Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces

Multi-Step Subway Passenger Flow Prediction under Large Events Using Website Data

An accurate and reliable forecasting method of the subway passenger flow provides the operators with more valuable reference to make decisions, especially in reducing energy consumption and controlling potential risks. However, due to the non-recurrence and inconsistency of large events (such as sports games, concerts or urban marathons), predicting passenger flow under large events has become a very challenging task. This paper proposes a method for extracting event-related information from websites and constructing a multi-step station-level passenger flow prediction model called DeepSPE (Deep Learning for Subway Passenger Flow Forecasting under Events). Experiments on the actual data set of the Beijing subway prove the superiority of the model and the effectiveness of website data in subway passenger flow forecasting under events

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network

The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA) method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks

Safety of Lithium Nickel Cobalt Aluminum Oxide Battery Packs in Transit Bus Applications

The future of mass transportation is clearly moving toward the increased efficiency and greenhouse gas reduction of hybrid and electric vehicles. With the introduction of high-power/high-energy storage devices such as lithium ion battery systems serving as a key element in the system, valid safety and security concerns emerge. This is especially true when the attractive high-specific-energy and power-chemistry lithium nickel cobalt aluminum oxide (NCA) is used. This chemistry provides great performance but presents a safety and security risk when used in large quantities, such as for a large passenger bus. If triggered, the cell can completely fuel its own fire, and this triggering event occurs more easily than one may think. To assist engineers and technicians in this transfer from the use of primarily fossil fuels to battery energy storage on passenger buses, the Battery Application Technology Testing and Energy Research Laboratory (BATTERY) of the Thomas D. Larson Pennsylvania Transportation Institute (LTI) in the College of Engineering at The Pennsylvania State University partnered with advanced chemistry battery and material manufacturers to study the safety concerns of an NCA battery chemistry for use in transit buses. The research team ran various experiments on cells and modules, studying rarely considered thermal events or venting events. Special considerations were made to gather supporting information to help better understand what happens, and most importantly how to best mitigate these events and/or manage them when they occur on a passenger bus. The research team found that the greatest safety concern when using such a high-energy chemistry is ensuring passenger safety when a cell’s electrolyte boils and causes the ventilation of high-temperature toxic material. A cell-venting event can be triggered by a variety of scenarios with differing levels of likelihood. Also, though the duration of a venting event is relatively short, on the order of just a few seconds, the temperature of the venting material and cell is extremely high. During a venting event, the high-pressure, burning gases tend to burn holes in nearby packaging materials. Most interestingly, the team discovered that following a venting event the large-format cells tested immediately reached and remained at extremely high external skin temperatures for very long periods, on the order of hours. The majority of this report covers the testing designed to better understand how high-energy cells of this chemistry fail and what materials can be used to manage these failures in a way that increases passenger survivability

Systemic risk approach to mitigate delay cascading in railway networks

In public railway systems, minor disruptions can trigger cascading events that lead to delays in the entire system. Typically, delays originate and propagate because the equipment is blocking ways, operational units are unavailable, or at the wrong place at the needed time. The specific understanding of the origins and processes involved in delay-spreading is still a challenge, even though large-scale simulations of national railway systems are becoming available on a highly detailed scale. Without this understanding, efficient management of delay propagation, a growing concern in some Western countries, will remain impossible. Here, we present a systemic risk-based approach to manage daily delay cascading on national scales. We compute the {\em systemic impact} of every train as the maximum of all delays it could possibly cause due to its interactions with other trains, infrastructure, and operational units. To compute it, we design an effective impact network where nodes are train services and links represent interactions that could cause delays. Our results are not only consistent with highly detailed and computationally intensive agent-based railway simulations but also allow us to pinpoint and identify the causes of delay cascades in detail. The systemic approach reveals structural weaknesses in railway systems whenever shared resources are involved. We use the systemic impact to optimally allocate additional shared resources to the system to reduce delays with minimal costs and effort. The method offers a practical and intuitive solution for delay management by optimizing the effective impact network through the introduction of new cheap local train services.Comment: 27 pages, 14 figure

Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data

Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods