Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Integrated charging scheduling and operational control for an electric bus network
No full textThe last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. Operational control is carried out through speed control of the vehicles and bus holding at the terminal. We propose a hierarchical control framework to solve this integrated problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules
Does it matter if you like it? exploring the relationship between travel mode choice, preference, and satisfaction
No full textUnderstanding the level of dissonance between travel mode and preference and its relationship with satisfaction can help develop transport strategies that encourage the use of sustainable modes. We study the difference in satisfaction levels for work and school trips of consonant travellers and dissonant travellers. The research uses a large-scale (N = 1,865) travel survey administered in Montreal, Canada. A binary logistic regression model reveals that both consonant and dissonant commuters have a high probability of satisfaction with their commute, except for dissonant car users. We find that consonant pedestrians have the highest probability of satisfaction when compared to all other groups, and that dissonant car users have the lowest probability of satisfaction. We further investigate the reasons preventing the use of preferred modes for dissonant car and transit users. Findings from this research help inform researchers and practitioners aiming to make sustainable mode choices the preferred one among travellers
Eco-driving strategy for connected electric buses at the signalized intersection with a station
No full textHigh energy consumption and low traffic efficiency are often caused by acceleration, deceleration, and secondary parking that do not consider traffic conditions in advance after exiting a station. To overcome this problem, this study establishes an eco-driving control strategy for the signalized intersection with a station. First, a control strategy before entering the station is established. Second, based on V2X, the decision-making model for the non-stop passing of the signalized intersection is established. Afterward, based on NSGA-II, an acceleration/deceleration strategy is established to obtain the eco-velocity curve. Finally, the effectiveness of the proposed strategy is verified based on natural driving data and other eco-driving strategies at intersections. The results show that the proposed eco-driving strategy not only saves 21.11 % of energy and 16.1 s of time compared to natural driving, but also saves an average of 5.83 % of energy compared to other eco-driving strategies at intersections
Knowledge integration in policy development and assessment for sustainable passenger transport transformation
No full textMitigation of greenhouse gas emissions has emerged as a crucial challenge for the passenger transport system. We introduce an inter- and transdisciplinary scheme for the development and assessment of socially and politically feasible transport policy packages to achieve net-zero transport emissions and illustrate the scheme via application to the EU member state of Austria. We find mutual disciplinary dependencies as well as the need for co-generation between science and practice, with a varying mix of stakeholders and disciplines relevant across different scheme steps. Results show that the assessment of legal feasibility and in particular proportionality rely on socioeconomic analysis. While economic incentives are crucial, an early announced ban on fossil fuel cars is needed to fully achieve carbon neutrality. Infrastructure development and education also need prompt redirection to keep the economic transition costs low. Overall, a such redesigned transport system imposes lower costs for society, providing a further implementation incentive
Innovative On-Demand Transit for First-Mile Trips: A Cutting-Edge Approach
No full textAs a result of the lack of access to efficient public transit in suburban areas, residents often have to use their own vehicles to commute either within the area, to neighboring regions, or to a public transit hub (PTH). Thanks to information and communication technologies, on-demand transit (ODT) is a potential solution being proposed and considered by transit agencies. Although ODT has shown the potential to enhance transit level of service, its efficiency depends on different parameters such as demand spatial and temporal distribution or the configuration of the service. In this study, we propose a novel configuration for an ODT service and apply it to the first part of a commuter’s trip, or the commuter’s “first mile.” The proposed configuration depends on the availability of smart devices installed at bus stops. Passengers request their rides via smart devices and receive real-time and personalized information about their ride requests to travel to a PTH. The proposed ODT service is modeled with the Simulation of Urban Mobility or SUMO simulation framework. To evaluate the performance of the ODT service, it is applied to the city of Terrebonne in Quebec, Canada. The proposed service is compared with existing bus transit operating in the area as well as a door-to-PTH service. The results of the comparison analysis reveal that the proposed ODT service may result in a significant 36% reduction in total travel time as well as a 41% reduction in detour time compared with the existing bus transit service. A detailed sensitivity analysis is also conducted to capture the impacts of different parameters, variables, and dispatching algorithms on the service performance
Mobility Energy Productivity Evaluation of On-Demand Transit: A Case Study in Arlington, Texas
