Spatial and Temporal Correlations of Freeway Link Speeds: An Empirical Study

Congestion on roadways and high level of uncertainty of traffic conditions are major considerations for trip planning. The purpose of this research is to investigate the characteristics and patterns of spatial and temporal correlations and also to detect other variables that affect correlation in a freeway setting. 5-minute speed aggregates from the Performance Measurement System (PeMS) database are obtained for two directions of an urban freeway – I-10 between Santa Monica and Los Angeles, California. Observations are for all non-holiday weekdays between January 1st and June 30th, 2010. Other variables include traffic flow, ramp locations, number of lanes and the level of congestion at each detector station. A weighted least squares multilinear regression model is fitted to the data; the dependent variable is Fisher Z transform of correlation coefficient. Estimated coefficients of the general regression model indicate that increasing spatial and temporal distances reduces correlations. The positive parameters of spatial and temporal distance interaction term show that the reduction rate diminishes with spatial or temporal distance. Higher congestion tends to retain higher expected value of correlation; corrections to the model due to variations in road geometry tend to be minor. The general model provides a framework for building a family of more responsive and better-fitting models for a 6.5 mile segment of the freeway during three times of day: morning, midday, and afternoon. Each model is cross-validated on two locations: the opposite direction of the freeway, and a different location on the direction used for estimation. Cross-validation results show that models are able to retain 75% or more of their original predictive capability on independent samples. Incorporation of predictor variables that describe road geometry and traffic conditions into the model works beneficially in capturing a significant portion of variance of the response. The developed regression models are thus transferrable and are apt to predict correlation on other freeway locations

Learning the Dependency Structure of Highway Networks for Traffic Forecast

Abstract-Forecasting road traffic conditions requires an accurate knowledge of the spatio-temporal dependencies of traffic flow in transportation networks. In this article, a Bayesian network framework is introduced to model the correlation structure of highway networks in the context of traffic forecast. We formulate the dependency learning problem as an optimization problem and propose an efficient algorithm to identify the inclusion-optimal dependency structure of the network given historical observations. The optimal dependency structure learned by the proposed algorithm is evaluated on benchmark tests to show its robustness to measurement uncertainties and on field data from the Mobile Millennium traffic estimation system to show its applicability in an operational setting