Journal of Public Transportation最新文献

Fare-free transit policy is not new to several public transit systems and communities in the U.S., as some local transit agencies have begun implementing fare-free transit policies or variations of them since the 1960s. Over time, the discussion regarding fare-free transit has been reignited by decreasing ridership trends in recent years and other thematic inquiries surrounding access, mobility and equity, operational efficiency, agency financial health, and community impacts. This research empirically investigates the effects of fare-free policy on transit ridership, labor force participation and income inequality. Using panel data regression models, we draw several conclusions: 1) Fare-free transit significantly increases ridership. 2) Fare-free transit neither significantly increases labor force participation rate nor reduces income inequality in small and medium-sized urbans. 3) Fare policy aside, external factors such as increased household income and work-from-home significantly reduce the demand for transit in small-urbanized areas.

This study analyzes the use of ride-hailing and bus services from a multi-modal user perspective by jointly modeling individuals’ monthly frequency of use of both bus and ride-hailing services. A bivariate ordered probit model is estimated to capture the influence of socio-demographic and attitudinal characteristics as well as unobserved factors that may simultaneously influence the travel frequency by both modes. We use data collected in a survey of travelers from Porto Alegre, Brazil. The results characterize the profile of local frequent ride-hailing users as young, medium-income, self-employed individuals with a propensity toward the use of technology and a low inclination for car ownership. Most importantly, we observe that after controlling for unobserved factors that simultaneously contribute to higher trip frequencies by both modes, frequent bus users demonstrate a lower propensity toward ride-hailing use.

As the airspace is increasingly gaining importance as a new frontier to improve urban mobility, aerial cable cars are being discussed and already appropriately implemented worldwide to supplement conventional modes of transport in urban areas. Transit planners and designers should carefully consider the interoperability and integration of cable car services with conventional modes of transport. In particular, excessive delays and overcrowding conditions due to deficits in interoperability should be avoided. This challenge of interoperability arises as conventional modes of transport operate predominantly on a timetable, and most cable car technologies operate in such close headways that they can be considered as almost continuous conveyors. The advantage of having almost always a transport vessel of a cable car ready for boarding ceases when large volumes of passengers arrive in batches, for example from higher-capacity modes of transport or at large events, resulting in long queues. Traditional manuals do not yet reflect these aspects of interoperability adequately. Consequently, this work filled this research gap about the interoperability of cable cars related to handling high volumes of incoming passenger arrivals that transfer in larger batches from feeder modes and often result in queues at cable car stations. The following objectives were targeted: (1) determine passenger capacity limits of conventional modes of transport acting as feeders to cable cars and (2) specify space requirements to be provided due to the potential queues that arise. To answer these, methods of Queuing Theory were used and results were placed in Levels of Services of traditional manuals. Key performance indicators included queue length, waiting time, and corresponding queue space. The results revealed that cable cars can be a useful complement to public transit but are of limited feasibility due to cumulative queues at arrival rates with larger crowds. High-capacity feeder modes (e.g., commuter rails) are limited to 20-minute headways depending on cable car technologies. Further, queuing areas of up to 1000 square meters (around 10,800 ft²) should be considered. Several operational limitations are presented as guidance for practitioners and policymakers.

This study aims to assess the public acceptance of Autonomous Modular Transit (AMT) and identify key factors influencing people’s intentions regarding the future use of AMT. While the integration of autonomy and modularity in transport systems has gained attention in recent years among researchers, the study of AMT acceptance is non-existent in the literature. To address this research gap, the study proposes an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model to explore public acceptance of AMT. It integrates the original model’s constructs with two additional factors: trust and perceived green usefulness, while also investigating the role of public transport usage behavior in intention. Data were collected through an online survey involving 1662 participants from different cities in Iran. Structural equation modelling was used to evaluate the conceptual model. Multiple t-tests and ANOVA tests were conducted to evaluate the effects of public transport usage behavior and demographics on the model’s constructs. The results showed that the majority of respondents showed their acceptance toward using AMT when available despite their limited prior knowledge about the system. Perceived usefulness was the strongest predictor of intention, followed by social influence and hedonic motivations. Although the COVID-19 pandemic significantly reduced the number of public transport (PT) regular users, this group showed a higher intention to use AMT compared to PT non-regular users. Furthermore, respondents aged over 60 years stated a lower intention to use AMT than their younger counterparts. Given that the mass adoption of AMT depends on public acceptance, this study is expected to serve as a benchmark for comparing countries with similar cultural contexts.

The positive impact of coordinated timetable innovations throughout national railway networks has been shown exemplarily in the 1970 s and 80 s, when so-called integrated periodic timetables (IPT) were installed in the Netherlands and in Switzerland and then gradually improved. After large-scale changes of the former train offer, rail passenger demand increased significantly. A similar timetable innovation was recently decided for the German railway network. However, the project’s impact on overall demand is uncertain. To approach this question, an elasticity-based forecast of long-distance passenger demand is proposed and adopted to a modelized railway network section that has changed to an IPT. Massive travel time reductions turned out as the most important factor for demand growth followed by demand effects due to the increase of train frequency and changes of a modelized ticket price system. Additional factors influencing nationwide rail passenger demand are conceivable but difficult to generalize.

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.

Transit agencies conduct system-level ridership forecasting for planning, budgeting, and other administrative purposes. However, the COVID-19 pandemic introduced substantial changes in transit ridership levels and seasonal patterns, which has impacted the performance of ridership forecasting. Although time series methods are commonly used for forecasting transportation demand, they have received limited use in practice for public transit ridership forecasting. This study compares the performance of seven time series forecasting methods for predicting system-wide, monthly transit ridership for heavy rail agencies in the continental United States. The forecasting methods are: ETS, ARIMA, STL with ETS, STL with ARIMA, TBATS, a neural network, and a hybrid model. Ridership was forecasted for pre- and post-COVID periods (pre- and post- March 2020), as well as for the full series (January 2002 to December 2023). The MAPE and MASE were used to compare forecast performance. Using the pre-COVID period, 43% of the models produced a MAPE below 5% and 82% produced a MAPE below 10%. Using the full-series and post-COVID periods, only about 10% of the models produced a MAPE below 5% and half produced a MAPE below 10%. The classical and hybrid methods outperformed the other models using the full series period, and the TBATS, neural network, and hybrid methods outperformed the other methods using the post-COVID period. The findings suggest that even a few years into the post-COVID era, patterns that were typical of heavy rail ridership before the pandemic have not returned at most agencies in the United States, posing challenges to forecasting post-COVID ridership.

With the frequent global breakouts of infectious diseases such as Covid-19 and the likes, passengers feel unsafe traveling in crowded trains. The reluctance to share public transport with others due to the risk of disease transmission may lower the ridership as well as decrease the comfort level of passengers. Providing them with future crowdedness levels may allow them to plan accordingly, hence regaining the lost confidence and improving their patronage. This study explores the less frequently investigated relationship among occupancy levels at a particular station over several train runs, to predict the future occupancy level with a delay of one run (day). Tackling the issue as a classification problem rather than a regression problem, train occupancy data, station data, and weather data are merged to develop the final dataset. Training data is stepwise increased from 1 month to 3 months. Similarly, 1–5 days of known occupancy levels are added to each data instance. Among the three classifiers used, XGBoost provides the best results. Some practical challenges to occupancy level prediction are also discussed at the end.

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.