emissions. Since 2000, many cities in China have been constructing P&R facilities, which have partially alleviated urban traffic congestion and provided a time-reliable mode of travel for commuters heading to urban centers. However, in recent years, due to the pricing policy of the P&R facility, there has been an insufficient supply of P&R facilities in many places. In fact, the P&R system prefers to welcome travelers who make long-distance subway rides and does not want those who make short-distance subway rides to occupy more parking spaces. To address this, this paper proposes a tiered pricing strategy that considers charging parking fees based on the distance traveled by commuters after switching to public transportation, to improve the utilization of P&R. That is, charge less for parking for long-distance subway riders and more for short-distance subway riders. Firstly, based on questionnaire data from SP surveys, a fixed pricing mixed logit model (FP model) and a tiered pricing mixed logit model (TP model) for P&R facilities are constructed. Utilizing two models, we explored the mechanisms underpinning traveler’s mode choice influenced by daily habits and travel considerations through the comparison of the two models to validate the effectiveness of the tiered pricing for P&R facilities. The study found that the implementation of a tiered pricing method for P&R facilities increases its attractiveness to long-distance subway ride travelers, resulting in a higher proportion of long-distance subway riders among P&R commuters. In the study’s last section, a marginal effect analysis was conducted on the per-kilometer cost (Pkm) within the P&R model. This analysis determined the optimal Pkm for three subway travel distances within the P&R model. Consequently, it calculated the corresponding P&R parking fees for these three subway travel distances. Additionally, we have predicted the implementation effects of the tiered pricing scheme. Full article

The analysis and modeling of parameters influencing parents’ decisions regarding school travel mode choice have perennially been a subject of interest. Concurrently, the evolution of artificial intelligence (AI) can effectively contribute to generating reliable predictions across various topics. This paper begins with a comprehensive literature review on classical models for predicting school travel mode choice, as well as the diverse applications of AI methods, with a particular focus on transportation. Building upon a published questionnaire survey in the city of Thessaloniki (Greece) and the conducted analysis and exploration of factors shaping the parental framework for school travel mode choice, this study takes a step further: the authors evaluate and propose a machine learning (ML) classification model, utilizing the pre-recorded parental perceptions, beliefs, and attitudes as inputs to predict the choice between motorized or non-motorized school travel. The impact of potential changes in the input values of the ML classification model is also assessed. Therefore, the enhancement of the sense of safety and security in the school route, the adoption of a more active lifestyle by parents, the widening of acceptance of public transportation, etc., are simulated and the impact on the parental choice ratio between non-motorized and motorized school commuting is quantified. Full article

Low-carbon travel is an important part of low-carbon cities and low-carbon transportation, and low-carbon transportation is an inevitable choice to slow down the growth of carbon emissions in China. All countries in the world are actively promoting new energy vehicles and attach great importance to the application of the new energy industry in urban transportation. Commuting is an important part of urban life, and the choice of travel behavior has an important impact on traffic and environmental protection. Taking the Xi’an metropolitan area as an example, this paper expounds on the integrated development path of the industrial chain of new energy + travel in the metropolitan area and clarifies the energy transformation model of the integrated development of low-carbon transportation and energy. From the perspective of green and low-carbon, 1000 commuters were interviewed using a questionnaire survey, and the cumulative prospect model was used to verify the internal mechanism affecting commuters in metropolitan areas to choose new energy commuting. The results of the study show that new energy transportation modes play an important role in the low-carbon economy, and under different scenarios and assumptions, there are significant differences in the cumulative prospect values of the subway, new energy buses and fuel private cars, and corresponding optimization measures are proposed to increase the proportion of new energy commuting trips. The results will help further promote the development of a low-carbon economy and energy integration in the field of transportation and provide a reference for the sustainable development of public transportation. Full article