Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Robust optimization of train timetables with short-length and full-length services considering uncertain passenger volume and service choice behavior
No full textTrain timetables with a combination of short-length and full-length services can adapt to spatial and temporal variations in demand. However, with such timetables, some passengers may choose to catch an earlier short-length service and then transfer to a full-length service to reach their destinations rather than waiting for a crowded full-length service. This uncertain service choice behavior frequently occurs, which, however, was not well considered in most studies on demand-oriented timetabling. Therefore, this study presents a robust timetabling approach based on the scenario-based method considering uncertain passenger volumes and service choice behavior for choosing different types of train services. A customized decomposition-based method with an iterative solution procedure is designed to solve the proposed model. In each iteration, a multi-agent-based simulation algorithm is developed to update passengers’ travel utility and the service choice preference proportion based on the previous iteration’s timetabling results. To obtain high-quality solutions in an acceptable computing time, a hybrid “ALNS + GUROBI” algorithm is developed to handle the timetabling problems for large-scale cases. The proposed method optimizes the train timetables for Xi’an Metro Line 3 in China. Our results indicate that the proposed method can account for uncertain passenger volumes and service choice behavior by adjusting the short-length plans and train headways to match the transport capacity to passenger demand
Modelling changes in accessibility and property values associated with the King Street Transit Priority Corridor project in Toronto
No full textDespite several decades of research, the relationship between transit accessibility and land values remains unclear. In practice, most research has focused on simple measures of proximity that, while easy to understand, fail to capture the potential for interaction using the transit network. Through the example of the King Street Transit Priority Corridor project, this research examines how transit accessibility, and changes in access over time that result from streetcar service upgrades, are capitalized into condominium prices in Toronto, Canada. Methodological and applied contributions include calculating streetcar travel time differences using disaggregate vehicle tracking data, calculating transit accessibility using a gravity-based measure with a calibrated impedance function, accounting for variations in accessibility over the course of a day as well as changes over time, incorporating measures of access to local amenities, transforming 2D transaction information to a 3D format, and specifying 4D spatio-temporal weights. Longitudinal model results indicate that transit accessibility is a significant determinant of condominium prices. While the service upgrades did not dramatically increase accessibility levels and the implicit value of accessibility did not change over time, panel model results find that condominium property prices appreciated by about 2.7% more on average in the King Street streetcar corridor relative to the Sheppard subway control after the introduction of the priority corridor pilot. This result suggests the corridor on the whole may have became more attractive relative to Sheppard in the pilot phase
How to optimize train lines for diverse passenger demands: A line planning approach providing matched train services for each O-D market
No full textLine planning decides critical service contents of a passenger railway schedule. In real-world scenarios, passengers’ travel expectations or preferences on service qualities tend to be heterogeneous among different origin–destination (O-D) pairs or even in the same O-D pair, which requires railway operators to schedule train services catering to those diverse passenger demands. However, previous line planning approaches either treated passengers homogeneously or roughly divided simple passenger groups by travel purposes or service types. In this paper, we view each passenger O-D pair as an individual O-D market and propose a line planning approach where concluded train lines can deliver matched service levels of trains for each O-D market. Based on the Set Covering Problem (SCP), we establish a novel bi-objective mixed integer linear programming (MILP) model that considers the benefits of both railway operators and passengers. Multiple service qualities for each passenger O-D pair, such as travel speed, direct or transfer connection, frequency, price, etc. are seen as the objects to be “covered” by train lines, to achieve a more accurate supply–demand match. Facing the potential conflicts between diverse passenger demands and limited railway supply, we formulate a series of non-rigid constraints (NRC) to achieve a non-rigid supply–demand match i.e., each O-D pair’s demands can be either guaranteed to be satisfied or satisfied as much as possible. In this manner, railway operators can testify different marketing policies and make marketing decisions regarding which O-D markets should improve, maintain, or degrade services. A heuristic rule-based adaptive iterative searching approach (ISA) is designed to solve large-scale model structures. We take the Beijing-Shanghai high-speed railway (HSR) line as the case study background. We comparatively evaluate the operation and service performances of multiple cyclic line plan scenarios, discuss the performance difference, and state our policy implication. We also recalibrate the service levels of the real-world non-cyclic line plan. The experiment results show that our proposed approach can efficiently help design the line plans based on customized marketing policies and improve the service levels of the real-world line plan
