Understanding the Resilience of Urban Rail Transit:Concepts,Reviews,and Trends

As the scale of urban rail transit(URT)networks expands,the study of URT resilience is essential for safe and efficient operations.This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research.First,URT resilience is defined by three primary abilities:absorption,resistance,and recovery,and four properties:robustness,vulnerability,rapidity,and redundancy.Then,the metrics and assessment approaches for URT resilience were summarized.The metrics are divided into three categories:topology-based,characteristic-based,and performance-based,and the assessment methods are divided into four categories:topological,simulation,optimization,and datadriven.Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods,such as conventional complex network analysis and operations optimization theory,with new techniques like big data and intelligent computing technology,to accurately assess URT resilience.Finally,five potential trends and directions for future research were identified:analyzing resilience based on multisource data,optimizing train diagram in multiple scenarios,accurate response to passenger demand through new technologies,coupling and optimizing passenger and traffic flows,and optimal line design.

Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Continuum modeling for two-lane traffic flow

In this paper, we study the continuum modeling of traffic dynamics for two-lane freeways. A new dynamics model is proposed, which contains the speed gradient-based momentum equations derived from a car-following theory suited to two-lane traffic flow. The conditions for securing the linear stability of the new model are presented. Numerical tests are can＇ied out and some nonequilibrium phenomena are observed, such as small disturbance instability, stop-and-go waves, local clusters and phase transition.

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.

Toward equitable,transparent,and collaborative human mobility computing for smart cities

Cities constitute vast and intricate systems in which diverse entities(e.g.,people,vehicles,and roads)interact collaboratively and dynamically.Analyzing and understanding the core elements of people in complex urban mobility systems provides a crucial means for smart city applications.In the era of global digitization,the exponential surge in geolocation data linked to human travel has profoundly transformed our understanding of human travel behavior.Data science enables us to comprehensively capture human mobility characteristics1 at the individual and population levels;these characteristics include regularity,diversity,and predictability.Empowered by data science,human mobility computing research has shaped a closed-loop scientific ecosystem involving data training models,model serving applications,and application feedback data(Figure 1).

大数据与人工智能时代下复杂系统管理研究的若干关键科学问题

当前,世界正在进入大数据与人工智能时代,复杂系统已呈现出人—机—网跨尺度耦合与互融互通的新形态。新形态下的复杂系统管理,既要对传统管理理论和方法进行突破创新,更要充分借助先进的技术手段对其进行赋能。本文面向大数据和人工智能时代下复杂系统管理的国家重大需求,阐述了当前复杂系统管理研究存在的重大机遇与挑战,分析了当前复杂系统管理研究的研究现状并指出了未来的发展趋势,在此基础上,进一步凝练了未来5~10年该领域的发展目标、拟开展的重大关键科学问题及其资助重点。

Future urban transport management

The incorporation of disruptive innovations into the transportation industry will inevitably cause major upheavals in the transportation sector.However,existing research lacks systematic theories and methodologies to represent the underlying characteristics of future urban transport systems.Furthermore,emerging modes in urban mobility have not been sufficiently studied.The National Natural Science Foundation of China(NSFC)officially approved the Basic Science Center project titled“Future Urban Transport Management”in 2022.The project members include leading scientists and engineers from Beijing Jiaotong University,Beihang University,and Beijing Transport Institute.Based on a wide range of previous projects by the consortium on urban mobility and sustainable cities,this project will encompass transdisciplinary and interdisciplinary research to explore critical issues affecting future urban traffic management.It aims to develop fundamental theories and methods based on social and technological developments in the near future and explores innovative solutions to implement alongside these emerging developments in urban mobility.

Coupling analysis of passenger and train flows for a large-scale urban rail transit system

Coupling analysis of passenger and train flows is an important approach in evaluating and optimizing the operation efficiency of large-scale urban rail transit(URT)systems.This study proposes a passenger–train interaction simulation approach to determine the coupling relationship between passenger and train flows.On the bases of time-varying origin–destination demand,train timetable,and network topology,the proposed approach can restore passenger behaviors in URT systems.Upstream priority,queuing process with first-in-first-serve principle,and capacity constraints are considered in the proposed simulation mechanism.This approach can also obtain each passenger’s complete travel chain,which can be used to analyze(including but not limited to)various indicators discussed in this research to effectively support train schedule optimization and capacity evaluation for urban rail managers.Lastly,the proposed model and its potential application are demonstrated via numerical experiments using real-world data from the Beijing URT system(i.e.,rail network with the world’s highest passenger ridership).

URBAN TRANSIT ASSIGNMENT MODEL BASED ON AUGMENTED NETWORK WITH IN-VEHICLE CONGESTION AND TRANSFER CONGESTION

This paper presents an augmented network model to represent urban transit system.Through such network model,the urban transit assignment problem can be easily modeled like a generalized traffic network.Simultaneously,the feasible route in such augmented transit network is then defined in accordance with the passengers＇ behaviors.The passengers＇ travel costs including walking time,waiting time,in-vehicle time and transfer time are formulated while the congestions at stations and the congestions in transit vehicles are all taken into account.On the base of these,an equilibrium model for urban transit assignment problem is presented and an improved shortest path method based algorithm is also proposed to solve it.Finally,a numerical example is provided to illustrate our approach.

