Emergency signal priority and social vehicle dynamic path induction collaborative optimization method

Abstract

The invention discloses an emergency signal priority and social vehicle dynamic path induction collaborative optimization method. The method comprises the following steps: constructing a road network toughness capability index based on a complex network theory; and deleting the nodes with relatively low toughness. An emergency rescue path double-layer planning model is constructed on the basis, and the upper-layer target is to maximize the reduction amount of emergency signal priority on emergency rescue vehicle delay, so that the travel delay of the emergency rescue vehicle is minimum; the lower-layer target is to optimize traffic flow distribution, so that the travel time of social vehicles is minimized. The emergency vehicle updates the sub-network, the emergency rescue path, the emergency signal priority strategy and the social vehicle guidance scheme according to the current road network traffic state after passing through each intersection until the emergency vehicle arrives at the accident site. According to the method, the negative influence of the emergency rescue task on the road network toughness is considered, the bilevel planning model is constructed to realize game balance between the emergency rescue efficiency and the social vehicle passing efficiency, and the occurrence of road network cascade failure and even secondary traffic accidents is effectively avoided.

Description

technical field [0001] The invention relates to a collaborative optimization method for emergency signal priority and social vehicle dynamic path induction, which belongs to the technical field of intelligent transportation. Background technique [0002] At present, the number of emergency incidents in China is increasing day by day. How to effectively formulate a reasonable emergency rescue path and emergency signal priority strategy has become an effective solution to alleviate casualties and property losses in emergency incidents. At present, my country has achieved initial results in improving the emergency response capabilities of road traffic accidents, but there is still a gap from the first-class level, especially for the key technologies of rapid emergency response to accidents (such as rescue vehicle route optimization, social vehicle guidance in emergency scenarios, etc.) etc.) Insufficient research has resulted in problems such as the lack of scientific nature of ...

AI Technical Summary

Problems solved by technology

[0008] 1. Most of the research on emergency rescue does not consider the negative impact of emergency signal priority on the resilience of the road network, and only takes the completion of rescue tasks as the sole goal

With the continuous development of urbanization in my country, the number of motor vehicles continues to increase, and the traffic pressure in the road network is also increasing. The traditional emergency rescue path planning method may cause traffic congestion in the road network and cascading failures at intersections. even lead to secondary accidents

[0009] 2. Most of the research on emergency rescue focuses on the scheduling of emergency rescue resources and the planning of emergency rescue paths. There are relatively few studies on the optimization of emergency signal priority strategies and the path induction of social vehicles in emergency scenarios.

[0010] 3. Most of the research on bi-level programming uses heuristic algorithms for path optimization. Once the scale of the road network increases, the calculation takes a long time, and it is difficult to meet the real-time requirements of emergency rescue tasks.

[0011] 4. Most of the dynamic changes in traffic conditions for emergency rescue are insufficiently considered

In fact, the real-time dynamic changes of road network traffic conditions have a greater impact on emergency rescue routes. In actual rescue missions, emergency vehicles are easily affected by complex traffic conditions, resulting in rescue mission failures.

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