Monash University, Institute of Transport Studies: World Transit Research (WTR)
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How to improve transportation capacity of oversaturated metro lines? A flexible operation approach with extra-long train compositions
No full textUnder regular metro operation conditions, one critical bottleneck to improving metro transportation capacity is fixed-length train compositions. These fixed-length compositions are mandated to not exceed station platform lengths, thereby limiting the potential for increasing capacity to effectively accommodate oversaturated passenger demand. To this end, we focus on a flexible metro operating system equipped with extra-long train compositions, which allows trains to protrude beyond both ends of the station platforms for additional capacity. Driven by oversaturated and time-dependent passenger demand, we develop a compact integer linear programming model to determine train composition lengths and train-platform alignment relationships. When using commercial solvers to directly handle this model, complexity analyses and computational practice show that it is less efficient for large-scale experiments. We thus reformulate it as a column-based optimization model, while employing a column generation algorithm to solve its linear relaxation version and customizing a dynamic programming method to generate promising column variables. To achieve high-quality integer solutions, we carefully embed the column generation into a branch-and-bound procedure and elaborate some accelerating strategies through theoretical analyses. The approach is applied to several test instances defined by using hypothetical and real-world lines. The computational results demonstrate that the proposed approach can significantly reduce passenger waiting times and effectively handle large-scale problems
Exploring bus drivers\u27 intentions to collaborate with level 4 autonomous buses: Integrating the technology acceptance model and assemblage theory
No full textAs AI proliferates, human-AI collaboration has become necessary in many domains, not least in public transportation, where highly automated, if not fully driverless buses, require human-AI cooperation. However, existing technology acceptance models lack insight into the unique factors that influence acceptance in collaborative human-AI contexts. This study integrates the Technology Acceptance Model (TAM) with Assemblage Theory to provide a comprehensive framework that does explicate key mechanisms underlying bus drivers\u27 behavioral intentions toward Level 4 autonomous buses. Drawing upon Assemblage Theory, we conceptualize the driver and the autonomous bus as a human-machine collaborative assemblage. Perceived usefulness and perceived ease of use from TAM are modeled as antecedents, with compatibility and trust from Assemblage Theory as mediators, predicting attitude and behavioral intention. The theoretical model is examined using structural equation modeling on data collected from 719 bus drivers of four major transit companies in Taipei. Results robustly support all hypotheses, with perceived usefulness exhibiting stronger positive effects on trust and compatibility than perceived ease of use. Trust and compatibility positively influenced attitude, which strongly predicted behavioral intention to cooperate with Level 4 autonomous bus introduction. The empirical findings show TAM is enriched by the integration of Assemblage Theory concepts, extending both theories\u27 ability to facilitate autonomous mobility human-AI collaboration
Passenger arrival patterns and its implications for bus operation: The impact of schedule reading behavior on average waiting times at bus stops
No full textInaccuracies in measuring passenger waiting times at bus stops can lead to significant inefficiencies in optimizing bus operation schemes. To address this issue, this paper introduces a refined methodology aimed at accurately representing passenger waiting times and estimating the distributions of passenger arrival patterns at bus stops, with a focus on low-frequency suburban buses, considering their schedule-reading behaviors. First, we conducted stated preference (SP) and revealed preference (RP) surveys to capture the factors affecting passengers’ arrival behaviors, revealing that bus vehicle headway and bus arrival punctuality (quantified as the standard deviation of arrival time deviations, SD-BATD) significantly influence passenger behavior. Second, we developed models to assess the proportions of schedule-reading passengers (SR-passengers) and their average waiting time (AWT) as well as standard deviation (SD-WT). By treating AWT and SD-WT as independent variables, we then characterized the arrival patterns of both SR-passengers and schedule-neglecting passengers (SN-passengers) using maximum extreme value and uniform distributions, respectively. Additionally, we conducted numerical experiments on bus headway optimization to validate the operational implications of the proposed model for bus services. The results demonstrate that the AWT model significantly reduces bus operation costs by up to 15.7% compared to the traditional assumption that AWT = 1/2 headway. This effect is particularly pronounced for routes characterized by lower demand and higher speeds, which are typical of low-frequency suburban buses. Furthermore, this paper highlights the importance of accurately estimating the passenger waiting times considering passenger schedule-reading behavior in optimizing bus services
Destination-to-gate assignment to mitigate congestion-related risks in oversaturated metro lines: A new passenger flow control strategy
