1 Video based Adaptive Road Traffic Signaling Indu S, Varun Nair, Shashwat Jain and Santanu Chaudhury Abstract—The ability to exert real time, adaptive control, of the transportation process is the core of an intelligent traffic system. We propose a video based adaptive traffic signaling scheme for reducing waiting period of vehicles at road junctions without detecting or tracking vehicles. The traffic signal timing parameters at a given intersection are adjusted automatically as functions of the local traffic conditions. The video sequences recorded at junctions are used for generating Spatial Interest Points (SIP) and Spatio-Temporal Interest Points (STIP). The traffic congestion at the junction is estimated using SIP and STIP. The decision rules are based on a definitive analogy between road traffic and computer data traffic wherein road vehicles are compared with data packets on the network. The system is similar in approach to the technique of Weighted Round Robin (WRR) queuing, a scheduling discipline used in data communication networks. Local traffic information is used to adjust the phase split keeping the cycle time constant. Two methods have been proposed. The first method, Optimal Weight Calculator (OWC), minimizes traffic at an intersection by determining the optimal phase splits or weights. The second method, Fair Weight Calculator (FWC), calculates weights relative to the road with minimum traffic to bring more fairness. After applying the respective algorithms mathematically on varying traffic conditions, OWC was found to be more equitable in the allocation of green time which is suitable for highly weight-sensitive junctions. For traffic with road priorities, FWC was found to be a much faster and effective control strategy. Keywords—Data communication; traffic; Distributed vision; transportation; Emerging applications ✦ 1 I NTRODUCTION Ever increasing number of vehicles on road, high fuel cost and environmental concerns are motivating researchers to come up with intelligent, simple and economical traffic systems. There are two types of conventional traffic lights control system in use. One type of control uses a fixed preset cycle time to change the traffic light. The other type of control is adaptive in nature as it combines preset cycle time with proximity sensors which can activate a change in the cycle time/phase splits/offset. Fixed preset is incapable of handling varying traffic con- ditions at an intersection. Therefore an adaptive control mechanism is the only solution. Current traffic control techniques involve data acquisition through magnetic loop detectors buried in the road [1], infra-red, proximity sensors and radar sensors which are subject to a high failure rate. Video-based systems provide more traffic information, are easily installed, and are scalable with progress in image processing techniques. Most of the decision making algorithms are based on case reasoning, fuzzy logic, neural networks or multi-agent systems. Authors of • Indu S, Varun Nair and Shashwat Jain are with the Department of ECE, Delhi Technological University, India, • Santanu Chaudhury is with EE department of IIT Delhi. [2] and [3] implemented a real time fuzzy logic traffic controller. Selecting an appropriate member- ship function for roads with unpredictable states is a major challenge / limitation for Fuzzy logic con- troller. Verma and et. al. [4] developed an ITS using Wireless Sensor network (WSN) installing external hardware both on vehicles and roads which makes it complex and costly. We propose a video based adaptive road traffic light control method motivated by network data traffic control methods. The road traffic junction is considered as a computer data traffic junction where vehicles are considered as data packets. Two methods have been proposed: Optimal Weight Calculator (OWC) and Fair Weight Calcu- lator (FWC). FWC is based on Variably Weighted Round Robin [5] where the weights are assigned according to the maximum value of Traffic State Ratio which ensures fairness in allocation of weight- s. OWC, based on [6], on the other hand tries to minimize traffic at an intersection by calculating optimal weights subject to certain constraints using linear programming technique.
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Video based Adaptive Road Traffic SignalingIndu S, Varun Nair, Shashwat Jain and Santanu Chaudhury
Abstract—The ability to exert real time, adaptive control, of the transportation process is the core of an intelligent trafficsystem. We propose a video based adaptive traffic signaling scheme for reducing waiting period of vehicles at roadjunctions without detecting or tracking vehicles. The traffic signal timing parameters at a given intersection are adjustedautomatically as functions of the local traffic conditions. The video sequences recorded at junctions are used for generatingSpatial Interest Points (SIP) and Spatio-Temporal Interest Points (STIP). The traffic congestion at the junction is estimatedusing SIP and STIP. The decision rules are based on a definitive analogy between road traffic and computer data trafficwherein road vehicles are compared with data packets on the network. The system is similar in approach to the techniqueof Weighted Round Robin (WRR) queuing, a scheduling discipline used in data communication networks. Local trafficinformation is used to adjust the phase split keeping the cycle time constant. Two methods have been proposed. The firstmethod, Optimal Weight Calculator (OWC), minimizes traffic at an intersection by determining the optimal phase splits orweights. The second method, Fair Weight Calculator (FWC), calculates weights relative to the road with minimum traffic tobring more fairness. After applying the respective algorithms mathematically on varying traffic conditions, OWC was foundto be more equitable in the allocation of green time which is suitable for highly weight-sensitive junctions. For traffic withroad priorities, FWC was found to be a much faster and effective control strategy.
