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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 2, JUNE 2013 627

Emergency Management of Urban RailTransportation Based on Parallel Systems

Hairong Dong, Senior Member, IEEE, Bin Ning, Senior Member, IEEE, Yao Chen, Xubin Sun,Ding Wen, Senior Member, IEEE, Yuling Hu, and Renhai Ouyang

Abstract—Integrating artificial systems, computational exper-iments, and parallel execution (ACP) is an effective approachto modeling, simulating, and intervening real complex systems.Emergency response is an important issue in the operation ofurban rail transport systems for ensuring the safety of peopleand property. Inspired by the ACP method, this paper introducesa basic framework of parallel control and management (PCM)for emergency response of urban rail transportation systems.The proposed framework is elaborated from three interdependentaspects: Points, Lines, and Networks. Points represent the modelingof urban rail stations, Lines describe the microscopic character-istics of urban rail connections between designated stations, andNetworks present the macroscopic properties of all the urban railconnections. Based on the given framework, a series of parallelexperiments, which were impossible to achieve in real systems,can now be conducted in the constructed artificial system. Fur-thermore, the constructed artificial system can be used to test anddevelop effective emergency control and management strategiesfor real rail transport systems. Therefore, this proposed frame-work will be able to enhance the reliability, security, robustness,and maneuverability of urban rail transport systems in case of anemergency.

Index Terms—ACP method, emergency response, parallel con-trol and management (PCM), urban rail transport.

I. INTRODUCTION

THE PROSPECTIVE future of modern rail systems hasled to great investments in recent years in the research

and development of more efficient, reliable, and secure railtransport systems [15], [17]. Among all the progress made byinvolved countries, China has demonstrated amazing rapidnesson an unprecedented scale. At the beginning of the 21st century,

Manuscript received July 20, 2012; revised September 30, 2012;accepted October 19, 2012. Date of publication December 10, 2012; dateof current version May 29, 2013. This work was supported in part by theNational High Technology Research and Development Program of China underGrant 2011AA110502, by the Fundamental Research Funds for the CentralUniversities under Grant 2010JBZ004, and by the Program for New CenturyExcellent Talents in University of the Ministry of Education of China underGrant NCET-10-0216. The Associate Editor for this paper was L. Li.

H. Dong and B. Ning are with the State Key Laboratory of Rail TrafficControl and Safety, Beijing Jiaotong University, Beijing 100044, China (e-mail:[email protected]; [email protected]).

Y. Chen, X. Sun, and R. Ouyang are with the School of Electronic andInformation Engineering, Beijing Jiaotong University, Beijing 100044, China(e-mail: [email protected]; [email protected]; [email protected]).

D. Wen is with the Research Center on Computational Experiments and Par-allel Systems, National University of Defense Technology, Changsha 410073,China (e-mail: [email protected]).

Y. Hu is with the State Key Laboratory of Management and Control forComplex Systems, Institute of Automation, Chinese Academy of Sciences,Beijing 100190, China (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TITS.2012.2228260

China embarked on its high-speed railway project [8]. Thetravail of the last ten years resulted in a series of encouragingachievements: China now owns the largest 7055-km high-speed rail tracks in the world; China has developed the fa-mous “Harmony” series high-speed trains with a peak speed of487.3 km/h, creating a world record in the history of raildevelopment; urban rail transportation system in China is alsorapidly developing; and urban rail systems are operating or areunder construction in nearly 30 cities.

However, booming urban rail development is often asso-ciated with unpredictable disasters. In 2003, in Daegu City,Korea, an arsonist set a train on fire, causing a serious firethat killed 198 people dead and injured 147. In 2005, anotheraccident happened in Japan. A train operating between thesuburb and the city of Amagasaki derailed and crashed into abuilding, resulting in 107 people dead and 549 injured. In themiddle of 2011, a Chinese bullet train departing from Beijingcrashed into another train departing from Hangzhou due to asignal failure, causing 40 people deaths and 200 injuries.

