Design of Urban Road Traffic Induction Algorithm based on DL Algorithm

Nowadays, the national life has been greatly improved, and the family car ownership has soared, leading to the increasingly serious urban traffic congestion (TC). To solve this problem, the traditional solution has been outdated. The application of intelligent traffic system to urban traffic induction can improve the current situation of TC, and then improve the efficiency of traffic operation. In addition, the collection of road traffic data is becoming more and more scientific and technological. With scientific and technological means, it can obtain real-time traffic flow (TF) in time, which provides a data analysis guarantee for the prediction and analysis of road conditions and traffic conditions. The traditional TF prediction model does not improve or integrate the technology combination, so the traditional model does not conform to the actual application, or cannot be applicable to all scenarios. Nowadays, deep learning (DL) has gradually entered the field of TFP, realizing the effect that traditional models do not have. Based on DL algorithm, the paper used literature research method, quantitative analysis method, adopted a section in February 2019 to March 2020 data for 38 months of DL simulation experiment, experiment shows that DL model error is lower than the error of the traditional model, reached the traditional model, the fusion mode supports the sustainable development strategy, urban TC has a breakthrough, to ensure the safety of urban road traffic.