1 INTRODUCTIONEver increasing number of vehicles on road, highfuel cost and environmental concerns are motivatingresearchers to come up with intelligent, simple andeconomical traffic systems. There are two types ofconventional traffic lights control system in use. Onetype of control uses a fixed preset cycle time tochange the traffic light. The other type of controlis adaptive in nature as it combines preset cycletime with proximity sensors which can activate achange in the cycle time/phase splits/offset. Fixedpreset is incapable of handling varying traffic con-ditions at an intersection. Therefore an adaptivecontrol mechanism is the only solution. Currenttraffic control techniques involve data acquisitionthrough magnetic loop detectors buried in the road[1], infra-red, proximity sensors and radar sensorswhich are subject to a high failure rate. Video-basedsystems provide more traffic information, are easilyinstalled, and are scalable with progress in imageprocessing techniques. Most of the decision makingalgorithms are based on case reasoning, fuzzy logic,neural networks or multi-agent systems. Authors of
• Indu S, Varun Nair and Shashwat Jain are with the Departmentof ECE, Delhi Technological University, India,
• Santanu Chaudhury is with EE department of IIT Delhi.
[2] and [3] implemented a real time fuzzy logictraffic controller. Selecting an appropriate member-ship function for roads with unpredictable states isa major challenge / limitation for Fuzzy logic con-troller. Verma and et. al. [4] developed an ITS usingWireless Sensor network (WSN) installing externalhardware both on vehicles and roads which makesit complex and costly. We propose a video basedadaptive road traffic light control method motivatedby network data traffic control methods. The roadtraffic junction is considered as a computer datatraffic junction where vehicles are considered as datapackets. Two methods have been proposed: OptimalWeight Calculator (OWC) and Fair Weight Calcu-lator (FWC). FWC is based on Variably WeightedRound Robin [5] where the weights are assignedaccording to the maximum value of Traffic StateRatio which ensures fairness in allocation of weight-s. OWC, based on [6], on the other hand tries tominimize traffic at an intersection by calculatingoptimal weights subject to certain constraints usinglinear programming technique.
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Fig. 1. Typical 4-way junction
2 PROBLEM STATEMENT
2.1 Related work
It has been more than two decades that road trafficmonitoring has been motivating researchers. Amongmany works, we discuss a few due to space limita-tion. Authors of [7] proposed a Vehicle Informationand Communication System (VICS) for travel timeprediction and congestion avoidance using numer-ous ultrasonic and radar detectors installed on roadsin addition to employing VICS units in vehiclesand infrared beacons. The method needs ultrasonicdetectors to be mounted in a down-looking config-uration as perpendicular as possible to the target(as opposed to side mounting), identifying lane-straddling vehicles and vehicles traveling side byside, and also susceptible to high wind speeds. Martiet. al [8] presented a rule -based road traffic manage-ment system for tackling weather induced problemson the road network. Based on specific logic rulesand decision rules which can be added and modified,the inference engine decides to whether send analarm or not. The system works only within thedomain of already created rules and does not involvelearning
2.2 Problem Description
In general traffic signal timings are decided ac-cording to the importance of the road. This mayincrease waiting period of vehicles at the intersec-tion. Figure.1 shows a four way traffic intersection.We propose a video based adaptive traffic signalingmethod for reducing the waiting period of vehiclesat the junction. Video cameras are placed alongthe road as well as near the junction. Two featuresare extracted from video sequence: Spatial Inter-est Points and Spatio-Temporal Interest Points, asshown in Figure.2. Spatial interest points are points(SIP) in spatial domain with significant variationin local intensities whereas spatio-temporal interest
Fig. 2. Red circles shows SIP and Blue circlesshows STIP
points (STIP) are points in space-time domain withsignificant variation in local intensities. All thevehicles on a road generate SIP and the movingvehicles generate STIP. Hence, the number of SIP isindicative of number of vehicles on a road and thenumber of STIP indicates the moving vehicles onthe road. It is not possible to count number of carsin real time without involving complex calculationsor processing. The ratio of STIP to number of SIP(Traffic State Ratio or TSR) gives an approximationof percentage of moving vehicles, which can beeasily calculated in real time. Higher the ratio lesseris the traffic. The local traffic information or the stateof the road is measured by the method proposedin [9], which is essentially a video-based trafficprediction algorithm based on Hidden-Markov Mod-el (HMM).In this paper we are proposing a noveltraffic control mechanism by modeling a road trafficjunction as a queue of a router as shown in Figure.1.The entire traffic cycle is seen as a round robinscheduling event [10] where weights for each routeare calculated using the Traffic State Ratio (TSR)in an adaptive manner. In both the methods it isassumed that during the green time the vehicles ona road are allowed to go straight and to other roadsas well.