Consequently, emergency response has become a criticalissue in the development of urban rail systems [12]. Emergencyresponse means the design and execution of effective strategiesto reduce casualties and property loss as much as possible incases of accidents [20]. On one hand, the rail transport systemplays an irreplaceable role in the economic development ofmost countries, bearing a large portion of passenger transport.On the other hand, the modern transport system is composed ofmillions of components with highly complicated interactionsamong them. Considering the high-speed and complex charac-teristics of modern urban rail transport systems, it is possiblethat some trivial and local accidents eventually evolve intounpredictable and global damage due to the high complexity ofthe transport systems [3]. Moreover, it is certain that the damageor collapse of an urban rail system will also impose a strongnegative impact on the national economy or policies. Therefore,designing good emergency response strategies (ERSs) for a spe-cific transport system is a very important but highly challengingtask [22], [24].

The operation of the modern urban rail system is a verycomplicated process, which typically includes transport plan-ning, train organization and dispatching, passenger flow anal-ysis, management of rail stations, transport fare management,system maintenance and evaluation, etc. [16]. For each of theseoperations, it is inevitable that human factors are involved,including both operators and passengers. It should be pointedout that the involvement of humans has apparently increasedthe uncertainty of the rail systems since the irregular behaviors

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628 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 2, JUNE 2013

of both operators and passengers are very difficult to predict.It has been demonstrated that many disastrous urban rail ac-cidents were caused by human factors, such as the fire in theDaegu Metropolitan Subway in 2003, the derailment accidentin Amagasaki in 2005, etc. Faced with these human-inducedaccidents, it is natural and important to study how to model andanalyze human factors appropriately [35]. Unfortunately, thereis no existing effective method for solving such a problem.

Based on the given description, one perceives that the designof ERSs is a central issue for the operation of rail systems inaccidental situations. Traditionally, designing an ERS is basedon the possible consequences of a specific accident, wherethe possible consequences are deduced by human expertsbased on their experience and prior knowledge [18]. Therefore,the design process itself inevitably includes the uncertaintybrought by incomplete knowledge and imprecise judgment ofthe experts. Considering this, the involvement of human factorsin the strategy design of emergency response can possiblymake the corresponding strategy unreliable and incomplete[21]. Consequently, how to design a comprehensive, scientific,and objective strategy for emergency response has become anurgent yet challenging issue in the development of modernurban rail transport systems.

The main difficulty in emergency management of urban railsystems is that the potential causes or incident scenarios ofrail accidents are impossible to repeat in real-life experiments[7], [30]. Fortunately, computerized simulation can provideus with an outlet for the evaluation of the effectiveness of aproposed emergency management strategy [19]. Nowadays, alarge number of excellent simulation software has been devel-oped and applied to the planning, design, and management ofurban rail construction. Typical platforms include VISION foranalyzing the duration of trains and capacity of lines, LOGSIMfor train scheduling and traffic control, and OpenTrack foroptimization of train scheduling. Other typical rail simulatorsinclude MEDYNA, SIMPACK, NUCARS, ADAMS/rail, etc. Itshould be mentioned that each of the aforementioned simula-tion platforms focuses on one specific aspect of the rail system,such as train dynamics, rail networks, or signaling systems.However, emergency response is a concern of every facet of therail system; hence, the construction of an emergence responseplatform should take the high complexity of the rail system intoconsideration. Furthermore, such a system should be designedto cope with the uncertainty of human factors induced byoperators and passengers. Considering these requirements, thedevelopment of such a platform is a very challenging task [10].During the past decade, many different methods have beenproposed for emergency response during disasters [23], [26],[31]–[34], but they are not very capable of coping with the highcomplexity and human factors of real systems.

Integrating artificial systems, computational experiments,and parallel execution (ACP) is a recently emerging approachto modeling, simulating, and intervening real complex sys-tems [1], [2], [4], [5], [37]. In 2004, Wang proposed a basicframework of the ACP method [2]. The implementation ofthis method can be divided into three different stages: 1) con-structing an artificial system that comes from the real system;2) experimenting on this artificial system to obtain related

Fig. 1. Framework of the proposed artificial system.

information on the potential consequences of the triggeringevents; 3) combining the obtained information with the realsystem to evaluate the real system and to make improvementson related strategies. By repeating the three steps iteratively,the plans to be executed can be optimized to realize its bestperformance, reaching multiple optimization indexes such asenergy, time, and cost savings [9]. ACP has been applied tomany real engineering projects [13], [14]. In 2011, Ning et al.introduced the method of parallel control to the managementof high-speed railway systems [9] and urban rail transportationsystems [10]. Following these existing works, the main purposeof this paper is to apply the ACP method to the emergencyresponse of urban rail transport systems.

