Journal of Public Transportation最新文献

The COVID-19 pandemic highlighted transit's crucial role as a social service, ensuring access to essential destinations. Despite this, unprecedented ridership lows forced agencies to implement service cuts, disproportionately affecting essential workers and vulnerable populations. However, the full extent of these impacts remains underexplored. While existing literature examines transit agency responses during the pandemic, much of the focus has been on public health and safety measures, overlooking the specifics of service adjustment strategies implemented. This study contributes to our understanding of transit agency pandemic responses throughout the pre-, peak-, and post-pandemic phases by 1) characterizing patterns in transit service adjustments and 2) extending pandemic accessibility literature by examining job-specific impacts. The framework integrates time series clustering, qualitative review of agency press releases, and transit accessibility analysis, using only publicly available data. Through a case study of Bay Area Rapid Transit, we find distinct clusters of stations characterized by patterns in weekday morning peak service restoration and station area demographics. While impacts to accessibility varied by time of day, the relative ordering of accessibility levels across income and race/ethnicity remained consistent throughout the pandemic. These findings contribute to our understanding of service adaptation impacts and inform equitable response strategies for future service planning and disruptions.

Rural areas typically register low accessibility. This fact negatively affects their attractiveness, the well-being of their population in general, and population subgroups with limited access to private cars or strong space-time constraints (like minors, the elderly or members of large households). Collective autonomous vehicles (AVs) might improve this situation, e.g. by enhancing standard line-based services or introducing alternative shared schemes. Nevertheless, the collective usage of AVs in rural transport and their potential impacts on accessibility are still underexplored, with most research focused on the urban context. This study aims to fill this gap by analysing the public transport accessibility impacts that five alternative AV supply scenarios might generate in the rural valley of Mühlwald (South Tyrol, Italy). To this end, a variant of the standard space-time accessibility model developed by the authors is used. This focuses on accessibility by public transport specifically, and measures it to both fixed activities and discretionary opportunities. Accessibility impacts are first estimated at the person-based level for a sample of residents. Then, they are aggregated for the whole sample and six subgroups that tend to experience more substantial accessibility issues based on the literature. Results show that line-based AV applications provide limited accessibility benefits. Conversely, time-flexible applications like ride-shared vans or combinations of line-based trunks and on-demand feeders over peak and off-peak hours may provide the most evident advantages, especially for the subgroups with the tightest space-time schedules. Although these results do not reflect other possible impacts of AVs (e.g. environmental effects), they provide policymakers with valuable insights into the collective AV usages that could be most suitable in the rural context regarding person-based accessibility gains.

Understanding how passengers perceive public transport interchanges is important to better explain current public transport mode and route choice behaviour and to better predict future demand levels. In this study we derive how passengers value a public transport interchange in a metropolitan context entirely based on recent, large-scale, Revealed Preference data, explicitly distinguishing between different types and modes of public transport interchanges. For this purpose we estimate three discrete choice models using maximum likelihood estimation, based on over 26,000 passenger route choices observed in June 2023 in the Greater London Area. We find that each public transport interchange is on average valued equivalent to 5 min uncrowded in-vehicle time. Additionally, our model results provide quantitative evidence that cross-platform interchanges between two metro journey legs are valued 20–25 % less negatively than a regular metro interchange where a level change is required. Multimodal bus-metro interchanges and out-of-station interchanges are perceived most negatively by passengers. Passengers value bus-bus interchanges on average about 60 % more negatively than metro-metro interchanges, possibly driven by factors such as comfort, service frequency, reliability and (perceived) safety. Our study results can be used for business case and appraisal purposes, when quantifying the impact of service changes which affect the number or type of interchanges.

Even though COVID-19 no longer poses a significant threat to public health, it is crucial to reflect on this large-scale event to design equity and adaptability into services like public transportation systems for cities of the future. The case of Seoul presents a unique opportunity to analyse the pandemic's impact on transit ridership, serving as a natural experiment which is characterized by the implementation of a non-lockdown policy coupled with proactive transit management strategies. This study introduces a resilience index that magnifies the “unaveraged clues” of changes in Origin-Destination (OD) pairs to quantify the spatially unequal response of Seoul public transit use to external shocks from 2020 to 2023. Our findings reveal spatial heterogeneity in the resilience index of OD pairs and the dynamic change of related factors. OD pairs with high resilience during the outbreak were often associated with long-distance and labour-intensive industries, highlighting the need for transit systems to cater to “captive” travellers during the outbreaks. Despite overall ridership recovery in Seoul, factors like car ownership and the diversity of spatial functionality continued to influence patterns, which suggests that transit systems should focus on improving attractiveness to regain lost passengers post-pandemic. These insights are valuable for aligning policy with spatial and temporal dynamics to create equitable and sustainable public transportation systems.

