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The severe impact of traffic accidents, along with a large number of deaths and disabilities, necessitates further improvements in rescue path optimization. To make the emergency rescue more efficient and furthermore ensure health care in life-saving and mitigating traffic congestion as soon as possible, a methodology for rescue vehicle path optimization, timing co-evolutionary path optimization (TCEPO), is proposed to optimize the rescue path. Distinguishing from conventional online re-optimization (OLRO) and co-evolutionary path optimization (CEPO), in TCEPO, each optimization process co-evolves with future traffic environment that keeps changing over time, and the best path will be modified timely based on the predicted routing environmental dynamics (PRED) and recent traffic data. Besides, for better computation efficiency, this research reports an improved ripple spreading algorithm (RSA) as a realization of TCEPO to resolve the optimality problem. The modeling and solutions of TCEPO are discussed in detail to illustrate the applications in emergency rescue path optimization. In order to compare the performance of three methods (OLRO, CEPO and TCEPO), the same optimization tasks and scenarios are presented, and numerical simulation is carried out 100 times. Experimental results clearly prove that the proposed TCEPO possesses stronger robustness and is about 17.65% to 40.02% shorter than CEPO, as well as about 26.34% to 38.47% shorter than OLRO in terms of the travelling time under the PRED with various uncertainties. These advantages have a great impact on raising efficiency and reliability of emergency rescue, which can help rescue vehicles reach the destination as quickly as possible and save more lives.

A Methodology of Timing Co-Evolutionary Path Optimization for Accident Emergency Rescue Considering Future Environmental Uncertainty