Traditionally, the evaluation of ERSs is based on the ana-lytical hierarchy process (AHP) method [37], [38]. The basicidea of an AHP is as follows: Given an evaluation object, fac-torize this object into different subobjects. Furthermore, eachsubobject can be also factorized into smaller factors. Based onthe given factorization, a hierarchical tree can be constructed.Consequently, the evaluation of the ERS can be performed fromtop to bottom and layer by layer, making the evaluation processorganized and efficient.

The given AHP method can be improved into different ver-sions, such as the method given in [37]. However, the evaluationprocess will inevitably involve the uncertainty of the experts,which is derived from their decision inaccuracy, subjectiveprejudice, and incomplete knowledge. Furthermore, the cause-and-effect relationship between the accidental events and thefinal scenario is too complicated to be precisely predicted byhuman deduction and experience. Therefore, a question arisesas how to eliminate the benevolent human factors in the processof evaluation. Fortunately, the ACP method can provide anoptimal solution for such a problem.

This paper is organized as follows: Section II introduces thestructure of the emergency response platform for urban railtransport systems based on parallel control and management;Sections III–V elaborate upon the proposed framework sepa-rately from three different scales: Points, Lines, and Networks;and Section VI concludes this paper and points out somepotential applications of the emergency response platform.

DONG et al.: EMERGENCY MANAGEMENT OF URBAN RAIL TRANSPORTATION BASED ON PARALLEL SYSTEMS 629

TABLE ICOMPARISON OF THE ACP METHOD AND THE TRADITIONAL SIMULATION METHOD

II. FRAMEWORK OF THE PROPOSED METHOD

The basic framework of the proposed method is demon-strated in Fig. 1. As shown, the construction of the artificialsystem is divided into three interdependent aspects: Points,Lines, and Networks.

Points represent the modeling of urban rail stations. Railstations are the basic components of the rail system and, hence,play the most important role for the successful operation ofthe rail system. In the construction of the artificial system,several critical aspects of the rail station will be considered,such as the station architectural structure, the characteristics ofpassenger flow, the arrival and departure of trains, the conditionof the station equipment, etc. Based on the aforementionedemergency-related aspects, an artificial system can be virtuallyestablished. Therefore, for emergency situations in the railstation, a lot of virtual experiments can be carried out basedon the artificial system. Furthermore, the experimental resultscan be used to improve the management of the rail station or torectify the defects of the given ERS.

Lines are the abstract urban rail connections between dif-ferent stations, together with the operation of trains along therail lines. Generally speaking, the operation of trains relates tothe following subsystems: automatic train surveillance (ATS),automatic train protection (ATP), and automatic train operation(ATO) [11]. Based on the basic functions of the aforementionedsubsystems, the modeling of Lines will consider the followingrelated factors: velocity and position surveillance, excessivespeed protection, headway control, temporary velocity restric-tion, signaling control, etc. In case of emergency, the congestionof the whole network usually originates from the accident insome specific links. Hence, the modeling of Lines will illustratewhy a given rail link is disconnected and how to restore theconnectivity of this link. The aforementioned two questions arecritical for the emergency response of the rail system. Aboveall, the modeling of Lines will design the scheduling strategybetween the failed station and its adjacent normal stations, theresolution of which will provide helpful information for theevacuation of passengers in case of emergency.

Networks can be viewed as the network composed of allthe urban rail lines of the transportation system. It should bepointed out that the difference between Lines and Networks isthat Lines focus on the microscopic modeling of the rail con-nections, whereas Networks focus on macroscopic modeling.The main purpose of Networks modeling is to explain howlocal failure of some specific stations leads to the congestionor breakdown of the whole rail system. The resolution of suchan issue will help us understand the mechanism of failurespreading over networks. In detail, Networks modeling willapply the theory of complex networks, network flows, andgraph theory into the analysis of the rail system. Furthermore,based on the experiment on the artificial network, the stationsand lines of the rail system will be classified according to

their importance in the process of emergency response, givingpotential guidelines for the design of ERS.

