System architecture of a decision support system for freeway incident management in Republic of...

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Transportation Research Part A 42 (2008) 799–810

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System architecture of a decision support system forfreeway incident management in Republic of Korea

S. Akhtar Ali Shah a,*, Hojung Kim a, Seungkirl Baek b,Hyunho Chang b, Byung Ha Ahn a

a System Integration Laboratory, Department of Mechatronics, Gwangju Institute of Science and Technology,

Gwangju 500-712, Republic of Koreab Highway and Transportation Technical Institute, Korea Highway Corporation, Republic of Korea

Received 31 March 2005; accepted 1 January 2008

Abstract

Traffic Management Centers play a vital role in efficient functioning of freeway network in the post-incident scenario.As per present practice in Korea, the traffic managers use a heuristic approach for incident analysis based on their expe-rience of similar scenarios. However, this approach induces uncertainty thereby reducing the overall effectiveness of thesubsequent incident management and rescue operations.

This paper proposes a decision support system to account for these shortcomings. We name our system as ‘FIAS’ –Freeway Incident Analysis System. The novel idea presented in this paper is the use of historical, real-time and spatial datasimultaneously to forecast post-incident traffic flows on a microscopic simulation platform, Cellular Automata. FIASincorporate two additional rules in the conventional model to depict more realistic incident flow characteristics. This paperfocuses on the system architecture of the model and tests its performance by comparing its predicted values with a realincident data. The evaluation results confirm the validity of FIAS as it can model the time dependent microstructure oftraffic flows with significant accuracy.� 2008 Elsevier Ltd. All rights reserved.

Keywords: Architecture; Incident; Freeway; Traffic management center; Micro simulation; Non-recurrent congestion; Intelligent trans-port

1. Introduction

Traffic congestion in Republic of Korea is a serious threat to efficient functioning of the road infrastructure.The cost of congestion is estimated about 4.4% of her GDP that amounts to US$37 billion (OECD, 2001).Considering the gross severity of the gridlock problem on freeways and in the perspective of higher traffic

0965-8564/$ - see front matter � 2008 Elsevier Ltd. All rights reserved.

doi:10.1016/j.tra.2008.01.024

* Corresponding author. Tel.: +82 62 970 2426; fax: +82 62 970 2384.E-mail addresses: [email protected] (S. Akhtar Ali Shah), [email protected] (H. Kim), [email protected] (S. Baek),

[email protected] (H. Chang), [email protected] (B.H. Ahn).

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800 S. Akhtar Ali Shah et al. / Transportation Research Part A 42 (2008) 799–810

growth in near future, Korea Highway Corporation has been taking a number of initiatives ranging fromphysical infrastructure expansion to demand management. It plans to extend 2610 km of existing freewayswith 22 routes to 3400 km by the end of 2006 that will eventually be expended to 6160 km freeway networkin a grid pattern with nine arterial expressways running east to west and seven running north to south by 2020(KHC, a).

In parallel with this supply-side approach, Korean government has also been promoting Intelligent Trans-port System (ITS) throughout the country and particularly at all major road networks to make traffic mobilitymore efficient and safer. This study is also a part of a similar initiative to optimize the working of Traffic Man-agement Center (TMC) which plays a vital role in the effective execution of ITS.

In this study, a tool named as ‘FIAS’ – Freeway Incident Analysis System is developed that has the missionof assisting the traffic manager working in TMC with the sitting Freeway Traffic Management System(FTMS). FIAS can simulate post-incident traffic flows based on historical data supplemented with the real-time data from Toll Collection System (TCS) and Vehicle Detection System (VDS) and spatial data. We havesuggestively discussed the feasibility aspect of FIAS along with its ability to forecast the inflow traffic on thefreeway network elsewhere (Kim et al., 2005). This paper, however, is a comprehensive sketch of the overallsystem architecture including specification of requirement and test the versatility of the model vis-a-vis long-term incident scenario that was not considered in the previous work.

Traffic mangers at the Traffic Management Center (TMC) are key stakeholder and their role has been dulyacknowledged (Ritchie, 1990; Zhang and Ritchie, 1994; Hu et al., 2003). Another unique feature of this modelis the incorporation of their knowledge at problem investigation stage. This adoption of pluralistic approach isimperative for conceiving the problem and winning the confidence of its potential users.

