Journal of Public Transportation最新文献

To use public transit, riders must complete first and last mile trip segments. However, transportation planning measures of access to opportunity often assume that transit riders can complete first and last mile trips with ease. This paper contributes to the understanding of how disabled transit riders experience the first and last mile of public transit trips. Using a mobility justice framework, interviews with disabled transit riders reveal accessibility challenges along the first and last mile in Austin, TX and Seattle, WA. Participants in both Austin and Seattle faced myriad accessibility challenges along the first and last mile, indicating that transit network size and local politics did not influence travel experiences. Findings indicate disproportionate and intersectional barriers to accessing public transit in both cities, especially among female transit riders. I find that gender, social conditions, built environment quality, connectivity, and public engagement experiences influence access to transit for disabled people along the first and last mile. This work reveals how planners can better engage with disabled transit riders about their experiences and incorporate mobility justice goals to improve first and last mile accessibility.

The outbreak of COVID-19 caused unprecedented declines in public-transport use. As travel frequencies rebound, ridership is recovering, although it remains considerably below pre-pandemic levels. This study compares pre- to post-pandemic public-transit use among workers and non-workers, and the changing impact of local and regional accessibility. Additionally, we assess the impact of increased telecommuting on workers’ transit use before, during, and after the pandemic. We estimate two weighted multilevel linear regressions using a three-wave panel survey over the years 2019–2022 in Montréal, Canada. Results indicate that the factors that determine workers’ and non-workers’ transit patterns have tended to diverge after the pandemic. For workers, the relevance of accessibility in promoting utilitarian transit use considerably decreased, being responsible for close to 10% of the post-pandemic transit-use reduction. The increase of telecommuting frequency due to the pandemic contributed more than 10% of the post-pandemic transit-use reduction, but the effect of transit commuting time has remained relevant. For non-workers, the effect of regional accessibility by transit has increased after the pandemic, which has partly mitigated non-workers’ transit-use decline. Moreover, we find there is a joint effect of local and regional accessibility that has maintained after 2019 for non-workers. Results from this work have relevant implications for transit planners and policymakers. To help transit-use recovery, results suggest that providing good transit connection to the workplace promotes workers’ transit use, while promoting transit accessibility in lower-local-accessibility areas is key for non-worker transit ridership.

Existing research underscores substantial gender-based variations in travel behavior on public transit. Studies have concluded that these differences are largely attributable to household responsibilities typically falling disproportionately on women, leading to women being more likely to utilize transit for purposes referred to by the umbrella concept of “Mobility of Care”. In contrast to past studies that have quantified the impact of gender using survey and qualitative data, we examine a novel data-driven workflow utilizing a combination of previously developed origin, destination, and transfer inference (ODX) based on individual transit fare card transactions, name-based gender inference, and geospatial analysis as a framework to identify mobility of care trip making. We apply this framework to data from the Washington Metropolitan Area Transit Authority (WMATA). Analyzing data from millions of journeys conducted in the first quarter of 2019, the results of this study show that our proposed workflow can identify mobility of care travel behavior, both in terms of (1) detecting times and places of interest where the share of women travelers in an equally-sampled subset (on basis of inferred gender) of transit users is 10 %–15 % higher than that of men, and (2) finding women significantly more likely to exhibit a consistent accompaniment patterns with riders who are children, elderly, or people with disabilities. The workflow presented in this study provides a blueprint for combining transit origin-destination data, inferred customer demographics, and geospatial analyses enabling public transit agencies to assess, at the fare card level, the gendered impacts of different policy and operational decisions.

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.

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.

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 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.

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.