Previous |  Up |  Next


travel time; automatic traffic incident detection (ATID); supporting decision model for police dispatching; police duty scheduling
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.
[1] Ahmed, S. A., Cook, A. R.: Time series models for freeway incident detection. Transport. Engrg. J. 106 (1980), 6, 731-745.
[2] Cai, X. Y.: Study of Urban Freeway Automatic Incident Detection Method. Doctor's Degree Thesis, Tongji University, Shangahi 2007.
[3] Cheng, Y., Zhang, M., Yang, D.: Automatic incident detection for urban expressways based on segment traffic flow density. J. Intelligent Transportation Systems: Technology, Planning, and Operations 19 (2015), 2, 205-213. DOI 10.1080/15472450.2014.977046
[4] Dinh, T.-U., Billot, R., Pillet, E., Faouzi, N.-E. El: Real-time queue-end detection on freeways with floating car data practice-ready algorithm. Transport. Res. Record 2470 (2014), 46-56. DOI 10.3141/2470-05
[5] Dong, W. L., Ding, H.: Development of emergency rescue decision scenarios deduction system of Tianjin. China J. Emergency Resuscitation and Disaster Medicine 6 (2015), 420-423.
[6] Dudek, C. L., Carroll, J. M., Nelson, B. N.: Incident detection on urban freeways. Transport. Res. Record 148 (2002), 1-46.
[7] Jain, S., McLean, C.: Simulation for emergency response: a framework for modeling and simulation for emergency response. In: Proc. 35th Conference on Winter Simulation: Driving Innovation, New Orleans 2003.
[8] Kirschfink, H., Hernández, J., Boero, M.: Intelligent traffic management models. In: Proc. European Symposium on Intelligent Techniques (ESIT) 2000, 9, pp. 36-45.
[9] Levin, M., Krause, G. M.: Incident detection: A Bayesian approach. Transportt. Res. Record 682 (1978), 52-58.
[10] Liu, Q., Lu, J., Chen, S.: Design and analysis of traffic incident detection based on random forest. J. Southeast University: English Edition 1 (2014), 88-95.
[11] Mountain, C. E.: Incident Detection Using the Standard Normal Deviate Model and Travel Time Information from Probe Vehicles. Master's Degree Thesis, Texas A and M University, Texas 1993.
[12] Pascale, A., Deflorio, F., Nicoli, M.: Motorway speed pattern identification from floating vehicle data for freight applications. Transport. Res. Part C: Emerging Technol. 51 (2015), 104-119. DOI 10.1016/j.trc.2014.09.018
[13] Payne, H. J.: Freeway Incident Detection Based Upon Pattern Classification. IEEE In Decision and Control including the 14th Symposium on Adaptive Processes, Houston, 1975. DOI 10.1109/cdc.1975.270592
[14] Raber, E., Hirabayashi, J. M., Mancieri, S. P., Jin, A. L., Folks, K. J., Carlsen, T. M., Estacio, P.: Chemical and biological agent incident response and decision process for civilian and public sector facilities. Risk Analysis 22 (2002), 2, 195-202. DOI 10.1111/0272-4332.00026
[15] Ritchie, S. G., Prosser, N. A.: A real-time expert system approach to freeway incident management. Transport. Res. Record 1320 (1990), 7-16. DOI 10.1016/0968-090x(96)00003-4
[16] Sermons, M. W., Koppelman, F. S.: Use of vehicle positioning data for arterial incident detection. Transport. Res. Part C: Emerging Technol. 4 (1996), 2, 87-96. DOI 10.1016/0968-090x(96)00003-4
[17] Sethi, V., Bhandari, N. S., Koppelman, F.: Incident detection using fixed detector and probe vehicle data. Transport. Res. Part C: Emerging Technol. 3 (1995), 2, 99-112. DOI 10.1016/0968-090x(94)00017-y
[18] Suyeong, K.: Theory of Human Intervention and Design of Human-computer Interfaces in Supervisory Control. Doctor's Degree Thesis, Massachusetts Institute of Technology, Boston 1997.
[19] Xu, X., Liu, L.: Comparative assessment of automatic incident detection algorithms based on simulated annealing algorithm. Technology and Economy in Areas of Communications 6 (2004), 2, 42-44.
[20] Xu, Z. X., Xi, S. R., Qu, J. Y.: Multi-attribute analysis of nuclear reactor accident emergency decision making. J. Tsinghua Univ. Science Technol. 3 (2008), 448-451.
[21] Yu, W., Park, S. J., Kim, D. S.: Arterial road incident detection based on time-moving average method in bluetooth-based wireless vehicle reidentification system. J. Transport. Engrg. 141 (2015), 3, 04014084. DOI 10.1061/(asce)te.1943-5436.0000748
[22] Zhang, W., Xu, J. M., Lin, M. F.: Map matching algorithm of large scale probe vehicle data. J. Transport. System Engrg. Inform. Technol. 7 (2007), 2, 39-45.
[23] Zhang, Q., Zhao, J. X., Dong, D. C.: Design of intelligent decision support system to traffic incident management. J. Railway Science Engrg. 5 (2008), 3, 83-88.
[24] Zhang, R. M., Zhou, Y., Xu, X. Y.: Traffic accident processingint elligent decision support system (YCIDSS). J. Computer Appl. 9 (2002), 60-61.
[25] Zhang, Y., Zuo, X., Zhang, L.: Traffic congestion detection based on GPS floating-car data. Procedia Engrg. 15 (2011), 5541-5546. DOI 10.1016/j.proeng.2011.08.1028
Partner of
EuDML logo