Road Traffic Forecasting: Recent Advances and New Challenges

Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. More recently, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. This paper aims to summarize the efforts made to date in previous related surveys towards extracting the main comparing criteria and challenges in this field. A review of the latest technical achievements in this field is also provided, along with an insightful update of the main technical challenges that remain unsolved. The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.

I. Introduction

Issues related to road traffic conditions are common practice in every major city around the world. Traffic congestion leads to social, economic and environmental problems, rationale for which public and private organizations have attempted at addressing them for more than 50 years. Efforts devoted to mitigate the effects of traffic congestion have been conducted in three directions [1]: increasing infrastructures, promoting transport alternatives and managing traffic flows. While the first direction is limited by topographical, budgetary and social factors and the second is mainly a matter of public policies, the latter has been continuously improving in the last decades with the expansion of data provided by sensors in roads and vehicles, as well as with the technology required to exploit those data. This allows measuring, modeling and interpreting traffic features such as flow, occupancy or travel times, which are useful to develop advanced traffic management systems (ATMS) and advanced traveller information systems (ATIS).