Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Perth is ready for urban consolidation led by light rail
No full textPerth is at a crossroad. A sprawling, low-density city, Perth is confronted with all the associated problems – car dependency, long commute times, large ecological footprint, growing transport costs and a myriad of health issues. In an age of climate crisis, energy vulnerability and high cost of living, Perth which is anticipated to grow to 3.5million people by 2050 can no longer afford suburban sprawl. A new model of growth is required, which is led by transport and urban consolidation, utilising Perth’s existing footprint and supported by light rail to create a more sustainable and affordable city. A consortium of 17 local governments is leading the way for a more sustainable future. Their approach incorporates light rail in inner and middle suburbs to create a ‘place based’ planning approach linking existing passenger rail stations with activity centres, and education hubs, combining this with housing diversity, that will create a more sustainable and affordable urban footprint. The approach aims to tackle the climate crisis by making transport more sustainable, helping to reduce the cost of living by reducing transport costs, and reducing Perth’s exposure to energy security as the supply of oil becomes ever more vulnerable in an increasingly unstable geopolitical climate
An integrated causal framework to evaluate uplift value with an example on change in public transport supply
No full textMany empirical applications aim to isolate the impact of implementing new public transport on real estate uplift value. While their conclusions generally point to positive impact, due to reduction in transportation cost, the methodological framework to investigate uplift value has largely evolved over time. This paper reviews the different methodological challenges in measuring causal uplift value and proposes an adjusted parametric approach inspired from the Alonso-Muth-Mills model, returning a complex 2-D price premium function allowing for spatial heterogeneous patterns of the average treatment effect. The proposed framework also accounts for other methodological challenges underlined by literature such as spatial autocorrelation, selectivity and representativity issues, and possible anticipation effects. To illustrate the importance of methodological choices on estimation results, the framework is applied to the case of the implementation of a bus rapid transit (BRT) system in Québec City, a medium-size Canadian city, as a specific case study
Operational design for modular electrified transit in corridor areas
No full textIn urban corridor areas, profound congestion creates a pressing need for public transit to deliver efficient, flexible, and sustainable “door-to-door” services. This paper introduces a modular electrified transit system tailored for urban corridor areas (MET-CA), with a primary focus on enhancing transportation efficiency and reducing electrical energy consumption. The MET-CA system comprises two core components: mainline transit, dedicated to providing rapid services along the corridor, and feeder transit, catering to individual service zones. Seamless en-route transfers are facilitated through designated docking sections along the corridor. To rigorously address the system’s optimization, we formulate it as a mixed-integer linear programming (MILP) problem on a time-expanded network. Subsequently, an inter-transit iterative heuristic with a problem-customized rolling method is developed within a bi-level framework to address the large-scale complexity. Its performance is validated through extensive numerical experiments, substantially compared to exact solutions obtained from the Cplex solver, showcasing an impressive average computation time savings of 49.7% with a minimal quality decrease of 0.18%. Through a real-world case study, Pareto optimality analysis underscores MET-CA’s 24.4% average electricity savings and highlights its advantages in efficiency and flexibility over bus rapid transit and demand responsive transit in urban corridor areas
A passenger flow spatial–temporal distribution model for a passenger transit hub considering node queuing
No full textPassenger transit hub is a quintessential complex system, characterized by intricate interactions among humans, facilities, and the surrounding environment. External disturbances often precipitate crowd congestion and safety risks. Modeling the spatial–temporal distribution of passenger flow within the hub is an important element in operational management. Prevailing research predominantly focuses on static models or monitoring data to assess the spatial–temporal characteristics of passenger flow throughout the hub. Nevertheless, few studies have been found in modeling passenger flow distribution for passenger transit hub from the vantage point of traveler behavior in the intricate ‘human-facility-environment’ complex network. Thus, this paper proposes a computational model for the passenger flow spatial–temporal distribution based on traveler behavior. First, combining consideration of critical spatial facilities and passenger flow streamlines, a passenger flow network is established. Second, the instantaneous travel times of link and node are defined while considering queuing and congestion at crucial facilities in the hub. Then, a passenger flow spatial–temporal distribution model for the passenger transit hub is constructed, which consists of dynamic route choice model and dynamic passenger flow loading model. A solution algorithm is simultaneously designed. Finally, the effectiveness of the model and algorithm are verified by a numerical example. The results show that the proposed model can effectively capture real-time congestion and the dynamic distribution of passenger flow in the hub. Therefore, this study contributes to the safety management and layout optimization of the hub, holding significant importance for improving hub operational efficiency and service levels
Joint optimization of bus scheduling and seat allocation for reservation-based travel
No full textReservation-based travel is an effective method for managing passenger demand during public health emergency. It helps limit the number of passengers boarding at each bus stop, thereby reducing the risk of infectious disease transmission among passengers. This study proposes a joint optimization method for bus scheduling and seat allocation with the consideration of travel time stochasticity based on the reservation-based travel, in which different buses with various capacities are put into operations to satisfy the uneven passenger demand in different periods. To characterize the problem mathematically, a mixed-integer nonlinear programming model is formulated to simultaneously minimize the passenger waiting cost and operating costs of the bus system. This model is further reformulated equivalently into a mixed-integer linear programming model via the linearization method. An effective heuristic algorithm is designed to find high-quality solution for the proposed model. Finally, two sets of numerical examples, including a small-scale example and a large-scale example based on the Beijing bus line 6, are conducted to validate the performance of the proposed method. The experimental results demonstrate that the proposed method can effectively reduce operation costs and satisfy passenger demand during different periods
