Simulation-Based Framework for Transportation Network Management in Emergencies

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Transportation Research Record: Journal of the Transportation Research Board,No. 2041, Transportation Research Board of the National Academies, Washington,D.C., 2008, pp. 80–88.DOI: 10.3141/2041-09

challenges. Planning involves the modeling of a transportation systemthat rapidly becomes oversaturated, compounded by the loss of vitaltransportation links. Increasing demand for transportation (from thepeople trying to evacuate and emergency response vehicles tryingto approach the affected area), combined with a limited networkcapacity (as a result of street closures because of debris accumula-tion and the establishment of cordons for rescue and relief work andthe blocking off of roads to minimize the risk from structurally dam-aged buildings), results in a chaotic situation with people makingdecisions under stress. Therefore, simulation modeling needs to berobust enough to test various levels of emergency evacuation, theloss of critical infrastructure, unusual travel patterns and behaviors,and different response plans and strategies. Another important con-sideration is the modeling of shifts in transportation mode, includingtransit and pedestrian movement. The ability to quickly and reliablyevaluate strategies and disseminate a generated response strategy tovarious mobile units and infrastructure elements is crucial to therestoration of normalcy in a network.

The importance of well-managed networks has been emphasizedin recent literature. In his last book, Unfinished Revolution, the lateMichael Dertouzos of the Massachusetts of Technology describes adisaster control scenario for a city struck by a major earthquake (2).Traffic redistribution is an important component of his hypotheticalexample.

Intelligent transportation systems can be used to improve andextend emergency response capabilities. Control and informationdelivery strategies, such as special traffic signalization and routingschemes, are important to a successful response during emergencysituations. However, the effectiveness of such strategies often cannotbe determined without the use of a model that captures the physicalnetwork infrastructure, the control systems, and the behavior of thedrivers who must be evacuated. This paper focuses on a simulationmethodology that can be used to assess the performance of the high-way network during evacuations and provides a tool for the determi-nation of key parameters, such as the time required for the evacuationof a region, the sufficiency of the roadway capacity along differentroutes, and the impacts of various countermeasures taken to augmentcapacity and reduce the evacuation time.

REVIEW OF STATE OF THE ART

During the past few years the topic of emergency and evacuationmanagement has been attracting increasing attention. This sectionoutlines some of the relevant recent research covering system-levelapproaches, as well as studies focusing on components of the emer-

Simulation-Based Framework forTransportation Network Management in Emergencies

Ramachandran Balakrishna, Yang Wen, Moshe Ben-Akiva, and Constantinos Antoniou

A simulation-based framework for the modeling of transportation net-work performance under emergency conditions is presented. The sys-tem extends the well-established dynamic traffic assignment (DTA)framework and provides the necessary support for the meaningful studyof a wide array of evacuation measures, the development of strategiesunder different prevailing conditions, and the generation of comprehen-sive emergency response plans. The system can be used to develop librariesto deal with emergencies and unplanned events, train response person-nel and traffic management center operators, provide decision supportand assistance for the evacuation of residents from affected areas, andensure unhindered access to first responders. A variety of practical issuesrelevant to evacuation modeling are discussed, and the modeling frame-work is demonstrated by using the Boston, Massachusetts, network asan example. DynaMIT, a state-of-the-art DTA model, is used in thecase study to illustrate how the benefits of network management strate-gies might be ascertained. The paper concludes with future directions,including the integration of simulation modeling as a real-time tool forthe management of evolving evacuations.

The transportation network is a critical infrastructure in the event ofnatural and human-caused disasters, when large populations mustbe expediently evacuated from dense urban areas. Evacuation demandis thus compressed into a relatively short time span, and the avail-able roadway capacity becomes a critical resource that must be usedjudiciously to minimize the evacuation time. Recent natural disas-ters such as Hurricane Katrina and the need for homeland securitypreparedness have mandated that evacuation plans be coordinatedacross jurisdictions and modes of travel. Such coordination is oftenlacking and can result in suboptimal operations during emergencyevacuations (1).

