Monash University, Institute of Transport Studies: World Transit Research (WTR)
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The characteristics of subsidised mobility services for disabled people
No full textThis research investigates subsidised mobility in countries other than Aotearoa New Zealand. The research is intended to inform the Ministry of Transport\u27s review of Total Mobility, which is a government-funded scheme that provides subsidised taxis for disabled people who cannot easily use public transport. The research comprises a literature review and case studies to explore how other countries subsidise transport for disabled people. We researched academic and industry (grey) literature. Results show that transport subsidies for disabled people are not widely researched. Most literature comes from Australia, Europe, the UK and the USA. While there are differences in how countries provide subsidies, these differences have not been analysed in sufficient depth to know what combination of approaches works best in terms of meeting the needs of disabled people, and the investment objectives of governments. The two main approaches to subsidised mobility are bottom-up support for transport run by community groups, and top-down government-run models that use for-profit taxi providers. These approaches have different advantages and disadvantages, and both meet some people\u27s needs for transport. We recommend that Aotearoa New Zealand researches both the impacts and return on investment in subsidised mobility, so that any changes in investment can be appropriately targeted
Assessing the Risks Associated with the Canadian Railway System Using a Safety Risk Model Approach
No full textCanada’s national rail network plays a vital role in moving goods and people, transferring $320 billion worth of goods and over 100 million passengers annually. Severe train occurrences are rare events. But they have the potential to cause fatalities and injuries, as well as environmental and property damage. Recent severe incidents, such as Burlington in 2012 and Lac-Mégantic in 2013, have shown that there is still a need for increased awareness and enhanced risk assessment. This work focuses on risk assessment on the Canadian railway system using the Safety Risk Model (SRM). The study applied a customized Canadian SRM (C-SRM) to two groups of hazardous events: main-track derailments and collisions with fatality and injury consequences, calibrated for data between 2007 and 2017. The model used Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) to identify the risks of hazardous events. The individual risks of the hazardous events were then evaluated for three groups of people: passengers, employees, and members of the public (MOP). Finally, the effectiveness of introducing a new control measure, Enhanced Train Control (ETC), was assessed. The results of the study showed that the collective risk of main-track derailments is higher than main-track collisions. Moreover, the risk to MOP and employees form the most significant proportion of individual risk. Finally, risk reduction analysis of the ETC revealed that developing this system reduced the risk of main-track derailments and collisions. This new control measure thus has the potential to make Canadian railways safer
Does docked bike-sharing usage complement or overlap public transport? the case of Brussels, Belgium
No full textThis paper complements the existing literature on Bike Share Schemes (BSS) by investigating their use from a spatiotemporal perspective to assess their relationship with public transport (PT). We address these questions through the case of Brussels and its long-standing docked BSS ‘Villo!’. Our study analyses comprehensive (consecutive 12 months) and disaggregated (station level) data on rentals and returns and finds that Villo! is used mostly in dense (although not all) districts also well served by PT. However, temporal structures suggest Villo! overtakes PT at night in vibrant districts and possibly in selected districts with lower PT services over weekends. In addition, Villo! stations at key PT hubs usually do not show specific temporal patterns, which suggests intermodality may work at all times during PT operations. There could be an evening peak effect combined with the Brussels’ topography, but this needs to be confirmed by on-site surveys
Forecasting the costs of battery electric buses: A system dynamics model perspective
No full textThe high investment costs of battery electric buses (BEBs), influenced directly by battery and infrastructure costs, pose a challenge that limits their large-scale adoption. Therefore, the success of this socio-technical transition requires economic feasibility analyses to identify the optimum adoption pathway. This study presents a system dynamics model (SDM) covering adoption scenarios from 2023 to 2030. The development of the model was established through interdisciplinary workshops conducted with local researchers from Brazil for collaborative conceptualization and modeling. We provide details of the costs involved, considering purchase, operation, maintenance, and infrastructure costs. Dynamic simulations show the estimated Total Cost of Ownership (TCO) for overnight and opportunity charging over the years, allowing a better understanding of the trade-off between large and small batteries for purchasing decisions and selecting charging infrastructure to reduce total costs. Our results indicate potential transport management concerning long-term cost planning associated with various battery sizes and charging strategies
