Journal of Public Transportation最新文献

Modular transit vehicles have the potential to transform public transport systems. Indeed, the possibility of dynamically adjusting capacity by assembling and disassembling multiple modular pods allows for improved transit service flexibility and efficiency, reducing operator and passenger costs. As such, modular buses are considered particularly advantageous for settings with large variations in passenger demand, permitting en-route capacity adjustment and seamless transfers. In this context, this study presents a model for the design of a feeder bus network operated by autonomous modular buses, accounting for en-route transfers, and a Genetic Algorithm to solve the non-linear mixed integer programming problem arising. Results for a case study in Athens, Greece for an area served by three metro lines are presented and discussed.

Public transport during COVID-19 has been crucial in ensuring the safety and health of both passengers and staff while maintaining essential public transport services. Currently public transport is gradually resuming its operations, the pandemic's influence is expected to persist for a long time. The vast majority of studies in this aspect concern the likelihood of spreading the virus inside the means of transport during travel. Nevertheless, there exists a substantial body of articles addressing the manner in which passenger movement within public transport systems has been impacted by the safety concerns and altered satisfaction levels following the propagation of the pandemic. This paper presents a model that accurately represents how passengers move through different parts of a public transport system, such as a bus or train station and stops. This model takes into account factors like how long it takes for passengers to board and exit a vehicle, how they move through different parts of the stops, and how their movements are affected by factors like crowding and delays. To reduce the risk of transmission on public transport focused on bus stops areas, the research paper formulated a passenger flow model using simulation programs like PTV Vissim and FlexSim with assumptions on minimum distance and concept of area cross sections. These programs were used to simulate passenger exchange scenarios, using data collected from real data. The paper aimed to develop a passenger exchange model that could reduce the risk of infection. By understanding the passenger flow model and how passengers interact with the public transport system, we can implement effective measures to minimize the spread of COVID-19 and other infectious diseases.

The pandemic led to the decrease of public transport use, which many passengers believed to have shifted from public transport to bike-sharing. We propose a novel methodology to quantify this shift and shed light on the causal factors of new preferences as well as the likelihood of their continuation. A first short-term intramodal analysis reveals important correlations between trip volumes and durations on the one hand, and COVID-19 policy measures on the other hand. COVID-19 significantly reduced all trips but public transport was hit harder and has not recovered so far. Shared-bike trip durations were extended during pandemic peaks. Then, we perform a second medium-term, disaggregate, and intermodal analysis to identify potential reasons explaining the shift from public transport to cycling. Logit models are specified on empirical ridership data from London and Washington DC. Results indicate that certain pre-covid factors (such as weather and type of day, travel purpose) remain influential under COVID-19. Also, we find facial covering obligation to be a key element in restoring public confidence towards public transport. Simple face covering recommendations seem, instead, to discourage public transport usage and play in favor of cycling.

Public transportation is an essential criterion that benefits several social sectors. Hence, most developing countries display an increase in the demand for enhanced public utility vehicle (PUV) systems. PUVs are prevalent in the Philippines; however, research on passenger satisfaction and public transportation is scarce. This research aimed to assess passengers' future intentions regarding PUVs through passenger satisfaction utilizing various latent variables. This study utilized an online survey with a total of 600 respondents that are using PUVs in the Philippines who voluntarily answered the questionnaire. The data were analyzed using different Machine Learning Algorithms (MLA) such as Deep Learning Neural Network (DLNN), Decision Tree (DT), and Random Forest Classifier (RFC). The study indicated that people vastly prefer a route-efficient way of traveling, safety, value for money, and passenger expectations as it highly affected passenger satisfaction and future intentions. The theoretical basis of this study provided an effective instrument for resolving the country's emerging traffic issues and served as the foundation for forming PUVs and policy initiatives. Future research may look into and concentrate more on particular types of service quality factors and public utility vehicle to provide a more in-depth analysis of the subject and extend the analysis. Researchers may also utilize MLA for the data as it provides a more efficient and accurate factor analysis in the transportation sector. Finally, managerial insights could be elevated, including service domains in different areas.

Although the COVID-19 pandemic highly impacted transit ridership as people reduced or stopped travel, these changes occurred at different rates in different regions across the United States. This study explores the impacts of COVID-19 on ridership and recovery trends for all federally funded transit agencies in the United States from January 2020 to June 2022. The findings of this analysis show that overall transit ridership hit a 100-year low in 2020. Changepoint analysis revealed that June 2021 marked the beginning of the recovery for transit ridership in the United States. However, even by June 2022, rail and bus ridership were only about two-thirds of the pre-pandemic levels in most metropolitan statistical areas (MSAs). Only in a handful of MSAs like Tampa and Tucson did rail ridership reach or exceed 2019 ridership. This retrospective study concludes with a discussion of some longer-term changes likely to continue to impact ridership, such as increased telecommuting and operator shortages, as well as some opportunities, such as free fares and increased availability of bus lanes. The findings of this study can help inform agencies about their performance compared to their peers and highlight general challenges facing the transit industry.