An important element of emergency management is the applica-tion of travel demand and simulation models that can estimate theperformance of the transportation system under various evacuationconditions. Planning for a catastrophic event presents numerous

R. Balakrishna, Caliper Corporation, 1172 Beacon Street, Newton, MA 02461.Y. Wen, Room 1-249, and M. Ben-Akiva, Room 1-181, Massachusetts Instituteof Technology, 77 Massachusetts Avenue, Cambridge, MA 02139. C. Antoniou,Department of Transportation Planning and Engineering, National Technical Uni-versity of Athens, 5 Iroon Polytechniou st., Zografou 15773, Athens, Greece.Corresponding author: R. Balakrishna, [email protected].

gency response problem, such as the demand and supply sides, andthe generation of evacuation plans.

Lively et al. discussed how advanced traveler information systems(ATISs; 511 call-in systems) enhance emergency and disasterresponse (3). Ullman et al. presented the findings from a preliminarystudy involving focus groups studies in Texas about the effectivenessand the appropriateness of major event messages on dynamic messagesigns (4). Zografos and Androutsopoulos presented a decision supportsystem for hazardous materials transportation risk management thatcovers integrated hazardous materials distribution and emergencyresponse decisions (5). El Mitiny et al. evaluated alternative plans forthe deployment of transit during an emergency situation in a transitfacility such as a bus depot (6).

Modeling and simulation are valuable tools in the arsenal of emer-gency management. Chen and Zhan investigated the effectivenessof simultaneous and staged evacuation strategies in different roadnetwork structures using agent-based simulation (7 ), whereas Liuet al. proposed a cell-based network model that captures critical char-acteristics associated with staged evacuation operations (8). On theother hand, Bronzini and Kicinger identified the limitations of exist-ing analytical tools for dealing with mass evacuations and proposeda conceptual model that combines cellular automata, evolutionarycomputation, and transportation science, along with infrastructuresecurity elements (9). Chiu et al. discussed the development of anemergency management scheme that includes a feedback loopthat uses surveillance systems (10), whereas Liu et al. presented amodel reference adaptive control framework for real-time trafficmanagement during an emergency evacuation (11). Jha et al. useda microscopic simulation model to evaluate emergency evacuationplans (12).

Geographic information systems are also prime candidates for usein emergency management and the development of evacuation plans(13–15). Dynamic traffic assignment (DTA) has also been used foremergency evacuation modeling (16, 17 ).

Alsnih and Stopher reviewed the procedures associated with thedevelopment of emergency evacuation plans (18). They assessed theavailable emergency evacuation models and identified key researchdirections for investigation of the effects of a mass evacuation oncurrent transport networks. Mitchell and Radwan evaluated variousheuristic strategies that can be used to improve evacuation of an at-risk region using a representative traffic roadway network (19). Manyresearchers have treated evacuation planning as an optimizationproblem (20–23).

The supply side is often affected by emergencies, both because ofspecial conditions (e.g., road closures because of flooding) and aspart of the response strategy (e.g., contraflow operations) (24–29).Ozbay and Yazici recognized the uncertainties that result in net-work capacity losses and approached the evacuation problem froma probabilistic point of view, discussing the reliability of evacua-tion performance measures, such as average travel times and clear-ance times (30). Patel and Falcocchio outlined gaps at the planning,design, and operational levels of managed lanes practice and pre-sented modifications required at the planning, design, and operationlevels of managed lanes practice (31).

When evacuation takes place, there often remains a need to pro-vide access for emergency vehicles and personnel to the threatenedarea, creating a conflict between the needs to maximize capacity forevacuation and continuing to provide access to the threatened area.Relatively little is known about when residents decide to evacuate.Models of evacuation behavior may predict the proportions of thepopulation that would leave within certain time periods (32, 33) and

Balakrishna, Wen, Ben-Akiva, and Antoniou 81

may be based on sequential binary logit models (34), evacuationresponse curves (35), activity-based simulation (36–38), and logisticregression and neural network models (39). Ozbay et al. conducteda comprehensive and critical review of demand generation and net-work loading models (40), whereas Fu and Wilmot presented twohazard-based survival analysis models with time-dependent variables,a Cox proportional hazards model and a piecewise exponentialmodel, that can be used to estimate the probability that a householdwill evacuate (41).

