Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Adaptive inference for dynamic passenger route usage patterns in a metro network considering time-varying and heavy-tailed travel times
No full textDue to the dynamic changes in timetables, passenger demand, and passenger composition, the distribution of passengers within a metro system becomes quite complex. Many studies divide a day into intervals to account for the dynamics of travel time. However, the intervals used in these studies are insufficient to capture the gradual and fine-grained changes in passenger travel patterns. This study proposes an adaptive dynamic route inference model (ADRIM) that overcomes these limitations. In the ADRIM, we introduce a constrained Expectation Maximization algorithm (CEM) by confining the parameters of the mixture log-normal distribution model (MLND) within confidence intervals, thereby reducing anomalous estimations. We use the concept of Hidden Markov Models (HMMs) to achieve a parameter-adaptive characterization for the dynamics of route choice and travel time distributions for MLND through an iterative process. For a Nanjing metro case study, the proposed model exhibits superior performance in fitting the actual distribution of travel times and accurately captures the dynamic trends in route travel times. Besides, it is revealed that the maximum difference in expected travel times among multiple valid routes for the same origin–destination (OD) pair primarily falls within the interval [5 min, 15 min], and the distribution range of the maximum ratio is mainly between [1.1, 1.6]. The high consistency in passenger route choice proportions observed for two consecutive weeks, along with an analysis of route choice patterns under dynamic conditions, serves as strong evidence supporting the reliability and practical utility of the dynamic route inference model in understanding and managing metro passenger flows
Choreographing mobilities & urban imaginaries: Case study of Dhaka Mass Rapid Transit (MRT)
No full textModern mega-cities are characterized by smart, efficient urban mobility infrastructures like the mass rapid transit. Yet, such infrastructural advancements are not always equitable. This paper delves into the core inquiry of how urban mobility is orchestrated in a mega-city such as Dhaka, particularly through the case study of the Dhaka Metro. We find that the introduction of the metro creates new spatial conditions and configurations for Dhaka\u27s middle class, where tropes like gender, inclusion and economic growth are leveraged to serve the state\u27s political agenda (and vested interests). Meanwhile, gatekeeping practices of surveillance, information policing, and muting dissent give insights into the political economy of ‘modernizing’ mega-cities. In other words, although the metro is built on public land, using public taxpayer money, for the sake of ‘public welfare’, it does not serve the masses. This illuminates the prioritization of top-down urban development and mobility imaginaries, which favour private and geopolitical interests. Consequently, working-class commuters are systematically excluded from the planning process, effectively designing them out of the city
Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression
No full textAccurate forecasting of bus travel time and passenger occupancy with uncertainty is essential for both travelers and transit agencies/operators. However, existing approaches to forecasting bus travel time and passenger occupancy mainly rely on deterministic models, providing only point estimates. In this paper, we develop a Bayesian Markov regime-switching vector autoregressive model to jointly forecast both bus travel time and passenger occupancy with uncertainty. The proposed approach naturally captures the intricate interactions among adjacent buses and adapts to the multimodality and skewness of real-world bus travel time and passenger occupancy observations. We develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm to approximate the resultant joint posterior distribution of the parameter vector. With this framework, the estimation of downstream bus travel time and passenger occupancy is transformed into a multivariate time series forecasting problem conditional on partially observed outcomes. Experimental validation using real-world data demonstrates the superiority of our proposed model in terms of both predictive means and uncertainty quantification compared to the Bayesian Gaussian mixture model
A bi-level optimization model for project scheduling and traffic flow routing in railway networks
No full textLong-term tactical infrastructure planning for a transportation network consists of deciding on renewals and major maintenance works. Such projects constitute large budget volumes and will impair the available traffic capacity during their execution, especially for railway systems. Quantitative methods that schedule and coordinate infrastructure projects together with traffic flow adaptations is however largely lacking today.
