Qiuping Li

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.

Urban Road Travel Speed Estimation Based on Low Sampling Floating Car Data

Using traffic data collected by floating car is a feasible way to determine road network traffic conditions. But conventional travel speed estimation from high sampling floating car data (FCD) is not applicable in practice. In this paper we propose a method for estimating road travel speed from low sampling FCD based on a trajectory-based approach, which also takes intersections into consideration. The experiment demonstrates that this approach can achieve a trade-off between low sampling FCD and higher estimation accuracy, and can be further used for traffic state analysis and short-term prediction.

Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data

Portraying urban functional zones provides useful insights into understanding complex urban systems and establishing rational urban planning. Although several studies have confirmed the efficacy of remote sensing imagery in urban studies, coupling remote sensing and new human sensing data like mobile phone positioning data to identify urban functional zones has still not been investigated. In this study, a new framework integrating remote sensing imagery and mobile phone positioning data was developed to analyze urban functional zones with landscape and human activity metrics. Landscapes metrics were calculated based on land cover from remote sensing images. Human activities were extracted from massive mobile phone positioning data. By integrating them, urban functional zones (urban center, sub-center, suburbs, urban buffer, transit region and ecological area) were identified by a hierarchical clustering. Finally, gradient analysis in three typical transects was conducted to investigate the pattern of landscapes and human activities. Taking Shenzhen, China, as an example, the conducted experiment shows that the pattern of landscapes and human activities in the urban functional zones in Shenzhen does not totally conform to the classical urban theories. It demonstrates that the fusion of remote sensing imagery and human sensing data can characterize the complex urban spatial structure in Shenzhen well. Urban functional zones have the potential to act as bridges between the urban structure, human activity and urban planning policy, providing scientific support for rational urban planning and sustainable urban development policymaking.

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.

Ant Colony Based Evacuation Route Optimization Model for Mixed Pedestrian-Vehicle Flows

Evacuation for large-scale activities usually involves a great number of pedestrians and vehicles. By applying ant colony optimization algorithm, an evacuation route optimization model for mixed pedestrian-vehicles flows is proposed in this paper. In this model, we construct a two-tier network structure in which the upper tier network is for path finding and evacuation route guidance, and the lower tier subnetwork which depicts the move directions of pedestrians and vehicles respectively is for the simulation of the movements as well as the conflicts between them. The experiment results show that the proposed model has the merit of modeling the interaction dynamics of pedestrians and vehicles and improving evacuation efficiency in an evacuation case of large-scale activities.

Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty

The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and tabu search (TS) was designed to solve the model. An experiment optimizing rescue routing plans under a real urban storm event, was carried out to validate the proposed model. The experimental results showed how our approach can improve rescue efficiency with high travel time reliability.

A spatial parallel heuristic approach for solving very large-scale vehicle routing problems

The vehicle routing problem (VRP) is one of the most prominent problems in spatial optimization because of its broad applications in both the public and private sectors. This article presents a novel spatial parallel heuristic approach for solving large-scale VRPs with capacity constraints. A spatial partitioning strategy is devised to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. An additional local search and perturbation mechanism around the border area of spatial cells is used to improve route segments across spatial cells to overcome the border effect. The results of one man-made VRP benchmark and three real-world super-large-scale VRP instances with tens of thousands of nodes verify that the presented spatial parallel heuristic approach achieves a comparable solution with much less computing time.