Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Cascading failure and recovery propagation of metro-bus double-layer network under time-varying passengers
No full textThis study proposes a dynamic model to elucidate the cascading failure and recovery propagation process under increased time-varying passenger flow. Firstly, a metro-bus double-layer network is constructed in which the increment of passenger flow fluctuation in each station is determined by its current connections. Then, we establish the cascading failure and recovery propagation model with two node failure states, failure and recovery propagation mechanism, and repair speed. The model considers the varying load of each node for the evolution process analysis under different load fluctuation. Finally, a real-world case study is conducted to examine the impact of three factors. The simulation results indicate the enhancements of initial load linear coefficient and node capacity lead to longer node repair time but shorter recovery propagation time. Faster repair speed can improve network lowest performance but shows a marginal decreasing trend. The findings have strategic implications for providing stable and efficient transportation services
Integrating Energy-Efficient Train Control in railway Vertical Alignment Optimization: A novel Mixed-Integer Linear Programming approach
No full textIncorporating train control into the railway design process enables a practical and comprehensive evaluation of the lifecycle utility of a track profile. This paper proposes a novel integrated approach, termed EETC-VAO, which combines railway track Vertical Alignment Optimization (VAO) and Energy-Efficient Train Control (EETC). Initially formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, EETC-VAO aims to meet various geometric constraints and simultaneously minimize construction costs, traction energy consumption, and section running times in both directions. The model is subsequently reformulated into an equivalent Mixed-Integer Linear Programming (MILP) model using linearization methods and is further enhanced with valid inequalities, logic cuts, and a warm start algorithm with random velocity generation. The model has been extensively tested across a variety of case studies and train types, from synthetic small-scale scenarios to challenging real-world cases spanning from 3 to 71.2 km. Our findings demonstrate that operational costs can be significantly reduced with only marginal increases in construction costs. The integrated approach achieves reductions in total lifecycle costs of up to 40%, revealing a critical trade-off between construction and operational expenses. Notably, our results also indicate that lower construction costs do not inherently conflict with reduced operational costs, emphasizing the critical importance of integrating the train control scheme into the VAO problem
Enhancing public transit adoption through personalized incentives: a large-scale analysis leveraging adaptive stacking extreme gradient boosting in China
No full textMotivating individuals to utilize public transportation through financial strategies, including both rewards and penalties, has been acknowledged as an effective approach to manage traffic demand and mitigate congestion-related issues. Personalized travel rewards, in contrast to economic sanctions like road tolls, tend to be more socially accepted. Nonetheless, insights into the effectiveness of personalized incentives remain limited, often constrained by studies that rely on small, non-representative samples of travelers. This study seeks to identify the variables that prompt individuals to switch to public transportation, drawing on extensive quasi-experimental data from a widespread public transit incentive program featured in one of China’s the largest navigation apps. This data encompasses the sociodemographic details of users, as well as their local and long-distance travel patterns. Both a binary Logit model and an adaptive stacking extreme gradient boosting (AS-XGB) model are applied to interpret and predict the changes in users’ public transit usage. Besides gender, job type and preferred travel mode, incentive reward category is found to be one of the significant determinants. In particular, rewards such as breakfast bread or travel vouchers have proven more effective than other types of incentives, like supermarket coupons or tissue gift bags. Female participants, individuals without children, and those who used public transportation in the week prior to receiving the incentives showed a higher propensity to embrace these rewards. However, the influence of education level, car ownership status, or preferred travel mode largely varies as the city’s development level. For intercity travel, regardless of whether the user owns a car or not, her/his income level and education level both have significant impacts on the incentive effectiveness
Designing a robust and cost-efficient electrified bus network with sparse energy consumption data
