Deriving on-Trip route choices of truck drivers by utilizing Bluetooth data, loop detector data and variable message sign data

Abstract

On important truck-dominated motorways, a large share of traffic consists of trucks. Our hypothesis is that these trucks do not always make optimal routing decisions which cause inefficiencies in the traffic system. Therefore, route choice of truck drivers is of interest to both transport planners and traffic management authorities. The objectives of this paper are two-fold. First, this paper models on-Trip route choices of the truck drivers. Second, we assess the inefficiencies of those routing decisions. This paper utilizes Bluetooth data, loop detector data, and variable message sign data to model the route choices of truck drivers. To the best of our knowledge, this is the first time that Bluetooth data have been used for the estimation of route choice models of truck drivers. The trucks are inferred from Bluetooth data by applying a Gaussian mixture model-based clustering technique. We apply both a binary logit model and a mixed logit model to derive the route choices of truck drivers on a case study between the port of Rotterdam and hinterland in the Netherlands. The predictive performance of the model is tested by conducting out-of-sample validation. The model results indicate truck drivers significantly value travel distance, instantaneous travel time and lane closure information en-route. The estimate of travel distance varies significantly among truck drivers. While 38% of truck drivers do not take the shortest time path, 48% of truck drivers do not choose the system-optimal path. These inefficiencies imply that traffic management solutions have the potential to improve the performance of truck-dominated motorways.