Dynamic Traffic Modeling From Overhead Imagery
This work provides a method for automatically generating dynamic traffic speed maps, which could be beneficial for urban planners and navigation services, though it appears to be an incremental improvement over traditional modeling approaches.
This paper addresses the problem of understanding traffic flow patterns from overhead imagery, specifically predicting travel speeds conditioned on location and time. The authors propose a convolutional neural network-based method that generates local motion models and demonstrate its ability to create accurate city-scale traffic models using historical data from New York City.
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.