GPU Based Genetic Algorithms for the Dynamic Sub-area Division Problem of the Transportation System

Abstract At the early stage, the transportation system was controlled in a centralized way. As it grows larger, the system becomes decentralized. Nowadays, most of the commercial transportation systems work in a distributed way. The whole city or town is divided into static or dynamic sub-areas by some rules or heuristics. In every sub-area, the strategy is determined independently. As the cloud computing becomes popular, we propose the idea to control and management the transportation in a new centralized way, that is, all the information is collected together at the cloud side. The effect of the centralized control can be no worse than the decentralized one, as the decentralized control strategy is also one strategy of the centralized control. The division of the sub-areas is determined by computational experiments for different scenarios. We adopt the Multi-Agent System (MAS) model for the traffic flow simulation. And we use the Genetic Algorithms (GA) as the method for the computation to obtain good divisions. To overcome the difficult of the heavy computational burdens, we employ the Graphics Processing Unit (GPU) to accelerate the GA. We test the method on a 5×5 lattice road network and the 18 intersection Zhongguancun road network of Beijing. A speedup factor of around 110 is achieved.

[1]  Kai Wang,et al.  Artificial Societies and GPU-Based Cloud Computing for Intelligent Transportation Management , 2011, IEEE Intelligent Systems.

[2]  R. D. Bretherton,et al.  Optimizing networks of traffic signals in real time-the SCOOT method , 1991 .

[3]  Wang Feiyue.  Concepts and Frameworks of Artificial Transportation Systems , 2004 .

[4]  Mo Han.  Automatic division of traffic control sub-area under condition of route guidance , 2002 .

[5]  Zuo Zhang,et al.  The optimality condition of the multiple-cycle smoothed curve signal timing model , 2013 .

[6]  Kai Lu,et al.  Research on Fast Dynamic Division Method of Coordinated Control Subarea: Research on Fast Dynamic Division Method of Coordinated Control Subarea , 2012 .

[7]  Bo Chen,et al.  A Review of the Applications of Agent Technology in Traffic and Transportation Systems , 2010, IEEE Transactions on Intelligent Transportation Systems.

[8]  Kai Wang,et al.  A GPU-Based Parallel Genetic Algorithm for Generating Daily Activity Plans , 2012, IEEE Transactions on Intelligent Transportation Systems.

[9]  Kai Nagel,et al.  Using common graphics hardware for multi-agent traffic simulation with CUDA , 2009, SimuTools.

[10]  R J Walinchus,et al.  REAL-TIME NETWORK DECOMPOSITION AND SUBNETWORK INTERFACING , 1971 .

[11]  Kai Wang,et al.  Agent-based traffic simulation and traffic signal timing optimization with GPU , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[12]  Kai Nagel,et al.  Multi-agent traffic simulation with CUDA , 2009, 2009 International Conference on High Performance Computing & Simulation.

[13]  Kai Wang,et al.  GPU based ordinal optimization for traffic signal coordination , 2012, Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics.

[14]  Nanning Zheng,et al.  Cognitive Cars: A New Frontier for ADAS Research , 2012, IEEE Transactions on Intelligent Transportation Systems.

[15]  E C Chang.  EVALUATION OF INTERCONNECTED ARTERIAL TRAFFIC SIGNALS , 1986 .

[16]  Ana L. C. Bazzan,et al.  A Distributed Approach for Coordination of Traffic Signal Agents , 2004, Autonomous Agents and Multi-Agent Systems.

[17]  Lu Kai,et al.  Research on Fast Dynamic Division Method of Coordinated Control Subarea , 2012 .

[18]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[19]  Shigeyoshi Tsutsui,et al.  Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study , 2009, GECCO '09.

[20]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

[21]  Kai Wang,et al.  A GPU based trafficparallel simulation module of artificial transportation systems , 2012, Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics.