Edge-Level Real-Time Traffic Estimation with Limited Infrastructure

In developing countries like India, limited Intelligent Transportation Systems (ITS) infrastructure is available and it is difficult to generate real time traffic information for city wide or larger regions. On the other hand, cellular network is widely deployed in India covering major part of the road network. However, cellular network based positioning data have large location error (250-500 meters) and is not directly useful for edge level travel time or speed estimation. Also, penetration of GPS enabled vehicles/phones is very low in India to generate useful real time traffic information. In this simulation based study we use the map matching algorithm, proposed in [1], that operates on a series of position points having large location error to get accurate vehicle count for every edge, with time lag of about 10 minutes. Temporal extrapolation using exponential moving average is used to overcome the time lag. The vehicle count is then used to estimate edgewise vehicle flow and occupancy. Simulation results show the mean estimation error of less than 10%. Then, the edge level congestion estimation is done using volume-capacity ratio (V/C ratio) and mean error of less than 10% is observed. These results are encouraging as they show feasibility of using widely deployed cellular network for real time congestion level estimation. For edge level speed estimation, a subset of edges are chosen for infrastructure deployment using historical data of congestion levels observed on various edges in a road network. The edges with ITS infrastructure are used to learn the occupancy-speed relationship which is then spatially extrapolated to infrastructure-less edges using GPS probe data to enable travel speed estimation on all the edges in the road network. The simulation results show that speed estimation with ninety percentile error of less than 15% is achievable.