Journal of Public Transportation最新文献

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.

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.