Journal of Public Transportation最新文献

Mobility as a Service (MaaS) integrates various transport modes into a single comprehensive service; thus, decrease in the inconvenience of using multiple mobility services is expected. This research focuses on the students of Budapest University of Technology and Economics (BME) and aims to complete a background study on MaaS, which offers a vision of how future MaaS studies could be designed and conducted. The research work investigates the influencing factors in BME students’ acceptance of MaaS. The preferences are categorized into two groups based on the travel captivity and the usage of shared mobility services. An online survey was conducted where a total of ca. 700 valid responses were collected. Structural equation modeling (SEM) was performed to examine the causal relationship between the variables. This study identifies effort expectancy as the most influential factor that affects BME students’ behavioral intention to adopt MaaS. On the other hand, there is no significant effect of group differences on the students’ MaaS acceptance, except for individual innovation for travel captivity and tech-savviness regarding the usage of shared mobility. Conducting MaaS studies with samples obtained from the general population is advised thus resolving the generalizability issue of current research.

Public transport (PT) systems face the challenge of retaining users and preventing a shift towards individual transport modes. While satisfaction is recognized as a key factor in user loyalty, there is a need to understand the specific PT attributes that contribute to passenger satisfaction and foster loyalty. This study aims to assess the impact of PT service attributes on user loyalty, controlling for socio-demographic characteristics. Data from an online survey conducted in the Grand Duchy of Luxembourg, a country with high car dependency, were analysed using logistic regression models. The findings highlight the importance of attributes such as reliable service, in-vehicle travel time, number of transfers, and feeling safe, while also identifying differences in attribute importance between bus and train loyalty. The study provides valuable insights for transport agencies and policymakers to enhance user loyalty and develop effective ridership retention strategies. These findings are particularly relevant in the post-pandemic scenario and can contribute to addressing car dependency challenges in diverse metropolitan areas. The paper concludes with policy recommendations to improve PT services based on the identified attributes.

Understanding the passenger demand impacts of public transport service changes is a fundamental aspect of transport planning. The main objective of this study is to derive an updated Generalised Journey Time (GJT) elasticity for urban and metropolitan public transport networks, by applying a revealed preference approach using individual passenger journey data. Based on more than 25 million empirical journeys subject to 9 different service interventions within the Greater London area, we find an average GJT elasticity of −0.61. The value implies that for every 1% increase in generalised journey time, on average public transport demand is expected to reduce by 0.61%, and vice versa. We also find that the demand response to service changes is most elastic during the midday period between the peak hours and most inelastic during the AM peak and early morning, possibly caused by a higher share of mandatory journeys. Our study results confirm the existence of a build-up rate from the initial short-run elasticity to a somewhat stronger longer-run elasticity. Besides, we find that at least within the short- and medium-term demand is more elastic to service degradations compared to service improvements. Our findings imply that it requires more time for demand to increase in response to a service quality improvement, compared to demand to decrease after a service quality reduction.

Planning public transport highly relies on the availability, quantity and quality of travel demand data of passengers. In the last two decades, smart card data has provided the opportunity to create comprehensive travel demand data as a byproduct of a fare-collecting system. One important attribute for the planning is the purpose of the trips, which is missing from the smart card data. This research study proposes and formulates a novel method to infer trip purpose in smart card data. Previous methods either lack the concept of trip chains or did not consider both spatial and temporal perspectives of a trip. Firstly, this method discovers relations between the sequence and temporal attributes of trips with their trip purpose attribute by running a clustering method on a rich travel survey dataset (This study only uses public transit records.) that contains all attributes. Secondly, the discovered clusters are labelled and transferred to the smart card data by calculating the closeness of the trip chain of each individual in the smart card data to the clusters. Thirdly, the proportion of relevant land use types near the destination of each trip is utilized to enhance the previously calculated closeness. The proposed method is implemented on datasets from South East Queensland, Australia. Also, two recently published methods were replicated and run on the same datasets to evaluate the proposed method. The results show improvements in the proposed method compared to the existing methods of the literature.

This study examines predictors of railway commuters' changes to departure time choice. Specifically, we sought to understand the impact of pre-departure information about in-carriage crowding on train choice behavior. We present the results of an online experiment, multiple-choice task, and a survey of UK rail commuters who regularly travel on crowded trains. Our findings show that most respondents are highly sensitive to crowding on trains. That notwithstanding, we identify a group of commuters who are free from constraints but do not use their flexibility to switch. This finding leads us to suggest further research into the decision-making processes of this specific sub-group of passengers to maximize the potential of personalized real-time and predictive provision of crowdedness information. Our study contributes insights relevant to practitioners grappling with innovative information provision to encourage operationally desirable behavior change among regular commuters.

Disruptions in public transit systems can have significant impacts on agency and passenger needs. Therefore, it is crucial to implement new transit policies to address these disruptions and to ensure efficient and reliable transit services. This study proposes a framework to assess alternative bus operating strategies to adapt with different disruptions to public transit systems. Conventional all-stop systems, visiting all the stops along a bus corridor, are compared against three alternative operational schemes: Skip-stop, express-local schemes with stop-skipping designs, and on-demand service with a fixed route but flexible stopping patterns, are compared to find the most efficient bus operating service under various circumstances. We developed an optimization model based on the total generalized system cost for each operating strategy using continuous approximation techniques and extended the previous models by comparing a wider range of alternative services and model flexibility to evaluate the optimum system in response to different disruptions. Different factors, such as the level of demand, demand patterns, and sensitivity to various components of transit trips for passengers and the operator, including crowding, denied boarding costs, and fleet constraints, are considered. We found that, given a different disruption scenario, demand, and travel patterns, the most efficient service can vary significantly between on-demand, all-stop, skip-stop, and express-local services. As such, it is suggested that the service scheme be chosen more adaptively by employing such frameworks.