Traffic signal control based on a predicted traffic jam distribution |
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Authors: | Cheng-You Cui Ji-Sun Shin Fumihiro Shoji Hee-Hyol Lee |
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Affiliation: | (1) School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China;(2) Stillman School of Business, Seton Hall University, South Orange, New Jersey, USA |
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Abstract: | In this article, we propose a new method of traffic signal control based on the predicted distribution of traffic jams. First, we built a forecasting model to predict the probability distribution of vehicles being in a traffic jam during each period of the traffic signals. A dynamic Bayesian network was used as the forecasting model, and this predicted the probability distribution of the number of standing vehicles in a traffic jam. According to calculations by the dynamic Bayesian network, a prediction of the probability distribution of the number of standing vehicles at each time will be obtained, and a control rule to adjust the split and cycle of the signals to maintain the probability of a lower limit and a ceiling of standing vehicles is deduced. Through a simulation using the actual traffic data of a city, the effectiveness of our method is shown. |
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