Active Traffic Management

Pravin Varaiya and Alex A. Kurzhanskiy∗

University of California, Berkeley, CA 94720, USA

Abstract

Active Traffic Management (ATM) is an approach to dynamic management of vehic-ular traffic, based on measurement of prevailing traffic conditions, in order to maximizethe efficiency of a road network. It is a continuous process of (1) obtaining and analyzingtraffic measurement data; (2) operations planning—simulating various scenarios and con-trol strategies; (3) implementing the most promising control strategies in the field; and (4)maintaining a real time decision support system that filters current traffic measurements topredict the traffic state in the near future, and to suggest the best available control strategyfor the predicted situation. ATM relies on a fast and trusted traffic simulator for the rapidquantitative assessment of a large number of control strategies for the road network undervarious scenarios, in a matter of minutes.

The paper describes three key components of an ATM system: the simulation modeland its use for evaluating performance under various scenarios and control strategies; thedatabase system used to calibrate the simulation model; and the traffic sensor systemneeded to collect traffic measurements that are input to the database.

1 Introduction

An Active Traffic Management (ATM) System enables dynamic management of vehicular traf-fic, based on measurements of prevailing traffic conditions. It combines automated controlsystems with strategic human intervention to manage traffic in order to maximize the efficiencyof road networks. This paper presents a particular structure of ATM and focuses on its keycomponents. The paper reflects the authors’ engagement in the TOPL (Tools for OperationsPlanning) project since 2006 [13]; the PeMS (California Freeway Performance MeasurementSystem) project since 2001 [10]; and the development of a new generation of wireless vehiclesensing systems at Sensys Networks, Inc. since 2005 [12].

ATM is a feedback process comprising (1) continuous traffic measurement and measurementdata analysis, without which attempts to manage a road network are blind; (2) operations

∗Research supported by National Science Foundation Award CMMI-0941326 and California Department ofTransportation.

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Figure 1: ATM workflow diagram.

planning, which includes evaluating the road network performance under various scenarios,such as demand increase, lane closures, special events, etc., developing control strategies thatimprove performance, and testing these strategies in terms of their cost and the benefits theybring under these scenarios; (3) implementing the most effective of these control strategies byinstalling necessary hardware and software in the field; and (4) running the decision supportsystem in real time, which includes filtering the measurement data, providing short termprediction of the traffic state, and selecting the best available control strategy for the next oneor two hours.

The process relies on a two-way communication network that feeds real time measurementsfrom the road network’s traffic surveillance system to the traffic control center database andtransfers commands generated by the control software (and human operators) to the controlactuators in the field.

The procedures in the ATM workflow process rely on an underlying macroscopic model oftraffic. That model and how it is built from data are described in §2. Model constructionis facilitated by the Aurora Road Networks Modeler (RNM) [1], an open-source tool set formodeling road networks that include freeways and arterials with signalized intersections. Themodel simulator and its use for predicting the traffic system’s performance of various controlstrategies under different scenarios is described in §3.

ATM relies on a database of historical and real time data. The California freeway performancemeasurement system or PeMS provides a database that stores detector data and allows theuser to conduct a wide range of performance analyses. A brief description of PeMS occupies§4. Ultimately, traffic measurements are made by vehicle detectors. A new generation wireless

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vehicle detection system offers a uniform, flexible platform for traffic measurement. This systemis described in §5.

2 Model

Aurora RNM is based on the macroscopic Cell Transmission Model [2; 3]. In Aurora, the roadnetwork consists of directed links and nodes. Links represent stretches of roads connected bynodes or junctions.

A link is specified by its length and number of lanes. Behavior of traffic on the link is character-ized by its fundamental diagram parameterized by its capacity, free flow speed and congestionwave speed (or, equivalently, the jam density). In the CTM model the state of the road networkat any time is given by the vehicle density in each link.

Demand is expressed by vehicles entering the network through source links and departingthrough sink links. Routing of vehicles through the network is governed by split ratios associ-ated with nodes.

Direct control of traffic is located at nodes: a node located at a signalized intersection controlsthe signal phase; a ramp metering controller is located at the node where an on-ramp joins ahighway. Indirect control, e.g., a variable message sign that advises drivers to take a particularexit, is expressed through changes in the associated split-ratios, as explained in §3.1.1.