It should be noted that the operation of the rail system isalways strongly interconnected with the behavior of humans,for example, the operation of train drivers, the dynamics ofpedestrians, and the directing of train scheduling by dispatchers[7]. In normal cases, it is not difficult to obtain the characteris-tics of human behavior. However, in emergency cases, the hu-man behavior characteristics will abruptly change, making theevolution of the rail system hard to predict. Take the behaviorof pedestrians for example. In the case of a fire, the velocityof the pedestrians will be much faster than that in the normalcase, and the density of pedestrians will be relatively higher insome regions, such as the exits. Based on this discussion, a keyproblem for emergency response is how to combine the humanfactors into the construction of the artificial system. For theaforementioned three aspects of the artificial system, the humanfactors mainly include pedestrian properties of the Points, theinteraction of train scheduling and passenger flows of the Lines,and the priori knowledge of passengers of the Networks.

By using the ACP method, an artificial system of an urbanrail system can be constructed, describing both the macroscopicand microscopic properties of a real urban transportation sys-tem. Basically, the advantages of the ACP method over thetraditional simulation method can be summarized as in Table I.

The ACP method can also provide a good solution to the ERSof urban rail systems. Based on the artificial rail system, a seriesof disastrous scenarios can be simulated beforehand. Therefore,it is also possible to integrate the ERS into the scenario simula-tion to observe the potential consequences under the conditionsof the given strategy. In this case, the evolution process of theaccidental scenario is perspicuous and, hence, can be easilyevaluated. After the execution of each computational exper-iment, the response strategy can be further improved basedon the simulation result. The process is repeated again andagain, allowing the final ERS to be optimized, catering to therequirements on the response efficiency, property safety, andexecution costs, etc.

III. FIRST MODELING ASPECT: POINTS

The rail station is the basic component of the rail transportsystem and is also the place in which most of the passengersstay. It has been demonstrated that most rail accidents originatefrom stations, and the emergency response of rail stations isquite concerned with the personal security of passengers. In theframework of the ACP method, the first step in the successfuldesign of an ERS for rail stations is the construction of anartificial system for the stations.

The constructed artificial system for stations is composed ofthe following modules: urban rail station architecture, scenelibrary, and pedestrian dynamics simulator.


Fig. 2. Planar structure of the Xuanwumen Subway Station.

A. Urban Rail Station Architecture

The architecture of the urban rail station has a close relation-ship with the design of strategies for emergency response. Theproposed artificial system contains the architecture graphs ofmany rail stations of the Beijing Subway, which is the busiestsubway network in China.

Take the Xuanwumen Subway Station, Beijing, as an exam-ple. As shown in Fig. 2, the basic components of the stationare provided, including platforms, tunnels, waiting zones, en-trances and exits, etc.

Designing the station architecture should facilitate the evacu-ation of passengers in case of emergency. However, the resolu-tion of such a problem is not easy since it is nearly impossible toconduct a real experiment on real station facilities. Luckily, theartificial system can provide an outlet for such a problem. Basedon the constructed rail station models and the related pedestriandynamics module (which will be introduced in Section III-C),evacuation experiments can be realized by using correspondingalgorithms, making it possible to assess the safety of relatedfacilities.

B. Scene Library

To cope with different accidents or disaster scenarios, itis necessary to build a corresponding scene library for theseevents. Based on the scene library, a series of computationalexperiments can be executed, generating a great amount ofuseful information for designing ERSs or improving the ex-isting strategies. Generally speaking, the disasters in the sta-tion can be categorized as follows: natural disasters such asearthquakes; operational accidents such as train crashes andfires; public hygiene accidents such as gas poisoning; and massdisturbance, which is usually caused by political reasons, suchas terrorist attacks. Among the given accidents, an operationalaccident is the most common accident and needs to be seriouslyaddressed [28].