No full textThis paper addresses the challenges of charging infrastructure design (CID) for electrified public transport networks using Battery Electric Buses (BEBs) under conditions of sparse energy consumption data. Accurate energy consumption estimation is critical for cost-effective and reliable electrification but often requires costly field experiments, resulting in limited data. To address this issue, we propose two mathematical models designed to handle uncertainty and data sparsity in energy consumption. The first is a robust optimization model with box uncertainty, addressing variability in energy consumption. The second is a data-driven distributionally robust optimization model that leverages observed data to provide more flexible and informed solutions. To evaluate these models, we apply them to the Rotterdam bus network. Our analysis reveals three key insights: (1) Ignoring variations in energy consumption can result in operational unreliability, with up to 55% of scenarios leading to infeasible trips. (2) Designing infrastructure based on worst-case energy consumption increases costs by 67% compared to using average estimates. (3) The data-driven distributionally robust optimization model reduces costs by 28% compared to the box uncertainty model while maintaining reliability, especially in scenarios where extreme energy consumption values are rare and data exhibit skewness. In addition to cost savings, this approach provides robust protection against uncertainty, ensuring reliable operation under diverse conditions
An exact and heuristic framework for rolling stock rescheduling with railway infrastructure availability constraints
No full textDisruptions on the railway network can lead to reduced availability of the railway infrastructure, which requires rolling stock dispatchers to adjust the planning of the rolling stock. In this paper, we develop fast rolling stock rescheduling methods which ensure feasibility with respect to the availability of the railway infrastructure. In particular, we explore the option of performing shunting movements at stations where shunting does not take place in current practice, due to the large number of trains that pass through or due to the complexity of the station layout. We introduce an exact rolling stock rescheduling algorithm and an iterative heuristic, which alternate between two mathematical formulations, namely one that creates an interim rolling stock schedule and one that tries to fit the suggested shunting movements between the remaining railway traffic. We test our solution approach with instances that contain complete railway blockages on the Dutch railway network. We successfully identify feasible shunting movements and find an average improvement in the objective function of 19% over the rolling stock schedule that would be obtained if performing shunting movements at the considered stations is prohibited
Joint bus dispatching and bus bridging timetabling for mass rapid transit disruption management
No full textThe mass rapid transit (MRT) systems play a pivotal role in urban mobility services but are frequently susceptible to various disruptions. Bus bridging service is a widely-applied substitute transit service in response to MRT disruptions, which requires a significant number of buses to transport stranded MRT passengers. In practical applications, these buses may be dispatched from the nearby bus lines or bus depots, which inevitably affect bus passengers. However, the literature has paid very little attention to the sources of buses and impractically assumed buses are immediately available. To bridge this gap, this paper proposes a joint optimization problem integrating bus dispatching and bus bridging timetabling, considering the balance between the impact of bus dispatching on bus passengers and evacuation of MRT passengers. A mixed integer linear programming model is developed to minimize total penalties caused by affected onboard passengers and cumulative waiting passengers in both bus and bus bridging systems. A tailored decomposition method is devised to find high-quality solutions efficiently. By applying the decomposition method, the model is split into three sub-problems, which are solved by three tailored and efficient methods developed based on their unique features. The efficiency of the proposed method is demonstrated using Singapore case studies. The computational results show that our method can guide bus dispatching efficiently to evacuate stranded passengers while minimizing the impact on bus passengers. Finally, the impacts of duration, bus frequency, passenger demand, and penalty coefficients are analyzed
A novel multi-objective evolutionary algorithm for transit network design and frequency-setting problem considering passengers’ choice behaviors under station congestion