2.3 Optimal Weight Calculator
The Optimal Weight Calculator algorithm is present-ed for a 4-Road junction shown in Figure. 1. Thiscan be extended for an n-Road junction. Weight (wi)is defined as the ratio of green time (i.e. the timefor which traffic light is green for a road in a cycle)to the cycle time (i.e. the summation of the greentime of all the roads in the junction). In other wordsit is the fraction of cycle time allotted to a road ’i’as green time.
wi ∗ Tcycle = Green T ime (1)
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In general, by increasing weight (wi) the traffic onthe road i decreases and hence the TSR increases.Similarly decrease in weight leads to decrease inTSR. In case there is no traffic, TSR tends to one.The TSR and weights allotted are assumed to be inlinear relation.
TSR = k ∗ w + c (2)
here, c = 0 and k is the slope. Without the lossof generality, the constant c is considered zero as itis not depending on the weights and it has no effecton the calculation of the weights.
TSR(w1, w2, wN) =N∑i=1
kiwi (3)
The Optimal Weight Calculator minimizes thetotal waiting time of vehicles or total number ofvehicles stopped in a cycle time at the junction bymaximizing the total TSR as shown in Equation.4.
MaximizeN∑i=1
kiwi (4)
Subject to constraints:-
Wthresh + yi ≤ wi ≤ 1 (5)
N∑i=1
wi = 1 (6)
Where, Wthresh = threshold or worst case weightand
yi αki(current) − ki(prev)
ri(prev)(7)
where ri - average TSR for the ith road forprevious 1 cycle
To incorporate the dynamic nature of traffic, theslope k associated with Equation.2 is calculated forevery upcoming cycle using the TSR and weightvalues of the current and previous cycles.
ki(next) = abs |ri(current) − ri(prev)
wi(current)
| (8)
The quantity yi in Equation.5 is introduced toaccount for sudden changes in the traffic pattern andat the same time keep the calculated weight values inaccordance with the current traffic pattern. Equation-s 4,5 and 6 represent a typical linear programming
formulation which gives us the required optimalweight values. The proposed method is computa-tionally light and independent of the number ofroads entering a junction.
2.4 Fair weight Calculator
[5] proposes a lightweight, simple QoS / CoScontrol method called VWRR (Variably WeightedRound Robin) for use on high-speed backbonenetworks. This method provides greater fairness interms of resource sharing among all kinds of trafficclasses, for a given processing load, than otherround-robin methods. We applied the same in RoadTraffic. In this case a proportionate weight withrespect to minimum traffic road is calculated foreach road by Equation.9. Hence for a large value ofri we get proportionately smaller value of wi whichin turn reduces the green time period. For avoidingnegligibly small green time we assign a minimumweight Wifnl as given bt Equation.12 Let M be themaximum value of average TSR of all roads
The proportionate weight is then calculated as
Wi =M
rii = 1, 2, ..N (9)
The value of Wi can be normalized to vary in therange [0 to 1], by normalizing Equation.9
Wi(norm) =Wi
Si = 1, 2.N (10)
where
S =N∑i=1
Wi (11)
If Wi(norm)<Wmin, minimum weight assigned
Wifnl = Wmin+(1−N∗Wmin)∗Wi(norm)i = 1, 2..N(12)
In case of clear traffic in all the roads, the corre-sponding weights assigned will be equal and forheavy traffic, maximum weight is assigned to thecorresponding road. The maximum weight and theweights for the rest of the road vary dynamicallywith the change in traffic over the course of time.
