Travel Behaviour and Society最新文献

Due to the issues of land redevelopment and changes of use within urban areas, many cities must adopt measures to reorganise and optimise parking space. This paper proposes a methodology to study one of them by implementing parking information systems (PIS). This solution offers users a competitive advantage by allowing them to know about the free parking spaces at the moment of decision-making. To achieve this goal, microscopic simulations are conducted to analyse the effects of various scenarios involving the implementation of PIS. The data used in these simulations is obtained from the Santander area in Spain. For the evaluation of results, a methodology has been developed that combines the evaluation of social factors for citizens and operational impacts for decision-makers. The results show significant improvements with increasing user information rate, e.g., the number of unsuccessful parking attempts before finding a final parking space is reduced by 55%, and 37% less particulate pollutants are emitted into the atmosphere.

Most ride-sourcing platforms, exemplified by industry leaders like Uber, Lyft, and Didi, provide a range of ride services tailored to the diverse preferences of their passengers. Passengers, driven by their distinct priorities, may opt for high-class (HC) ride services, such as Luxury rides, if they value service quality, while those more cost-conscious may gravitate toward low-class (LC) ride services, including basic solo and shared rides. However, this market fragmentation can manifest as an excess of HC vehicles idly cruising the streets, while an insufficient number of LC vehicles struggle to meet passenger demand for LC services. To mitigate this issue, upgrading strategy is proposed where some LC vehicle requests are elevated to HC ride services without incurring additional charges. This study embarks on an initial exploration of the impacts of upgrading within the ride-sourcing system. We develop a mathematical model to depict the equilibrium conditions and analyze the collective influence of operational strategies, encompassing upgrading, spatial pricing, and vehicle repositioning, on system performances. Our research identifies scenarios in which the platform should employ these strategies to balance supply and demand and curb superfluous idle vehicle movements, supported by both theoretical and numerical analyses. The results offer operational insights that guide platform decisions, allowing them to adapt their strategies effectively in response to various supply–demand dynamics.

Recently, several new concepts and innovative technologies have emerged to overcome the problems of urbanization, which can be hardly solved with using exclusively private vehicles or conventional public transport services. One of the new solutions is the Mobility-as-a-Service (MaaS) concept, a user-centric mobility distribution scheme, in which the user needs are satisfied via a single platform, and multiple transport options are offered by one MaaS operator (MO). In the last years, a couple of MaaS pilots were performed, but previous papers fail to focus on the pilot development and the proper description of the minimum viable product. A pilot of MaaS in Budapest has been developed by using the innovative Scrum methodology successfully involving six mobility service providers, such as public transport, shared mobility, and taxi, in the live demonstration. Current article provides detailed information about the pilot development including technical, legal, and business use cases for all service providers. The results of the recruitment and the characterization of the early-bird users are presented, too. The iterative pilot development process can be utilized by those MOs and governmental organizations that would like to initiate a new mobility project based on the MaaS concept.

In response to the absence of demographics in increasingly emerging big data sets, we propose a novel method for inferring the missing demographic information based on similarity in people’s daily multi-dimensional activity-travel patterns as well as the characteristics of the area they move about. Instead of using isolated activity-travel attributes to infer social demographic features, our proposed method first calculates the similarity of people’s multidimensional daily activities and travels as well as characteristics of their visiting locations, between those for whom the social demographics are to be imputed (target) and those with known demographics (base) using a polynomial function. The weights of the function are determined using the permutation feature importance method, and then dynamic time warping is used to align the multidimensional activity sequences of the base and target sample and measure their similarities. For each person in the target database, a matched list is created consisting of those with the most similar activity-travel sequences in the base sample. A support vector machine is then trained using the base sample as input to impute the demographics of the target sample. The proposed model is trained using a national travel survey and validated by applying it to a GPS dataset. The results show that the proposed method outperforms existing methods in predicting four selected demographics: gender, age, education level, and work status, with an accuracy range between 91% and 94% for the national dataset and 88% to 91% for the GPS data. This study highlights the importance of considering the multidimensional and sequential nature of peoples’ daily activity-travel patterns in the imputation of demographic features.

Using 2021 5-year American Community Survey, we empirically examine the effect of English proficiency on public transit ridership among immigrant commuters. To solve the endogeneity problem of English proficiency, we employ an instrumental variable strategy designed based on the interaction term between the age at arrival and non-Anglophone linguistic origin. English skills have a mixed role in explaining commuters’ travel behaviors: On the one hand, immigrants from English-speaking countries are more likely to commute by public transit than those from non-English-speaking countries. On the other hand, controlling for countries of origin, there is a moderate, negative association between English proficiency and public transit commuting among immigrants. Such an association is spatially heterogeneous: the association is statistically significant only in counties where public transit is a common commuting option.

