Short-Term Traffic Flow Prediction with Recurrent Mixture Density Network

Mingjian Chen,Fu Cai,Wanli Li,Guangyun Li,Naikun Guo,Rui Chen,Bartlomiej Blachowski

doi:10.1155/2021/6393951

Abstract

Traffic situation awareness is the key factor for intelligent transportation systems (ITS) and smart city. Short-term traffic flow prediction is one of the challenging tasks of traffic situation awareness, which is useful for route planning, traffic congestion alleviation, emission reduction, and so on. Over the past few years, ubiquitous location acquisition techniques and sensors digitized the road networks and generated spatiotemporal data. Massive traffic data provide an opportunity for short-term traffic flow prediction in a data-driven manner. Most of the existing short-term traffic flow prediction methods can be divided into two categories: nonparametric and parametric. Traditional parametric methods failed to obtain accurate prediction, due to the nonlinear and stochastic characteristics of short-term traffic flow. Recently, deep learning methods have been studied widely in the fields of short-term prediction. These nonparametric methods yielded promising results in practical experiments. Motivated by the current study status, we dedicate this paper to a short-term traffic flow prediction approach based on the recurrent mixture density network, the combination of recurrent neural network (RNN), and mixture density network (MDN). This approach is implemented on real-world traffic flow data and demonstrates the prominent superiority. To the best of our knowledge, this is the first time that the recurrent mixture density network is applied to a real-world short-term traffic flow prediction task.

Highlights

Over the past few decades, the number of megalopolises has increased rapidly, especially in developing country as China
Two widely used methods are involved in comparison experiments, including the long short-term memory (LSTM) network and Autoregressive Integrated Moving Average (ARIMA) model. e optimal parameter configuration of the recurrent mixture density network is determined after several experiments, where the number LSTM layer is set to 4, the number of LSTM units in each layer is set to 256, and the number of mixture components in the mixture density layer is set to 15
E prediction performance comparison of the three methods is given in Table 1. e lowest errors of all metrics are highlighted in bold font, which demonstrate the prominent performance of the recurrent mixture density network

Summary

Introduction

Over the past few decades, the number of megalopolises has increased rapidly, especially in developing country as China. In the process of city development, projects in various aspects such as urban layout plan, environment protection, and traffic management are still tricky challenges [1]. Great demand for smart city construction and intelligent transportation systems (ITS) emerges [2]. Short-term traffic flow prediction aims to provide future traffic information of road network, which is useful for both individual users and transportation management [3]. And accurate traffic situation awareness contributes to a wide spectrum of applications, such as route planning, traffic congestion alleviation, and emission reduction. With the rapid development of sensors, communication techniques, and location acquisition techniques, such as Global Navigation Satellite System (GNSS) and 5G technology, massive multisource data including trajectory data and traffic flow data are generated and communicated by prevalent traffic information acquisition devices [4]

Objectives

Methods

Results

Conclusion