This brief prologue will be followed with the definition and investigation of the problem from users’ per-spective. In the next section, an improved version of an already presented (Kim et al., 2004) system architec-ture of the model will be described. It will be followed with the evaluation of the incident that needs longertime for clearance. Experimental results conclusions and discussion will be followed in the later sections.

2. Traffic incident

Although traffic incident embraces a range of activities, planned or unplanned, which restrict the free move-ment of traffic (Ozbay and Kachroo, 1999), this paper adopts only accident as a working definition of incident.In the post-incident scenario free-flow restoration is an important task and involves a series of typical stepsthat are elaborately discussed by Nathenail and Zografos (1995). Based on the time required to remove theobstruction, the incident has been categorized as short-term and long-term incidents. An incident with clear-ance time as 30 min or below is defined here, as short-term incident while the incident requiring more than 30minutes shall be regarded as long-term incident. For model validation, this paper considers only long-termincident.

3. The problem investigation

3.1. Literature review

Uncertainties in the incident analysis results in inefficient traffic management and use of rescue resourcesand need to be addressed (Hu et al., 2003; Ivan and Chen, 1996; Zhang and Ritchie, 1994). The traffic man-agers at TMC generally employ a heuristic approach based on their individual experience that may vary fromincident-to-incident. Some of the disadvantages of this approach are outlined as below (Hu et al., 2003):

(a) Complex spatio-temporal distribution of traffic conditions limits the scope of individual’s experience tocope with all eventualities.

(b) Inconsistency as the response depends on non-deterministic parameters like type, diversity and time ofthe incident.

(c) Inefficiency of the overall management and rescue infrastructure.

S. Akhtar Ali Shah et al. / Transportation Research Part A 42 (2008) 799–810 801

Besides this conventional practice does not consider traffic flow information like origin and destination,which is readily available in countries, like Korea, with closed ended freeway network and limited numberof stakeholders’ involvement in the rescue efforts. The present incident impact prediction tools are basedon real-time data (Zhang and Ritchie, 1994) and historical data (Heygi et al., 2000). Some of the suggestedsystem focused on distributed architecture of the agents, their communications and decision-making charac-teristics (Flippo and Ritchie, 2002). Some researcher employed GIS in Houston traffic management projectbut it was not proved efficient in tackling real-time traffic management system (Ozbay and Kachroo, 1999).

3.2. User participation

An assessment survey on existing management system was conducted to help the traffic manager to react ina consistent and controlled way to the non-recurrent traffic congestion. The primary stakeholders were visitedat TMC and informally interviewed on the basis of an open ended questionnaire regarding their problems,needs, jobs, existing system of incident detection, verification and restoration. Besides they were also inquiredabout degree of their satisfaction in tackling the incident related gridlocks and their perception of a helpingtool in the post-incident scenario. Their responses can be attributable as

(i) The existing detection and verification procedure based on VDS speed maps, drivers’ and CCTV surveil-lance cameras are significantly useful.

(ii) It has been difficult to forecast the clearance time in some ‘unfamiliar’ incident scenario.(iii) In post-incident scenario it entails a vigilant tool for the analysis of the situation in least time and with a

satisfactory degree of correction.(iv) The working of TMC can be improved if the managers are timely provided with reliable predictions

about the impact of an incident in some tangible format.

4. System concept

The framework of ‘FIAS’ is based on four basic modules that include data accessing, manipulation, sim-ulation and output module (Fig. 1). The data-accessing module consists of the existing FTMS components,i.e., TCS and VDS data servers. The historical and real-time data are fused and filtered in this module. It alsotackles the geographical data obtained from a database prepared in Geographic Information System (GIS)software for the spatial analysis and the incident data that the operator injects at the simulation stage.

Fig. 1. Conceptual framework of FIAS.


Data manipulation module is an enabling entity that duly formats the warehouse data for the simulation.Simulation module, being the heart of the system, has autonomous but cooperative components designed tohandle the operational requirement of the specified system. At this stage the handler encapsulates the function-alities with regard to initial traffic assignment, incident generation, Cellular Automata (CA) based microscopicsimulation kernel and computation of Measures of Effectiveness (MOE). CA kernel delivers driver behaviorwhereas the analysis of TCS data reveals trip trends, which are amalgamated to generate the logistic via outputmodule that display the predicted traffic flow information. Additionally, the output module has incident pro-gress monitoring capability with 2D/3D animation features. Fig. 1 illustrates the schematic diagram of theframework.