This paper addresses the joint planning of temporary capacity restrictions and traffic flow adaptions during track work closures, by proposing a bi-level optimization model which separates the problem into project scheduling (upper level) and traffic assignment (lower level). The latter model uses a novel traffic flow formulation for routing volumes of trains through the transportation network under the capacity restrictions given by the project scheduling. An aggregated network is used together with time discretized into uniform periods, which makes it possible to treat large national planning problems with a planning horizon of up to a year and a period length of a couple hours. The computational properties are evaluated, both for the individual models, and for their joint usage. Furthermore, results from applying the models on two case studies, concerning Northern and South-Western Sweden, are presented.
The main conclusion is that the model formulations are capable of solving realistic planning cases and to provide support for capacity planners at an infrastructure manager, even for a large national railway. The results show that a good overview over the collective traffic impact is obtained, but also that details of particular traffic relations or capacity usage over individual network links and their variation over time can be studied. One major deficiency has been identified in the flow-based traffic assignment model, which can lead to incoherent train flows over long traveling distances and many time periods
The Fundamental Issues and Development Trends of AI-Driven Transformations in Urban Transit and Urban Space
No full textChanges in transportation demand driven by artificial intelligence (AI) are reshaping urban spatial structures, and the continued development of AI is expected to exacerbate the spatiotemporal imbalance between urban spatial structures and transportation behaviors. Studying the interaction between urban transit and spatial factors helps to achieve precise alignment between structures and behavior. This study demonstrates the immense potential of AI technologies in uncovering complex, high-dimensional, non-linear interactions between pertinent factors using clustering analysis and further reveals the urban transformations induced by Urban AI and their broader macro impacts. A multi-factor equilibrium model of human and artificial intelligence is also proposed as a direction for future research, aiming to help scholars familiarize themselves with the latest trends and emerging technologies as well as to provide inspiration and guidance for future studies
Built Environment’s Non-Linear Impact on Subway Passenger Flow Through Improved Interpretable Machine Learning
No full textUnderstanding the complex correlation between the built environment and subway passenger flow can provide unique insights for the development of transportation operations and urban coordination policies. Few studies have systematically analyzed the rationality of selecting built environment variables and further explored the non-linear relationships. In this study, we integrated various sources of built environmental factors and developed an interpretable machine learning analysis framework using backward elimination extreme gradient boosting and SHapley Additive exPlanations (SHAP) values analysis (BE-XGBoost-SHAP). The framework was validated by analyzing passenger flows during the morning peak, non-peak, and evening peak periods at the station level. The research results indicate that there are significant differences between built environment factors and the time-varying passenger flow. Land use characteristics significantly dominate across all three temporal periods. The importance of other variable types in relation to passenger flows varies significantly across the three time periods. It is worth noting that the relationships between all variables and passenger flow at different time periods are non-linear, with the majority displaying threshold effects. Compared with the gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models, the proposed interpretive framework performs better as regards R-square, root mean square error (RMSE), and mean absolute error (MAE) metrics. This study offers valuable insights, elucidating the pivotal land use attributes that notably affect passenger flow, the significance of varied built environment factors across distinct time spans, and the acknowledgment of non-linearities and threshold effects within these relationships. These findings are imperative for urban planning and the enhancement of station area design
Future of Passenger Mobility in the U.S.A.: Scenarios for 2030
No full textTo develop long-term strategies for transportation planning, it is necessary to understand and address the uncertainties related to mobility. This task is challenging, given the rapid evolution of passenger transportation in the U.S.A. To aid transportation planning, we combine expert insights and quantitative modeling in a two-step process to create scenarios for U.S. passenger transportation in 2030. In the first part, we use inputs from 34 experts on 18 socio-demographic, macro-economic, and technological factors that influence transportation to generate combinations of projections that are reasonable and coherent: we call them scenarios. For the three distinct scenarios thus developed, we use a spreadsheet-based transportation activity accounting tool to estimate nationwide vehicle miles traveled, carbon emissions, and electrification levels. The Hop & Drive scenario is dismal, characterized by a slower economy, greater suburban growth, and higher driving levels. Mapped by Directives envisions a future shaped by ambitious local and federal policies accelerating electrification, reviving U.S. mass transit, and lowering driving levels. The third scenario, Technology Dazzles, imagines the outcomes of rapid technological improvements and adoption, leading to an increasingly automated world with a mobility-as-a-service paradigm beginning to be realized. The projections developed for each scenario should serve as markers for transportation planners at the federal and regional levels who monitor transportation trends. These markers can be used to adjust regional and national transportation funding priorities and understand the broader implications of development and funding strategies under uncertainty