A variable-splitting Lagrangian decomposition for train timetabling and skip-stopping with train-type decision
No full textWhile designing the train timetabling and skip-stopping plan in a space–time network shared by multi-type trains, a prevailing approach is to predesignate a specific type for each train to simplify the problem. However, such a setting is unreasonable and inaccurate to a considerable extent. This paper focuses on how to jointly optimize the train timetabling and skip-stopping problem with train-type decision for a high-speed rail corridor. With the help of a time-dependent and preference-grouped demand representation, this problem is formulated as an integer linear programming model, in which the optimization objective is to minimize the total train- and passenger-related costs. Under the Lagrangian relaxation framework, we employ a variable-splitting technique to decompose the proposed model into several solvable subproblems. By further exploiting the dual solution information, a three-stage heuristic method is developed to generate the expected feasible solution to the problem under consideration. Finally, we conduct a series of numerical experiments to demonstrate the efficiency and effectiveness of the proposed approach
Racial Representation and Diversity on Non-Elected Transit Advisory Bodies
No full textNon-elected advisory and planning bodies of transit agencies help planners determine the agency’s service, operations, and future developments. The Federal Transit Administration (FTA) requires reporting of the racial and ethnic makeup of those bodies to receive federal funding. This paper collects those data and compares them with the service area demographics of those agencies. This paper is an attempt to determine if those bodies are representative of the people they serve. This paper sets forth possible metrics to judge representation by and finds that most bodies in the United States are not representative, and instead over-represent the white population. This implication suggests that people of color in the United States are not being represented in the decision-making and planning processes of the transit agencies that serve them
Parameter Adaptive Research of Automatic Train Control Algorithm Based on Sliding Mode PID
No full textIn existing urban rail systems, most trains are operated by automatic control, which places high demands on the control effectiveness of automatic train operation (ATO). In this study, a train operation model considering the response delay is firstly constructed. Subsequently, by analyzing and comparing the existing mainstream research methods, neural network and sliding mode control techniques were selected and incorporated into the speed control of the train. Among them, the dynamic sliding mode technique, is used to optimize the PID control effect of ATO. Single neuron and back propagation (BP) neural network algorithms are applied to the selection of PID control parameters. The study selects the difference between the actual speed of the control train and the target speed as the control objective. Through continuous optimization and iteration of the control parameters, the control accuracy of the train operation was improved. Finally, this study validates and simulates different model control methods using the actual operation data of urban rail transit. The results show that the sliding mode PID control model optimized with BP neural network performs better in terms of error distribution, average error value, and control effect variance under different simulation scenarios, showing good tracking ability and robustness. The related research results are expected to be applied to the initial selection of rail vehicle control parameters, dynamic adaptive operation control, and other fields, providing practical help to rail operators and rail signaling companies
Identifying the Optimum Combination of Use of Smartphone Apps and Hedonic Motivation for Increasing Public Transit Loyalty: An fsQCA Approach
No full textIntensive use of public transit plays an important role in commuting and daily travel in China and many other countries. With the widespread use of smartphones and other information and communication technologies, using mobile applications while on public transit has become increasingly popular, and more passengers rely on them during travel. To understand how the use of smartphone apps during transit travel and hedonic motivation can improve passengers’ willingness to use public transit, this study examines their combined effects. Smartphone apps used during transit travel can be classified into four categories: entertainment; information retrieval; e-ticketing; and mobile working. Drawing on complexity and configuration theories, we propose a theoretical model that is empirically tested using fuzzy set qualitative comparative analysis. Based on an empirical study of 567 Shanghai residents, we present eight different combinations of the use of smartphone apps during transit travel and hedonic motivation that explain a high degree of public transit loyalty. These can be categorized into three types: (a) mobile working oriented; (b) hedonic motivation oriented; and (c) digital management oriented. The findings suggest that satisfaction and happiness with public transit are sufficient but not necessary conditions for public transit loyalty. If passengers use smartphone apps or experience hedonic motivation during public transit travel, their satisfaction and happiness levels are likely to increase. Overall, this paper provides insights to guide policymakers and public transit operators in considering more external factors that might improve passengers’ travel satisfaction, happiness, and loyalty
Study on Tram Travel-Time Reliability Characteristics for Optimal Traffic Signal Control
No full textThis paper quantitatively investigates the effects of the number of intersections between adjacent stations (N) and different periods on tram travel-time reliability to provide a decision basis for adopting optimal signal-control methods. Station spacing data, intersection information on the line, 10 day planned operation schedules, and actual operation data of Zhangjiang tram line 1 were collected. The tram travel-time distributions were analyzed under seven typical scenarios (i.e., N = 0, 1, 2, 3, off-peak hours, morning peak hours, and evening peak hours). The results showed that the lognormal distribution described the tram travel time better than did the normal and gamma distributions. It was demonstrated that scenarios N=0, N=1, and off-peak hours were of high reliability; morning peak hours and evening peak hours were of medium reliability; and scenarios N=2 and N=3 were of low reliability. The unreliability of travel time increased along with the increase of the mean and the fluctuation of travel time. An optimized signal-control strategy based on travel-time reliability was put forward and VISSIM simulation of an interval was conducted, verifying the effectiveness of the proposed strategy. The findings will help tram-management agencies to adopt suitable and flexible signal-control strategies according to the actual varying condition