Road-RFSense: A Practical RF-Sensing Based Road Traffic Estimation System for Developing Regions. Rijurekha Sen, Abhinav Maurya, Bhaskaran Raman, Amarjeet Singh, Rupesh Mehta, Ramakrishnan Kalyanaraman. ACM Transactions on Sensor Networks, 2014. [abstract]
An unprecedented rate of growth in the number of vehicles has resulted in acute road congestion problems worldwide, especially in many developing countries. In this article, we present Road-RFSense, a practical RF sensing--based road traffic estimation system for developing regions. Our first contribution is a new mechanism to sense road occupancy, based on variation in RF link characteristics, when line of sight between a transmitter-receiver pair is obstructed. We design algorithms to classify traffic states into two classes, free-flow versus congested, at timescales of 20 seconds with greater than 90% accuracy. We also present a traffic queue length measurement system, where a network of RF sensors can correlate the traffic state classification decisions of individual sensors and detect traffic queue length in real time. Deployment of our system on a Mumbai road gives correct estimates, validated against 9 hours of image-based ground truth. Our third contribution is a large-scale data-driven study, in collaboration with city traffic authorities, to answer questions regarding road-specific classification model training. Finally, we explore multilevel classification into seven different traffic states using a larger set of RF-based features and careful choice of classification algorithms.
KyunQueue: A Sensor Network System To Monitor Road Traffic Queues. Rijurekha Sen, Abhinav Maurya, Bhaskaran Raman, Rupesh Mehta, Ramakrishnan Kalyanaraman, Nagamanoj Vankadhara, Swaroop Roy, Prashima Sharma. 10th ACM Conference on Embedded Networked Sensor Systems, 2012 (acceptance rate: 18.7%). [abstract]
Unprecedented rate of growth in the number of vehicles has resulted in acute road congestion problems worldwide. Better traffic flow management, based on enhanced traffic monitoring, is being tried by city authorities. In many developing countries, the situation is worse because of greater skew in growth of traffic vs the road infrastructure. Further, the existing traffic monitoring techniques perform poorly in the chaotic non-lane based traffic here. In this paper, we present Kyun Queue, a sensor network system for real time traffic queue monitoring. Compared to existing systems, it has several advantages: it (a) works in chaotic traffic, (b) does not interrupt traffic flow during its installation and maintenance and (c) incurs low cost. Our contributions in this paper are four-fold. (1) We propose a new mechanism to sense road occupancy based on variation in RF link characteristics, when line of sight between a transmitter-receiver pair is obstructed. (2) We design algorithms to classify traffic states into congested or free-flowing at time scales of 20 seconds with above 90% accuracy. (3) We design and implement the embedded platforms needed to do the sensing, computation and communication to form a network of sensors. This network can correlate the traffic state classification decisions of individual sensors, to detect multiple levels of traffic congestion or traffic queue length on a given stretch of road, in real time. (4) Deployment of our system on a Mumbai road, after careful consideration of issues like localization and interference, gives correct estimates of traffic queue lengths, validated against 9 hours of image-based ground truth. Our system can provide input to several traffic management applications like traffic light control, incident detection, and congestion monitoring.