Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab

TL;DR

This paper presents a comprehensive approach to city-wide traffic forecasting using multi-channel image data, evaluating various neural network architectures and data transformations to improve prediction accuracy in a competitive setting.

Contribution

The authors systematically analyze data properties and explore new methods to incorporate additional spatio-temporal context for traffic prediction.

Findings

Evaluated multiple neural network architectures for traffic forecasting.

Identified key data properties influencing model performance.

Enhanced prediction accuracy through data transformation and context integration.

Abstract

The goal of the IARAI competition traffic4cast was to predict the city-wide traffic status within a 15-minute time window, based on information from the previous hour. The traffic status was given as multi-channel images (one pixel roughly corresponds to 100x100 meters), where one channel indicated the traffic volume, another one the average speed of vehicles, and a third one their rough heading. As part of our work on the competition, we evaluated many different network architectures, analyzed the statistical properties of the given data in detail, and thought about how to transform the problem to be able to take additional spatio-temporal context-information into account, such as the street network, the positions of traffic lights, or the weather. This document summarizes our efforts that led to our best submission, and gives some insights about which other approaches we evaluated, and why they did not work as well as imagined.