DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

TL;DR

DeepTFP is a deep learning model that predicts city traffic flow by analyzing mobile data using residual neural networks, outperforming traditional LSTM methods in accuracy.

Contribution

The paper introduces DeepTFP, a novel deep residual neural network-based approach for comprehensive citywide traffic flow prediction using mobile data.

Findings

DeepTFP outperforms LSTM-based methods in prediction accuracy.

Using three residual neural networks improves modeling of temporal properties.

Contrast experiments validate the effectiveness of DeepTFP on real-world data.

Abstract

Traffic flow prediction is an important research issue to avoid traffic congestion in transportation systems. Traffic congestion avoiding can be achieved by knowing traffic flow and then conducting transportation planning. Achieving traffic flow prediction is challenging as the prediction is affected by many complex factors such as inter-region traffic, vehicles' relations, and sudden events. However, as the mobile data of vehicles has been widely collected by sensor-embedded devices in transportation systems, it is possible to predict the traffic flow by analysing mobile data. This study proposes a deep learning based prediction algorithm, DeepTFP, to collectively predict the traffic flow on each and every traffic road of a city. This algorithm uses three deep residual neural networks to model temporal closeness, period, and trend properties of traffic flow. Each residual neural network consists of a branch of residual convolutional units. DeepTFP aggregates the outputs of the three residual neural networks to optimize the parameters of a time series prediction model. Contrast experiments on mobile time series data from the transportation system of England demonstrate that the proposed DeepTFP outperforms the Long Short-Term Memory (LSTM) architecture based method in prediction accuracy.