2.1 Building the Model

Building a model ready for simulation consists in (1) specifying a road network as a list nodesand links with correct lengths and lane counts, and possibly the correct shapes for purposes ofdisplay; (2) calibrating the model, that is, assigning a fundamental diagrams to each link; (3)defining the time-varying demand profiles for the source links and split ratio matrices for thenodes.

Model-building is a time-consuming process, as no single data source provides the informationnecessary for all three tasks.

The network is typically specified using GIS data. Aurora RNM provides tools to help inextracting the network from Google Maps. Figure 2 shows an example 9-node urban networkconstructed in this way. The application allows the network builder to select nodes on themap and indicate which pairs of nodes are connected by links. The application automaticallyconstructs the network graph, including link length, as well as link shapes for visualization.

Measurement data from freeway detectors archived in systems such as PeMS [10] or PORTAL[11] allow the use of Aurora statistical procedures to estimate the parameters of the fundamen-tal diagrams for the freeway links. The full description of the procedures with applications, isfound in [4]. Calibrated models of several California freeways are available [1].

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Figure 2: Illustration of network construction. Source:Google Maps, 2010.

The most difficult part of calibrating urban streets or arterials is to estimate their capacities.The capacity of an arterial link is determined from available measurements or assigned byguess, the free flow speed can be set to the speed limit assigned on that street, and the jamdensity can be set as the number of vehicles this link can store divided by the link length.

Lastly, it remains to define the demand profiles for the sources and split ratio matrices for thenodes. For highways, database systems like PeMS [10] and PORTAL [11] may provide on-rampflow data from which to construct the demand profiles. Split ratios could be computed frommeasurements of mainline and off-ramp flows. In practice, however, on- and off-ramp flowsmay not be available, as is today the case for several California highways. The demands andsplit ratios must then be imputed so that when used in the model, the model produces mainlineflows that match the measurements. The imputation problem is thoroughly addressed in [8].Sources of demand and split ratio data for arterials vary from city to city.

Once the model is built one or several base case scenarios can be run to check whether simu-lation results match with measurement. Figure 3 is an example. The measured speed contourfor the 24 hours of February 19, 2009 and a 21-mile section of I-80 East (from postmile 10to postmile 31) is placed next to the speed contour obtained from the calibrated model. Al-though the figure only offers a visual comparison, since traffic moves from left to right, one canimmediately see that the bottlenecks revealed by the two contours match in terms of locationand activation duration. Of course, one can assign in a variety of ways numerical scores to thedifference between the simulation model behavior and measurements to decide whether the

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model is reliable.Simulated vs. measured speed contours

I-80E: Feb 19, 2009 (Th)

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Model captures all bottlenecksFigure 3: Matching simulation results with measurement.

3 Simulator

Central to the ATM workflow is the ‘fast and trusted simulator’. The Aurora macroscopicsimulator is fast: it simulates several 24 hour-long scenarios in a few minutes. It is trustedbecause it is founded on a sound theory of traffic flow [6]; it is parsimonious, only includingparameters that can be estimated; and it is tested for reliability.

The simulator has three modes of operation. In the operations planning mode, many sim-ulations are run to model scenarios and test potential control strategies for improving roadnetwork performance. Scenarios incorporate known past events such as a festival or an acci-dent, or future events considered plausible on the basis of statistical learning techniques thatcombine historical data with the current estimate of the state of the system (e.g., high like-lihood of an accident occurring at a specific location, conditional on the current traffic stateand weather conditions [? ]). The configurations for the implemented control strategies arestored in the repository, readily accessed by the real time decision support system.

The second mode of operation is the dynamical filter : the simulation, with some uncertaintyin system parameters and inputs, runs in real time as the noisy measurements arrive from thetraffic sensors. By filtering the measurement data through the simulation, the traffic state isestimated and fed back into the traffic responsive control algorithms [7].

The third mode of operation is the short term prediction and strategy selection: with the initialconditions coming from the filtered real time measurements and predicted with some uncer-tainty in short term future inputs, the simulator runs a number of plausible near term scenarios

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with available control strategies and calculates the resulting congestion and potentially seriousstresses on the road network. The strategy promising the greatest benefits, is deployed bysending the corresponding commands to the actuators in the field.

