Link and Route Travel Time Prediction Including the Corresponding Reliability in an Urban Network Based on Taxi Floating Car Data

Abstract

This study addressed the modeling of route travel times (including their associated uncertainty) in urban networks based on taxi floating car data. The model decomposes observed link travel speeds into the expected speed (modeled with daily and seasonal profiles) and deviations thereof. The latter were shown to be strongly heteroscedastic by providing an explicit model for the time variance. Temporal and spatial correlations were considered with a vector autoregression framework. Modeling was supported by automatic model selection methods for identifying the relevant effects and providing one-step-ahead predictions. The potential of the proposed model was investigated with taxi floating car data from a real-world test site near the city core of Vienna, Austria. Various specifications of the vector autoregression model were tested and compared. Taxi floating car measurements of local speeds were found to be strongly heteroscedastic, a factor that must be considered in estimation of models for expected travel speeds. The modeling of the mean suggested no remaining daily or weekly patterns, and it was superior to simple models explaining travel speeds as a linear function of the travel speeds in the last time period. The variance model successfully captured heteroscedasticity. More complex models for link travel speeds, including temporal and spatial correlation, do not increase prediction accuracy consistently; the lack indicates that a sampling frequency of 15 min for floating car data in urban settings is too low for use of temporal dependencies for prediction. An introduced method for computing route travel time uncertainty showed variability over the day for a highly frequented route.