Adaptive Traffic Light Control For Traffic Network

Abstract Our objective is to reduce the delay experienced by the green wave in a traffic network. We propose two algorithms which based on two different baselines – Adaptive Green Wave Algorithm (based on Conventional Green Wave) Adjust phase offset adaptively according to local queue information – Predictive Algorithm (based on phase fairness) Introduces a predictive total waiting time as metric Predicts the arrivals so that lights can turn green before cars arrive

Conclusion Adaptive green wave algorithm decreaes the delay of cars in the green wave direction (excluding cars that turn into the green wave) Predictive algorithm decreases the green wave delay compared to basic phase fairness, but does not create a green wave, however it improves overall system’s average delay

What is a Conventional Green Wave? – Fixed sequence of phases to be green in one round – Fixed green length for each phase, divide the cycle time according to the flow fraction – Consecutive traffic lights on dominant diretion has a fixed offset Baseline Description Cars won’t stop if no cars waiting in the next light, if there is queue accumulating, then the flow has to be slowed down or even stopped Problem:

Baseline Description What is a Phase Fairness Algorithm? – The objective is to minimize the sum of the waiting time for each phase. A system is fair when we cannot reduce the sum of the waiting times for a phase without increasing the sum of the waiting times for a phase with a larger sum. – Use the metric of computing the total waiting time every cycle and picks the largest one to be green next – The vehicles in a phase with fewer vehicles may wait longer, but a single vehicle waiting at a red signal will eventually accumulate a longer delay than a larger number of vehicles in a more heavily travelled lane.

Conventional Green Wave At T = t Turn green at T = t 1 Length of Street: 216 Units Speed of a car: 8 units/cycle Offset: 216 / 8 = 27

Network Model Single Intersection

Network Model 10*10 Torroidal Network – Manhattan street network is torroidal in nature – Its a wrap-around system in which the cars go back from the other side of the network when reaching the edge. – There is no edge – effect, every node is the same in the network Turn Ratios – Determine the traffic flows for four directions to create different strength of green wave – If the turn ratio of other three directions is n times as the turn ratio of green wave direction, then the flow of green wave is n times as each other three flows

Adaptive Green Wave Algorithm Gives priority to the cars in the green wave direction. Retrieve the time the previous intersection turned green to adjust the timing of following light. Offset of the green wave is shifted according to its current queue length – Clear the queue length before the incoming traffic flow Fixed time allocation to green wave, and variable time allocation to other phases. – Fair allocation depends on the fraction of total waiting time for each phase out of the all phases – The phase has largest fraction goes first – Due to satisfying the green wave always, the phase that goes last might be punished, but it will get a higher chance to go first since it may have higher total waiting time

Adjust Offset Turn green at T = t 1 - Delta(t) Turn green at T = t 1

Adjust Offset Turn green at T = t 1 - Delta (t) - 1.2*Queue Length Shifting the queue back based on the information of the cars waiting in the current queue, so as to empty the queue before other cars arrive The remaining of the light time is fairly allocated to the other 3 phases based on the total waiting time. Turn green at T = t 1

Adjust Offset Green Phase=68 Other Phases fairly allocated Turn green at T = t 1 Turn green at T = t 1 - Delta (t) - 1.2*Queue Length

Predictive Algorithm Based on the proportional fairness according to total waiting time Introducing a new factor, predicted waiting time, which predicts the total waiting time for each phase with following 5, 10 or 15 seconds assuming it will face red light The one who has the largest predictive total waiting time turns green

Predictive Algorithm 10s Out of consideration Take into consideration

Predictive Algorithm The longer time we consider for prediction, the more we give priority to the heavy flows. Decreases the average delay in the green wave, but increases the delay for others, which might end up increasing the average delay Among the three trials of 5s, 10s, 15s prediction, we pick the one which has the smallest green wave delay under the condition that average delay is smaller than the phase fairness algorithm as the best one.

Parameter NameValue Time Unit(s)1 Length Unit(m)1 Velocity of Vehicle(m/s) 8 Length of Lane(m)216 Simulation Time(s)4000 Turn Ratio0.05/0.15 or 0.05/0.3 Amber Light time(s)2 Simulation Parameters

Simulation Results Do simulations under 3 traffic load and 2 turn ratio settings Compare 4 algorithms (2 baselines and 2 proposed algorithms) in terms of : – Average delay of cars in green wave – Average delay of all cars in network

Average Delay Comparison For 3 times the flow from the north direction

Average Delay Comparison For 6 times the flow from the north direction

Green Wave Delay Comparison For 3 times the flow from the north direction GW LOAD=15%GW LOAD=30%GW LOAD=60%

Green Wave Delay Comparison For 6 times the flow from the north direction GW LOAD=41%GW LOAD=82%

Conclusions In predictive algorithm, the total average delay is significantly decreased in comparison to the fixed green wave, and is smaller than phase fairness Average delay improvement compared to conventional green wave Average DelayBaseline Compared Average Improvement 3 times flow in GW 6 times flow in GW Predictive Algorithm Conventional Green Wave 63.0%76.0% Basic Phase Fairness 16.9%14.1% Adaptive GW Algorithm Conventional Green Wave 13.8%37.9%

Looking at the perspective of green-wave delay, the offset shift algorithm with fair allocation works best. Both our algorithms perform better than the fixed green wave which is static in nature. GW DelayBaseline Compared Average Improvement 3 times flow in GW 6 times flow in GW Predictive Algorithm Conventional Green Wave -- Basic Phase Fairness 36.6%30.9% Adaptive GW Algorithm Conventional Green Wave 66.6%72.5%