Only the operations planning mode is described in some detail. A highway example is thenpresented to illustrate how all three modes are used together.

3.1 Operations Planning

The objective of the operations planning is to develop traffic control strategies that improveroad network performance. Historical data may pinpoint weaknesses in road network opera-tions, for example by locating bottlenecks and accident hot spots. With a reliable simulationmodel, weaknesses are revealed in more detail since a variety of scenarios can be considered.Operational strategies can be designed and simulated under these scenarios to determine whichstrategy is well suited to each scenario.

The devised strategies may temporarily expand capacity at the bottleneck (for example, bypermitting traffic in a shoulder lane that is otherwise restricted to emergency vehicles, ortechniques of demand management that focus on reducing the excess demand during peakhours (for example, by increasing toll charges); incident management, which targets resourcesto reduce incident clearance times at ‘hot spots’; providing traveler information, so travelersmay plan trips to ensure on-time arrival; and direct traffic flow control through ramp meteringat highway on-ramps and signal timing plans at signalized intersections. Operations planningneeds quick quantitative assessment of the performance benefits that can be gained from thesestrategies, so that, in conjunction with a separate estimate of their deployment costs, promisingstrategies may be selected.

General link performance measures are:

Traffic speed, measured in miles per hour (mph);Travel Time, measured in hours for each start time;Vehicle Miles Traveled (VMT), a measure of the ‘output’ of the link in a particular time period;Vehicle Hours Traveled (VHT), a measure of the ‘input’ to the link in a particular time period;Delay, measured in vehicle-hours, indicating the additional time spent driving below somereference speed; andProductivity Loss, in lane-mile-hours (lmh)—the degree of under-utilization of the link due tocongestion.

From the travel time, VMT, VHT, delay and productivity loss for each individual link, onecan compute the same quantities for any specified route in the network.

Arterial links whose end nodes are signalized intersections have additional performance mea-sures:

Delay per cycle, the number of vehicle-hours spent waiting at the signal;Queue size, the number of vehicles in the link;

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Phase utilization, the percent of the green phase time used during cycle;Cycle failure, the percent of vehicles waiting for more than one red light;Volume to capacity ratio, which characterizes the utilization of the available capacity;Progression quality, the percent of vehicles arriving during the green phase; andLevel of service, a performance measure favored by the Highway Capacity Manual.

Knowing these performance measures for each incoming link of an intersection, one can assessthe overall efficiency of the intersection, and indeed, for a network of intersections.

3.1.1 Scenario Design

To evaluate road network performance under different scenarios characterized by incidents, laneclosures, special events, or demand fluctuations, Aurora provides ‘switches’ in the macroscopicmodel parameters (fundamental diagrams at links and split ratio matrices at nodes) or inputs(demands at source links). The switches can be set at specified times.

Switches in fundamental diagrams can model incidents and lane closures. For example, supposethe original fundamental diagram of some link has capacity of 4000 vehicles per hour (vph), andan accident blocking half of the lanes occurs at 10.00 AM and lasts until 10.30AM at whichtime the road is cleared. To model this accident one sets two switches in the fundamentaldiagram—2000 representing the capacity drop at 10.00 AM, and 4000 representing the returnto normal operation 30 mins later. Evaluating the impact of faster reaction times (how muchbetter would the system perform if the accident were cleared in 20 minutes instead of 30?) canhelp allocate resources for incident management.

Special events and the impact of providing traveler information are modeled by switches in thesplit ratio matrices at given nodes.

In Aurora these switches in parameters and inputs are implemented as events that can begenerated by the user and triggered at user-specified times during the simulation. The impactof each scenario can be quickly assessed, as the simulator computes the general performancemeasures for links and routes.

3.1.2 Traffic Control

Aurora provides a facility to model three types of direct traffic control, as indicated in Figure4. The facility can be used to design sophisticated control strategies. The effectiveness of thesestrategies is evaluated by simulating them in various scenarios.

• Local controller is assigned to a particular input link of a node and controls only the flowcoming from that link. An example is a ramp meter shown in Figure 4a. Here, the nodehas two input links, uncontrolled highway link 1 and controlled on-ramp 2.

