Implications of link range and (In)stability on sensor network architecture
WiNTECH '06 Proceedings of the 1st international workshop on Wireless network testbeds, experimental evaluation & characterization
Surface street traffic estimation
Proceedings of the 5th international conference on Mobile systems, applications and services
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
A Traffic Congestion Estimation Approach from Video Using Time-Spatial Imagery
ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems
Vehicle speed estimation using acoustic wave patterns
IEEE Transactions on Signal Processing
Road traffic estimation using in-situ acoustic sensing
Proceedings of the ACM SIGCOMM 2010 conference
Kyun queue: a sensor network system to monitor road traffic queues
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Using mobile phone sensors to detect driving behavior
Proceedings of the 3rd ACM Symposium on Computing for Development
Participatory sensing based traffic condition monitoring using horn detection
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Information fusion based learning for frugal traffic state sensing
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.