The designed emergency response platform can providecomputational experiments for rail stations under different sce-narios, such as a fires or terrorist attacks. As shown in Fig. 3,the circular region represents an explosion area. In the case ofan explosion, the passengers located in that circle will escapefrom it as soon as possible; therefore, pedestrian dynamics will

Fig. 3. Three-dimensional scene graph of explosion.

Fig. 4. Three kinds of forces between passengers.

be vastly different from those in the normal case. In detail, thesedifferences mainly come from passengers’ velocities, densities,and rationality indexes. To describe the main property of thepassenger dynamics in the case of a fire, the platform providesan interface to adjust those human-related parameters, such asvelocity and rationality index.

C. Pedestrian Dynamics Simulator

As was pointed out in the previous section, the pedestriandynamics in an emergent scenario are quite different fromnormal. Generally speaking, the characteristics of passengerflow in the emergent case include high velocity in movement,unevenness of distribution, and irrationality in decision-making[39]–[41]. The constructed simulator for pedestrian dynamicshas taken the aforementioned characteristics into consideration.The basic idea for the construction of the simulator is to takeeach passenger as an autonomous agent and to have any twoadjacent agents interact via some specified local rules [42].Moreover, the simulator also considers the interaction betweenthe agents and the walls of the stations, making the simulationsimilar to real cases.

As shown in Fig. 4, the existing model for the pedestriandynamics includes three different local forces: repulsion, align-ment, and attraction. It should be pointed out that the repulsion


TABLE IIEVACUATION TIME OF LINE 2, BEIJING SUBWAY

TABLE IIIEVACUATION TIME OF LINE 2, BEIJING SUBWAY

region in the case of emergency is very small relative to the nor-mal case due to human survival instinct. Therefore, governedby this human psychology, the passenger density in the stationwill be quite unevenly distributed: Most of the population willcenter around the entrances or exits of the station, leaving mostof the remaining station areas vacant. However, in the normalcase, most of the passengers are distributed along the outerregion of the platforms.

Calculation of the evacuation time is an important aspect ofemergency response [36]. The artificial system provides twobasic methods for computing the evacuation time: the Chinesestandard algorithm and the U.S. standard algorithm.

The Chinese standard algorithm calculates the evacuationtime based on the following formula:

TCH =Q1 +Q2

0.9[A1b(N − 1) +A2B]+ I

where

T evacuation time in the platform, min;Q1 number of passengers in the train, which varies with the

types of trains;Q2 total number of passengers waiting for the trains and the

operators in the station in peak hours;A1 passage capacity of elevators, person/(min-m);A2 passage capacity of stairs, person/(min-m);N number of elevators;B, b width of the rung of an elevator, m;I response time of a person;0.9 practical passage capacity of stairs, where moving stair-

cases are reduced to 90%.

It is required by Chinese standard GB50157-2003 that thewidth of the ladders and the evacuation aisles should guarantee

the evacuation of all the passengers in the platform and all therelated workers within 6 min in case of a fire during peak hours.

By using the Chinese standard algorithm as an example,the evacuation time of Line 2, Beijing Subway, is given inTable II.

The evacuation time obtained by the U.S. standard algorithmcan be given by the following formula:

TUS = T +Wp +

n∑i=1

Wi

where

T walking time spent on the road to outlet, min;Wp waiting time of the outlet of the platform, min;Wi waiting time of the other moving zone, min.

The details for the calculation of the given items are omittedhere due to space restrictions. By using the U.S. standardalgorithm, the evacuation time of Line 2, Beijing, is given inTable III.

IV. SECOND MODELING ASPECT: LINES

The operation of a train from one station to another concernstwo different aspects: One is the train itself, which containsthe related control, operation, and communication systems; theother is the rail track between two stations, which contains therelated signaling and detection systems. Therefore, the artificialsystem of lines mainly focuses on the aforementioned twoaspects.

The constructed artificial system for lines has the followingtypical properties: accurate modular structure and fast genera-tion of the train diagram.


Fig. 5. Multilayer structure of the artificial system.

Fig. 6. Structure of the artificial ATP system.