No full textThe transit network design and frequency-setting problem (TNDFSP) plays a critical role in urban transit system planning. Due to the conflict between the level of service and operating costs, extensive research has been conducted to obtain a set of trade-off solutions between the interests of users and operators. However, most studies ignored the effects of station congestion in TNDFSP, resulting in unrealistic solutions or a failure to achieve optimal design schemes. Therefore, this study investigates the multi-objective optimization of TNDFSP considering users’ choice behaviors under station congestion. To address the problem, a multi-objective bilevel optimization model is first formulated. The upper level is a bi-objective optimization model with two conflicting objectives: minimizing users’ cost and minimizing operator’s cost. The lower-level problem is a passenger assignment problem under station congestion. Moreover, a novel multi-objective evolutionary algorithm based on objective space decomposition (MOEA-OSD) is proposed to solve the complex problem. When dealing with multi-objective optimizations, a decomposition mechanism is developed to convert the problem into multiple subproblems. These subproblems are optimized using an evolutionary approach with newly designed selection process and elite preservation strategy to achieve desirable convergence and diversity. The computational results obtained using Mandl’s benchmark demonstrate the efficacy of MOEA-OSD and the advantage of the proposed model in achieving more comprehensive trade-off solutions
Enhancing public transportation sustainability: Insights from electric bus scheduling and charge optimization
No full textThis study presents a joint optimization model for optimizing the scheduling and charging of electric buses in urban transit systems, integrating fleet size determination, trip scheduling, and charging infrastructure planning. The model is solved using a genetic algorithm and validated through constrained particle swarm optimization. Results demonstrate that by efficiently incorporating time-of-use pricing, optimized partial charging, and dynamic speed variations, the model achieves a 2.5% cost reduction compared to full charging and improves operational efficiency by over 7% within changing speed scenarios. Sensitivity analyses confirm the model’s robustness, identifying the minimum charge duration of 15 min and discharge depth of 90% as economically optimal. The study provides valuable insights for transit agencies seeking to optimize electric bus fleet operations and transition to more sustainable and cost-effective public transportation
The flex-route transit planning problem with meeting points
No full textAs an innovative alternative to ridesharing, flex-route transit (FRT) is widely acknowledged as a promising solution, especially in scenarios in which transportation demand is low or dispersed. This paper addresses the FRT planning problem with meeting points (FRTPP-MP), which conceptualizes each passenger’s pick-up/drop-off request as a set of points (i.e., a cluster) containing the designated pick-up/drop-off point and alternative points (i.e., meeting points), stipulating that only one point in each cluster needs to be visited to fulfill the request. The aim is to minimize both the travel cost of vehicles and the walking cost of passengers by simultaneously optimizing the routes of vehicles and the selection of nodes within their respective clusters. We formulate the FRTPP-MP as a mixed-integer programming (MIP) model and develop an exact branch-and-price (BAP) algorithm to solve it. To tackle the specific challenges of cluster visit restrictions in the pricing problem, we design a tailored bidirectional label correction algorithm (TBLCA), which is further enhanced by a novel acceleration strategy. Extensive computational experiments are conducted based on benchmark instances generated from a real-life FRT system. The numerical results highlight our solution algorithm’s satisfactory performance. Furthermore, managerial insights from a sensitivity analysis suggest that introducing meeting points can substantially reduce the costs associated with FRT
“I let him go because I wanted him to have independence”: How parents and adolescents negotiate using public transport
No full textThis paper explores the perspectives of parents and adolescents in Melbourne, Australia, regarding the use of public transport by adolescents. The primary goals are to study how parental views influence their adolescents’ use of public transport, as well as how adolescents build confidence to navigate using public transport at different destinations. A qualitative research approach was employed, involving semi-structured interviews with 20 parents and 10 of their adolescents aged 11–17. The findings show that parental concerns about safety, particularly harassment and unpredictable conduct from strangers, greatly impact their decisions to allow their adolescents to take public transport. However, parents realise the developmental benefits of granting independence and fostering life skills through public transport use. Adolescents, on the other hand, were far less concerned about personal safety and far more concerned about navigation and ticketing. They expressed a desire for autonomy and freedom while acknowledging challenges such as navigating unfamiliar routes. Additionally, the presence of siblings or friends enhances their experiences and influences parental decisions. Highlighting these concerns provides valuable insights for creating an inclusive, safe, and supportive public transport system. Further research is needed to understand the impact of parental and child anxieties on actual rates of independent mobility by adolescents