OVERALL EMERGENCY RESPONSEFRAMEWORK

The real-time response to emergencies can be conceptually repre-sented as a feedback loop, in which data become available from avariety of surveillance sources. The data are transferred through com-munications networks and are gathered at central locations, wherethey can be accessed by the relevant authorities. The informationmust be processed and analyzed (by using modeling and simulationtools) to generate potential control strategies that may be reviewed,amended, and implemented by experienced operators in a decisionsupport center. The implementation of the control strategies resultsin a new network state, which is represented in new surveillanceobservations, and a new iteration of the feedback loop is initiated.The elements of the feedback loop include

• Data collection. Data collection involves the detection of theactivities, changes, and conditions on the city’s physical infrastruc-ture by the use of advanced sensor technologies. For example, in-pavement loop detectors can monitor roadway traffic conditions inreal time, whereas embedded sensors can monitor the conditions ofbridges and tunnels. These data can then be used to direct trafficaway from vulnerable components.

• Data transmission. Data can be transmitted through wirelessand other broadband technologies, and redundancy can be includedto ensure that the flow of data is maintained, even when some of thecommunication channels fail.

• Information processing, modeling, and simulation. A scalabledata-fusion module is required to synthesize data from multiplesources and evaluate the performance of the network under a givenmanagement strategy

• Decision support. State-of-the-art decision support requires therapid evaluation of multiple scenarios to allow the correspondingcontrol strategies to be generated and implemented in the field inreal time.

• Control strategies. Emergency control strategies can be timesensitive and must account for the chain-reaction effects among thedifferent infrastructure components before field implementation.

The challenges in building such a comprehensive emergencyresponse framework lie in two different areas. Communication andsensor technologies must be designed and deployed to ensure thatdata and information can be exchanged in the field. These data includetraffic conditions, disabled transportation links, and hazard disper-sions. Several initiatives are already moving toward providing sucha communication infrastructure (42). In addition, network condi-tions must be simulated and comprehensive control strategies mustbe generated before they can be communicated to field operatorsfor deployment. However, in the event of a crisis (such as a majorearthquake or a terrorist attack), the amount of information that is

rapidly collected by the control center is vast (depending on the out-reach of the incident), and this information is unlikely to be effi-ciently digested by human control center operators. A computerizedsimulation system can gather and exploit this information in a morecomprehensive and efficient way, identify problem areas, evaluatecontrol strategies, and recommend and disseminate the best controlstrategy to the infrastructure elements through the appropriate com-munications channels. DTA systems are unique in their ability tofuse multiple sources of information, estimate and predict the evo-lution of networkwide traffic conditions, and generate route guidanceon the basis of these predictions.

DTA systems are typically designed to reside in traffic manage-ment centers (TMCs) and support real-time applications, such asthe on-line evaluation and implementation of guidance and controlstrategies, incident management and control, and emergency responseoperations. DTA models can also be used for short-term planningapplications. A DTA system integrates historical data and informa-tion from multiple sensor sources to perform two main functions(43): (a) state estimation and (b) prediction-based informationgeneration

During the state estimation phase, real-time surveillance informa-tion is combined with historical data and a priori model parameter val-ues to capture the current traffic conditions in the network. Detailedtraffic data from instrumented portions of the network are used toinfer the conditions in the parts of the network for which no real-time information is available. This is achieved through an iterativesimulation of demand–supply interactions designed to reproducereal-time observations from the surveillance system.

The role of the prediction-based information generation processis to generate unbiased and consistent traffic information for dissem-ination to travelers. Information based on predicted network condi-tions (i.e., anticipatory information) is likely to be more effective thaninformation based on current traffic conditions because it accountsfor the future evolution of traffic conditions. A detailed treatment ofthe demand–supply interactions within a state-of-the-art DTA systemcan be found in the work of Ben-Akiva et al. (43).

To provide the functionality described above, DTA systems usedetailed travel demand and network supply simulators to synthe-size multiple sources of information and perform state estimationand prediction. A demand simulator captures networkwide demandpatterns through time-dependent origin–destination (O-D) matricesand models the travel choices of individual motorists (e.g., routechoice). A supply simulator is usually based on high-level (meso-scopic or macroscopic) models that represent traffic dynamics byusing speed–density relationships and elements from queuing the-ory, as described previously (43, 44). In a DTA model, the complexdemand–supply interactions are represented by detailed algorithmsthat estimate the current network state, predict future conditions, andhelp with the generation of anticipatory route guidance and controlstrategies.