A. Modular Structure

The operation of trains includes three interdependent sys-tems: ATS, ATP, and ATO. Since each of the aforementionedsystems contains a multitude of components, the modular struc-ture is beneficial for the design of the artificial system.

The artificial system of ATS has a multilayer structure withmultifunctions. As shown in Fig. 5, the software structure ofATS is composed of three layers: user layer, processing layer,and data layer. The user layer processes the interfaces forhuman–machine interaction. The processing layer integratesvarious logic rules and equipment principles, receiving thecommands from the user layer, whereas the data layer stores theinformation obtained by the processing layer and also providesrelated information for the processing layer when necessary.

The ATP system focuses on security-related tasks, such asposition and velocity detection, velocity surveillance, excessspeed protection, control of trans–trains distance, control ofdoors, accident recording, etc.

Based on the basic function of ATP, the artificial ATP systemis designed with the structure as shown in Fig. 6, which isdivided into the following major modules:

1) Train and environment. This module is used to initializethe related states of trains and lines.

Fig. 7. Different surveillance curves of the artificial system.

2) Trajectory computation of trains. This module calculatesthe positions and velocities of trains to provide data forexcessive speed protection.

3) Excessive speed protection. This module provides a real-time protection curve based on the information of trainsand lines.

4) Emergency braking. This module provides the strategy ofemergency braking based on the braking model and theconditions of trains and lines.

5) Information recording and analysis. This module inte-grates and analyzes the information of the aforemen-tioned modules.

According to the description of the aforementioned modules,the artificial system centers around the protection of trains inthe case of overspeed. In fact, there are many requirements foroverspeed protection of trains, i.e., it is forbidden to exceed themaximal secure velocities of the train, the rail tracks, and thecurrent operational mode. The artificial system integrates threedifferent surveillance curves. The principle of these curves isshown in Fig. 7.

B. Fast Generation of Train Diagram

A train diagram is an auxiliary method for the description ofthe schedules of trains. Specifically, the generation of a traindiagram has several advantages in emergency situations, forexample, transparency, by which it is easy to obtain the relatedoperational information from the diagram; maneuverability, bywhich the reschedule design of trains is relatively easy by usingthe diagram; and detectability, by which the design errors andfaults can be easily detected from the diagram.

Generally, the real numerical data of train operation are notbeneficial for the intuitive analysis of the train schedule. Theartificial system provides a method for quick transformation ofthe numerical data into a visible diagram, which is given inFig. 8. According to Fig. 8, one can easily get the operation


Fig. 8. Train diagram.

information on the uplink and downlink of Line 5, BeijingSubway.

In fact, the design of the train schedule has a close rela-tionship with the operational safety of trains. During the pastdecade, many mathematical models have been proposed todesign the timetable of metrolines. It should be noted that op-erational safety is an important aspect in such a design process.In general, the operational safety of the trains is quantized byseveral factors, such as the train headway, the highest velocity,the braking distance, etc. Therefore, the involvement of thesesafety factors in the design of timetables is quite helpful foraccident avoidance in rail systems.

V. THIRD MODELING ASPECT: NETWORKS

Networks represent the topology formed by all the railconnections, which is also the topmost structure of the urbanrail system. According to the extensive data of rail accidents,many of them are caused by Networks-level reasons, such asincorrect scheduling of trains, unpredicted rail congestion, etc.However, most of the Networks-level reasons are indeed humaninduced, which can be avoided by serious preplanning and strictorganization. This section mainly introduces some Networks-level modeling of the rail system, providing possible solutionsto avoid the Networks-level accidents.

The constructed artificial system for networks is concernedwith the following aspects: rescheduling of trains, searchingfor the shortest path in cases of emergency, and the impact ofpassengers’ prior knowledge on the choice of lines.

A. Search of Candidate Paths

In normal cases, a major objective of rail operation is tooptimize the passenger flow of the rail network. However, inan emergency situation, the abnormal flow in one station orrail link may lead to the breakdown of the whole network.Therefore, it is necessary to reschedule the trains to meet theneeds of emergency response. The framework for the design ofNetworks is shown in Fig. 9.

