Xinlu Zong

Multiobjective evacuation route assignment model based on genetic algorithm

Emergency evacuation in public places becomes a research focus in recent decades. One major objective of evacuation is to maximize the efficiency of the whole evacuation system. The paper proposes a multiobjective evacuation route assignment model to plan an optimal egress route set for the individual evacuees. The three objectives in the proposed model are to minimize the total evacuation time, minimize the total travel distance of all the evacuees and minimize the congestion during the evacuation process. These objectives need to be satisfied simultaneously while some of them conflict with each other. The travel speed on each road segment is related with the time and the number of evacuees on it during a certain time period. The congestion of a road segment is modeled as the density of evacuees passing it in time and space dimensions. The evacuation route assignment problem can be treated as a combinatorial optimization problem. A multiobjective optimization model based genetic algorithm is adopted to solve the proposed evacuation routing problem. Wuhan Sport Center in Wuhan city of China was taken as the experiment scenario to test the performance of the proposed algorithm. The results showed that it can provide some system optimal evacuation plans. Meanwhile, the multi-objective optimization model based genetic algorithm can produce a pareto optimal set rather than single optimal point, thus the model can give alternative strategies for the evacuation policy makers.

Multi-ant colony system for evacuation routing problem with mixed traffic flow

Evacuation routing problem with mixed traffic flow is complex due to the interaction among different types of evacuees. 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 process. In this paper, an approach based on multi-ant colony system was proposed to tackle evacuation routing problem with mixed traffic flow. Total evacuation time is minimized and traffic load of the whole road network is balanced by this approach. The experimental results show that this approach based on multi-ant colony system can obtain better solutions than single ant colony system and solve mixed traffic flow evacuation problem with reasonable routing plans.

A conflict-congestion model for pedestrian-vehicle mixed evacuation based on discrete particle swarm optimization algorithm

A simulation model based on temporal-spatial conflict and congestion for pedestrian-vehicle mixed evacuation has been investigated. Assuming certain spatial behaviors of individuals during emergency evacuation, a discrete particle swarm optimization with neighborhood learning factor algorithm has been proposed to solve this problem. The proposed algorithm introduces a neighborhood learning factor to simulate the sub-group phenomenon among evacuees and to accelerate the evacuation process. The approach proposed here is compared with methods from the literatures, and simulation results indicate that the proposed algorithm achieves better evacuation efficiency while maintaining lower pedestrian-vehicle conflict levels.

Particle swarm and NSGA-II based evacuation simulation and multi-objective optimization

Because of the high-dense population and complex structure, the large public building faces a unique challenge in developing effective emergency evacuation plans. And due to the large scale and numbers of evacuees in real evacuation, real tests are impractical. Therefore, the simulation of evacuation becomes a wonderful choice in program planning. Particle Swarm is as one of the multi-agent based simulation method that can simulate complex behaviors of individuals. NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a kind of optimization method for multi-objective optimization problem. In this paper, we propose a novel multi-objective evolutionary algorithm (named as PNMO, Particle swarm & NSGA-II based Multi-objective Optimization) which simulates evacuation process as well as optimizing the generated evacuation plans. The experiment shows that this method possesses superior performance in evacuation planning.

Positive point charge potential field based ACO algorithm for multi-objective evacuation routing optimization problem

Multi-objective evacuation routing optimization problem is defined to find out optimal evacuation routes for a group of evacuees according to multiple evacuation objectives. For improving the evacuation efficiency, we abstracted the evacuation zone as a positive-point-charge-potential-field-like model (PPCPF-like model), and we proposed PPCPF-ACO algorithm to solve this problem based on the proposed model. In PPCPF-ACO algorithm, we use non-dominated sorting based roulette wheel routing method (NSRWR) to further improve evacuation efficiency. In Wuhan Sports Center case, we compared PPCPF-ACO with HMERP-ACO (hierarchical multi-objective evacuation routing problem - ant colony optimization) and traditional ACO according to three evacuation objectives, namely, total evacuation time, total evacuation route length and cumulative congestion degree. The experimental results show that PPCPF-ACO has a better performance than HMERP-ACO algorithm and traditional ACO algorithm while solving multi-objective evacuation routing optimization problem.

Multi-Objective optimization for massive pedestrian evacuation using ant colony algorithm

Evacuation route planning is one of the most crucial tasks for solving massive evacuation problem In large public places, pedestrians should be transferred to safe areas when nature or man-made accidents happen A multi-objective ant colony algorithm for massive pedestrian evacuation is presented in this paper In the algorithm, three objectives, total evacuation time of all evacuees, total routes risk degree and total crowding degree are minimized simultaneously Ants search routes and converge toward the Pareto optimal solutions in the light of the pheromone The experimental results show that the approach is efficient and effective to solve massive evacuation problem with rapid, reasonable and safe plans.

Space-time simulation model based on particle swarm optimization algorithm for stadium evacuation

In this paper, a space-time simulation model based on particle swarm optimization algorithm for stadium evacuation is presented. In this new model, the fast evacuation, going with the crowd and the panic behaviors are considered and the corresponding moving rules are defined. The model is applied to a stadium and simulations are carried out to analyze the spacetime evacuation efficiency by different behaviors. The simulation results show that the behaviors of going with the crowd and panic will slow down the evacuation process while quickest evacuation psychology can accelerate the process, and panic is helpful to some extent. The setting of parameters is discussed to obtain best performance. The simulation results can offer effective suggestions for evacuees under emergency situation.

Multiobjective optimization of evacuation routes in stadium using superposed potential field network based ACO

Multiobjective evacuation routes optimization problem is defined to find out optimal evacuation routes for a group of evacuees under multiple evacuation objectives. For improving the evacuation efficiency, we abstracted the evacuation zone as a superposed potential field network (SPFN), and we presented SPFN-based ACO algorithm (SPFN-ACO) to solve this problem based on the proposed model. In Wuhan Sports Center case, we compared SPFN-ACO algorithm with HMERP-ACO algorithm and traditional ACO algorithm under three evacuation objectives, namely, total evacuation time, total evacuation route length, and cumulative congestion degree. The experimental results show that SPFN-ACO algorithm has a better performance while comparing with HMERP-ACO algorithm and traditional ACO algorithm for solving multi-objective evacuation routes optimization problem.

Multi-objective optimization model based on steady degree for teaching building evacuation

In this paper, the process of evacuation in teaching building is considered. The concept of steady degree based on cellular automata and potential field is introduced and it can describe the behavior tendency of evacuees during the evacuation process. With the help of steady degree, the model simulates the indoor evacuation behavior. To reduce the congestion and evacuation time, a multi-objective optimization model considering steady degree and evacuation clearance time is proposed. Finally, an experiment in the Teaching Building No.1 of Wuhan University of Technology is carried out. The results show that this model can reduce the clearance time of emergency evacuation in teaching building compared to other models.

PSO-based emergency evacuation simulation

The Emergency Evacuation Simulation (EES) has been increasingly becoming a hotspot in the field of transportation. PSO-based EES is a good choice as its low computation complexity compared with some other algorithms, especially in an emergency. The selection of fitness function of each particle in PSO is a key problem for EES. This paper will introduce some fitness functions for EES and present a new fitness function called Triple-Distance Safe Degree (TDSD). Through theoretical analysis and experimental validation, the TDSD is proved to be much better than other fitness functions introduced in this paper.