5. System architecture of FIAS

The indispensable part of modeling is to establish a logical information exchange between the user andthe system besides in vitro processing and simulation methods that takes place within the system. Systemarchitecture constitutes interactions amongst various components of the system for achieving predefineduser objective. The two constituting elements of system architecture of FIAS are described in greater depthbelow.

5.1. Logical architecture

The logical architecture of FIAS is primarily considered as a ‘specification of requirement’ upon which theworking model of deployment guide exists. The three fundamental units of the architecture includes trafficinformation checking post, simulation core and display block; characterizing data flow diagram at variousstages of the system and the tasks performed by the TMC manager at each stage (Fig. 2).

As the incident type, location, severity and timing are uncertain; it is imperative to associate discretionaryfunctions in the architecture along with the obligatory functions. These optional functions are incorporated atall stages of the logical architecture.

For example, at the initial stage, additional information about traffic flows like volume, average speedalong with information about tollgate, VDS identification and time need to be added for a greater degreeof precision in analysis. Similarly historical traffic patterns can also be separated from the real-time data atthe time of analysis. Likewise, default simulation time and animation parameters deserve suppleness at themanager’s end. At the final stage again managers have the interactive option regarding the output of the sim-ulation. They can interact to obtain predicted freeway speed maps, 2D/3D animation or additional parametersthat may be presented in graphical and tabular format.

5.2. Physical architecture

Based on specification of requirement, information flow mapping and logical modeling, the physical archi-tecture of FIAS is developed as illustrated in Fig. 3. It supports a range of evaluation conditions and effectu-ation strategies in terms of standardized framework of the logical architecture. The architecture generatesinformation in a format that is more tangible and can ascertain the unknown impacts of an incident. The highlevel structure around the three steps defined in the logical architecture is realized in four main shaping com-ponents. These system components are regarded as ‘subsystems’ and are described below.

Subsystem 1: This is the data warehouse that includes the traffic and network data source as the basis of thearchitecture. Here the real-time traffic data is collected at various independent servers periodi-cally that includes: (i) flow data gathered at VDS stations (ii) TCS data and (iii) freeway networkinformation. Besides, it also includes information regarding geometry of the freeway and otherimportant geographical features, such as, location of interchanges, service areas, VDS, CCTVcameras, emergency services and tollgates. This spatial information is incorporated using theGIS database. This data is a part of existing FTMS and is used for a variety of independentfunctions such as, speed mapping, volume and travel pattern studies.

Fig. 2. The logical architecture of FIAS.


Subsystem 2: This maps the data manipulation unit with two building blocks that include FTMS databasemodule and simulated network generating module. It extracts real-time VDS data after everyfive minutes and TCS data after every fifteen minutes from FTMS respective data server,

FTMS Access Module

Real-Time Communication

Module

TCS\VDS Data Manipulation

Module

FTMS Server

TC

VD

Network Generation

Module

GIS Data Manipulat-ion Module

Simulation Network

Generation Module

GIS

VDS Location

FRETASIM

Pattern Analysis Module

O/D Pattern

Demand Patten

O/D Path Generation

Module

Global Network O/D Path

Simulation Network O/D Path

Traffic Data Monitoring

Module

Real-Time Traffic

Monitoring

Future Traffic Monitoring

Simulation Module

Micro Simulation

Based On CA Model

Incident Generation

MOEs Computation

Micro Animation

Current Situation

Traffic Volume/ Average Speed on the Section

Traffic Volume/ Average Speed on the Section

Current Freeway Speed

Monitoring

Future Situation

3D Animation

3D CCTVEmulation

VariousMOEs

Affectedrange byIncident

Operation Parameter

Fig. 3. The physical architecture of FIAS.


employing open database connectivity method. Simulation network generation module tacklesthe graphical data in GIS file format along with the locations of various VDS stations. It thenmarks a rectangular corridor between two consecutive stations with width of the corridordepending upon the roadway width. This corridor is represented on MS Access file format.