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Figure 4: Controller hierarchy: examples of (a) local controller (ramp meter); (b) node con-troller (signalized intersection); (c) complex controller (coordinated ramp metering).

• Node controller is assigned to a node and controls the flows coming from all the inputlinks of that node. An example is a signalized intersection shown in Figure 4b. Here, onlytwo non-conflicting input flows (1 and 5, 2 and 6, 3 and 7, or 4 and 8) are allowed at anyone time, while the others are blocked. The individual input flows are still controlled eachby its own local controller, but the local controllers are synchronized by the centralizednode control.

• Complex controller operates on multiple local, node, or even other complex controllerscoordinating their action toward some common objective. An example is a coordinatedramp metering system shown in Figure 4c. This is a highway with two identified bottle-necks, which divide the highway into zone 1 and zone 2, each ending at the correspondingbottleneck. Bottlenecks are expected to move throughout the day. Zone controllers areresponsible for coordinating local ramp meters at the on-ramps within their respectivezones, whereas the main controller keeps track of the bottleneck locations and zone con-figuration. Main and zone controllers are both complex controllers.

TUC [5] is an example of a closed-loop complex controller that coordinates multiple nodecontrollers operating on signalized arterial intersections. Users can design and implement theirown control strategies, but Aurora also provides several pre-designed classes of controllers. Forramp metering there are time of day, ALINEA [? ] and a coordinated version of ALINEA,

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known as HERO [9]. For intersection signal control there are fixed-time and several traffic-responsive controllers.

3.2 Illustration

Figure 5: Short term prediction from 6 to 8 AM for I-210 West (top Source Google Maps, 2008):(a) projected density bounds for 7.30 AM without ramp metering; (b) projected density boundsfor 7.30 AM with ALINEA ramp control; (c) projected bounds for total network delay withoutramp metering; (d) projected bounds for total network delay with ALINEA ramp control.

In this section we briefly consider a plausible future scenario that may lead to poor performance,and consider control strategies as countermeasures. The base case is represented by a modelof highway I-210 West in Los Angeles using measurements for 25 July 2009, for 25 miles of

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I-210 from Baseline Road (postmile 52) to junction with I-710 (postmile 27). The scenario isas follows. Current time is taken as 6:00 AM and measurements up to that time are available.Demand is forecast for the next two hours, until 8:00 AM. This forecast is uncertain, with a±5% stochastic variation around the mean. The result is a stochastic simulation. Aurora cancarry out stochastic simulations, but it also gives a much more revealing computation thatprovides an upper and lower bound of the evolution of the state (link densities) of the highway.

Figure 5a is a snapshot of the projected density bounds (lower bound in green, upper boundin red) along the highway at 7.30 AM (1.5 hours into the future) computed with no trafficflow control at nodes. At the location marked by the ellipse (between postmiles 40 and 35)the projected density uncertainty interval is large, with its upper bound exceeding criticaldensity and lower bound staying in the free flow state, indicating that the system may developcongestion if left to its own devices, or stay in free flow if managed properly. Such locationsare primary candidates for aggressive ramp metering (RM).

Figure 5b is a snapshot of projected density bounds at the same time if ALINEA ramp meteringwere applied within the marked highway segment. The size of the projected density intervalis significantly reduced: the controlled system is more predictable than the uncontrolled one.Figures 5c and 5d show the evolution of the total network delay (including on-ramp queuedelay) for the uncontrolled and ALINEA-controlled cases respectively. From 4 to 6 AM thedelay is computed from the measurements (blue), and from 6 to 8 AM the predicted delaybounds (lower bound in green, upper bound in red). One can see that ALINEA ramp meteringpotentially yields a noticeable delay reduction.

This example evaluates only one strategy, namely ALINEA. In practice, the traffic operatorshould test several potential control strategies, study their relative benefits and costs and applythe most effective one. A macroscopic traffic simulator such as Aurora RNM [1] is able to runa hundred such simulations with preprogrammed scenarios in a matter of several minutes. Theoperator can thereby explore a large set of strategies.

