Linqing Li

Bio: Linqing Li is an academic researcher from Southwest Jiaotong University. The author has contributed to research in topics: Flow network & Girvan–Newman algorithm. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Resilience model and recovery strategy of transportation network based on travel OD-grid analysis

Abstract: Transportation is the key to a city's prosperity, however, there is possibility that the development and expansion of city make the transportation system complicated, uncertain and vulnerable, especially when in the face of damage. Although resilience is a critical factor for understanding and managing the damage and recovery of the transportation system, study on model of transportation resilience, which is able to effectively utilize actual data and is based on real cases, is still not enough. Therefore, this paper proposes a resilience model and investigates the optimization of the recovery strategy according to this model. It constructs a modeling framework based on the OD-grid network and provides a brand-new performance metric for transportation network resilience analysis based on grid capacity. It also develops two resilience assessment models and compares their characters. Focusing on the recovery of transportation resilience process, it presents two recovery strategies considering recovery sequence and resource allocation respectively and uses the GA algorithm to optimize them and solve the problem. It then demonstrates the effectiveness of the proposed model and the recovery algorithm through the resilience analysis and recovery strategy optimization of a city's transportation network model, which is built according to the real GPS data.

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

Abstract: Western China is a sparsely populated area with dispersed transportation infrastructure, making it challenging to meet people’s accessibility and mobility requirements. Rescue efficiency in sparse networks is severely hampered by the difficulty rescue teams have in getting to the scene of abrupt traffic accidents. This paper develops a layout optimization model for multiple rescue points to address this issue, suggests an improved particle swarm algorithm by including a variation that can reduce optimization time and avoid the disadvantage of precocity, and designs a MATLAB program using an adaptive variation algorithm. The proposed approach increases the effectiveness of rescue in a sparse network and optimizes the distribution of emergency resources.