The computational efficiency of DTA models makes them idealfor evacuation planning and management. They represent a power-ful trade-off between modeling accuracy and running time, thusscoring over more realistic (yet slow) microscopic approaches.Several DTA runs can therefore be performed in a short time, allow-ing operators to test various strategies in faster than real time andselect those interventions that can have the most impact on reducingthe duration of the evacuation process.

DTA systems combine individual models into a complex systemwith many inputs and parameters. The proper calibration of thesemodels and inputs is essential to improving their ability to predict

82 Transportation Research Record 2041

future conditions accurately. Calibration can take place offline andonline. Offline calibration is performed with archived data. Commoncalibration variables include time-varying O-D flows, route choicemodel parameters, other parameters used by the O-D estimation andprediction modules and, depending on the nature of the supply simu-lator used by the DTA system, segment capacities and speed-densityfunctions. The resulting historical parameters represent averageor expected conditions. Online calibration exploits the continuousflow of surveillance information to allow the dynamic adjustment ofmodel inputs and parameters. By using the offline calibration as astarting point, online calibration fine-tunes the model parametersso that they capture the prevailing traffic conditions more accu-rately and can therefore lead to better predictions. Traffic dynamicsdepend on factors that cannot always be anticipated (such as weatherconditions, incidents, unscheduled maintenance work, and emer-gencies). Even when they can be predicted, it would be impracticalto calibrate traffic dynamics models and develop associated data-bases for every possible scenario. The capacity of the network facil-ities (segments) is affected by several factors (including weatherand lighting conditions, driver mix, and traffic composition) andmay therefore change as prevailing conditions change. Althoughsome of these factors (e.g., driver mix and traffic composition) areimportant, they are difficult to observe. Online calibration is theonly means of adjusting the parameters to capture their effectstemporarily.

MODELING CHALLENGES

The modeling of the transportation system during emergencies isassociated with challenges that arise from deviations from the normalenvironment. For example, the network is likely to be significantlydifferent. Other variations, such as driver behavior, are more diffi-cult to quantify and are induced by the uncertain nature of unfold-ing events. This section discusses the important deviations that mustbe captured by the model.

Network Characteristics

Network supply can be substantially altered during an emergency.Some of the links in the network may be disrupted or closed becauseof incidents, such as floods, chemical spills, and earthquakes. Thestructural reliability of bridges may also be affected because ofseismic activity. Furthermore, depending on the response plans inuse, some of the main arteries may be operating in contraflow modethrough lane reversals. Main evacuation routes may also be givenpriority over side streets through changes in traffic signal plans. Themodel must quickly accommodate these changes. A report of a smallnumber of meaningful and useful measures of effectiveness alsoneeds to be provided to the decision makers.

Demand Patterns

Precalibrated demand patterns typically represent normal condi-tions and can largely be irrelevant during emergencies. Althoughthe origins of evacuation trips can be inferred from available O-Ddemand matrices, their destinations are gathered to a few key loca-tions deemed safe under the emergency situation. It is thereforeimportant to determine these modified demand patterns, keeping

in mind the changes to the physical transportation infrastructurethat have taken place.

Behavioral Aspects

Lack of data, population heterogeneity, and a number of other factorsmake the task of modeling drivers’ behavior a challenging task evenunder normal conditions. These issues become considerably morepronounced during emergencies. Reliable data capturing drivers’behavior during real emergencies are essentially unavailable, whereasthe use of data calibrated under regular conditions could lead tobiased results. Two approaches can be used to handle this situation:

1. Obtain behavioral data from people under stress or encoun-tering emergencies. The collection of data during actual emergen-cies is difficult for a number of reasons, including the possiblebreakdown of telecommunication and surveillance systems duringemergencies and the other priorities of response personnel. Artifi-cial environments (such as driving simulators or web-based sur-veys) may be used to develop stress-inducing scenarios and collectinformation. However, such approaches are typically expensive andtime-consuming, may lack realism, and can be difficult to transferto different scenarios.

2. Develop and apply online calibration approaches that can steerthe DTA model’s parameters closer to the ground truth as the emer-gency unfolds. This approach has an additional advantage, as theresulting parameters would be readily applicable and relevant to theemergency or application and not to a generic or similar one usedfor calibration.