No full textThe accumulation of large passenger flows at metro stations often poses congestion risks to urban rail systems, including an increased likelihood of accidents (e.g., slips, trips, and falls) and train departure delays. However, during peak hours, the priority boarding rights of passengers at upstream stations often lead to congestion and overcrowding at downstream popular stations. We propose a new passenger flow control strategy to address this issue, namely destination-to-gate assignment. This approach assigns specific gates to destinations served by the station, enabling passengers to board the appropriate train carriages. This strategic allocation facilitates a more even distribution of passengers, reducing congestion and enhancing spatial equity in passenger travel, thereby mitigating operational risks associated with overcrowding. For the problem of interest, we propose a nonlinear integer programming model to optimize the destination-to-gate assignment, aiming to simultaneously minimize risks related to passenger crowding and waiting times. The model adopts a first-come, first-served (FCFS) boarding rule to accurately capture the dynamic nature of passenger flow while considering the capacity limitations of train carriages. Leveraging the model’s characteristics, we employ a set of linearization methods to equivalently transform it into a mixed-integer linear programming (MILP) model. To address the computational challenges posed by real-world scale, we develop a customized heuristic algorithm that uses Variable Neighborhood Search (VNS) combined with passenger flow simulation to efficiently generate high-quality solutions. Finally, we conduct a series of numerical experiments using data from Guangzhou Metro Line 9 to demonstrate the effectiveness of our proposed approach. The results show that the proposed destination-to-gate assignment strategy effectively alleviates congestion-related risks across all stations and promotes spatial equity in passenger travel, even under varying levels of passenger compliance, demand, and train delays. It can thus be recommended as a self-organizing and easily implementable passenger flow control method
Utilizing a data-driven methodology to resolve the passenger-to-train assignment problem
No full textUnderstanding the passenger distribution within the metro system is a prerequisite for metro network planning and operation. However, as automatic fare collection (AFC) data records only entry and exit information, directly obtaining passenger distribution through AFC data and established timetables remains challenging. Although many studies have explored passenger distribution in metro systems based on accurate timetable inputs, parameter collection and calibration are challenging due to the spatiotemporal dynamics of both passenger demand and headway. This study proposes a data-driven passenger-to-train assignment model (PTAM). The posterior probability of passengers boarding the train is computed using a two-stage Gaussian mixture model (GMM). This method does not require precise timetable inputs, and both the initial parameter collection and final estimation processes are automated, eliminating the need for manual calibration. Using the Nanjing metro as a case study, the effectiveness of the PTAM is demonstrated. Additionally, the study computes in-vehicle passengers, left-behind passengers, and passengers’ willingness to pay (WTP) using PTAM. The results demonstrate significant differences in crowding level and left-behind at different stations on the same line. During the evening peak, passengers bear about 50% of welfare costs. The findings can provide managers with a basis for passenger flow organization and guidance for passenger’s travel decision
Method for Predicting Environmental Vibration Impact of Existing Subway Lines and Its Practical Application
No full textTo predict accurately the impact of track vibrations caused by subway trains on adjacent buildings and address the problem of their excessive dynamic response, a method for predicting the environmental vibration impact of existing subway lines is proposed in this paper. A coupled numerical dynamical model of the train/track/tunnel system is established using the multi-scale method. Using field-measured soil vibration data, empirical formulas for soil frequency and material damping coefficient are calibrated. By combining the numerical model and the empirical formulas, buildings’ indoor vibrations and noise level can be assessed. At the same time, a solution to the problem of excessive vibration of buildings along a subway line is also proposed that optimizes the track fastener parameters. Field tests verified the accuracy of the prediction method and the effectiveness of the optimized track parameters. These research results provide a reference for predicting the vibration of buildings along subway lines and using the proposed preventive solutions
Towards an efficient electric bus system: Multi-phase optimization model for incremental electrification of bus network with uncertain energy consumption
No full textElectrifying bus transit systems emerges as a practical solution to environmental degradation resulting from the unprecedented level of mobility nowadays. In the U.S., with the intensified efforts to expand EV infrastructure, a special emphasis is now placed on providing emission-free transit services. This initiative is central to America’s push towards a net-zero-emissions future. In response, a growing number of cities have started replacing diesel buses with battery electric buses (BEBs). However, technological, operational, and economic barriers related to charging infrastructure and power supplies make the electrification of bus systems a gradual process, where only a part of the system is electrified at each stage. Moreover, due to the limited battery capacities of BEBs and their stochastic discharge rates influenced by factors like weather, traffic, and road conditions, BEBs often require daytime charging to be able to continue operating throughout the day. Therefore, transit agencies need to