4 Database

The ATM system must be supported by a database that keeps historical data. The Californiafreeway Performance Measurement System or PeMS is a good example. Established in 2000,PeMS archives real time measurements from 24,000 detectors in California freeways, amountingto 50 billion data samples each year. PeMS also receives and stores event data such as incidentsreported by the California Highway Patrol and lane-closure data reported by the CaliforniaDepartment of Transportation (Caltrans). Today PeMS holds 10 TB of original and deriveddata.

As sensor samples arrive, PeMS subjects them to statistical tests to determine whether themeasurements are valid or erroneous. If a measurement is judged to be in error, it is replacedby an imputed value. The data are processed to produce a wide range of freeway performancemetrics in real time (delay, travel time, bottleneck locations, etc.) as well as metrics that cover

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longer periods (annual congestion, average peak period VMT) and larger regions (Bay Area,Southern California). The variety of performance metrics computed routinely in PeMS hasgrown over time in response to the need of different user groups.

Figure 6: PeMS real time dashboard.

The entire PeMS database and all performance metrics are online, so traffic engineers can gaina quick appreciation of what is happening at the moment as in Figure 6 which is a screen shotof the real time dashboard, showing current and projected performance metrics of aggregateVMT, VHT and Q (a measure of speed); the previous days worst bottlenecks; the current daysworst incidents; and, for specific routes, a comparison of their current travel time with theirhistorical average.

Because PeMS keeps all past data online, it also provides transportation planners, consultantsand researchers the tools for retrospective studies. Figure 7 gives an example. It plots the25th, 50th and 75th percentile of travel time for trips on the I-50 freeway near Hodges Lake foreach month from January 2007 to May 2008. As is evident there is a dramatic and sustaineddrop in travel time after August 2007, when an additional lane was opened. Planners couldcompare the actual benefit in travel time with what was predicted when the lane construction

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Figure 7: PeMS plot of travel time over a route.

was approved.

5 Measurement

An archive of historical and real time traffic measurement data is needed for Active TrafficManagement. The measurement data are generated by vehicle detection systems and transmit-ted to the database in the Traffic Management Center. Effective traffic management requiresa geographically extensive network of reliable and accurate vehicle detection systems, togetherwith a communication network over which these measurements are forwarded to the TrafficManagement Center as indicated in Figure 1.

Today the overwhelming proportion of these systems rely on inductive loop sensors. Theseloop detector systems are technologically obsolete: they have a high failure rate (e.g., at any

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time up to 30% of loop detectors in California have failed); require frequent re-calibration inthe field, which is rarely undertaken, so their measurements are not accurate; they are veryexpensive to maintain. Lastly, installing new loop detectors is disruptive, requiring closure ofa lane for several hours or days.

A new generation of wireless magnetic sensors is briefly described in this section [? ]. Eachsensor is housed in a 3-inch cube containing a magnetic sensor, microprocessor, radio, antennaand a battery with a 10-year lifetime. The cube is buried in a 4-inch diameter drilled hole inthe middle of a lane. The sensor detects the change in the earth’s magnetic field caused bythe passage of a vehicle over it, and transmits the detection to a 6-inch Access Point (AP)on the side of the road. The AP sends the detection signal to the local signal controller andvia GSM cellular modem to the Traffic Management Center’s database server. Installation ofthe wireless detector station takes a few minutes [12]. Once installed the sensors immediatelybegin to deliver data. These sensors may be used to replace broken loop detectors and toaugment the existing detector network. They are currently deployed in more than 150 citiesin the U.S. An effective ATM system for a city should be supplied by data from sensors thatserve all the functions suggested in Figure 8.Traffic Sensing Applications

Freeway TrafficData

access point

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Stop-BarDetection

Advance Detection

ParkingEnforcement

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170

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Figure 8: Network of sensors for urban traffic measurement.

An array of magnetic sensors placed across a lane generates a signal from which a vehicle-specific signature can be extracted. If such arrays are placed in successive links across anarterial, these signatures can be matched to provide an accurate measurement of vehicle traveltime in each link. The travel times, together with volume, occupancy and speed measurementsprovided by the sensors can be processed to produce a range of performance metrics [? ]. Thetravel time measurement system installed on approaches to Dodgers Stadium in Los Angelesis depicted in Figure 9. During game events, queues of vehicles form at these approaches andsigns are posted telling drivers the time they will take to get to the entrances to the Stadium.