The previous sections have presented the main requirements of atool used to manage urban transportation networks under emergencyconditions, outlined a general framework for the use of such a tool,and discussed the modeling challenges that must be considered in thedevelopment of a practical and effective methodology. DTA is onemodeling approach that is suitable for use as a real-time evacuationmanagement tool. The next section presents an example of an onlineDTA, DynaMIT for emergencies (DynaMIT-E), and how it may beapplied to evaluations of evacuation control strategies in real time.

EMERGENCY RESPONSE SIMULATION:DynaMIT-E

TMCs in many major U.S. metropolitan areas are already linked tosurveillance and monitoring infrastructures. However, when unusualevents result in extreme conditions, the conventional practices and thepredefined control strategies of current TMCs are not sufficient to pro-vide the necessary reactions. An extension to existing state-of-the-artDTA models would provide the necessary framework for meaning-fully organizing the vast amount of information that becomes avail-able in real time, synthesizing a complete and representative viewof prevailing conditions, and generating comprehensive emergencycontrol strategies. DynaMIT-E is one such extension and is basedon dynamic network assignment for the management of informationto travelers (DynaMIT).

DynaMIT is a simulation-based real-time system designed to esti-mate the current state of a transportation network, predict future traf-fic conditions, and provide consistent and unbiased information totravelers (43). Designed to reside in a TMC, DynaMIT combines


real-time data from a surveillance system (composed, for instance,of loop detectors, probe vehicles, and incident detection systems)with historical data to estimate the current state of the network, pre-dict future traffic conditions, and provide travel information andguidance through ATISs.

Figure 1 presents the DynaMIT-E framework. The basic externalinputs comprise surveillance information (such as sensor data), net-work characteristics (changes to the network topology or character-istics because of the emergency), and characteristics of the emergency(e.g., the dispersion path of a toxic plume because of a fire at a chem-ical plant or the need for medical personnel to access the disasterzone). The diverse information is then combined and homogenizedin a data-fusion step and the output is fed to an online calibrationstep that obtains the model parameters that capture the prevailingconditions because of the developing emergency. This step does notrequire the operator to intervene manually to define the type of emer-gency and configure the system appropriately. Instead, the system“senses” that an emergency is ongoing and switches back to the useof regular parameters when conditions normalize.

As sensor data become available, the state estimation module usesfixed-point optimization techniques to reach a meaningful estimateof the current conditions prevailing on the network. This step involvesiterations between the demand and supply components to reachconvergence. Using this information as a proxy, an evaluator com-ponent interacts with an emergency control generator to generateoptimal (according to user-defined criteria) emergency control strate-gies. The emergency control generator can adopt various approaches.One such approach is to consult a predefined library of strategiesand select the one(s) that closely matches the main elements of theactual emergency. The performance of the selected strategies isthen evaluated and possibly refined. The best strategy is output asan emergency control plan. The state estimation and evaluationcomponents are closely related to their counterparts in the Dyna-MIT framework. Therefore, the only major component within theDynaMIT-E framework that needs to be developed is the emergencycontrol generator.

As new information becomes available, DynaMIT-E is rerun andthe control plan generated is updated accordingly. Furthermore, mul-tiple evaluators can be run in parallel to speed up the ability of thesystem to process control strategies. The system is also sensitive to theurgency of the situation. For example, at the outbreak of a new emer-gency, DynaMIT-E provides a control plan as quickly as possible.However, given a reasonable control plan, subsequent DynaMIT-Eiterations may consider a larger number of emergency controls.

DynaMIT-E is expected to run constantly. When there is no emer-gency, DynaMIT-E quietly monitors the available information andperforms self-calibration tasks to reflect changing trends in trafficpatterns and enrich its databases. When an actual emergency occurs,DynaMIT-E is able to react immediately. This takes place whenDynaMIT-E is actuated by an operator or when it detects unusualfield measurements indicating an emergency. DynaMIT-E can thusserve as a secondary means of detecting potential emergencies.