As shown in Fig. 9, the core component of this frameworkis the algorithm library, which is composed of four classes

of algorithms. These are the generation algorithm of avail-able pathways, the matching algorithm between passengersand trains, the adjustment algorithm of programs in case ofemergency, and the optimization algorithm of a train diagram.

The passengers always make their decisions based on someintuitive criteria, such as the minimum number of transfers, theshortest riding time, or the shortest distance from the startingpoint to the destination. Therefore, to model the passenger flowof the rail network, the artificial rail system should providedifferent generation strategies based on these human-inducedcriteria. As shown in Fig. 10, the developed artificial systemprovides the transfer strategies of the Beijing Subway based onthe criteria of shortest path and minimal transfers.

In case of an emergency, the preference and choices ofpassengers will be abruptly changed. Qualitatively speaking,passengers will become more irrational in choosing candidatepaths, making the traditional algorithm ineffective. Based onthis fact, it is necessary to quantitate the emotional factors inboth normal and abnormal cases.

B. Impact of Passengers’ Prior Knowledge

Faced with different candidate pathways, passengers do notmake their choices randomly; the choice of the final pathis usually dependent on prior knowledge of the passengers.Consequently, an interesting question arises: How great is theprobability that the passenger will choose a given candidatepath.

It should be noted that the choice of paths is a very sub-jective issue, and different passengers usually have differentrequirements. To evaluate the different requirements of thepassengers, a new concept named generalized expenditure isusually considered in the mathematical modeling of passengerdistribution. By generalized expenditure, we mean the total costfrom one station to another, including traveling time, comfortlevel, transfer times, etc.

A logit model is a famous mathematical model for the cal-culation of the choosing probability among several given paths.Let ωf be the weight of passenger familiarity on a specific path,which is defined as

θ = f(ωf ) = tan

(πωf

2ωfmax

), ωf ∈ [0, ωfmax

].

According to the logit model, the distribution ratio of thecandidate paths is given as follows:

Pwm =

exp {θf (Twm/Tw

min)}∑n exp {θf (Tw

m/Twmin)}

where the denominator is the sum over all the n candidatepaths, Pw

m denotes the choosing probability of the mth path,Twm represents the time spent on the candidate path m, and

Twmin represents the minimal travel time of the candidate paths.

It is easy to verify that when ωf → ωfmax, there is Pw

m →1; when ωf → 0, there is Pw

m → 1/n. Based on the givencalculation, the choosing probability of the four candidate pathsin the condition of different familiarity indexes is shown inFig. 11.


Fig. 9. Framework for the designing of Networks.

Fig. 10. Transfer strategies provided by the artificial system.

VI. CONCLUSION

This paper has proposed an ACP framework for emergencyresponse of urban rail systems. The proposed framework iscomposed of three parts: Points, Lines, and Networks, repre-senting urban rail stations, railtrack-related facilities, and railnetworks, respectively.

For each of the three components, related emergency casesare correspondingly discussed. Points concentrate on the mod-eling of accidental or disastrous events, such as a fire, a terroristattack, and a public hygiene problem. Lines focus on the model-ing of events such as rail congestion, trains crashing, and signal-ing failure. Networks focus on network-induced accidents, suchas train rescheduling, large-scale passenger evacuation, and thespread of an epidemic.

Since all of the three components have been integrated intothe artificial system, every aspect of the accident scenario canbe simulated on both macroscopic and microscopic scales. As

Fig. 11. Choosing probability varies with familiarity.

a result, computing experiments on the artificial system canprovide accurate and guiding information for the consequenceof a given accident, which is impossible to implement in thetraditional simulation platform of rail systems.

Based on the constructed artificial system, the ERS can beevaluated and improved via repeatedly computational experi-ments and by integrating the modified strategies, making theemergency response fast, easy, and efficient.

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Hairong Dong (SM’12) received the B.S. and M.S.degrees in automatic control and basic mathematicsfrom Zhengzhou University, Zhengzhou, China, in1996 and 1999, respectively, and the Ph.D. degreein general and fundamental mechanics from PekingUniversity, Beijing, China, in 2002.

