Monash University, Institute of Transport Studies: World Transit Research (WTR)
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    9860 research outputs found

    Collective and individual spatial equity measure in public transit accessibility based on generalized travel cost

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    This study devises a novel two-dimensional analysis framework for spatial equity in public transit accessibility. We incorporated door-to-door travel time and ticket price by public transit into a generalized travel cost function to measure utility-based accessibility following a log-sum formulation. Then, this study designed a Palma ratio of accessibility and a neighboring accessibility gap index to respectively examine the collective spatial equity and individual spatial equity of public transit. Finally, we took an empirical case of Kunming city, China to analyze the unity-based accessibility distribution and checked collective and individual spatial equity levels reaching business centers, transport hubs, and 3-A hospitals. The results show that the utility-based accessibility reaching transport hubs by public transit is less than 50% to business centers and 32% to 3-A hospitals. Collectively, the expected minimum generalized travel cost by public transit in the poorest 40% of traffic analysis zones is almost 2.5 times as much as in the richest 10% of traffic analysis zones, and the collective spatial equity in old blocks is superior to new blocks. Individually, the number of traffic analysis zones with no spatial inequity, slight inequity, and medium inequity in public transit accessibility respectively occupied almost 57%, 38%, and 5%. We also found individual spatial equity may be associated with the walking distance during taking public transit and the number of neighboring traffic analysis zones

    A Weibit-Based sequential transit assignment model based on hyperpath graph and generalized extreme value network representation

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    This study aims to develop a weibit-based stochastic transit assignment model for situations in which passengers’ travel choice strategies are stochastic. The weibit choice model, which was originally developed for the stochastic traffic assignment problem, is adapted using a hyperpath graph network representation. Unlike the traditional logit model, the weibit model can account for the homogeneous perception variance problem. Besides, a joint choice network representation originated from the network generalized extreme value (N-GEV) model, known as the GEV network, is adopted to depict the correlation among the arcs within the hypergraph consisting of all efficient hyperpaths. The GEV network uses a link-based parameter, the degree of membership, to capture the overlap effect, which is thus well incorporated with the link-based transit assignment approach. For implementation, a link-based stochastic network loading approach is proposed for solving the weibit-based transit assignment model in the sequential process. This sequential assignment approach does not need to enumerate and store the transit hyperpaths in the solution process. This model is then extended to a stochastic transit equilibrium model under congestion conditions, which is formulated as a fixed-point problem. Thus, a recent fast line-search scheme, i.e., the Barzilai and Borwein step-size, is adopted for solving the stochastic transit equilibrium model. Numerical examples are provided to illustrate the features of the proposed weibit-based stochastic transit assignment model. A real transit network is used to demonstrate the algorithm efficiency and the detailed results of the weibit-based transit equilibrium model

    Integrated design framework for on-demand transit system based on spatiotemporal mobility patterns

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    On-demand transit is a flexible transit service designed to adjust the service schedule and route based on passengers’ dynamic demand. The operation of on-demand transit operates in accordance with physical and socioeconomic environments, and demand patterns. In order to meet the diverse mobility needs in urban areas, integrating different transit services is essential to improve both passenger convenience and operational efficiency simultaneously. We propose a data-driven design framework for an on-demand transit system that operates three types of services: planned-and-inflexible (PI), planned-and-flexible (PF), and unplanned-and-flexible (UF), each with varying levels of responsiveness to real-time demand. We classify historical demand data into three classes based on their spatiotemporal density. Then, we use the trip data of each class to plan and operate the PI, PF, and UF services. The performance of the proposed system is evaluated using real public transit data from Sejong city. Simulation studies reveal that the proposed system outperforms the existing on-demand transit system. Specifically, we observe that the PI and PF services, which are planned based on the historical spatiotemporal mobility patterns, highly compatible with requests that follow the major mobility patterns. At the same time, the UF service, which offers real-time routing without prior planning, covers areas and times beyond those served by the PI and PF services that do not correspond to major mobility patterns. Furthermore, we found that the proposed system is flexible enough to accommodate various real-world demand patterns by proving suggestions on the optimal vehicle operation for each service

    What do riders say and where? The detection and analysis of eyewitness transit tweets