As it is constantly online and operational, DynaMIT-E does notrequire a warm-up period. By providing comprehensive assessmentsof the expected impact of each alternative strategy (in the form ofappropriate measures of effectiveness), DynaMIT-E enables the con-trol center operators to select and initiate the best control or evacu-ation strategy quickly. Assuming that the appropriate interfaces tothe infrastructure elements are present, DynaMIT-E may be able toapply the selected control strategy under proper supervision fromthe human operators.

CASE STUDY

DynaMIT-E is demonstrated through numerical tests studying theevacuation of the central business district (CBD) of the city of Boston,Massachusetts to suburban evacuation centers (Figure 2). The justifi-cations, assumptions, and data underlying the application are based onlessons learned during the 2004 Democratic National Convention(DNC) held in Boston. The DNC created unprecedented challenges totransportation agencies in the Boston area and significantly affected thefreeway system, the Massachusetts Bay Transportation Authority railand bus system, Boston Harbor ferry transport, and the air transportsystem. Restrictions were implemented on portions of the freeway androadway system during the convention, including the designation ofemergency lanes in some areas. Restrictions were also implementedon Boston Harbor and the transit and commuter rail system, includingclosure of the North Station on the city’s commuter rail system.

More than 75% of the employment destinations in the CBD arelocated within a 10-min walk to a subway or bus stop. Also, Bostonhas the third highest percentage of transit use in the country, withmore than 31% of workers using public transportation to com-mute to work. Additional complications arise from the high per-centage of schools and hospitals in the downtown area and thepresence of Logan Airport only 2 mi from downtown. The airportis connected to downtown Boston by three highway tunnels and asubway tunnel.

Although the total metropolitan area contains more than 4 millionpeople, the case study was limited to the evacuation of only the day-time commuter employee and student populations (approximately200,000 people) from downtown Boston and the surrounding com-


munities. In the event of an actual emergency, a portion of the resi-dential population of Boston (approximately 650,000 people) mayalso evacuate the city, creating induced demand on the roadwaynetwork. Similarly, some number of commuters may shift travelmodes in an emergency situation through spontaneous carpoolingand transit. Although DynaMIT does not simulate transit or pedestrianmovements, these elements are critical to any evacuation strategy.Transit services and pedestrian destinations on the periphery of theCBD were therefore treated as demand sinks, with an assumed flowrate of the number of evacuees per hour that represented the exodusfrom the CBD by transit and on foot.

Performance Measures and Base Case

The effectiveness of an emergency response may be evaluated byusing several metrics. The most common measure is the time requiredto clear the affected area to ensure that further casualties are mini-mized. This statistic may easily be obtained from the simulationmodel. In addition, the temporal evolution of flow, speed, density,and queue length can be reported for each road segment. Thesestatistics serve as useful guidelines in determining the performanceof localized sections of the network and in identifying underusedcapacity that may be harnessed through a redistribution of the evac-uation demand. For example, police personnel could divert residualdemand onto less used roadways that may not have been identifiedas evacuation routes.

The base case for numerical testing consisted of an evacuationof Boston’s CBD by using the available freeway capacity in the

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FIGURE 1 DynaMIT-E framework.

region. The evacuation demand was assumed to be routed to specificperipheral holding centers on the I-95 ring, reached primarily throughthe radial I-93 (north–south) and I-90 (west) links. A time-varyingdemand profile representing the demand to be evacuated from down-town Boston was simulated in DynaMIT-E. The focus was the per-formance of the westbound direction of I-90, which represents theshortest distance from the affected area to relative safety beyond theI-95 ring. DynaMIT-E was run to quantify the severity of the baseevacuation setting. Subsequently, contraflow along I-90 (westbound)was simulated to study the impact of partial lane reversal, by whicha single inbound lane was operated to allow security and rescue per-sonnel to access the city center. All remaining lanes were reversedto augment the exodus capacity. Note that the inbound lane alsoserves Boston-bound vehicles already on the network. These vehicleswould be forced off the highway before the city limits.


The impact of the contraflow strategy is illustrated through com-parisons of traffic density by time of day. Figure 3 shows the evolutionof congestion on I-90 near the downtown Boston area. The suddenloading of evacuation demand onto the downtown network resultsin the rapid buildup of congestion to jam density, resulting in grid-lock. Increasing the capacity on I-90 through contraflow allows themaximum density to stabilize at a much lower level, which keepstraffic moving.

