Vehicular Traffic Detection, Scheduling,

and Simulation

Abdulmomn Kadhim {a.bluemix@gmail.com}Supervised by:

Dr. Muayad Sadik Croock Dr. Shaimaa Hameed Shaker

December 29, 2013

Contents

1 Magnetic sensors and vehicle detection 21.1 Wireless magnetic sensor networks [3] . . . . . . . . . . . . . . . 21.2 On-road sensors deployment . . . . . . . . . . . . . . . . . . . . . 3

2 Simulation of urban traffic 4

3 Swarm intelligence for traffic light scheduling [2] 53.1 Cycle program of traffic lights . . . . . . . . . . . . . . . . . . . . 63.2 Experiments and results . . . . . . . . . . . . . . . . . . . . . . . 7

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Why ad-hoc sensor networks are can be preferred to infrastructure ones

How magnetic sensor nodes are deployed and used to detect vehicles

Simulating urban vehicular traffic using SUMO

How swarm intelligence approach used to find successful cycle programsof traffic lights

Report outlines

1 Magnetic sensors and vehicle detection

The presence, direction, and speed of a vehicle can be determined by employingmagnetic sensors. The technique requires a magnetic field of known strengthand direction. A moving vehicle can disturb the distribution of the magneticfield either by producing its own mag- netic field or simply by cutting across it.As the magnitude and direction of the disturbance depends on the speed, size,density, and permeability of the vehicle, it is possible to use magnetic sensorsto quantify the disturbance.

Sensors that can measure the Earths magnetic field comprise an alloy ofnickel and iron. Typical examples are anisotropic magnetoresistive (AMR) sen-sors whose resistive property changes according to the Earths magnetic fieldstrength. AMR sensors can measure both linear and angular positions anddisplacement in the Earths magnetic field.

Almost all road vehicles, including those with polymer body panels, containa large mass of steel. Since the magnetic permeability of steel is much higherthan the surrounding air, it has the capacity to concentrate the flux lines ofthe Earths magnetic field. The concentration of magnetic flux (disturbance) ata particular location varies as the vehicle moves and can be detected from adistance of up to 15 m. Fig. 7 demonstrates how an AMR sensor can be usedto measure the disturbance in the Earths magnetic field caused by a movingvehicle. [1]

For detecting the presence of a vehicle, measurements of the (vertical) z-axisis a better choice as it is more localized and the signal from vehicles on adjacentlanes can be neglected. [4]

1.1 Wireless magnetic sensor networks [3]

Wireless magnetic sensor networks offer a very attractive alternative to inductiveloops for traffic surveillance on freeways and at intersections in terms of cost,ease of deployment and maintenance, and enhanced measurement capabilities.These networks consist of a set of sensor nodes (SN) and one access point (AP)1.A SN comprises a magnetic sensor, a microprocessor, a radio, and a battery.Each SN is encased in a 5- diameter smart stud container that is glued to thecenter of a lane.

1This is an infrastructure arrangement, in an ad-hoc one, only sensor nodes exist, i.e.,

peer-to-peer communication

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Figure 1: Detection of a moving vehicle with an AMR magnetic sensor

1.1.1 The use of ad-hoc sensor networks in vehicular traffic [5]

All nodes in a wireless ad-hoc network act as a router and host at the same time(peer-to-peer communication), alongside, the network topology is dynamicallyvarying, because the connectivity between the nodes may vary with time dueto node departures (e.g., the node gets corrupted) and new node arrivals (e.g.,placement of a new node instead of corrupted one, or extendign the coverage ofthe netword). The special features about ad-hoc networks is that all the nodesare responsible to organize themselves dynamically the communication betweeneach other and to provide the necessary network functionality in the absence ofa fixed infrastructure or we can call it ventral administration. It implies thatmaintenance, routing and management, etc., have to be done between all thenodes. This case called Peer level multi-hopping and that is the main buildingblock for ad-Hoc networks. In the end, we conclude that the ad-hoc nodes aredifficult and more complex than other wireless networks. Therefore, ad hocnetworks form sort of clustering for effective implementation and performanceof such a complex process.

In summary, here are some of the core benefits of ad-hoc networks:

Ad-hoc networks are simple to set up. Plug in your wireless networksensors and youre off and running.

Ad-hoc networks are inexpensive. You save the cost of purchasing anaccess point (the centralized device in infrastructure networks).

Ad-hoc networks are fast. Throughput rates between two wireless net-work adapters (nodes) are twice as fast as when you use an access point(infrastructure networks cut the data transfer rate about in half, becauseof the time it takes to send the signal to and from the access point ratherthan directly to its destination, as in an ad-hoc network).

