An algorithm to estimate mean vehicle speed from MPEG Skycam video
Multimedia Tools and Applications
A High Efficient System for Traffic Mean Speed Estimation from MPEG Video
AICI '09 Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 03
Building a transportation information system using only GPS and basic SMS infrastructure
ICTD'09 Proceedings of the 3rd international conference on Information and communication technologies and development
EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
An algorithm to estimate mean traffic speed using uncalibrated cameras
IEEE Transactions on Intelligent Transportation Systems
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Monitoring traffic density and speed helps to better manage traffic flows and plan transportation infrastructure and policy. In this paper, we present techniques to measure traffic density and speed in unlaned traffic, prevalent in developing countries, and apply those techniques to better understand traffic patterns in Bengaluru, India. Our techniques, based on video processing of traffic, result in about 11% average error for density and speed compared to manually-observed ground truth values. Though we started with intuitive and straight-forward image processing tools, due to a myriad of non-trivial issues posed by the heterogeneous and chaotic traffic in Bengaluru, our techniques have grown to be non-obvious. We describe the techniques and their evaluation, with details of why simpler methods failed under various circumstances. We also apply our techniques to quantify the congestion during peak hours and to estimate the gains achievable by shifting a fraction of traffic to other time periods. Finally, we measure the fundamental curves of transportation engineering, relating speed vs. density and flow vs. speed, which are integral tools for policy makers.