The sustainability appeal of urban rail transit
No full textUrban rail transit (URT) has expanded rapidly since the dawn of the century. Here we examine the impact of URT development on bus service supply and usage, auto ownership, and traffic congestion, by applying fixed-effects panel regression to time series data sets compiled for major urban areas in China and the US. We find that URT development is strongly and negatively correlated with auto ownership in both countries, after controlling for standard social-economic and natural/built environment variables. Importantly, the impact transpires only after a URT system reaches a tipping point that sets in motion a network effect. We also uncover strong evidence of cannibalization by URT of bus market share in both countries. However, rather than undermining the supply of bus services, developing URT is strongly and positively correlated with its growth and adaptation. Finally, no de-congestion benefit of URT is detected in the data. In the US where the analysis is performed, URT development is significantly associated with worsening traffic conditions
A data-driven mixed-integer linear programming approach for real-time rescheduling of urban rail transit under rolling stock faults
No full textUrban rail transit operations are susceptible to unexpected disturbances or disruptions, with rolling stock faults being a particularly common cause. Therefore, this paper focuses on the integrated rescheduling of the train timetable and rolling stock circulation in an urban rail transit line under rolling stock faults. Three typical scenarios arising from such faults are studied simultaneously, i.e., delay, out-of-service, and rescue. Taking general key practical constraints and scenario-specific constraints into account, multi-objective mathematical models are formulated for each scenario to optimize various dispatching measures, such as retiming, cancellation, short-turning, and backup rolling stock utilization. For computational tractability, the proposed models are transformed into equivalent mixed-integer linear programming (MILP) reformulations using some linearization techniques. In order to satisfy the real-time requirements of train rescheduling, a data-driven approach is developed to accelerate the solving process by fixing some decision variables in advance. Specifically, the prediction of binary variable values is treated as a classification task. After creating a dataset including different rolling stock faults and their respective optimal solutions generated by GUROBI, the correlations between optimal solutions and instance features are extracted through supervised learning based on the multilayer perceptron. By generalizing the extracted correlations to unseen instances, high-quality solutions can be found in a short time. Finally, numerical experiments are carried out based on the Beijing Yizhuang Metro Line. Compared to directly solving the original model using GUROBI, the proposed solution approach can reduce the average computation time by up to 91.49% with an average optimality gap of only 0.77%
Individual response prediction and personalized guidance strategy optimization in urban rail transit networks
No full textAdvanced travel information systems play a crucial role in alleviating network-wide congestion in urban rail transit. However, existing studies overlook the heterogeneity of passengers’ compliance with information and their personalized requirements, leading to inefficiencies in guidance. To address these limitations, this paper explores the personalized guidance problem. An integrated framework is proposed to strategically provide passengers with differential route suggestions, ultimately minimizing systematic cost. The framework includes two modules, the first of which predicts individual route decisions under information. Passenger preferences are incorporated into the gradient boosting decision tree model to capture the heterogeneity of compliance with information. Additionally, this module integrates automated fare collection data with stated preference data, thereby avoiding the large-scale and costly data collection. The second module formulates and solves the personalized guidance problem. The problem is modeled as a Markov decision process encompassing an extensive solution space. Moreover, the deep deterministic policy gradient approach is utilized to overcome the dynamicity and dimensional disaster of the problem. A case study of the Beijing Subway is provided to highlight the effectiveness of the proposed framework. The findings show that the guidance strategy significantly decreases the network-wide generalized travel cost by 18.7%, with considerable benefits in overcrowded regions by guiding passengers toward less crowded areas. Moreover, the proposed framework accurately predicts individual behavior responses in route choice, reducing the mean squared error by at least 18.5 %. This study offers valuable information for subway managers to effectively organize passenger flow and improve the quality of passenger travel
