Abstract
The traffic condition can be improved with the real-time traffic information that is obtained from vehicle detectors (VDs) or probe vehicles (PVs). Using PVs has a lower cost and a broader coverage, but cannot measure the traffic flow like using VDs. Most studies on PVs used Fundamental Diagram (FD) models to investigate the speed-density-flow relationship. However, they didn’t notice that the driving speed is varied with the traffic signal. Accordingly, we propose an approach, Flow Estimation with Traffic Signal (FETS), to estimate the traffic flow in urban roads by considering the traffic signal. The speed is calculated at green light and the density is acquired by the queue length at red light. The experiment results show that the mean relative error of FETS is 44.4% while the best one of the FD models is 117.3%, representing that FETS has better accuracy than FD models in urban roads.
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Acknowledgement
The authors would like to thank Ministry of Science and Technology of Republic of China, Taiwan, for financially supporting this research under Contract No. MOST 106-3114-E-011-003 and MOST 106-2221-E-011-013.
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Lai, YC., Huang, SY. (2017). Accurate Traffic Flow Estimation in Urban Roads with Considering the Traffic Signals. In: Peng, SL., Lee, GL., Klette, R., Hsu, CH. (eds) Internet of Vehicles. Technologies and Services for Smart Cities. IOV 2017. Lecture Notes in Computer Science(), vol 10689. Springer, Cham. https://doi.org/10.1007/978-3-319-72329-7_5
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DOI: https://doi.org/10.1007/978-3-319-72329-7_5
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