Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Two-step optimization of train timetables rescheduling and response vehicles on a disrupted metro line
No full textMetro disruption management is currently one of the hot issues in metro research. Existing research has primarily focused on rescheduling normal train timetables or the design of bus bridging services, with limited consideration of the traffic dynamics. In this paper, we introduce a two-step optimization framework to derive a comprehensive evacuation plan encompassing the rescheduled train timetable and the response vehicle scheduling scheme. In the first step, an integer programming model is proposed to reschedule the normal train timetable. The objective function of this model is to minimize total passenger waiting time while considering various constraints such as the timetable rescheduling strategies (i.e., cancellation and short-turning), train headway, and train capacity. In the second step, the response vehicle scheduling model is established based on the Cell Transmission Model (CTM). This model aims to minimize the total travel time of the response vehicles and is capable of capturing traffic dynamics on the evacuation network. To bridge the gap between the mathematical models of the first and second steps, we establish a demand transformation process, which provides a formula for transforming the stranded passenger demand into the demand for response vehicles. Numerical cases of Beijing Metro Line 9 verify the efficiency and effectiveness of our proposed model, and results show that: (1) the direction with fewer train services experiences a greater impact from the disruption. The disruptions occurring within the central region of the metro line tend to affect a greater number of normal train services during peak hours, whereas disruptions occurring within the terminal areas of the metro line tend to affect a greater number of normal train services during off-peak hours; (2) compared with the static shortest route scheme, the dynamic shortest routes of response vehicles contribute a 7% reduction in total travel time
Exploiting the flexibility of modular buses in an urban transit system
No full textUrban transit systems usually operate according to fixed-route and fixed-schedule schemes by employing fixed-capacity vehicles, despite the mobility demand is unevenly spread out in both space and time. Modular buses are an emerging technology in which modules of relatively small capacity can be dynamically docked together to form greater capacity buses and can, therefore, make the transit system capable of adapting the capacity to the actual mobility demand. A module can be shifted from one line to another at pre-defined intersections and can be relocated when empty, if beneficial. We call these two operations sharing and rebalancing, respectively. Given a transit network comprising multiple bus lines and a mobility demand, we present an integer linear program to determine an optimal assignment of modules to lines, so that the mobility demand is met with a minimum total number of modules. Computational experiments show that, by exploiting the flexibility of modular buses, the total capacity deployed can be reduced by 49% with respect to a conventional transit system, whereas the average occupancy ratio increases from 41.22% to 72.85%
Collaborative generative adversarial networks for fusing household travel survey and smart card data to generate heterogeneous activity schedules in urban digital twins
No full textConventional activity-based models (ABMs) relying on household travel survey (HTS) data suffer from low spatiotemporal heterogeneity and outdated information due to low sampling rates and infrequent data collection of HTS. Transit smart card (SC) data can aid with continuously collected spatiotemporally heterogeneous mobility patterns of transit users. However, its integration with HTS in activity schedule generation is challenging due to differences in spatial resolutions, missing activity purpose, and lack of non-transit trips. To tackle these issues, we propose a novel data fusion method based on a deep generative model: collaborative generative adversarial networks (CollaGAN), which leverage the complementary strengths of HTS and SC data. CollaGAN generates activity schedules by harmonizing the differences in spatiotemporal heterogeneity and information between the two datasets in the latent space. The novel architecture of CollaGAN involves a discriminator for each HTS and SC data to simultaneously preserve the comprehensive information in the small-scale HTS data and the heterogeneous patterns in the large-scale SC data. We also devise novel boundary-based and domain-specific regularization to maintain the feasibility of the generated activity schedules. Using HTS and SC data in Seoul, a semi-synthetic simulation study quantitatively demonstrates multi-fold enhancements in the heterogeneity of the activity schedules from CollaGAN compared to those from single-source models, with a pronounced increase in heterogeneity of spatial attributes. A case study qualitatively shows that mobility patterns overlooked by HTS are captured through fused joint probability distributions, generating heterogeneous mobility patterns of non-transit users that existing data fusion methods fail to capture. By simulating heterogeneous activity schedules, the model provides more precise and policy-relevant insights into urban mobility, ultimately enhancing the overall accuracy of the urban digital twin by preventing error propagation to other interconnected energy and land use systems
Extending electric bus charging infrastructure considering charging scheduling and energy pricing
No full textThe transition to fully electrified public transport is a pivotal step towards sustainable urban mobility. However, the efficient deployment of charging infrastructure poses significant challenges, particularly in optimizing charger placement to meet operational and economic constraints. This article introduces a novel strategic planning model designed to calculate the optimal locations for extending an existing charging station network that supports the daily operations of an electric bus fleet. The planning model is a bi-objective mixed integer-linear program that considers both the operational needs of the fleet operator and the energy pricing schema as imposed by energy management authorities. By accounting for parameters such as charging station installation costs, Time-of-Use tariffs and peak demand charges, the model addresses an integrated planning problem with two objectives: the minimization of the monetary cost required for the operation of the bus fleet and the buses’ deadhead times. An implementation in a network using data from Manhattan, New York and a case study in Limassol, Cyprus, demonstrate the model’s efficacy in providing actionable insights for urban planners and transport authorities, ensuring cost-effectiveness and reduced environmental impact