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Los Angeles Dodger StadiumLos Angeles Dodger Stadium

Travel Time to the Gate on Game Day

Figure 9: Travel times to gates A and B are posted at signs at green locations. Source: YahooInc. 2010

6 Conclusion

A reliable, spatially dense network of detectors is essential for the data needed for any effectivetraffic management system. Wireless magnetic sensors offer a flexible means for building sucha traffic surveillance network. The network can be incrementally expanded. Data from such anetwork must be archived to be of use.

Archived data are needed for an Active Traffic Management (ATM) System. The data areused to estimate and validate dynamic macroscopic models of the road network, and revealweaknesses and stress points. The model has to be macroscopic for it to be fast enough for theATM system operator to run scores of simulations in an operational setting. As advocated here,ATM provides tactical decision support to the operator by evaluating a large range of controlstrategies in the context of plausible near term future scenarios. In this view, ATM is proactive:it asks what adverse events are more or less likely to occur, and what countermeasures canan operator take to prevent or mitigate their ill effects. Thus, ATM is risk management.As transportation systems experience greater stress (growing demand without correspondingcapacity increase), proactive risk management becomes more important. This is a differentview of ATM than a frequently expressed view of ATM as a set of ‘advanced’ control strategiessuch as ‘variable speed limit’ and ‘lane management’.

Risk management needs a capability to assess risk. Such a capability requires a fast simulatorthat can rapidly assess scores of possibilities. The Aurora Road Network Modeler appears tofulfill this need. It comprises a collection of software tools for building a model and a disciplined

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way of specifying scenarios and control strategies, together with a set of performance metricsthat can be used for a comparative evaluation of system performance. Its simulation model isaustere, characterized by a few parameters, so estimation provides statistical guarantees, andeach parameter has an understandable impact. This is in contrast to slow microsimulationmodels, which with their abundant parameters are impossible to estimate with any statisticalprecision and whose influence in traffic behavior is difficult to trace, even though the visualsthat microsimulation packages offer lend a deceptive air of verisimilitude.

References

[1] Aurora RNM Homepage. http://code.google.com/p/aurorarnm.

[2] C. F. Daganzo. The cell transmission model: A dynamic representation of highway trafficconsistent with the hydrodynamic theory. Transportation Research, B, 28(4):269–287,1994.

[3] C. F. Daganzo. The cell transmission model II: Network traffic. Transportation Research,B, 29(2):79–93, 1995.

[4] G. Dervisoglu, G. Gomes, J. Kwon, A. Muralidharan, and P. Varaiya. Automatic cali-bration of the fundamental diagram and empirical observations on capacity. 88 AnnualMeeting of the Transportation Research Board, Washington, D.C., USA, 2008.

[5] C. Diakaki, M. Papageorgiou, and T. McLean. Integrated traffic-responsive urban corridorcontrol strategy in glasgo, scotland. Transportation Research Record, (1727):101–111,2000.

[6] G. Gomes, R. R. Horowitz, A.A. Kurzhanskiy, J. Kwon, and P.Varaiya. Behavior of thecell transmission model and effectiveness of ramp metering. Transportation Research, PartC, 16(4):485–513, August 2008.

[7] A. A. Kurzhanskiy. Set-valued estimation of freeway traffic density. Proceedings of the12th IFAC Symposium on Control in Transportation Systems, 2009.

[8] A. Muralidharan and R. Horowitz. Imputation of ramp data flow for freeway trafficsimulation. Transportation Research Record, 2009.

[9] I. Papamichail, M. Papageorgiou, V. Vong, and J. Gaffney. HERO coordinated rampmetering implemented at monash freeway, australia. 89th Annual Meeting of the Trans-portation Research Board, Washington, D.C., USA, 2010.

[10] PeMS Homepage. http://pems.eecs.berkeley.edu.

[11] PORTAL Homepage. http://portal.its.pdx.edu.

[12] Sensys Networks. http://www.sensysnetworks.com.

[13] TOPL Project. http://path.berkeley.edu/topl.

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