The needs for transit station classification are ever-growing as the planning process, be it at a strategic or operational level, becomes increasingly automated, data-oriented, and short-cycled. Whilst most existing models have used binary methods, this study applied a fuzzy clustering approach and examined cluster memberships (i.e., to what degree a station belongs to each cluster) of London rail transit stations by using entry and exit data with intra-day and intra-week variations. A method of hyperparameter selection in fuzzy clustering considering the context of transportation and a framework of ridership variation analysis was proposed. The results suggest that fuzzy clustering can maximise the information from high-resolution temporal passenger flow data of urban rail transit. The membership breakdowns allow users to have a better understanding of station characteristics and help to avoid inadequate plans by treating the stations belonging to multiple clusters in a different manner from the binary clustering, where each station only belongs to one cluster. Furthermore, fuzzy clustering can capture the ridership variation patterns and reveal special clusters. The results can be potentially applied in operation planning, such as service timetabling, station staff working-hour designs and fare strategy designs, etc.

The Public Bus Transit Network Design and Operations Planning Problem (PBTNDOPP) is a complex transportation problem. Analytical approaches are one of the key approaches to studying this problem, often leading to optimal or near-optimal solutions with reduced computational complexity compared to mathematical programming. This article leverages the Critical Path Method (CPM) to visualize the historical applications of analytical approaches in PBTNDOPP. Then, it reviews the applications of these approaches to some of the recently emerged sub-problems identified via CPM, i.e., sustainable public bus transit network design and introducing emerging transportation technologies to these systems. Our review aims to shed light on the current state of the literature and its future direction in the selected sub-problems by analyzing published studies in the last decade from various angles, such as the problems investigated, modeling methods, decision variables, network structures, and findings. The review shows that the existing body of literature on the application of analytical approaches to the selected problems is immature and at an early stage of development. For example, most of the studies on sustainable PBTNDPPP have not included all dimensions of sustainability and have only focused on environmental sustainability, while considering social criteria such as fairness and equity in PBTNDOPP is crucial for designing a sustainable PBTS. Another important gap is related to hybrid methods based on analytical approaches that take realistic assumptions and uncertainty in various problem parameters and variables into account.

In proof-of-payment transit systems worldwide, fare inspection is the most widely adopted strategy against fare evasion from transit authorities and public transport companies. Although these actors attempt to make the inspectors’ work as easy, effective, and equitable as possible, several issues need to be analysed according to a unifying approach, i.e., “How, where and when to inspect”, “Who and why evades the fare”, “How many and how to distribute inspectors” as well as improve the inspectors’ effectiveness. Since no study exists in the literature investigating all these issues together, this paper aims to fill this gap with a review of several key papers that covered the full spectrum of relevant literature which, whole or partially, focused on fare inspection. Results show fare inspection is a beneficial strategy against fare evasion, but there are still many challenges and research limits that should be overcome in the years ahead. A possible research agenda is provided. It calls for specific options (i.e., data collection and fare evasion risk in hotspot definition, digital support and bottom-up approaches, size of inspection staff and scheduling of inspectors under realistic conditions and follower responses, effectiveness of actions focused on the visibility of fare inspection, managing interactions between fare inspectors and passengers) and integrated approaches (i.e., linking the planning, organisation, and activities of fare inspection with who and why evade). Nevertheless, even if this review may not be conclusive, these results support a unifying literature development on fare inspection.

The evaluation of performance of public transportation, such as bus lines for example, is a major issue for operators. To be able to integrate specific and local behaviors, microscopic simulations of the lines, modelling each buses on a daily basis, brings an actual added value in terms of precision and quality. A scientific deadlock then appears regarding the parameterization of the simulation model. In order to be able to gather relevant performance indicators on a potential evolution of the configuration of the line, validated and modifiable simulation models need to be developed. This study aims at proposing a model development methodology based on a multi-agent simulation framework and data inputs extracted by a hybrid approach combining machine learning (ML) trained on actual bus data to predict travel times and probabilistic distributions to accurately estimate travel time variability. It also aims to propose a two-step validation framework that exhibits the performance of the obtained model on a case study based on actual data. The results of the proposed approach are validated by a real case study of three bus lines, including a number of simulation scenarios, to study the impacts of bus recovery time and bus control strategies on bus punctuality. The results obtained show that proposed hybrid approach combining ML with probabilistic distributions outperforms probabilistic distributions on average. Overall, the results show a good fit with the actual Key Performance Indicator (KPI) used by bus operators.

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.