TY - GEN
T1 - Dynamic highway congestion detection and prediction based on shock waves
AU - Huang, Dijiang
AU - Shere, Swaroop
AU - Ahn, Soyoung
PY - 2010
Y1 - 2010
N2 - Existing highway traffic monitoring system requires to deploy a large number of sensors and video cameras to detect traffic congestions, which is costly and prone to errors and failures [1]. In this paper, we present a distributed traffic detection and prediction solution by using shock wave traffic model. We develop a Hello protocol to maintain the vehicle sequence on the same lane. Based on the measurements of velocity and distance between immediate leading and following vehicles, a vehicle can detect and compute shock wave velocity incurred by vehicle merges or obstacles on the highway. When velocity changes occur continuously, congestions will be formed, which can be detected and predicted by the vehicles through a shock wave detection procedure. Our solution is effective since we only require vehicles to communicate with its neighboring vehicles within its wireless communication range VANET, Traffic Modeling, Congestion Detection and Prediction.
AB - Existing highway traffic monitoring system requires to deploy a large number of sensors and video cameras to detect traffic congestions, which is costly and prone to errors and failures [1]. In this paper, we present a distributed traffic detection and prediction solution by using shock wave traffic model. We develop a Hello protocol to maintain the vehicle sequence on the same lane. Based on the measurements of velocity and distance between immediate leading and following vehicles, a vehicle can detect and compute shock wave velocity incurred by vehicle merges or obstacles on the highway. When velocity changes occur continuously, congestions will be formed, which can be detected and predicted by the vehicles through a shock wave detection procedure. Our solution is effective since we only require vehicles to communicate with its neighboring vehicles within its wireless communication range VANET, Traffic Modeling, Congestion Detection and Prediction.
KW - Congestion detection and prediction
KW - Traffic modeling
KW - VANET
UR - http://www.scopus.com/inward/record.url?scp=78649284884&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649284884&partnerID=8YFLogxK
U2 - 10.1145/1860058.1860061
DO - 10.1145/1860058.1860061
M3 - Conference contribution
AN - SCOPUS:78649284884
SN - 9781450301459
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 11
EP - 20
BT - Proceedings of the 7th ACM Int. Workshop on VehiculAr InterNETworking, VANET '10, Co-located with MobiCom'10 and 11th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc'10
PB - Association for Computing Machinery
T2 - 7th ACM International Workshop on VehiculAr InterNETworking, VANET '10
Y2 - 20 September 2010 through 24 September 2010
ER -