Road network congestion propagation situation prediction method and system based on infectious disease model

Abstract

The invention relates to a road network congestion propagation situation prediction method and system based on an infectious disease model. The method comprises the following steps of acquiring road network data at the current moment, performing road section division on the road network data according to the road section types to obtain a road section set of each road section type, wherein the road section types comprise a congested road section, an unblocked road section which is easily propagated and congested and an unblocked road section which is not easily propagated and congested, according to the road section set of each road section type, based on an infectious disease model, predicting a road network congestion condition at each moment within a set duration to obtain road networkprediction data, wherein the road network prediction data comprises a road section set of each road section type corresponding to each moment, acquiring a road network congestion propagation evaluation index, wherein the road network congestion propagation evaluation indexes comprise a propagation scale, a propagation duration and a propagation speed, and in combination with the road network congestion propagation evaluation index, analyzing the road network prediction data to obtain a road network congestion propagation situation at the current moment. The real-time performance of road network congestion propagation situation prediction can be improved.

Description

technical field [0001] The invention relates to the field of road network congestion analysis, in particular to a method and system for predicting road network congestion propagation situation based on an infectious disease model. Background technique [0002] With the continuous increase of car ownership, there is a serious imbalance between the supply of transportation resources and travel demand, and the problem of road congestion is increasing day by day. Taking Beijing as an example, at present, more than 90% of road sections in the road network are saturated or supersaturated during morning and evening peak hours; the average load of the road network has reached 70%, and the arterial road system exceeds 90%. It is worth noting that the congestion of a single road section will spread to the road network along the line and the surrounding road network in a certain order, resulting in large-scale congestion of the road network, which will lead to problems such as low road...

AI Technical Summary

Problems solved by technology

However, this method has limitations mainly in the following three aspects: first, congestion has become the norm during actual operation, and the traffic demand is rigid, making it difficult to control the overall demand in the short term; second, the MFD relationship of the system is relatively unstable. The volatility is strong, so it is difficult to support real-time decision-making of road network management; third, MFD cannot reflect the spatio-temporal law of congestion propagation, and it is difficult to support traffic management regulation and decision-making

In the evaluation of road network reliability, the propagation mechanism of congestion is ignored, resulting in a relatively rigid regulation process

In terms of decision-making, the spread of congestion in the spatial range is often ignored, and only micro-local regulation is emphasized, inter-regional correlations are not considered, and algorithms and capabilities for global optimization are lacking; in terms of prediction, the spread of congestion in the time range is often ignored. Lack of comprehensive forecasting ability of historical data and real-time data, lack of dynamic evaluation and intelligent update of plans; in research and judgment, ignoring congestion propagation makes it impossible to understand the indirect and hidden causal relationship between traffic events, making the research and judgment process blind sex

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