Energy-saving operation approaches for urban rail transit systems

With the accelerated urbanization in China, passenger demand has dramatically increased in large cities, and traffic congestion has become serious in recent years. Developing public urban rail transit systems is an indispensable approach to overcome these problems. However, the high energy consumption of daily operations is an emerging issue due to increased rail transit networks and passenger demands. Thus, reducing the energy consumption and operational cost by using advanced optimization methodologies is an urgent task for operation managers. This work systematically introduces energysaving approaches for urban rail transit systems in three aspects, namely, train speed profile optimization, utilization of regenerative energy, and integrated optimization of train timetable and speed profile. Future research directions in this field are also proposed to meet increasing passenger demands and network?based urban rail transit systems.

Robust train speed trajectory optimization: A stochastic constrained shortest path approach

Train speed trajectory optimization is a significant issue in railway traffic systems, and it plays a key role in determining energy consumption and travel time of trains. Due to the complexity of real-world operational environments, a variety of factors can lead to the uncertainty in energy-consumption. To appropriately characterize the uncertainties and generate a robust speed trajectory, this study specifically proposes distance-speed networks over the inter-station and treats the uncertainty with respect to energy consumption as discrete samplebased random variables with correlation. The problem of interest is formulated as a stochastic constrained shortest path problem with travel time threshold constraints in which the expected total energy consumption is treated as the evaluation index. To generate an approximate optimal solution, a Lagrangian relaxation algorithm combined with dynamic programming algorithm is proposed to solve the optimal solutions. Numerical examples are implemented and analyzed to demonstrate the performance of proposed approaches.

MODELING THE CONGESTION COST AND VEHICLE EMISSION WITHIN MULTIMODAL TRAFFIC NETWORK UNDER THE CONDITION OF EQUILIBRIUM

Traditional system optimization models for traffic network focus on the treatment of congestion, which usually have an objective of minimizing the total travel time. However, the negative externality of congestion, such as environment pollution, is neglected in most cases. Such models fall short in taking Greenhouse Gas （GHG） emissions and its impact on climate change into consideration. In this paper, a social-cost based system optimization （SO） model is proposed for the multimodal traffic network considering both traffic congestion and corresponding vehicle emission. Firstly, a variation inequality model is developed to formulate the equilibrium problem for such network based on the analysis of travelers＇ combined choices. Secondly, the computational models of traffic congestion and vehicle emission of whole multimodal network are proposed based on the equilibrium link-flows and the corresponding travel times. A bi-level programming model, in which the social-cost based SO model is treated as the upper-level problem and the combined equilibrium model is processed as the lower-level problem, is then presented with its solution algorithm. Finally, the proposed models are illustrated through a simple numerical example. The study results confirm and support the idea of giving the priority to the development of urban public transport, which is an effective way to achieve a sustainable urban transportation.

Percolation-based health management of complex traffic systems

System health management,which aims to ensure the safe and efficient operation of systems by reducing uncertain risks and cascading failures during their lifetime,is proposed for complex transportation systems and other critical infrastructures,especially under the background of the New Infrastructure Projects launched in China.Previous studies proposed numerous approaches to evaluate or improve traffic reliability or efficiency.Nevertheless,most existing studies neglected the core failure mechanism(i.e.,spatio-temporal propagation of traffic congestion).In this article,we review existing studies on traffic reliability management and propose a health management framework covering the entire traffic congestion lifetime,from emergence,evolution to dissipation,based on the study of core failure modes with percolation theory.Aiming to be"reliable,invulnerable,resilient,potential,and active",our proposed traffic health management framework includes modeling,evaluation,diagnosis,and improvement.Our proposed framework may shed light on traffic management for megacities and urban agglomerations around the world.This new approach may offer innovative insights for systems science and engineering in fiiture intelligent infrastructure management.

Analyzing and evaluating the three-line rail traffic

In this paper, we propose a method to simulate the three-line rail traffic. The aim is to evaluate the carrying capacity of the three-line rail traffic by studying the rail traffic flow when the passenger flow is unsymmetrical. The simulation results demonstrate that under the unsymmetrical condition, the three-line rail traffic system has almost the same carrying capacity as that of a four-line rail traffic system. Compared with the four-line rail traffic system, the three-line rail traffic system has better utilization of rail line. As a result, building the three-line rail traffic system is a more economical and rational selection.

Special issue:Reliability management of complex system

Nowadays,reliability is moving toward interdisciplinary research with ever-increasing connotations for full life-cycle system management,including system design,analysis,modeling,test,operation,optimization,etc.Meanwhile,complex systems,such as transportation system,power system,communication system and other various critical infrastructure systems,have posed a big challenge,which attracts great attention both in theory and application.Characterized by nonlin ear interaction,emerge nt response,and high dime nsional coupling,the complex systems are in the face of extremely high uncertainty and vulnerability.

Special issue: Management of road and railway traffic and transportation engineering

In recent years,with the development of road and railway transportation industries,a variety of complicated decisionmaking problems have emerged in real-world applications.It is urgent to analyze these problems from the perspective of theoretical and methodological innovations,and provide methods in management,decision-making and application so as to achieve efficient operations of traffic and transportation systems.These problems have

Implementation of a flexible Major Vacation Toll-free Program for small passenger vehicles in China

On July 24,2012,the State Council of the People’s Republic of China implemented'a toll-free program for small passenger vehicles during major vacations1).In the several months that followed,local governments issued implementation articles.The current Major Vacation Tollfree Program for small passenger vehicles(MVTP)pioneers the concept(Xu and Gao,2016),'Benefaction to People.'The current MVTP has received