Subsystem 3: Freeway Traffic Simulator (FRETASIM) is a simulation platform that is made up of four inde-pendent components namely, Pattern analysis module, Origin and Destination (O/D) path gen-erating module, Traffic Data Monitoring Module (or TDMM) and Simulation Module (SM).At the pattern analysis module, TCS data of 8 weeks is used to determine the initial O/D flowpattern. As the demand pattern module analyzes the demand for various links of the network,the O/D path-generating module processes data regarding the freeway networks and tollgatelocation to provide O/D information between tollgates. In O/D path-generating modules, globalnetwork paths are transformed into simulation network O/D paths that are then used to predictinflow traffic volumes of a controlled section. It is important here to note that until this point, weare only using historical data for the purpose of ascertaining O/D pattern, route pattern andpredicting inflow volumes from tollgates.The analyzed TCS data is transferred to simulationmodule where the manager injects incident parameters, which is subsequently augmented withthe VDS data.FIAS uses the adaptive cellular automata model for the prediction of selectedparameters, i.e., volume and speed, as a measure of Effectiveness (MOE) in the post-incident sce-nario.The distinguishing feature of the adaptive CA is the incorporation of two additional rulesto the conventional model of Nagel and Schreckenberg (1992). These rules are Stopping Maneu-ver Rule (SMR) and Low Acceleration Rule (LAR); former describes a decelerating vehicle toarrive and stop in the tail of a jam while later is used for an accelerating vehicle which follows astopped leading vehicle. These adaptations are implicitly discussed in Kim et al. (2005); nonethe-less, a comparison of the two models is shown in Fig. 4 that illustrates the impact of two adap-tations on the flow-density diagram. The original NaSh model (Fig. 4, left) shows an unrealisticbehavior of maximum speed transition to immediate zero in one time step to avoid backcollision in addition to not acknowledging the behavior of accelerating vehicle in jam. These

Fig. 4. Vehicle trajectories on a 1-lane periodic system with parallel update: dots represent vehicles that move to the right. The horizontaldirection is discrete space to the right and the vertical direction to the down is discrete time. Left: NaSh model. Right: the integrated modelwith SMR and LAR (Source: Kim et al., 2005).


adaptations in the modified CA model can simulate breakdown on the bottleneck sections alongwith simulation of Stop/Go phenomena more realistically.

Subsystem 4: It corresponds to output view of the logical architecture that presents the simulated information.It handles the output of present and future situation of traffic flows. Assessment and monitoringof the VDS and TCS data in animated form is conformable. Particularly, traffic functional datacan be displayed in 2D vehicle animation (Fig. 5) and 3D CCTV emulation (Fig. 6) with playand speed control interface options.

6. Performance evaluation

FIAS validation is based upon a case study approach. In this context 78.4 km of Seohean expressway, nearSeoul (capital of Korea) is focused as the study section. It is a recently completed road (in 2001) and hence candeliver significantly accurate VDS data. The study section extends southward from West Seoul tollgate atReduced Distance (RD) 327 km to Seosan tollgate at RD 248.6 km (Fig. 7).

6.1. Incident parameters

Temporal incident data must be used as a priori for a comparative study. The MOEs from 6 VDS stationsare taken for 4 h (1.00 p.m. to 5.00 p.m.) with a priori based on an incident that had occurred in the studysection on November 21, 2003 at RD 317 km. The data was captured after every 5 min making 12 data setsper hour from each VDS station or 48 observations per station in the stipulated time. The observed attributes

Fig. 5. A screen shot of the demo indicating 2-D animation at an interchange.

Fig. 6. A screen shot of the demo indicating 3D CCTV emulation.


Fig. 7. Study section of Seohean line near Seoul (Source: extracted from KHC, b).


of the incident are summarized in Table 1, while Fig. 8 indicates the incident point and location of VDS sta-tions selected for data collection.

6.2. Experimental results

In this experiment a Pentium-IV desktop system was used with the specification as: CPU 1.6 GHz, RPM7200, 512 MB DDR-RAM, HDD 40 GB. The total simulation time required in this case of the long incidentswas 1230 s for 4 h of traffic. The simulation results of FIAS to model the time dependent micro structure oftraffic flows are validated using percent Mean Absolute Relative Error (MARE) which is defined as

Table 1Description of a priori for comparative study

Description Parameters

Incident point 317.0 kmIncident day 2003.11.21 ThursdayIncident occurrence time 1:40 p.m.Incident clearance time 70 minIncident effect sweeping point 14 km to upstream (RD #: 303 km)Net capacity of the incident section 460 veh/h/lane

Fig. 8. Incident point and location VDS stations for data collection in case of short clearance time.


