City traffic forecasting using taxi GPS data: A coarse-grained cellular automata model

TL;DR

This paper introduces a coarse-grained cellular automata model for city traffic forecasting that leverages large-scale taxi GPS data to predict traffic flow patterns and speeds with reasonable accuracy.

Contribution

It proposes a novel coarse-grained cellular automata approach that simplifies vehicle interactions and uses data-driven parameters for city-wide traffic prediction.

Findings

Model captures city-wide traffic flow patterns

Predicts road speeds one hour ahead effectively

Utilizes large floating car datasets for parameterization

Abstract

City traffic is a dynamic system of enormous complexity. Modeling and predicting city traffic flow remains to be a challenge task and the main difficulties are how to specify the supply and demands and how to parameterize the model. In this paper we attempt to solve these problems with the help of large amount of floating car data. We propose a coarse-grained cellular automata model that simulates vehicles moving on uniform grids whose size are much larger compared with the microscopic cellular automata model. The car-car interaction in the microscopic model is replaced by the coupling between vehicles and coarse-grained state variables in our model. To parameterize the model, flux-occupancy relations are fitted from the historical data at every grids, which serve as the coarse-grained fundamental diagrams coupling the occupancy and speed. To evaluate the model, we feed it with the historical travel demands and trajectories obtained from the floating car data and use the model to predict road speed one hour into the future. Numerical results show that our model can capture the traffic flow pattern of the entire city and make reasonable predictions. The current work can be considered a prototype for a model-based forecasting system for city traffic.