A traffic flow partitioning model based on similar evolution mode clustering and dynamic time zone partitioning

Abstract

According to the method, a traffic flow time sequence partitioning model based on similar evolution mode clustering and dynamic time zone partitioning is provided, the dynamic time-space characteristics of traffic flow changing along with time are tried to be excavated for the first time, and the challenge of traffic flow time non-stationarity in short-time traffic flow prediction is solved. The invention specifically comprises the following steps: firstly, automatically identifying road sections with similar traffic flow evolution modes in a road network by using an affinity propagation clustering algorithm (APC); and secondly, for the intra-day evolution difference of the traffic flow, performing dynamic time zone division on the traffic flow in the similar evolution mode by using a curvature K-Means algorithm, and mining the space-time state characteristics of the road network traffic flow in a deeper level; after similar mode identification and automatic time zone division, performing modeling on traffic flows in different time zones in different modes, and quantifying the state information of the traffic flows, so that the prediction precision of the model is more accurate; and finally, verifying the validity of the provided model by using a real data set.

Description

1. Technical field [0001] The present invention relates to the field of intelligent transportation, in particular to short-term traffic flow forecasting. Specifically, it is a method of clustering traffic flow data of each section of the road network based on similar evolution patterns, and then performing intra-day traffic analysis for each section of the road network with similar evolution patterns. A traffic flow partition model for dynamic time zone partitioning of flow data. 2. Background technology [0002] Accurate real-time short-term traffic flow forecasting can effectively alleviate urban traffic congestion and reduce urban air pollution, which is of great social significance. Traffic flow data has characteristics such as trend, periodicity and dynamic randomness. Among them, trend and periodicity belong to the regular characteristics of traffic flow, which are mainly manifested as trends or fluctuations according to the time law, which is the premise that traffic...

AI Technical Summary

Problems solved by technology

Dynamic randomness is the fundamental reason why traffic flow is difficult to be accurately predicted

Images