Research On Multi-objective Spatial-temperal Evacuation Model Of Pedestrian-vehicle Mixed Traffic Flow

Pedestrian-vehicle mixed evacuation is an important and urgent issue, which confronted commonly by modern transportation, earth observation, geography, artificial intelligence, computer science, modern communication and public health and security. The existed research on evacuation, on the one hand does not reach the demand of evacuation mixed with pedestrians and vehicles. Most of evacuation models focus on pedestrian evacuation inside building while the models based on traffic flow are directly vehicle-oriented. As for passing characteristics of pedestrian-vehicle mixed traffic in China, however, the current studies on evacuation are lack off analysis on movement pattern and evolutionary process of pedestrian-vehicle mixed evacuation, which leads to inapplicable in practice and difficult to form scientific evacuation measures. On the other hand, there are few works studying evacuation modeling from the perspective of optimization, the existing optimization models only consider optimizing one objective for single transportation mode or converting multi-objectives optimization problem into single objective optimization problem, whose single optimal solution can not offer manager decision foundation on multiple objectives.To solve these problems described above, based on the computer simulation and multi-objective optimization, many researches are carried out:Focusing on the evacuation routing problem in large public place, a hierarchical directed network is presented, which describes a destination-oriented structure. The hierarchical directed network offers hierarchical guidance for pedestrian during the process of searching for exits. Only a pedestrian whose current position belongs to a certain rank achieves the rank's corresponding destination, he/she is allowed to enter into next rank and finally reach one of the terminal exits. This strategy can effectively avoid blindness in evacuation process. On the basis of hierarchical directed network, a multi-objective optimization evacuation model is proposed to minimize three objectives simultaneously, total evacuation time, total evacuation distance and cumulative congestion degrees. A hierarchical multi-objective evacuation routing problem algorithm based on ant colony optimization algorithm is designed, which is tested using a stadium. The results of four different optimization algorithms are analyzed, and congestion degree, space-time distribution of pedestrian number and evacuation paths are discussed. The proposed model and algorithm in this paper can solve the problem and provide multiple safe, efficient evacuation plans.Based on the analysis of characteristics of traffic flow mixed pedestrians and vehicles, a multi-objective optimization model was proposed to tackle pedestrian-vehicle mixed evacuation problem under emergency situation. This model aims to minimize total evacuation time and maximize mixed road utilization of the whole road network simultaneously. To solve this model, a multi-objective ant colony optimization algorithm is designed. In order to tackle the deficiency of the algorithm in pedestrian-vehicle mixed evacuation problem, an improved ant colony optimization algorithm with proper heuristic information, self-adaptive tabu adjustment and pheromone updating strategy constrained by threshold is described. The proposed model and algorithms are tested using a case integrated a stadium of Wuhan Sports Center (in China) and road network around it. Evacuation performances with different mixed proportion of pedestrians to vehicles are analyzed. The experimental results show that this model and algorithm are effective for evacuation problem with mixed traffic flow, and mixed evacuation results are better when the mixed ratio of pedestrian ranges from 50% to 80%. By comparing the solutions and spatial-temperal performances of two methods, the improved approach is better in solving pedestrian-vehicle mixed evacuation problem. The research in this paper can offer decision support for planning pedestrian-vehicle mixed evacuation in large common place.Studying the behavior characteristics of evacuees, interrelationship between the same individuals and interaction between different individuals in the process of pedestrian-vehicle mixed evacuation, evaluation criteria such as minimal evacuation time and balanced traffic load, are designed. The positive feedback mechanism of single ant colony system may lead to congestion on some optimum routes. Like different ant colony systems in nature, different components of traffic flow compete and interact with each other during evacuation. According to the group effect in evacuation process, an approach based on multi-ant colony system evolution is proposed to tackle mixed traffic flow evacuation problem. A multi-objective model is established to minimize total evacuation time and balance traffic load of the whole road network. Communication mechanism between colonies is used to simulate the interaction between pedestrians and vehicles. Performances of the two approach based on single ant colony system and multiple ant colonies system are analyzed, including distribution of solutions, evacuation efficiency and distribution of individuals in each exit. The experimental results indicate the superiority of coevolution of multi-ant colony system over single ant colony system in mixed evacuation problem.By studying some special psychology and behaviors of individuals in emergency evacuation, including the psychology of going with the crowd, sub-group phenomenon, and swarm intelligence theory is utilized to simulate and optimize evacuation process mixed with pedestrians and vehicles in this paper. Pedestrian-vehicle spatial-temperal conflict and spatial-temperal congestion are defined. And an evacuation model base on minimizing both spatial-temperal conflict and congestion is presented. Discrete particle swarm optimization with neighborhood learning factor algorithm is proposed to solve pedestrian-vehicle mixed evacuation problem. By the approach each evacuation individual is considered as a particle. The particles moves impacted by self-experience and best individual in swarm, in addition, they learn from the best particle in their respective neighborhood particles. This local and global learning mechanism simulates the individual's behavior of going with the crowd as well as accelerates the process of searching exits. The simulation results of ant colony optimization algorithm, multi-ant colony optimization algorithm, particle swarm optimization algorithm and discrete particle swarm optimization with neighborhood learning factor algorithm are compared and analyzed.