Monash University, Institute of Transport Studies: World Transit Research (WTR)
Not a member yet
11112 research outputs found
Sort by
Pursuing higher compliance for the mandatory electric bus program in Vietnam: Should we keep a traditional business model or promote a fleet leasing model?
No full textThis paper investigates and compares the economic and environmental benefits of a traditional business model as opposed to a fleet leasing model for electric buses (e-buses). A combination of the emission model and financial model, along with secondary data, was adopted to simulate the environmental and economic impacts of three bus technologies (i.e. diesel buses, compressed natural gas buses, and electric buses). The environmental aspects were evaluated by setting the energy flow of CO2 emission simulation, considering both direct and indirect emissions. The economic impacts of e-bus versus baseline diesel and CNG alternatives were estimated using the metric total cost of ownership (TCO) per km. We determined that although the mandatory e-bus program might represent a viable option when compared to the combustion alternatives from an environmental standpoint, e-buses are less appealing to bus companies due to their significantly lower cost-competitiveness under a traditional (current) business model. In contrast, a fleet leasing model brings large financial benefits to bus companies, increasing more probability of acceptability and intention of compliance from bus operators. Specifically, our analysis estimates that implementing the e-bus mandatory program with a new leasing model will result in a maximum of 47 %, 90, and 97 % emission removal in 2030, 2040, and 2050 respectively. In the same period, the TCO per km reduces a maximum of 41.8 %, 31.0 %, and 17.4 %, respectively, compared to the traditional business model. The findings can be valuable for cities, practitioners, and bus companies, aiding in a better understanding of the challenges and benefits of the bus leasing model in order to better plan for e-bus uptake in Vietnam
Giving Voice to Women in Public Transport: Understanding “(Im)Mobility of Care” and female travel patterns
No full textRegarding the latest developments in transport research and policy, “mobility of care” (MoC) is a concept that has started to be recognised. MoC refers to trips generated by activities of care for home/family. These activities are mostly associated with women and affect their mobility patterns, thus requiring observing mobility from a gender perspective. Using analysis with the gender-perspective of Santiago\u27s mobility survey and our survey, we obtain characteristics of caregivers\u27 mobility patterns. Findings highlight significant inequalities between genders. Specific results show: (1) twice as many women than men make chained trips because of care reasons; (2) minors conduct care-chain trips, which suggests that in the Global South, minors also conduct care-task trips; (3) the presence of children in the household creates a gender gap between women and men that is not present in households without children; (4) immigrant women and single mothers make more stages in a chained trip; (5) 31,2 % of trips are done for care-related reasons, with a significant difference between women and men. Our results show that including gender-perspective in transport planning can help reduce gaps between genders and offer ways of reducing poverty, which makes mobility more equitable and sustainable – environmentally, economically, and socially
Nonlinearities and threshold points in the effect of contextual features on the spatial and temporal variability of bus use in Beijing using explainable machine learning: Predictable or uncertain trips?
No full textIn pursuing sustainable transport, understanding the dynamics of transit passengers\u27 travel demand is necessary for establishing more attractive public transport relative to cars. However, to what extent daily transit use displays geographic and temporal variabilities or predictability, and identifying what are the contributing factors explaining these patterns have not been fully addressed. Drawing on smart card data in Beijing, China, this study adopts new indices to capture the spatial and temporal variability of bus use during peak hours and investigates their associations with relevant contextual features. Using explainable machine learning, our findings reveal non-linearities and threshold points in the spatial and temporal variability of bus trips as a function of trip frequency. Greater distance to the urban centres (\u3e10 km) is associated with increased spatial variability of bus use, while greater separation of trip origins and destinations from the subcentres reduces both spatial and temporal variability reflecting highly predictable of trips. Higher availability of bus routes is linked to higher spatial variability but lower temporal variability. Meanwhile, both lower and higher road density is associated with higher spatial variability of bus use especially in morning times. These findings indicate that different built environment features moderate the flexibility of choosing travel time and locations influencing the predictability of trips. Understanding highly predictable trips is key to develop more effective planning and operation of public transport
Big data for decision-making in public transport management: A comparison of different data sources
No full textThe conventional data used to support public transport management have inherent constraints related to scalability, cost, and the potential to capture space and time variability. These limitations underscore the importance of exploring innovative data sources to complement more traditional ones.
For public transport operators, who are tasked with making pivotal decisions spanning planning, operation, and performance measurement, innovative data sources are a frontier that is still largely unexplored. To fill this gap, this study first establishes a framework for evaluating innovative data sources, highlighting the specific characteristics that data should have to support decision-making in the context of transportation management. Second, a comparative analysis is conducted, using empirical data collected from primary public transport operators in the Lombardy region, with the aim of understanding whether and to what extent different data sources meet the above requirements.