develop an integrated strategy that can address various costs of electrification and minimize the planning and operational costs. This study proposes a framework to facilitate the incremental electrification of bus systems. We formulate the problem as a two-stage stochastic mixed-integer linear programming model. The first stage optimizes long-term strategical decisions related to fleet sizing, charging station siting, and charging-station-route assignments under random BEB charging demand and time-of-use electricity tariffs. The second-stage optimizes the charging operations of the BEB fleet for a realized charging demand scenario while maintaining the service schedule for passenger convenience. We also develop a Benders decomposition method to solve the problem with better computational efficiency than existing solvers. To validate the proposed model, we test it on a real-world bus network to design an incremental electrification plan. We show the efficacy of the solution approach and study the managerial insights including the deployment of fast charging and potential battery technology enhancement in the future
On solving the suburban commuting problem in megacities: Integrating ridesharing with urban rail transit
No full textThe increasing spatial separation between workplaces and residences, coupled with the continued rise in motor vehicle ownership, has significantly strained urban traffic during rush hours. Suburban commuters, in particular, experience prolonged travel times. To enhance suburban commuting efficiency and alleviate congestion, this paper introduces a novel approach to address the Suburban Commuting Problem (SCP) in megacities. The proposed solution integrates ridesharing with urban rail transit (URT) systems. By promoting ridesharing in suburban areas, commuters can broaden their options for URT stations, no longer restricted to the nearest but often overcrowded end stations. This approach enhances the accessibility of URT and helps alleviate queuing congestion at end stations. Consequently, this approach shortens travel times for suburban commuters. We formulate the SCP as an arc-flow mixed-integer linear programming model, as well as a set-partitioning formulation. We introduce a tailored branch-and-price (BP) algorithm based on the set-partitioning approach to accurately solve the SCP. To expedite the solution process for the pricing sub-problem, we devise a tailored label-setting algorithm incorporating a bi-directional search strategy. Finally, we evaluate our model and algorithm’s performance through extensive computational experiments and provide valuable managerial insights. The case results based on part of road network in Beijing indicate that the proposed optimized solution for the integrated commuting mode can reduce vehicle commuting distance by 34.65%, thereby mitigating traffic congestion and reducing pollutant emissions
Resilience-Based Approach to the Measurement of Headway Inconsistency
No full textMaintaining a consistent headway is a primary goal in bus operation, but it poses a significant challenge because of fluctuating traffic conditions and uneven passenger demand. This leads to inconsistent headways and even bus bunching, resulting in negative consequences such as increased travel time and reduced vehicle capacity. Various control strategies have been developed to alleviate headway inconsistencies, but most of them have focused on whether an inconsistency occurs while overlooking its duration. This study proposes a resilience-based approach to measure headway inconsistencies considering both the depth and duration of the impacts with deviations from the optimal headway. The approach is devised to analyze the cumulative impacts of inconsistent headways comprehensively. It goes beyond addressing only extreme cases, such as bus bunching, and considers persistently uneven headways. We first identify vehicle trajectories from smart card data, then compute deviations from the center of the preceding and following vehicles as an index of impact. Using the concept of resilience, cumulative impacts are measured and their distribution is utilized to set the failure criterion. The approach is applied to 320 bus lines in Seoul to measure the deviations of headways and is used to analyze the characteristics of the bus lines and links where inconsistencies frequently occur. The proposed method enables transportation planners to identify factors that can lead to headway inconsistency, facilitating tailored and data-informed planning processes. This study provides a comprehensive framework that recognizes line-specific and link-specific factors, thereby contributing to enhancing the reliability and efficiency of public transportation systems
Transit Board Diversity and Pandemic Service Cuts in Vulnerable Communities
No full textDespite its primary role in shaping policy and service characteristics, board governance is an understudied topic in the field of urban transit. Existing research on board management and representative bureaucracy theory suggests that the race and gender diversity of boards has a significant impact on organizational activity but that these relationships are highly dependent on the cultural context and industry analyzed. In this paper, we evaluate how the diversity of transit boards (with respect to race, gender, and disability) in the U.S.A. correlates with service changes authorized by these boards during the COVID-19 pandemic. Utilizing a database on board governance and general transit feed specification data for 36 agencies, we find a positive relationship between the presence of women on transit boards and vertically equitable service cuts, defined as increasing or maintaining transit service in more vulnerable neighborhoods. Overall, transit agencies with more female board members had more equitable service cuts, on average, during the COVID-19 pandemic