City Bus Reliability Measurement Based on Sparse Field Data Supported by Selected State Space Models
No full textMeans of transport are an important part of today’s cities. Bus transport in particular is considered to be a reliable mode of transport. In cooperation with a city’s transport company, we process in this article data collected from two fleets of buses. The data records are related to the failures of individual bus subsystems. We focus on the study of data from engine and brake subsystems, the consequences of failures of which are the most serious in relation to traffic safety. The data are seemingly austere, as the records only contain information such as “operating/fault” during a given month (no known causes, mechanisms, or other more precise time information about the failure). On the basis of such sparse data, however, it is still possible to estimate the trend or predict the development of certain measures over time. For the study and subsequent prediction, we used approaches based on state space models. Specifically, we worked with a linear trend model and a periodic component model. For both fleets of buses, we have also analyzed what the respective model and its prediction could look like if we knew selected and more detailed time information about the failures. This model therefore provides a general idea of the rate of occurrence of failure trend development, expected number of failures within single months, and respective bus subsystem failure occurrence forecasts. Based on this information, operators and entrepreneurs can rationalize the processes related to operations, maintenance, and repair planning
Towards sustainable neighbourhoods? Tensions and heterogeneous transport priorities among suburban residents
No full textA major challenge in North America’s car-centric suburbs is developing sustainable transportation strategies that align with residents’ diverse needs and preferences. Using a survey of 1,850 residents in Scarborough, an eastern suburb of Toronto, we used descriptive statistics and an exploded logit model to identify which environmental factors, sociodemographic characteristics, travel behaviors, political values, mobility options and transport barriers, and aspirations influence residents’ transport priorities in terms of space and investment.
Overall, transit investments are considered the top priority, followed by walking, driving, and cycling, with clear neighbourhood-specific variations. Newcomers, older adults, and racialized groups prefer sustainable transport options, while women, white and right-wing individuals prioritize car investment. Moreover, transport priorities are closely linked to people’s lifestyles and neighborhood aspirations, as reflected in the destinations they want near their homes. These findings enhance our understanding of transportation preferences and offer valuable insights for developing effective, context-specific sustainable transportation strategies
Resilience assessment of an urban rail transit network under natural disasters
No full textAssessment of the resilience of an urban rail transit (URT) network under natural disasters is of practical importance, especially with the intensification of global warming. This study develops a train schedule-based resilience assessment model to assess the resilience of a URT network under natural disasters, addressing a critical gap in existing research that often ignores the influence of the train schedule on resilience assessment. Numerical experiments on the Chengdu Metro network demonstrate that the travel time-based performance indicator can effectively evaluate the network performance, revealing that the train schedule has a significant influence on the network’s resilience. Counties Shuangliu and Wuhou are identified as the most important county-level administrative districts in Chengdu during rainstorms and strong wind events, respectively. Furthermore, this study explores the influence of parameters on the network’s resilience and provides some policy implications based on the results obtained from the sensitivity analysis
Heat stress mitigation by trees and shelters at bus stops
No full textMunicipalities are attempting to create safe and comfortable transit systems in the face of climate change. Herein, we determined how trees and different shelter designs impact heat stress at bus stops. Over 13 summer days in 2023, we used sensors to measure wet bulb globe temperature in the shade from trees and four different shelter designs to compare with unshaded areas at 17 bus stops in Houston, Texas. Results from multilevel linear mixed effects modeling revealed that tree-shaded areas were 3.2 °C (5.8 °F) cooler than unshaded areas (p \u3c 0.001). Shelters provided less cooling than trees, and enclosed shelters were less effective than open designs. Further, heat stress was more than 3 °C (5.4 °F) higher under unshaded, enclosed shelters than unshaded areas outside of shelters (p \u3c 0.001). Tree planting at transit stops may be a top option to improve heat safety, and shelters, if improperly designed, may be a form of maladaptation, amplifying health risk
Exploration of the tram-involved crashes’ characteristics and contributing factors to fatality in tram crashes in Japan
No full textModern trams have garnered worldwide attention as an alternative urban rail transit system since they offer advantages such as higher accessibility, convenience, lower construction and maintenance costs compared with subways. Nevertheless, due to the system’s characteristics, particularly regarding right-of-way issues, crashes involving trams often lead to severe consequences. This study utilizes traffic crash data from across Japan, including 1,121,299 crashes that occurred from 2019 to 2022, of which 304 were tram involved. The study has two major analyses using random-parameter model with heterogeneity in means: (1) tram-involved crashes’ characteristics (vs. non-tram-involved) and (2) factors affecting the probability of fatality in tram crashes. The first analysis’ findings suggest that fatalities and crashes at rail crossings are more likely to be associated with tram crashes, while non-tram crashes are more likely to occur during daytime, at intersections, or on narrower lanes. Furthermore, the presence of a median between opposing directions increases the likelihood of a non-tram crash. The second analysis to identify the factors influencing fatality reveals how factors such as the season, lighting conditions, road conditions, crash locations, and the age and category of victims affect the characteristics of tram crashes and fatalities. Specifically, it explains the heterogeneous effects of daytime and intersections on tram crashes, as well as the heterogeneous impacts of rail crossings and densely populated areas on tram fatalities. The findings are expected to assist authorities in formulating strategies to minimize the incidence of tram crashes and related fatalities, potentially saving lives and bringing about economic benefits