Dynamic Traffic Modeling From Overhead Imagery

TL;DR

This paper introduces a CNN-based method to generate dynamic traffic speed maps from overhead imagery, conditioned on location and time, enabling city-scale traffic modeling without extensive data collection.

Contribution

The paper presents a novel CNN approach that models traffic flow from overhead images, generalizing to new areas and reducing data requirements compared to traditional methods.

Findings

Accurately models city-scale traffic speeds from overhead imagery.

Generalizes to new locations without extensive data collection.

Effective in capturing traffic patterns at different times.

Abstract

Our goal is to use overhead imagery to understand patterns in traffic flow, for instance answering questions such as how fast could you traverse Times Square at 3am on a Sunday. A traditional approach for solving this problem would be to model the speed of each road segment as a function of time. However, this strategy is limited in that a significant amount of data must first be collected before a model can be used and it fails to generalize to new areas. Instead, we propose an automatic approach for generating dynamic maps of traffic speeds using convolutional neural networks. Our method operates on overhead imagery, is conditioned on location and time, and outputs a local motion model that captures likely directions of travel and corresponding travel speeds. To train our model, we take advantage of historical traffic data collected from New York City. Experimental results demonstrate that our method can be applied to generate accurate city-scale traffic models.