Experimental Design

Three demand levels (low, medium, and high), analogous to varia-tions in automobile occupancy rates and the availability of transitservices, were analyzed. Because a significant portion of the CBD

FIGURE 2 Boston metropolitan area map. (Source: www.mapquest.com.)

population may comprise public transit users, it is hypothesized thatany available train and bus infrastructure will be pressed into serviceto assist with the evacuation process. The remaining demand willuse the road network either with personal vehicles or via carpooling.For each demand level, two capacity settings on I-90 were selected.Although one setting corresponded to the base case capacity undernormal conditions, the second setting (designated “low capacity”) isintended to represent the effects of panic or incidents that mightresult in bottlenecks. In addition, a contraflow strategy was tested toaugment the evacuation capacity. DynaMIT-E was run for each ofthe resulting nine scenarios.

Results

Figure 4 summarizes the impact of the contraflow strategy on the O-Dpair most affected by the evacuation. These trips begin in downtownBoston and proceed westwards on I-90. As expected, the capacityreduction is observed to result in a slight increase in the average triptime between downtown Boston and the safety zone due west of thecity. It can also be seen that the provision of added capacity alongI-90 significantly reduces the average trip time for all three demandscenarios. The contraflow strategy is therefore effective under theconditions of the evacuation scenario being modeled.

These numerical tests demonstrate the potential use of DynaMIT-Ein assessing the performance of various traffic management strategies


during evacuations. Apart from providing a graphical overview ofthe entire network on a minute-by-minute basis, detailed statistics(such as travel times, queue lengths, and the total time for evacuation)can also be collected. This provides a valuable tool for planners sothat they may determine a library of suitable actions to be consideredfor various emergencies. Together with real-time surveillance infor-mation about the current state of the network, DynaMIT-E can pre-dict short-term congestion and assist emergency personnel with theefficient management of the network.

CONCLUSION

This paper presents a framework for the planning and managementof highway networks under emergency conditions. The frameworkaddresses the need for a robust model that can be used to evaluate theperformance of the network under a wide range of mitigation mea-sures, such as contraflow, signal priority, and the staged release ofthe evacuation demand. Critical issues in the modeling process arediscussed, including data collection, evacuation demand estimation,and driver behavior under stress. DynaMIT, a state-of-the-art simu-lation model based on DTA, is adapted for emergency analysis. Thesimulation outputs from DynaMIT-E are processed into several per-formance measures that can be used both offline (for planning andtraining) and online (for real-time management). It is expected thatthe resulting model performance metrics (such as time to evacuate,corridor congestion levels, and intersection levels of service) willprovide decision makers with useful data that they can use to vali-date their assumptions about the duration and the severity of a poten-tial evacuation. For example, if the time to evacuate takes more than14 h instead of 5 h, then the availability of police who would beneeded to manually control critical intersections during the evacua-tion would be severely underestimated. The benefits of DynaMIT-Eare demonstrated through a case study based on contraflow schemesto reduce the evacuation time for the city of Boston.

Future work involves the integration of DynaMIT-E into TMCsto demonstrate its ability to interface with surveillance and incidentdetection systems. Steps in this direction have already been successfulwith online DynaMIT implementations in Los Angeles, California,and Hampton Roads, Virginia. The Hampton Roads applicationfocused on predicting point-to-point travel times. In Los Angeles,DynaMIT automatically obtained freeway and arterial loop detectorcounts every 5 or 15 min and predicted network conditions (such aslink flows and speeds) for the next hour. These predictions were to

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be used by incident detection algorithms. Wen et al. presented adetailed discussion of several practical issues involved in integratinga DTA model into a TMC (45). These include the synchronizationof all data transfers and the need to know about sensor malfunctionsin real time. Wen et al. further analyzed the lessons learned from theintegration exercise (46 ). Several other issues must be investigatedin the context of emergencies. For example, data from a wider rangeof sources must be synthesized while simultaneously providingdata-sharing functionalities across agencies. Furthermore, the TMCmust have the ability to evaluate multiple control strategies to ensuretheir timely dissemination and deployment.

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The Transportation Safety Management Committee sponsored publication of thispaper.

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Simulation-Based Framework for Transportation Network Management in Emergencies