Train Rescheduling of Urban Rail Transit Under Bi-Direction Disruptions in Operation Section
No full textAs urban rail transit systems expand rapidly and passenger volumes grow, disruptions in operations increasingly occur, leading to adverse effects such as train delays and passenger retention. This paper introduces an organizational scheme to address train rescheduling in urban rail transit during bidirectional operational disruptions. It uses crossovers for short-turning services on either side of the disruption. Train rescheduling following a disruption is categorized into three stages: the disruption response phase, the disruption duration phase, and the recovery phase. This study presents a cooperative adjustment model for the network train timetable with the optimization objectives of reducing total passenger waiting time and the penalty time incurred from exceeding train capacity. The model considers the interrupted line and other lines connected to the interrupted line, employing a genetic algorithm for solution. Real operational data from a local subway line in a city are used as an example to adjust the train operation of urban rail transit with the bidirectional interruption. The results show that the short-turning strategy can better ensure the service level under the urban rail transit disruptions. Furthermore, the network train timetable collaborative adjustment model outperforms strategies that only modify the timetable of the disrupted line, effectively reducing both total passenger waiting time and penalty time for exceeding train capacity. This approach enhances operational efficiency and service levels in urban rail transit
Fusing Repeated Cross-Sectional Revealed Preference Datasets based on Rational Inattention Theory: Accounting for Changing Modal Preferences
No full textTo address the methodological limitation of cross-sectional studies and the data constraints of longitudinal/panel studies, this paper presents a model-based method to fuse repeated cross-sectional travel survey data based on the theory of rational inattention (RI) in discrete choice modeling. In the proposed framework, older cross-sectional data are used to model the prior probability of choice alternatives, and more recent cross-sectional data are used to capture conditional heterogeneous choices. The fusion method is theoretically more robust and computationally less burdensome than existing data pooling techniques. The method is empirically tested using data from two cycles of a large-sample post-secondary student travel survey in the Greater Toronto and Hamilton Area to investigate the commuting mode choices of post-secondary students. Parameter estimates of the RI-based multinomial logit (MNL) model indicate that the proposed method can generate behaviorally consistent results. Validation of the estimated model using a holdout sample indicates its improved forecasting performance compared with the classical random utility maximizing MNL model. The fusion method can be extended to more than two cycles of repeated cross-sectional data by updating the prior probabilities whenever new cross-sectional data become available. Thus, the study presents a continuous framework for fusing information from multiple time points using repeated cross-sectional datasets to capture preference evolution better and enhance the forecasting robustness of discrete choice models
Undercutting Transit? Exploring Potential Competition Between Automated Vehicles and Public Transportation in the United States
No full textAutomated vehicles (AVs) have the potential to dramatically disrupt current transportation patterns and practices. One particular area of concern is AVs’ impacts on public transit systems. If vehicle automation enables significant price decreases or performance improvements for ride-hailing services, some fear that it could undercut public transit, which could have significant implications for the environment and transportation equity. The extent to which individuals adopt automated transportation modes will drive many system-level outcomes, and research on public preferences for AVs is immature and inconclusive. In this study, we used responses from an online choice-based conjoint survey fielded in the Washington, D.C. metropolitan region (N = 1,694) in October 2021 to estimate discrete choice models of public preferences for different automated (ride-hailing, shared ride-hailing, bus) and nonautomated (ride-hailing, shared ride-hailing, bus, rail) modes. We used the estimated models to simulate future marketplace competition across a range of trip scenarios. Respondents on average were only willing to pay a premium for automated modes when a vehicle attendant was also present, limiting the potential cost-savings that AV operators might achieve by removing the driver. Scenario analysis additionally revealed that for trips where good transit options were available, transit remained competitive with automated ride-hailing modes. These results suggest that fears of a mass transition away from transit to AVs may be limited by people’s willingness to use AVs, at least in the short term. Future AV operators should also recognize the presence of an AV attendant as a critical feature for early AV adoption