The impacts of extreme weather events on U.S. Public transit ridership
No full textClimate change is expected to dramatically change weather patterns across the U.S. To understand its impact on public transit, we use regression analysis to investigate: 1) the relationship between public transit ridership and very hot and cold days and days with heavy precipitation across 48 U.S. cities between 2002 and 2019, 2) how this relationship has changed over time, and 3) if there are differences in this relationship based on sociodemographic characteristics. We find a modest reduction in unlinked passenger trips (UPT) per capita, our proxy for public transit ridership, for each additional very hot day, very cold day, or day with heavy precipitation. The greatest reductions associated with very hot days occur toward the end of our study period and in lower-income cities. We also find greater reductions in UPT on buses associated with several consecutive days of cold and heat, but less so with rail
Effects of Covid-19 pandemic restrictions on zonal transit demand: Evidence from a low-density city
No full textStudies investigating the effects of COVID-19 pandemic on transit ridership of low-density cities are scarce. There exist three unanswered questions in the context of low-density cities: (i) how much patronage losses can be attributed to pandemic restrictions? (ii) which land use zones are more vulnerable to demand declines? and (iii) what factors contribute to zonal vulnerability? The present study intends to answer these questions by investigating zonal level boardings of bus transit system in the city of Winnipeg, Canada. For this purpose, two multivariate adaptive regression splines (MARS) models were developed: (i) a time-series MARS model based on historical transit demand patterns at zonal level, and (ii) a regressive MARS model to predict demand decline as a function of land use, socio-demographic, and zonal-level variables. The magnitude of the demand decline was found to be highest in April 2020, with a total loss of 1.74 million boardings attributable to the COVID-19 pandemic. Among 840 zones, transit usage in commercial (50 %–60 % reduction), education (80 % reduction), and recreational (60 %–80 % reduction) zones are most affected by pandemic restrictions. The findings are valuable for transit officials of low-density cities to effectively plan response strategies for long-term operational disruptions due to pandemic situations
Urban transport system changes in the UK: In danger of populism?
No full textMany cities seek to change their transport systems to reduce negative outcomes. This generally involves measures supporting active and public transport, restricting vehicle use. Infrastructure modification and legislative developments are often perceived as ‘attacks’, and lead to resistance by specific groups. This paper uses critical discourse analysis to evaluate a convenience sample of 185 social media threads opposing Ultra-Low Emission Zones (ULEZ), Clean Air Zones (CAZ), and Low Traffic Neighbourhoods (LTN) in four cities in the UK, Oxford, London, Birmingham, and Bradford. Themes are identified through MaxQDA to determine the range of discursive strategies used, as well as to understand their interrelationships. Findings highlight intersections of populist politics and (sustainable) transport policymaking and planning in UK cities. The understanding of the mechanisms at work can facilitate the development of less divisive strategies for transforming urban transport systems
The recovery from the pandemic: A spatial-temporal analysis on the changes in mobility and public attitude in Singapore
No full textThe COVID-19 pandemic has significantly affected individuals\u27 daily lives. The reduction in mobility, decreased social interactions, and heightened uncertainty, negatively affected the public\u27s emotional well-being. As restrictions eased, many countries transitioned towards a new normal. Understanding the connection between public attitude and urban mobility during the recovery phase is crucial for the planning of resilience urban environment. This study investigates the changes in urban mobility and public attitudes by analyzing public transportation and social media data. Findings indicate a significant increase in mobility during the recovery compared to the semi-lockdown period, with better resilience to subsequent outbreaks. Commercial areas and major public transit interchanges exhibited stronger recovery in mobility after new (sub)variants waves. Train mobility demonstrated robust recovery. Relaxation in April 2022 played a crucial role in increased mobility and reduced negative feelings. This policy shift contributed to a surge in positive emotions, particularly in locations where individuals could engage in social and recreational activities. Moreover, the increased beyond-neighborhood mobility was related to decreased negative emotions during the easing of restrictions. Commuting behaviors exhibited long-term changes after 30 months from the initial outbreak, e.g., preference shifted to trains from buses and reduced visits to destinations for international travel