1.2 On-road sensors deployment

We [4] use intersections controlled by four traffic lights. Each traffic light isresponsible for controlling traffic on three lanes. We assume the right lane turns

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Figure 2: Road intersection configuration

Figure 3: Deploying sensor networks at freeway (left), intersection (right)

right only, center lane goes straight or left and left lane goes left only. We deploysensor nodes on every lane (see Fig. 2). We place the sensor nodes where theycan monitor the traffic before entering the intersection and after leaving theintersection. We use the nodes placed after the intersection to locally determinethe direction of the vehicle within one intersection.

Another general configuration proposed by [3], is show in Fig. 3.

2 Simulation of urban traffic

SUMO (Simulation of Urban MObility) is an open source, highly portable,microscopic and continuous road traffic simulation package designed to han-dle large road networks. It is mainly developed by employees of the Instituteof Transportation Systems at the German Aerospace Center. SUMO is open

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Figure 4: Flowchart showing the traffic model creation process.

source, licensed under the GPL.The open-source SUMO simulation environment was chosen for a number

of reasons including portability, presence of an active development communityand availability of a graphical user interface [6].

A realistic traffic model based on a section of the downtown area of theCity of Ottawa was developed for use within the SUMO microscopic trafficsimulation environment, to demonstrate the effectiveness of an intelligent trafficcontrol system that system should be tested on realistic traffic scenarios [6].

Fig. 4, shows a flowchart of the traffic model creation process, which isexplained in [6].

Fig. 5 shows an example intersection with four groups (and thus four signalsets) within a SUMO simulation [6].

And, finally, University of Technology road street map is shown in Fig. 6.2

3 Swarm intelligence for traffic light schedul-

ing [2]

The growing number of traffic lights that control the vehicular flow requires acomplex scheduling, and hence, automatic systems are indispensable nowadaysfor optimally tackling this task.

In this work, we propose a Swarm Intelligence approach to find successfulcycle programs of traffic lights. Using a microscopic traffic simulator, the solu-tions obtained by our algorithm are evaluated in the context of two large andheterogeneous metropolitan areas located in the cities of Ma laga and Sevilla (inSpain). In comparison with cycle programs predefined by experts (close to realones), our proposal obtains significant profits in terms of two main indicators:

2This map is being displayed in jsom, which can be converted to be used in SUMO.

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Figure 5: Example of an intersection with four groups, showing the four corre-sponding signal sets. (a) Signal set for Group #1, (b) Signal set for Group #2,(c) Signal set for Group #3 and (d) Signal set for Group #4.

the number of vehicles that reach their destinations on time and the global triptime.

3.1 Cycle program of traffic lights

Cycle programs are refereed to the time span a set of traffic lights (in a junction)keep their color states. At the same time, these programs have to coordinatetraffic lights in adjacent intersections with the aim of improving the global flowof vehicles circulating according to traffic regulations. In this context, our mainobjective is to find optimized cycle programs (OCP) for all the traffic lightslocated in a given urban area.

In our approach, the OCP (optimized cycle program) is encoded by means ofa vector of integers (see Fig. 7) following the SUMO structure of programmingcycles, where each element represents a phase duration of one state of the traffic

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Figure 6: University of Technology street map.

lights involved in a given intersection.

An example of this mechanism can be observed in Fig. 7 where the inter-section with id =i contains seven phases with dura- tions 40, 5, 40, 10, 36, 6,and 22 s (simulation steps). In these phases, the states have fourteen signals(colors), corresponding each one of them to one of the fourteen traffic lightslocated in the studied intersection. These states are the valid ones generatedby the simulator (SUMO in this work) attending to real traffic rules. In thisinstance, the fifth phase contains the state rrrr GGr rrrr GGr meaning thatfour traffic lights are in green (G), and the ten others are in red (r) during 36s. The following phase changes the state of the four traffic lights to other validcombination, for example, GGGG yyr GGGG yyr (y means yellow) during 6 s,and so on.

The last phase is followed by the first one, and this cycle is repeated duringall the simulation time. All the intersections in the complete scenario performtheir own programming cycles of phases at the same time, hence conformingthe global schedule of traffic lights. As commented before, computing OCPconsists in optimizing the combination of phase durations of all traffic lights (inall intersections) with the aim of improving the global flow of vehicles circulatingin a urban scenario instance.

3.2 Experiments and results

3.2.1 Instances

In Fig. 8, the selected areas of the two cities are shown with their correspondingsnapshots of Google Earth, OpenStreetMap, and SUMO. This figure illustratesthe process of generating the traffic network instances.

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Figure 7: Cycle program (phase duration) of traffic lights within intersections.Integer codification inside a PSO tentative solution.

3.2.2 Results and comparisons

In this section, we are first interested to analyze the internal performance of ourPSO (particle swarm optimization). Graphically, Fig. 9 plots the trace progressof the obtained in 30 independent runs of our technique when solving the Malaga instance.