She is currently a Professor with the State KeyLaboratory of Rail Traffic Control and Safety,Beijing Jiaotong University. She was a Visit-ing Scholar with the University of Southampton,Southampton, U.K., in 2006; The University of

Hong Kong, Pokfulam, Hong Kong, in 2008; the City University ofHong Kong, Kowloon, Hong Kong, in 2009; The Hong Kong PolytechnicUniversity, Kowloon, in 2010; and the KTH Royal Institute of Technology,Stockholm, Sweden, in 2011. In 2007, she served as a Project Level-3 Expertwith the Department of Transportation for the Beijing Organizing Committee ofthe Olympic Games. Her research interests include stability and robustness ofcomplex systems control theory, intelligent transportation systems, automatictrain operation, and parallel control and management for high-speed railwaysystems.


Bin Ning (SM’12) received the B.S., M.S., andPh.D. degrees from Beijing Jiaotong University,Beijing, China.

From 1991 to 1992, he was a Visiting Scholar withBrunel University, London, U.K., and, from 2002 to2003, with the University of California, Berkeley.He is currently a Professor with the State Key Lab-oratory of Rail Traffic Control and Safety, BeijingJiaotong University, and is also the President ofBeijing Jiaotong University. He has directed manykey national scientific projects in China. His sci-

entific interests include intelligent transportation systems, communication-based train control, rail transport systems, system fault-tolerant design, faultdiagnosis, system reliability, and safety studies.

Dr. Ning is a Fellow of the Institution of Railway Signal Engineers andthe Institution of Engineering Technology (IET), a Senior Member of theChina Railway Society, and a member of the Western Returned ScholarsAssociation. He is the Chair of the Technical Committee on Railroad Systemsand Applications of the IEEE Intelligent Transportation Systems Society.

Yao Chen received the B.S. degree in mathemat-ics from China Three Gorges University, Yichang,China, in 2007 and the Ph.D. degree from theAcademy of Mathematics and Systems Science, Chi-nese Academy of Sciences, Beijing, China, in 2012.

From November 2009 to March 2010, he was aResearch Assistant with the Department of Math-ematics, City University of Hong Kong. FromNovember 2010 to November 2011, he was a Re-search Assistant with the School of Electrical andComputer Engineering, Royal Melbourne Institute of

Technology, Melbourne, Australia. He is currently a Postdoctoral Fellow withthe School of Electronic and Information Engineering, Beijing Jiaotong Univer-sity. His current research interests include complex networks and applications,emergent behavior of multiagent systems, and rail traffic control.

Xubin Sun received the B.S. degree in electricalengineering and automation from Beijing JiaotongUniversity, Beijing, China, in 2002 and the Ph.D.degree in control theory and control engineeringfrom the Institute of Automation, Chinese Academyof Sciences, Beijing, in 2007.

He is currently a Lecturer with the School of Elec-tronic and Information Engineering, Beijing JiaotongUniversity. His research interests include parallelcontrol and management of urban rail transit andhigh-speed railway systems, emergency response op-

timization, and stochastic control.

Ding Wen (SM’99) is a Professor with the National University of DefenseTechnology, Changsha, China, where he is also a Senior Advisor with the Re-search Center for Computational Experiments and Parallel Systems. His mainresearch interests include behavioral operation management, human resourcemanagement, management information systems, and intelligent systems. Hehas extensively published and received numerous awards for his work in theseareas.

Yuling Hu received the B.S. degree in electrical en-gineering and the M.S. degree in pattern recognitionand intelligent systems from Beijing University ofTechnology, Beijing, China, in 1996 and 2004, re-spectively. She is currently working toward the Ph.D.degree with the State Key Laboratory of Manage-ment and Control for Complex Systems, Institute ofAutomation, Chinese Academy of Sciences, Beijing.

She is currently an Associate Professor withBeijing University of Civil Engineering and Archi-tecture. Her research interests include evacuation

strategies in high-rise building fires and emergency management.

Renhai Ouyang received the B.S. degree in 2011from the School of Electronic and Information Engi-neering, Beijing Jiaotong University, Beijing, China,where he is currently working toward the Ph.D.degree with the School of Electronic and InformationEngineering.

His current research interests include complexnetworks and applications, emergency response forurban rail transit networks, and parallel control andmanagement of urban rail transit.