The findings of this study support transport operators in selecting data sources aligned with different decision-making domains, highlighting related benefits and challenges. This underscores the importance of integrating different data sources to exploit their complementarities
A Case Study of Ridership and Equity Implications of All-Day Massachusetts Bay Transportation Authority Commuter Rail Service
No full textAs the COVID-19 pandemic emerged from the acute phase and vaccines became widely available in 2021, transit agencies like the Massachusetts Bay Transportation Authority (MBTA) faced a daunting challenge of drawing riders back into their systems. Despite systemwide staffing shortages, service disruptions, and ridership patterns that have yet to return to 2019 levels, ridership on MBTA’s Commuter Rail has consistently outperformed the agency’s other services in the current COVID recovery era. In April 2021, as part of a multiyear vision to overhaul the system, MBTA switched from a schedule focused on serving traditional peak-period commuters to providing steady, all-day service modeled on more legible “clockface” departures. This study used regression analysis to show this change was consistent with the Commuter Rail outperforming other modes in ridership recovery, generating over 7,000 average daily weekday boardings and over 9,000 average weekend boardings. Latent class analysis demonstrated that this schedule shift primarily benefited riders traveling for a variety of purposes, rather than just traditional office work. This group, which we referred to as general riders, was more likely to be low-income (household income below $75,000), young (25 or younger), and Hispanic. This research demonstrated the ability of all-day service on Commuter Rail to serve MBTA’s key aims of increasing ridership and providing more equitable, accessible service
Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach
No full textRecent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system
Urban transport during the COVID-19 pandemic: a case study of Poland
No full textOngoing improvement in the quality of public transport calms and slows road traffic while causing desirable changes in the transport behaviours of residents and the urban structure of city centres. These efforts were thwarted by the outbreak of the COVID-19 pandemic, which resulted in a significant decrease in public transport ridership. Its scope varied with the passage of time and with the dynamics of the pandemic itself, which were significantly different for individual cities. This article undertakes an analysis of these changes in Poland and identifies factors describing public transport accessibility that may influence them. The novelty of this article is examining the correlation of changes in public transport ridership in 17 Polish cities caused by the COVID-19 pandemic with the presented range of factors and conducted longitudinal studies. A multiple regression analysis was made possible by collecting data describing various aspects of public transport and populations. The introduction of remote work and learning also had a noticeable impact in public transport ridership during this period. In some sectors and fields, they have proven to be at least as efficient, but less expensive than their traditional forms, which suggests that they may have an impact on reducing the public transport ridership after the end of the pandemic
Simulating Impacts from Transit Service Enhancements in the San Francisco Bay Area
No full textPreemptively assessing the potential impacts of large transportation projects is an essential step in achieving better outcomes. However, for transformative public transit projects, it can be difficult to weigh the many complicated downstream impacts on individual travelers in a coherent, cost-effective, and comprehensive way. This research focuses on leveraging the Behavior, Energy, Autonomy & Mobility Comprehensive Regional Evaluator (BEAM CORE) to gauge regional responses to changes in existing and planned public transit services, capturing service performance, system impacts, and users’ responses. We applied BEAM CORE to a case study in the San Francisco Bay Area to simulate the effects of recent and upcoming transit projects, showcasing its potential for transportation planning. By simulating individual traveler movements, it becomes possible to delve deeply into the equity and accessibility ramifications of transit system enhancements. The analysis of ridership, mobility, accessibility, and equity presented for this study highlights the benefits of this method in providing a clear understanding of the performances of public transit projects, facilitating more informed and efficient decision-making for transport stakeholders. The results obtained from BEAM CORE aligned closely with expectations and observed data, demonstrating its effectiveness and reliability. Finally, because of the BEAM CORE model’s responsiveness to changes in the systems, the method can in the future be applied not only to test existing or planned interventions but to a large variety of hypothetical scenarios to identify the optimal solution, including other transport modes
TMS-GNN: Traffic-aware Multistep Graph Neural Network for bus passenger flow prediction
No full textBus network plays a critical role in urban transportation affecting the use of private vehicles, traffic congestion, and urban accessibility. The accurate prediction of bus passenger flow is key to improving transit passenger experience and increasing the efficiency of bus network operations. In line with recent advances in deep learning for passenger flow prediction, graph neural networks (GNNs) have become increasingly popular due to their ability to account for the network structure between stops. Existing GNN-based models for bus passenger flow prediction, however, face several limitations. First, they do not take into account some distinctive characteristics of bus networks, such as their coexistence with vehicular traffic and their high sensitivity to urban traffic conditions. Moreover, sequence prediction models that have been widely applied to multistep passenger flow prediction suffer from a critical issue, called “exposure bias.” This results in the propagation and accumulation of errors through prediction steps while making predictions for farther time horizons. To address these issues, this study presents the Traffic-Aware multistep Graph Neural Network (TMS-GNN) model with Scheduled Sampling, a graph-based deep-learning framework designed to forecast multistep bus passenger flows at individual stops across a bus network. The model takes into account factors such as bus stop connectivity, urban traffic impacts, and multi-dimensional temporal patterns; and addresses exposure bias by employing a curriculum learning strategy called Scheduled Sampling. The comparison between the proposed model and other popular baseline models on two real-world networks with different geographical and urban patterns in Canada and USA shows that TMS-GNN outperforms the baselines in both the overall network-wide task, as well as multistep prediction. Also, to verify the contribution of the proposed components of the model, an ablation study is conducted. The results of the ablation study validate the design choices as well
Comparing skip-stop and all-stop transit network designs
No full textSkip-stop service is a public transportation scheme designed to improve operational speed and travel efficiency by skipping certain stops along a route. This paper focuses on the optimal design of a specific type of skip-stop service, known as AB-type service. While previous research primarily addressed corridor-level designs for this service, we present a continuous model to optimize an AB-type skip-stop service network in a square city layout. Our objective is to minimize the combined costs of the agency and patrons’ travel time by making optimal decisions about network design—including line and stop spacings—and the operational plan, consisting of the number of skip-stop routes and service headways.
We conduct extensive numerical case studies to compare the performance of the skip-stop service with two variants of all-stop services: one incorporating non-transfer stops and one without. We examine two prevalent transit modes: rail and bus. The results indicate that the optimal skip-stop network outperforms all-stop networks in most scenarios for rail systems. In contrast, for bus systems, the all-stop service without non-transfer stops performs best. Interestingly, skip-stop service results in a lower commercial speed compared to all-stop services, and it also significantly reduces agency costs for rail systems. Moreover, we observe that the advantages of skip-stop service at the network level decrease as travel demand increases. These findings are contrary to those from corridor-level studies, highlighting the importance of understanding network-level dynamics when planning skip-stop services in a broader range