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Road State of Green Time Green Time Green TimeRoad (TSR) (sec) Normal (sec) OWC (sec) FWC
Road N Heavy(0.2432) 63 66 73Road E Mild(0.5605) 61 52 31Road W Heavy (0.2456) 63 66 72Road S Heavy (0.2393) 63 66 74
TABLE 1Green Timings for Scenario-I
Road State of Green Time Green Time Green TimeRoad (TSR) (sec) Normal (sec) OWC (sec) FWC
Road N Open (0.8022) 63 58 37Road E Open(0.8311) 61 57 35Road W Open(0.7904) 63 57 38Road S Heavy(0.2123) 63 78 140
TABLE 2Green Timings for Junction shown in Figure.4
3 RESULTS AND DISCUSSION
3.1 Simulation
We used Synchro Studio 8 to simulate two differenttraffic scenarios and compare the green timingsassigned by the OWC and the FWC with the con-ventional traffic signal timings (fixed green times).The results are shown in Table.1. The video framein Figure.3 shows the first traffic scenario. SIPsand STIPs were generated, using the current frameand the 5th succeeding frame. Further, green timeis calculated assuming the cycle time to be 250seconds.
Figure.4 shows the second scenario .The state ofroad ’S’ is heavy and other roads are open. Theweights for normal or non- adaptive method are stillthe same for all the roads while in OWC and FWCthe weights have changed adaptively and becomehigher for the more congested road and lower forthe less congested road as shown in Table.2.
Fig. 3. Scenario-I
Fig. 4. Scenario-II
Fig. 5. Unequal traffic pattern
The results shown in Table. 1 and Table 2 indicatethat both the proposed adaptive methods fare betterin comparison to conventional traffic signaling. Onlya single cycle readings have been shown here sincethe traffic pattern in the simulation remained thesame throughout the simulation time. The relativedifference in the weights is higher for FWC than forOWC. This is indicative of the fact that the formeris more prioritative towards the road with maximumtraffic.
3.2 Experimental EvaluationWe applied the said algorithms on an actual trafficscenario- a 4-way road junction. 4 separate cameraswere placed at the junction (Figure.5) that recordedtraffic for a period of 2 hours on 2 different days.SIPs and STIPs were generated from each of therecordings and average values of the TSR werecalculated per traffic cycle. One traffic cycle lastedfor approximately 250 seconds.
The tables.3,4,5 and 6 show the day-wise greentimings calculated for each of the 4 roads for 5traffic cycles by both the methods. The TSR valuesfrom Table.3 show traffic in Road 3 to be relativelyheavier as compared to the other roads. This is con-sistent with the values obtained for Day 2 (Tables.4and 6)as well. The green timings, as one would
expect, are also significantly higher for Road 3 forboth OWC and FWC. The distinctive feature in theresults of the two methods is the value of greentime assigned to the road with maximum traffic.Tables 3 and 5 show that the FWC has a tendencyto allocate a higher weightage to the road withmaximum traffic than the OWC. The OWC tends tobe more equitable in its allocation especially whenthere is lesser relative difference in the TSR valuesof the roads.
Figure.6 depicts the plot of the TSR for 4 roads(simulated values) at an intersection and the corre-sponding distribution of weights for the respectiveroads calculated by the Optimum Weight Calculatoralgorithm. Each road is in a particular traffic state(Open, Mild, Heavy or Stop). This is desirable sinceallotting a higher green time to the stopped road willhelp to reduce traffic in the road over time. Thefigure clearly shows that the weight assigned to astopped road is high and that of an open road is verylow. Thus the OWC method attempts to minimizethe traffic at the junction.
Figure.7 depicts the TSR (simulated values) andWeight plot calculated by Fair Weight Calculator.Here we have considered a mixed traffic distribu-
tion in the 4 roads. As can be inferred from thegraph and Equation.10, this method gives priorityto the more congested road during the allocation ofweights. Higher the TSR, Lower will be the weightand vice versa. At all times during the observationperiod, the road with the highest TSR is given themaximum green time. But this maximum value isdetermined taking into consideration the dynamictraffic condition of the other roads. So if traffic inany other road(s) begins to build up, this maximumvalue will decrease over time. This is where thealgorithm achieves fairness.
4 CONCLUSIONThis paper has presented a video based adaptivetime sharing model for road traffic management. Thetwo proposed methods have different approachesbut the essential idea is the same i.e. to minimizethe waiting period of vehicles at the junction. Inour findings FWC prioritize road states and allocategreen time in comparison to OWC which in turnwas found to be more equitable in the allocationof green time. The results have demonstrated thatthe model guarantees dynamic time allotment fordifferent traffic characteristics. Besides, it is worth
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Road TSR TSR TSR TSR TSR Fixed Green Green Green Green GreenCycle 1 Cycle 2 Cycle 3 Cycle 4 Cycle 5 time time 1 time 2 time 3 time 4 time 5
TABLE 6Green Timings for Each Road shown in Figure. (5)calculated by FWC (Day 2)
noticing that the developed model can be applieduniversally on multi road junctions irrespective ofnumber of roads.
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