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    Information shared on social media by transit system customers is often candid, localized, and includes in the moment information about emerging events or issues. Twitter provides an unfiltered and timestamped feed of information that can be aggregated to generate valuable insights. Our research aims to identify passenger-related transit incidents from a public Twitter feed. Detecting these incidents in real time enables transit agencies to immediately respond to them by dispatching security, safety, or maintenance crews or by rapidly replying to requests made to the agency that are urgent in nature. We leverage natural language processing to extract latent information from identified eyewitness tweets about transit, focusing on location details, topic classification, and sentiment analysis. We outline an approach to developing a useful corpus of transit-focused tweets, detecting in the moment events, classifying these tweets into topics, and detecting locations where possible. We then demonstrate the approach through an application to Calgary Transit in Calgary, Canada. The results demonstrate that key incidents can be identified and prioritized for an agency

    Sustainable transport choices in public transit access: Travel behavior differences between university students and other young adults

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    This research investigates the socioeconomic and travel characteristics of student transit users in comparison to other young adults and quantifies behavioral differences in public transit access between these two population groups. Using data from a 2015 system-wide on-board survey in the Denver-Aurora region, CO, we seek to understand whether college and university students make more environmentally sustainable choices when accessing bus and light rail transit as well as identify the determinants of their choices. Our results indicate that student transit riders live in larger households with more vehicles per household member and are located substantially farther from the city center and the light rail compared to other young adults. The majority of student light rail users drive alone to light rail stations and typically do not park at the station that is the closest to their home. On the other hand, most other young adults walk to light rail stations. We also find that travel time and vehicle ownership per household member have a significantly lower impact on student choices. The identified travel differences and behavioral variations between the two population groups may be associated with the lack of affordable housing for students in the central and transit-rich neighborhoods of large metropolitan areas

    Measuring activity-based social segregation using public transport smart card data

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    While social segregation is often assessed using static data concerning residential areas, the extent to which people with diverse background travel to the same destinations may offer an additional perspective on the extent of urban segregation. This study further contributes to the measurement of activity-based social segregation between multiple groups using public transport smart card data. In particular, social segregation is quantified using the ordinal information theory index to measure the income group mix at public transport journey destination zones. The method is applied to the public transport smart card data of Stockholm County, Sweden. Applying the index on 2017–2020 data sets for a selected week, shows significant differences between income groups’ segregation along the radial public transport corridors following the opening of a major rail project in the summer of 2017. The overall slight decrease in segregation over the years can be linked to declining segregation in the city center as a travel destination and its public transport hubs. Increasing zonal segregation is observed in suburban and rural zones with commuter train stations. This method helps to quantify social segregation, enriching the analysis of urban segregation and can aid in evaluating policies based on the dynamics of social life

    The equity of public transport crowding exposure

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    Public transport crowding exposure is known to cause discomfort, stress and dissatisfaction. However, the distribution and equity of crowding exposure across socioeconomic groups has been largely unexplored. This paper opens a new research topic connecting crowding exposure in public transport to travelers’ socioeconomic characteristics. We present a framework for assessing the equity of in-vehicle crowding exposure based on automatic data sources. Two metrics are considered for quantifying the travelers’ in-vehicle crowding exposure: (1) the excess perceived travel time and (2) the relative excess perceived travel time. The proposed methodology computes the two metrics based on travel diaries and in-vehicle loads inferred from automated fare collection data. We implement Lorenz curves, Gini and Suits coefficients to evaluate horizontal (across the population) and vertical equity (considering income as well as mobility ability and need). The vertical equity is further discussed using clusters of socioeconomic groups and results from spatial lag regression models to assess the distribution of crowding exposure across socioeconomic characteristics. The results for the Stockholm Region case study indicate that crowding exposure varies substantially across the service area, with the highest values found in the denser urban areas close to Stockholm City. We find that the distribution across socioeconomic groups is relatively even, but travelers from areas that are wealthier, higher educated, have higher share of rental housing or lower vehicle ownership areas tend to be exposed to more crowding. The paper provides tools to support public transport planners in decision-making, showing where to intervene to reduce crowding exposure efficiently to achieve urban equity and sustainability

    Charging Infrastructure and Schedule Planning for a Public Transit Network with a Mixed Fleet of Electric and Diesel Buses