There is limited availability of travel survey data on households with electric vehicles (EVs) and a lack of evidence on factors influencing EV ownership levels at a finer geographic level, which are crucial for optimizing public charging infrastructure investments. To address this gap, we propose an integrated approach utilizing a discrete choice model and a Bayesian network-generated synthetic population. Applied to Maryland, the model analyzes the impact of public charging stations (level-2 and DC fast chargers) on EV ownership at the census tract level. Access to fast charging, workplace charging, and the possibility of teleworking are key factors influencing EV ownership. The model, applied to the synthetic population, predicts higher EV growth in suburban regions compared to urban areas and a larger increase in EV adoption among high-income groups. This highlights potential disparities in EV adoption and demonstrates the application of this methodology in understanding micro-level EV adoption rates for informing targeted policies and infrastructure development to promote equitable adoption.

With growing concern about the climate impact of travel, a central question is the extent to which working from home (WFH) can reduce commuting. Recently, the question has received even more attention as WFH has increased sharply with the onset of the COVID-19 pandemic. However, the state of research is marked by mixed results and lacking longitudinal evidence. We investigate the link between WFH and total weekly commuting time by applying fixed effects regression to panel data from the Australian HILDA Survey, covering the period 2002–2019. We go beyond previous research by examining the moderating roles of the extent of WFH, the duration of the WFH episode, and gender. Overall, we find that doing any work from home is associated with a significant decrease in employees’ weekly commuting time of about 14% on average. The reduction sets in immediately with the start of WFH and tends to further increase thereafter. However, only high shares of WFH are associated with substantial drops in commuting time, and reductions are larger for women than men. Taking into account Australian workers’ reported WFH preferences, our results suggest maximum potential future commuting time savings of about 17–25% compared to 2019.

Changes in transportation ridership during COVID-19 indicate several important factors, including the need to serve changing spatial and temporal patterns of travel demand, and the equity implications of pandemic impacts across lines of race, age, and income. Various papers have sought to understand changes in transportation ridership during the pandemic, but have been focused solely on a single mode (often public transit), and have been limited to a single data source for analysis. This paper examines and compares the changes in public transit and ride-hailing ridership in Chicago during the COVID-19 pandemic, investigating ‘who’ stopped using transit and Transportation Network Company (TNC) services from a demographic perspective, how remote work relates to changes in transit use, how pandemic ridership changes are clustered in space, and what factors will impact a return to regular travel. Analysis integrates datasets spanning over a year of the pandemic, including aggregate spatial ridership counts that are used to form spatial regression models, and a six-month panel survey that received input from approximately 1,000 Chicago Transit Authority (CTA) riders. Continued transit use is correlated with areas with a greater percentage of African American and Spanish-speaking people, and with a greater percentage of pre-pandemic bus riders and off-peak riders, while peak-period, frequent, and rail system riders stopped using transit to the greatest extent. Areas with a higher share of young, college-educated people, and those with a high walkability metric, generally saw the greatest decreases in TNC use, reflecting a potential loss of trips for those who used TNCs to access social events or employment, and moved to virtual work during COVID-19. Our findings can help to guide transportation service providers and policy-makers in planning service for public safety and a changing demand profile, advancing equity of access to mobility, and anticipating long-term mobility patterns.

To address the unbalanced distribution of dockless bike sharing around metro stations, existing studies usually examine local-scale characteristics while neglecting the correlation between station location and travel demand which exert a more substantial influence on travel behavior. This study bridges this gap by conducting a comprehensively analysis of the impacts and underlying mechanism of station location on DBS-metro integrated demand. The results uncover a notable negative correlation between station location − measured by both proximity to the city center and distance from subcenters and DBS-metro integration demand. Moreover, job-housing imbalance and public transport allocation around metro stations, inherently influenced by station location, further shape DBS-metro integration demand. Notably, our results show significantly higher demand during the morning peak compared to the evening and at metro stations with medium-level ridership, highlighting the heterogeneous patterns. In addition, subcenters compensate to some extent for the decline in transport resources caused by distance from the city center. These insights bear critical implications for policy formulation concerning on-demand public transport services and the exploration of transport equity, facilitating a more informed approach towards rectifying transportation disparities.

As the core force in logistics transportation, long-haul truck drivers profoundly impact the global economy. However, the monotonous and demanding nature of the transport task often results in truck drivers neglecting their health and well-being, and the resulting issues of psychological safety and fatigue pose a significant challenge to truck drivers’ transport performance. Couples traveling together as a strategy can increase driver-family interactions and significantly improve driver transport performance. However, there is a gap in research on the specific pathways of the effects of traveling together as a couple on truck drivers’ transport performance. This study combined perceived risk theory, conservation of resources theory, and job demand-resource theory with structural equation modeling to investigate 167 long-haul truck drivers from China. The comprehensive evaluation found that, under the mediating role of psychological capital and job burnout, couples traveling together can reduce the perceived health, financial, social, and performance risks of long-haul truck drivers, thereby increasing the psychological capital of long-haul truck drivers, reducing job burnout, and ultimately improving transportation performance. This study reveals the jurisprudential relationship between truck driver couples traveling together and transport performance. This study also provides useful insights for transportation companies and government departments to formulate business strategies and management policies for truck driver couples traveling together.