No full textOn-demand transit (ODT) systems are increasing in number and size. To evaluate and quantify outcomes, the research team utilizes the mobility energy productivity (MEP) metric, which is a holistic accessibility measure, to analyze and compare the mobility of various transportation modes in Arlington, Texas. The MEP tool is applied to the ODT system in Arlington, Texas, as well as to five existing transportation modes (driving, transportation network company, transit, biking, and walking). Six ODT scenarios are also analyzed and compared. The analysis is focused on the opportunities that an ODT system presents for transportation disadvantaged communities (DACs) with low rates of car ownership. Although driving received the highest MEP score—a finding typical for a U.S.A. city— the results for the ODT system reveal that it serves those in DACs effectively, helping to achieve an equity design goal. ODT improved the average MEP score across the service area by 83% when considering only accessible, nonprivate vehicle modes (biking, transit, and ODT). For the ODT scenarios, decreasing the wait time by 50% compared with the baseline scenario led to a nearly 160% increase in the MEP score, whereas increasing the ODT travel speed by 21% led to an 80% improvement in the MEP score. As analyzed through the MEP tool, this paper demonstrates how ODT can enhance mobility, particularly for DACs. The results of an MEP analysis can be used by researchers and transit agencies to compare transportation modes and improve the effectiveness of transportation systems across a service area
Road to health: Evidence from subway construction in China
No full textThis paper uses data on city subway openings to match data on the health status of respondents from the China Health and Nutrition Survey database between 1991 and 2015. Our conclusions show that urban subway opening significantly improves the health of the population and reduces the incidence of diseases. Furthermore, the closer residents live to a subway station, the more they will be affected by the subway opening. After conducting a series of robustness tests to eliminate possible biases in the model, the results remain robust. We also find that the effects of using subways on health varies considerably among different populations. People belonging higher-income brackets, lower-middle-aged demographic, and moderately educated groups tend to be more affected by subway opening. Based on the analysis of the existing literature on the influence path of subways, subways mainly affect people\u27s daily lives by reducing transportation costs and promoting information exchange. Therefore, we consider that the influence of subway use on health may come from the following mechanisms: subway openings enhance the level of medical services sought by residents, promote the purchase of commercial insurance, and reduce the severity of diseases
Investigating Anticipated Changes in Post-Pandemic Travel Behavior: Latent Segmentation-Based Logit Modeling Approach Using Data From COVID-19 Era
No full textThe unprecedented situation created by the COVID-19 pandemic in the year 2020 has drastically changed daily mobility patterns around the world. Various measures were implemented to prevent the transmission of the virus, which have resulted in short- and long-term impacts on the activity systems and daily travel. To capture the impacts of the pandemic on travel behaviors and activity systems, a web-based survey was designed and administered in April–May 2020 in Montreal, Canada. In addition to questioning on pre- and during COVID-19 behaviors, it included a section on how people expected to travel, telework, shop online, and so forth in the post-pandemic era. Using data from this survey, which gathered 1,620 completed questionnaires, this paper proposes insights into how people are planning to travel in a post-COVID-19 world using latent segmentation-based logit modeling technique. Three models are estimated to identify factors related to expected trip frequency, expected transit usage, and expected bike usage. Undertaking such modeling approach provides opportunity to understand different types of individuals’ preferential behaviors. This study probabilistically identifies two latent segments, suburbanite and urbanite people, and finds considerable heterogeneity across sample individuals. For example, urbanite people tend to increase their expected number of trips after COVID-19 if they have at least one bike in their household. Suburbanite people exhibit an opposite relationship, and they are more likely to keep their trip frequency the same as before. Findings of this study will assist decision makers in developing effective policy measures to better prepare for the changes in travel behaviors after COVID-19
COVID-19 and Public Transport in Auckland, New Zealand: Investigating Vulnerable Population Groups’ Ridership Behavior
No full textPublic transit ridership was severely affected during the COVID-19 pandemic in 2020 and the effects have continued since. The present study examines changes to ridership immediately post-pandemic in 2021. Research investigating the effects of COVID-19 on disadvantaged population groups is limited and the present study addresses this knowledge gap. Ridership of socially-disadvantaged groups such as low-income, female, and ethnic minority people is examined using order logit regression models. The study uses data from an online travel survey conducted in Auckland, New Zealand, immediately after all COVID-19-related restrictions were lifted. This allowed the collection of revealed preference data for the post-pandemic period. The regression models included the effects of socio-demographic characteristics of individual riders, travel attributes, and built environment factors. Findings suggest that those with lower income and from an ethnic minority group are likely to continue using transit frequently post-pandemic. Younger riders from the ethnic minority group are less likely to use transit frequently, while pre-COVID-19 they were more likely. Access to transit stops near home and work are significant factors for the ethnic minority group. Higher land use mix near the residence and work locations are found to induce more transit trips for all. It is critical for transit agencies to understand how the usage has evolved post-pandemic. These findings highlight the importance of considering the effects of the pandemic on different disadvantaged groups. Public transport service providers are encouraged to consider equity as they develop strategies to improve transit ridership