In this figure, we can observe that for all executions our algorithm practicallyconverged after the first 200 iterations (20,000 evaluations), using the remainingtime to only slightly refine solutions. In addition, all the computed solutionsare close each other in quality, but different among them. They are almost allin the range of fitness values between 1 and 3. In terms of convergence androbustness, these are desirable features since we can offer to the expert a variedset of accurate cycle programs in a first stage of optimization.

3.2.3 Analysis of resulting traffic light schedules

In this section we focus on the cycle programs obtained by our PSO, and thepossible profits they can offer to the actual users in this field. Then we showthe real impact of using our optimization technique, able of computing realistic

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Figure 8: Process of creation of real-world instances for study. Urban centre ofSevilla and Malaga instance views. After selecting our area of interest (GoogleEarth view), it is interpreted by means of the OpenStreetMap tool, and thenexported to SUMO format.

and comprehensive traffic light cycle programs.A representative example can be observed in the optimization process of the

Ma laga instance. First, in Fig. 10 we can see the trace of the number of vehiclesthat did reach their destinations (upper continuous curve) versus the numberof vehicles that did not reach their destinations (lower dotted curve) for eachiteration step in a run of PSO. The overlapped curves show the mean number ofvehicles (out of 30 independent runs) that did arrive and did not arrive to theirdestinations. In addition, this figure also shows the results (in dotted straightlines) of the SCPG for this same instance.

From a different point of view, Fig. 11 plots the trace of the average triptime employed by the vehicles in the resulted solutions of PSO through all theiterations of an example run. In this case, the trip time becomes shorter as thealgorithm approaches the stop condition. We must notice that, in the calculationof the trip time, the vehicles that did not arrive to their destinations took 500s, the complete simulation time. For this reason, SCPG solutions showed anaveraged trip time of 660 s while PSO solutions obtained a trip duration of 557s, which represents an improvement of 15.7% respect to the SCPG (SUMO cycleprogram generator) solution.

Finally, with the aim of better understanding the final implica- tions of using

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Figure 9: Trace progress of the best fitness values in 30 independent runs ofPSO tackling the Malaga instance.

(or not using) an optimized cycle program, Fig. 12 shows the simulation tracesof the traffic flow resulted from solutions generated by both, SCPG (left) andPSO (right). The pictures were captured at the final of the simulation time,and correspond to two simulation procedures of a selected area of the Ma lagainstance including: Andaluc a avenue, Aurora avenue, and Guadalmedina street.As we can observe, the traffic density of the SCPG cycle program is clearly higherthan the one of PSO, even showing the former several intersections with trafficjams. As to the PSO cycle program, all intersections are unblocked at the endof the study.

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Figure 10: Number of vehicles that did reach their destinations (continuouslines) versus vehicles that did not reach their destinations (dotted lines). Over-lapped curves show the mean number of vehicles (out of 30 independent runs)that did arrive and did not arrive to their destinations. SCPG results are alsoshowed with dotted straight lines.

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Figure 11: Mean trip time of vehicles calculated for each one of the simulationsperformed through a representative run of PSO. SCPG (SUMO cycle programgenerator) results are also showed with a dotted straight line. Y axis representsthe trip time in seconds.

Figure 12: Simulation traces of the traffic flow (cars in white) resulting fromthe cycle programs generated by both, SCPG (left) and PSO (right) in Ma laga.The pictures show snapshots at the end of the simulation time. The reader cannotice that the SCPG leaves a dense traffic while PSO has cleaned the routesand the traffic is very fluid and sparse.

References

[1] Fundamentals of Wireless Sensor Networks - Theory and Practice - Dargie,Waltenegus;( WILEY, 2010)

[2] Swarm intelligence for traffic light scheduling: Application to real urbanareas (Elsevier, 2011).

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[3] Traffic Surveillance with Wireless Magnetic Sensors, Sing Yiu Cheung,Sinem Coleri Ergen and Pravin Varaiya University of California, Berkeley,CA 94720-1770, USA

[4] Adaptive Traffic Light Control with Wireless Sensor Networks: MalikTubaishat, Yi Shang and Hongchi Shi; Department of Computer ScienceUniversity of Missouri - Columbia; Columbia, MO 65211-2060

[5] An overview of mobile ad-hoc networks for the existing protocols andapplications; Saleh Ali K.Al-Omari , Putra Sumari; School of ComputerScience, Universiti Sains Malaysia, 11800 Penang, Malaysia. (Internationaljournal on applications of graph theory in wireless ad hoc networks andsensor networks, Vol.2, No.1, March 2010)

[6] Distributed and adaptive traffic signal control within a realistic trafficsimulation; Dave McKenney, Tony White (Elsevier 2012)

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Magnetic sensors and vehicle detectionWireless magnetic sensor networks 3On-road sensors deployment

Simulation of urban trafficSwarm intelligence for traffic light scheduling 2Cycle program of traffic lightsExperiments and results