The paper presents a statistical model for urban road network travel time estimation using low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency samplings of vehicle traces. We combine link-specific effects with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. This makes travel time estimation with sparse probe vehicle data feasible also in areas with very few observations. The approach also reveals the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood and the statistical significance of the results is assessed by reporting standard errors. The model is applied in a case study for the network of Stockholm, Sweden. We find that link attributes and trip conditions (including rain and snow) have significant effects on travel times and that there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system. 3
Jenelius et al. estimated travelling time based on low frequency GPS data in REF , and demonstrated that there is significant correlation between segments and showed the feasibility of using low frequency GPS data for monitoring the performance of transport system.
Travel time estimation for urban road networks using low frequency probe vehicle data
Transportation Research Part B-methodological
Economics