Kybernetika 52 no. 1, 106-130, 2016

Automatic detection of urban traffic incidents and supporting decision model for police dispatching based on travel time

Guangyu Zhu, Jingxuan Zhang, Haotian Lin and Peng ZhangDOI: 10.14736/kyb-2016-1-0106


It is very important to get the complete and timely information of urban road traffic incidents and then to make a reasonable strategy for police dispatching. By improving the efficiency of sending police, the loss of traffic incidents and the pressure of traffic police will be reduced greatly. An assistant decision model of police dispatching based on the information of automatic traffic incident detection is proposed in this paper. Firstly, an automatic traffic incident detection algorithm is put forward based on travel time of urban road section. Two severity dimensions of traffic incidents could be detected, and the average detection time could be reduced significantly. Then, an assistant decision model of police dispatching is established based on Bayesian decision theory by utilizing the results of automatic incident detection algorithm as well as the experience of police officer who in charge of police duty scheduling. Even though the police officer doesn't get the clear and complete information of incidents; he can also qualify the probability of actual states of different traffic incidents with the aided model. Case studies indicate that the model can help police officer to make the strategies of police dispatching and then reduce the risks of decision-making at a certain extent.


travel time, automatic traffic incident detection (ATID), supporting decision model for police dispatching, police duty scheduling




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