A Short-term Forecasting Method of Real-time Traffic Flow

Abstract

The invention discloses a real-time traffic flow short-term forecasting method, which comprises the following steps: step 1, determining a target city range to be predicted; step 2, acquiring traffic flow historical observation data of the target city range according to a time period; Step 3, preprocessing the obtained historical traffic flow observation data within the target city to form corresponding training sets and test sets; Step 4 constructing a traffic flow prediction model based on fuzzy self-adaptation; Step 5, using the formed The training set and test set are used to train the traffic flow prediction model; step 6, using the trained traffic flow prediction model to predict the traffic flow within the target city.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular, fuzzy rules are adaptively generated by using a deep convolutional network, which is a short-term traffic flow prediction method based on fuzzy self-adaptation. Background technique [0002] In modern society, with the increase of the number of vehicles, many problems also appear, such as traffic jams and traffic accidents. These problems cause people to waste more time on the road, so obtaining timely and accurate traffic flow forecast information has become an urgent need for travelers. [0003] In today's big data era, traffic flow data is also growing explosively. Using traffic big data to predict traffic flow will further ensure safe travel and plan efficient travel. Large-scale traffic flow prediction depends heavily on historical traffic data and other relevant information, such as weather conditions, traffic accidents, etc., and is considered an important part of ...

AI Technical Summary

Problems solved by technology

[0005] However, traffic flow prediction is very complex, and the uncertainty of large-scale data makes it very challenging to predict traffic flow

The existing deep learning models for traffic flow prediction are deterministic and do not consider the uncertainty of the data, which leads to low prediction accuracy

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