Method for compensating real time traffic information data

Abstract

The invention relates to a data compensation method for real time traffic information, which comprises the following steps: optimizing a road net according to the traffic information generated by real-time processing; conducting the abnormal data rejection to the traffic information generated by real-time processing, so as to obtain data which complies with current traffic tendency; choosing historical data which complies with the change of the traffic tendency from a historical database according to the obtained data of real-time traffic tendency to be taken as an auxiliary information source for compensating vacant information; taking a vacant road chain road as a center to construct a compensatory area according to the auxiliary information source and the road net after being optimized, taking the area as a unit to conduct compensatory calculation to road chain which is not covered with traveling track information; determining a filling mode according to the road chain role of a vacant road chain and the number of the vacant road chain in the compensatory area, thereby accomplishing the compensation. The data compensation method fully utilizes the characteristic of the change of traffic flow tendency to develop the information in the historical database to realize the real-time calculation of large-scale data, and the data compensation method has the advantages of high computational efficiency and versatility which is not restricted by areas.

Description

technical field [0001] The invention belongs to the category of real-time processing of traffic information in the field of intelligent traffic systems, and relates to a data compensation method for real-time traffic information based on historical data. Background technique [0002] The cause of traffic congestion is either beyond the capacity of the congested road or due to the occurrence of special events (such as traffic accidents or traffic control). The biggest problem that traffic congestion brings to public travel is the uncertainty of travel time, which leads to the inability to compensate for the time required from the departure point to the destination (OD distance). Dynamic path planning is to search for an optimal or suboptimal path from the initial state to the target state according to a certain performance index (such as distance, time, energy, etc.) based on real-time traffic information, to help users avoid congested roads as much as possible and calculate ...

AI Technical Summary

Problems solved by technology

The historical trend method cannot respond to the external factors and abnormal factors that affect the travel time, and cannot overcome the uncertainty and nonlinear characteristics of the traffic flow process, especially the influence of random disturbance factors; the ARIMA model is only suitable for stable traffic conditions. The situation; the neural network model can accurately simulate complex traffic conditions, but its parameter training is very complicated and the calculation time is too long, so it is not suitable for online applications

In addition, the floating car data obtained in one processing cycle may have only a few floating cars or even no floating cars passing by on some roads, which is a small sample data, which is affected by factors such as driver behavior, vehicle conditions, and the environment, and cannot reflect It is an extremely unstable data source, and the above methods cannot be overcome if used to process real-time data.

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