Understanding Incident Effects on Subway Operations: Clustering Analysis of Severity Patterns
No full textIncidents pose challenges to the reliable operation of urban rail transit systems. Given the high frequency of subway services, even minor incidents can cause cascading delays across multiple trains. Understanding incident effects is crucial for improving response time and enabling efficient recovery strategies. This study uses operational records from the Montreal subway system to quantify the overall impact of incidents including the number of affected trains and total delay time. The proposed approach involves integrating operational records with incident data to identify the source of delays and subsequent knock-on effects. To recognize distinct propagation patterns among various incident types, K-means clustering is applied to categorize incidents into three clusters. Cluster 1 represents incidents with the lowest impacts, affecting only one direction of a subway line and imposing an average total delay time of 16 min. Cluster 2, which comprises most incidents, causing moderate operational impacts with an average total delay time of 52 min. Cluster 3 includes severe incidents, affecting an average of 26 trains and causing a total delay time of 273 min. Peak hour analysis indicates that morning and evening peak hours have the highest average number of affected trains, emphasizing the impact of peak hours on incident severity. Investigation into the causes of incidents highlights that the most frequent incidents fall into Cluster 2, implying moderate impacts on subway operations. This research provides valuable insights into subway incident management, laying the groundwork for further studies aimed at enhancing the performance of urban rail transit systems during service disruptions
Enhancing Urban Mobility with Aerial Ropeway Transit (ART): Future Accessibility Impacts of Multimodal Transit Expansion Scenarios
No full textAerial ropeway transit (ART) systems are emerging alternatives to augment existing transit systems in congested cities in the Global South, especially in urban areas with limited transit coverage because of road width constraints or topography. Integration of aerial cable car stations to an existing transit network can improve the overall accessibility of various population segments with significant positive benefits in relation to reducing transport-related social exclusion. This study evaluated the impact of introducing ART in the city of Varanasi (India) and assessed the spatial accessibility improvements to critical facility locations such as heritage sites, educational institutions, hospitals, and employment centers. Several multimodal transit expansion scenarios were considered in this study and the potential benefits of each case were quantified using the two-step floating catchment area (2SFCA) method. A multi-criteria decision-making (MCDM) approach was subsequently employed for identifying the optimal locations of ART stops. Microlevel analysis findings suggest that the mean accessibility values could increase up to 10.92% in the first phase of the ART implementation, which could subsequently increase to 24.7% and 49.8% for the subsequent transit expansion scenarios. The study also investigated the Varanasi ART DPR prepared by Varanasi Development Authority (VDA) and showed that a significant increase of 16% in accessibility levels could be achieved if optimal stop locations identified in this study were implemented. The proposed two-step (2SFCA+MCDM) method for identifying the optimal locations of ART stations in a multimodal transit network is expected to be an effective tool for transit system redesign using place-based accessibility measures
Optimal planning and scheduling for fast-charging electric bus system with distributed photovoltaics
No full textThe global focus on emissions reduction has grown significantly, leading to the emergence of Battery electric buses (BEBs) as a promising eco-friendly alternative to conventional buses. However, the high charging power induced by fast charging can burden the power grid and incur high demand charges. Distributed solar photovoltaics (PV) can play an important role in reducing peak load and charging costs. In this study, we address the problem of optimal planning and charging schedule for an electric bus system using fast-charging technology. We proposed a mathematical model to optimize the BEB charging schedule and PV planning such that the total cost of the system is minimized. A real bus network in Utah was adopted to validate the efficacy of the proposed models. The results demonstrated that integrating an energy storage system (ESS) and distributed PV can significantly reduce costs and increase the proportion of renewable energy used for BEB charging