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    Promoting battery electric buses (BEBs) can reduce fuel consumption and air pollution from the transit system. A complete transition from the current diesel fleet to BEBs is costly and time-consuming. Thus, the intermediate solution is a combination of diesel, hybrid, and BEBs. Therefore, a planning framework is required that simultaneously tackles three contiguous aspects of transit electrification and their interconnections, namely charging infrastructure, fleet configuration, and scheduling. Accordingly, this study considers a mixed fleet of diesel and BEBs. It aims to concurrently find (i) the optimal location and capacity of charging infrastructure, considering micro-grid specifications, the impact of distributed energy resources, and time-of-use electricity rates and (ii) optimum operation and refueling strategies. Another objective of this study is to capture the impacts of adverse weather conditions on transit electrification. A mixed-integer problem is proposed and solved using a metaheuristic algorithm based on simulated annealing to minimize system costs, including infrastructure, fleet, and operation costs. A subnetwork of transit in Worcester, Massachusetts, is selected as a case study, including three routes, five candidate charging locations, and three bus types. Findings suggest that BEBs can operate and serve the passenger demand with sufficient charging infrastructure. Sensitivity analyses show that even though high-power chargers are more expensive per piece, they reduce the overall cost as fewer chargers are required. The cost rises for chargers with power of 350 kW or more. It is worth noting that the benefits of BEBs are more significant in smaller buses and are heavily affected by adverse weather conditions

    Impact of COVID-19 on Public Transit Accessibility and Ridership

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    COVID-19 has radically transformed urban travel behavior throughout the world. Agencies have had to provide adequate service while navigating a rapidly changing environment with reduced revenue. As COVID-19-related restrictions are lifted, transit agencies are concerned about their ability to adapt to changes in ridership behavior and public transit usage. To aid their becoming more adaptive to sudden or persistent shifts in ridership, we addressed three questions: To what degree has COVID-19 affected fixed-line public transit ridership and what is the relationship between reduced demand and -vehicle trips? How has COVID-19 changed ridership patterns and are they expected to persist after restrictions are lifted? Are there disparities in ridership changes across socioeconomic groups and mobility-impaired riders? Focusing on Nashville and Chattanooga, TN, ridership demand and vehicle trips were compared with anonymized mobile location data to study the relationship between mobility patterns and transit usage. Correlation analysis and multiple linear regression were used to investigate the relationship between socioeconomic indicators and changes in transit ridership, and an analysis of changes in paratransit demand before and during COVID-19. Ridership initially dropped by 66% and 65% over the first month of the pandemic for Nashville and Chattanooga, respectively. Cellular mobility patterns in Chattanooga indicated that foot traffic recovered to a greater degree than transit ridership between mid-April and the last week in June, 2020. Education-level had a statistically significant impact on changes in fixed-line bus transit, and the distribution of changes in demand for paratransit services were similar to those of fixed-line bus transit

    Vehicle Design Strategies to Reduce the Risk of COVID-19 Transmission in Shared and Pooled Travel: Inventory, Typology, and Considerations for Research and Implementation

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    The global COVID-19 pandemic has given rise to a plethora of ideas for modifying and redesigning public transportation and shared mobility vehicles to protect workers and riders from contracting the disease while traveling. This research seeks to inventory these strategies, and to organize and distill them in a way that enables researchers, policymakers, and public transport and mobility service operators to more systematically and efficiently evaluate them. Through literature search and analysis, the COVID-19 risk-mitigating vehicle design (CRVD) typology was developed, articulating 12 categories of strategies (e.g., Seating Configuration, Barriers) and 12 mechanisms (e.g., physical distancing, physical separation) by which the strategies may reduce COVID-19 spread. A secondary contribution of this research is to gather opinions of experts in fields related to COVID-19 and its transmission, about the identified CRVD strategies and mitigation mechanisms. The typology and expert opinions serve as a launching point for further innovation and research to evaluate the effectiveness of CRVD strategies and their relationship to user preferences and travel behavior, within and beyond the current context. Public transport and shared mobility service operators can use the CRVD typology as a reference, in conjunction with industry guidance and emerging research on strategy effectiveness, to aid decision-making in their continued response to the pandemic as well as for future planning

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    Monash University, Institute of Transport Studies: World Transit Research (WTR) is based in Australia
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