Horn-ok-please

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Abstract
Road congestion is a common problem worldwide. Existing Intelligent Transport Systems (ITS) are mostly inapplicable in developing regions due to high cost and assumptions of orderly traffic. In this work, we develop a low-cost technique to estimate vehicular speed, based on vehicular honks. Honks are a characteristic feature of the chaotic road conditions common in many developing regions like India and South-East Asia. We envision a system where dynamic road-traffic information is learnt using inexpensive, wireless-enabled on-road sensors. Subsequent analyzed information can then be sent to mobile road users; this would fit well with the burgeoning mobile market in developing regions. The core of our technique comprises a pair of road side acoustic sensors, separated by a distance. If a moving vehicle honks between the two sensors, its speed can be estimated from the Doppler shift of the honk frequency. In this context, we have developed algorithms for honk detection, honk matching across sensors, and speed estimation. Based on the speed estimates, we subsequently detect road congestion. We have done extensive experiments in semi-controlled settings as well as real road scenarios under different traffic conditions. Using over 18 hours of road-side recordings, we show that our speed estimation technique is effective in real conditions. Further, we use our data to characterize traffic state as free-flowing versus congested using a variety of metrics: the vehicle speed distribution, the number and duration of honks. Our results show clear statistical divergence of congested versus free flowing traffic states, and a threshold-based classification accuracy of 70-100% in most situations.

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