Impact of Pandemic on Commuters’ Mode Choice Behavior: A Case Study of Dalian City, China
No full textFear of COVID-19 infection has influenced individuals’ decisions on making travel choices. Consequently, an unprecedented change occurred in travel behavior around the world. This study analyzes commuters’ mode choice behavior during COVID-19 using stated preference data collected in Dalian. In addition to the usual practice of taking sociodemographic and travel characteristics into account, the study also quantifies the effect of safety regulations on commuters’ mode choice behavior. A multinomial logit (MNL) and mixed logit (ML) model with panel correlation are developed to understand the impact of various factors on commuters’ mode choice. It is found that commuters prefer the mode with more stringent COVID-19 safety regulations than the one with lenient regulations. Additionally, safety regulations implemented during the corona crisis significantly increased subway ridership relative to bus; however, a noticeable shift from transit to the private car is observed. We found income, education, employment status, car ownership before pandemic and safety regulations were the variables that have a significant association with subway use. Similarly, gender and car ownership, both before and during COVID-19, have a significant positive impact, while education and employments are negatively associated with the choice of private cars. The findings will be useful for planners and policymakers for further regulating the transportation system during a crisis like the corona pandemic
Evaluation Framework for Multi-Modal Public Transport Systems Based on Connectivity and Transfers at Stop Level
No full textMulti-modal public transport (PT) networks within metropolitan areas are often characterized by complexity resulting mainly from their infrastructure, design, operations, and demand. This complexity leads to a significant amount of effort on behalf of the transit agencies to properly evaluate their performance at certain locations and proceed with improvements. This study proposes a methodology based on clustering techniques that facilitates the evaluation of PT networks. The evaluation framework refers to the comparison between the levels of supply and demand at a certain stop. Service supply is quantified through an existing connectivity index, whereas demand is considered through the number of transfers that are performed at each stop. Transfers are critical within multi-modal mobility and often serve as a hindrance for choosing PT. The case study here is the Helsinki PT network in Finland. General Transit Feed Specification (GTFS) data are used for quantifying connectivity and a dataset deriving from smartphone ticketing application for quantifying transfers. Results include the evaluation for each PT mode and for the overall multi-modal PT network. Focusing on the evaluation of the overall multi-modal PT network, connectivity and transfers levels for 75.60% of stops are found to be well aligned. Therefore, these stops could be eliminated from the list of candidate stops for performing improvements. Of the remaining stops, 19.73% belongs to the case of higher connectivity than transfers and 4.67% to the case of lower connectivity than transfers. Stops included in these two cases require further attention and prioritization during planning processes
Assessing Bike-Transit Accessibility
No full textBicycles are a potential first-last mile mode that can augment the service area of public transit, yet it is difficult to fully account for bike-to-transit trips in planning and travel demand modeling processes. This paper presents a methodology for assessing bicycle first-last mile trips from one area to many possible areas using three visualizations on accessibility, travel times, and transit mode(s) utilized. Two configurations of bicycle first-last mile travel are considered: bringing the bicycle aboard transit to have the bicycle for biking at both ends of the trip (bike-transit-bike) and leaving the bike at the first stop (bike-transit-walk). Three locations in and near Atlanta, GA, U.S., are selected for analysis, and the optimal routes to all possible destinations in the transit service area are calculated for walk-transit-walk, bike-transit-walk, and bike-transit-bike. The walking and biking portions of trips are modeled using Dijkstra’s algorithm, and the transit portion is modeled using the round-based public transit optimized routing (RAPTOR) algorithm. Results indicate that bike-transit-bike and bike-transit-walk decrease travel and wait times for transit, and in many cases reduce the number of transfers required compared with walk-transit-walk. Transit services with higher travel speeds or frequencies, such as heavy rail, greatly increased the number of accessible destinations and reduced travel times. Thus, an origin’s distance to rail service had a major impact on the number of accessible TAZs. Planners and engineers can use this research to examine how public transit service changes and new cycling infrastructure can affect the accessibility of bike-transit trips