Vehicle forecasting method and forecasting system

Abstract

The invention relates to the field of traffic forecasting. A road traffic forecasting method comprises the steps that information collecting is carried out, namely vehicle driving related information and traffic forecasting auxiliary information are collected; driving path analyzing is carried out, so as to calculate the driving path, which takes a road as an element, of each vehicle to acquire driving path data; road traffic threshold calculation and vehicle behavior chain analysis are carried out, wherein road traffic threshold calculation is used for calculating the traffic threshold of each road in different flow states, and vehicle behavior chain analysis is used for analyzing and calculating the behavior chain of each vehicle; vehicle behavior mode analysis is carried out to analyze and calculate the travel mode of each vehicle; vehicle travel forecasting is carried out to forecast the travel purpose, the starting point, the driving path, the probability and the time of each vehicle in future; road traffic forecasting is carried out to forecast the flow, the flow patterns, the vehicle sources and the destinations of different roads at different times in future; and information releasing is carried out on a road traffic forecasting result.

Description

【Technical field】 [0001] The invention relates to the field of traffic forecasting, in particular to a traffic forecasting method and a forecasting system. 【Background technique】 [0002] In the prior art, there are mainly three modes commonly used in the vehicle identity and location collection system: [0003] 1) Vehicle identity and location information collection system based on RFID electronic license plate. The system installs RFID identification cards for each vehicle, and installs RFID card reading devices in multiple traffic sections; when the vehicle passes the installation site of the card reading device, the system can read the RFID information on the vehicle and know the identity information of the passing vehicle , and know the current location of the vehicle according to the installation location of the card reading device. Since the installation location of the card reading device is sparse, the current location information of the vehicle is also sparse. ...

AI Technical Summary

Problems solved by technology

It is impossible to implement targeted measures according to the source / flow direction of the traffic flow and the traffic conditions of the associated road sections, and the reference and guidance effect of traffic relief is low

[0013] 2) Unable to assist in solving the problem of parking difficulties; because the macro-flow forecasting method fails to grasp the departure / destination of the traffic flow, it cannot provide prediction information about the vehicle's stay, and it cannot assist in solving the problem of parking difficulties

The macro-traffic forecasting method fails to provide individual vehicle forecast information, so it fails to take corresponding measures according to the characteristics of different groups of people, affects the travel behavior of different groups, has weak influence on public travel behavior, and has a weak role in guiding future traffic.

[0015] 4) It is impossible to provide personalized traffic information services based on personal characteristics

Inundated by a large amount of traffic information, the role of public travel assistance is low

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