Adaptive Traffic Control Brian Dong & Graham Roff

Motivation Traffic Jams: gridlock at rush hour, urban sprawl Current implementation: Fixed-cycle or even fixed-interval, environment insensitive design Optimizing every intersection is not practical: requires collection of data at each intersection in the city to analyze traffic patterns at all hours of day Would be nice if a traffic light controller could figure this out all on its own…

Design Challenges Goal: Develop an adaptive algorithm and embedded traffic controller that can optimize traffic flow without any a priori information other than which lanes can be green simultaneously. Algorithm complexity: how to weigh the various factors/parameters affecting the intersection lanes (previous wait times, traffic volume, current cars waiting) Simulation: how to create a reasonably accurate representation of cars arriving at an intersection

System Design Modular design: intersection module contains lane modules, each containing a hardware interface Simulator communicates by triggering the hardware (car sensor) and responding to hardware state changes (light changed) Intersection module controls changing state (lights) of intersection Decisions based on state “metric delta”

System Diagram

Algorithm Design Decisions based on state “metric” for each lane A given state causes a change of metric over time: The metric delta is evaluated over every possible next state State with best reduction in metric is chosen

Algorithm Challenges Time: how to determine t in the above equations Lanes go green until idle or max timeout expires Red light time for other lanes is minimum time light will be red for based on light concurrency Simultaneous lanes: multiple lanes may be green at the same time, and light changes are asynchronous Algorithm must handle switching the first idle lane vs. waiting for all green lanes to go idle before switching to new state

Simulator Design Needs to represent the bursty nature of traffic: groups of cars arrive sequentially at an intersection, there are time delays between these groups/bursts. Solution: Use two Gaussian random variables of different means and variances. First variable determines the time delta between cars during a burst Second variable determines the time delta between the bursts Use a uniformly distributed random variable to determine when the burst occurs Interface with TLC controller: Generate test vectors using Matlab and compile into SystemC. Simulator module actuates the TLC hardware sensors based on the delta-queue created in Matlab

Example and Demonstration

Results Three different control algorithms were implemented for comparison Fixed time Fixed cycle “Optimum” Measured the mean and variance of the waiting time, and the percentage of stopped cars A basic four-way intersection with eight lanes (four straight and four left) was used for analysis Number of traffic scenarios examined, including: All lanes equally busy All straight lanes busy North/south straight lanes busy

Results - 2 The results for the equally busy intersection are shown below:   Fixed Interval Fixed Cycle Optimum Wait time mean 67.0 52.2 42.1 Wait time variance 42.7 36.2 35.2 Stopped cars 94% 92% Fixed interval is worst performer, followed by fixed cycle Optimum intersection performs the best Relatively small difference between fixed cycle and optimum – however optimum requires no additional state change configuration Plot of lane queue length versus time is also generated to examine algorithm behavior

Results - 3

Future Directions Basic principle is proven, further testing remains Support for pedestrians, emergency vehicles, etc…is not included as these are not relevant to algorithm efficiency Further improvement could be obtained by allowing intersection communication – beyond the scope of this project

Conclusions Complex problem: Traffic light control is more complicated than one might expect Many possible algorithms: Impossible to declare one algorithm “optimal” as there is no real definition of optimum Our general “metric” based approach allows easy tweaking of details to optimize the system for a given algorithm For a simple waiting time evaluation this algorithm performs the best of the three tested and is simplest to configure Testing: Biggest concern in a production environment is testing – any solution needs to be tested across every possible scenario

Questions?