MAREð%Þ ¼ 1

n

Xn

i¼1

jxi � yijxi

� 100 ð1Þ

where xi is the real data at ith time interval; yi is the predicted data at the i th time interval and n is the numberof data sets or observation ranging from 1 to n.

6.2.1. Volumes study

The volume data from all six selected VDS stations is compared with the FIAS predicted values and theanalytical results are shown on the respective figures (Fig. 9a–f). The error analysis indicates that the net aver-age error is 17% though it varies from 12% to 25%. The comparison designates greater fluctuation in the realdata than simulation results. Thus the stations with larger difference shows higher values of mean absoluterelative error as evident from (Fig. 9b–d).

However, our model very precisely picks up the post-incident drop and rise in the volume. Thus eventhough our simulation data look significantly lower at some VDS stations (Fig. 9b–d) from the managers

(a) VDS at RD 317 km (MARE=12%)

0

200

400

600

1 5 9 13 17 21 25 29 33 37 41 45

Veh

/5m

in

Real data

Simulation data

(b) VDS at RD 316.1 km (MARE=18%)

0

200

400

600

1 5 9 13 17 21 25 29 33 37 41 45

Veh

/5m

in

Real data

Simulation data

(c) VDS at RD 313.8 km (MARE=25%)

0

200

400

600

1 5 9 13 17 21 25 29 33 37 41 45

Veh

/5m

in

Real dataSimulation data

(d) VDS at RD 310.8 km (MARE=20%)

0

200

400

600

1 5 9 13 17 21 25 29 33 37 41 45

Veh

/5m

in

Real data

Simulation data

(e) VDS at RD 308.4 km (MARE=14%)

0

200

400

600

1 5 9 13 17 21 25 29 33 37 41 45

Veh

/5m

in

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Simulation data

(f) VDS at RD 307.3 km (MARE=12%)

0

200

400

600

1 5 9 13 17 21 25 29 33 37 41 45

Time section #Time section #

Time section #

Veh

/5m

in

Real dataSimulation data

Time section #

Time section #Time section #

Fig. 9. Comparison of real and simulated volume data at the selected VDS stations.

Fig. 10. Comparison of real and simulated vehicle speed data at the selected VDS stations.


perspective it is more important to know the behavior of the flows and forecast the time when the effectedsection will be experiencing most retarded flows. Our model depicts that phenomenon quite correctly(Fig. 9a–f).

6.2.2. Speed study

Fig. 10 illustrates almost similar pattern of error in speed comparison as of volumes. Here the traffic withinthe 4-km stretch shows greater vulnerability to the incident as evident from greater fluctuations of volumeswhereas our proposed model is slightly lagging behind resulting in error as high as 24% (Fig. 10b). A bit awayfrom the incident point (Fig. 10e and f), the simulation data appears to be in harmony with the real data andthe error shrinks down to as low as 12% at RD 307.3 km (Fig. 10f). Even when the local error is 24% (Fig. 10b)our model is capturing quite accurately the disturbed flow period from time section 8 to time section 22 (i.e.,1:40 p.m. to 2:50 p.m.).

Another important aspect of our model is that it is identifying the stretch of the road that will be experi-encing the impacts of the incident. For example, the simulation data corresponds to the 6 VDS stations thatshow the impact, which is gradually reducing from 70 min at RD 317.0 (incident point) to 10 min at RD 307.3.

7. Conclusions

FIAS is a microscopic simulation based model that predicts the post-incident traffic impacts using historicaldata supplemented with real-time and spatial data. This predicted data is imperative for the real-time incidentanalysis and improve the functioning of TMC. This proposed decision support system not only predict the


required impacts but also present them in both in 2D and 3D CCTV environment as per requirements of thetraffic management personals. It is anticipated that the incident analysis result in this format will help the traf-fic managers to take significantly consistent steps based on tangible information and not the speculativeapproach.

The performance validation results assure the significance of the proposed model, as the predicted data isconsiderably closer to the real post-incident. However, occasionally, it does stray from the real data curve thatindicates scope of further improvement to fully incorporate driver’s behavior, particularly in the vicinity of theincident. The study thus identifies it as a potential avenue for further investigation.

Acknowledgement

This research is a part of a project of Korea Highway Corporation. We acknowledge Transportation Re-search Group, Highway and Transportation Technical Institute, Korea Highway Corporation. We would alsothank Sim Tech Systems Inc. for her support.

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