The paper presents a method to estimate utility functions in route choice models from GPS-data. Compared to the present use of Stated Preference techniques and other interview techniques to estimate choice models, the work in this paper derives the utility functions from the observed trips and routes. This leads to several methodological challenges, especially since the explanatory variables are highly correlated. The methodological discussions are tested on data from the AKTA road pricing experiment in Copenhagen. A total of 500 cars were equipped with GPS over three experiment rounds in a 2-years period. The normal travel pattern for each participant was estimated on observations from a control period over 10 to 12 weeks. A pricing scheme was then implemented over 12 to 14 weeks. The corresponding payment for a given road-pricing scheme assuming no change in behaviour could then be calculated. The main idea in the experiment was that the participants were paid the savings by changing behaviour compared with the control period. The behavioural impact of road pricing is thus revealed. The processing of GPS-data includes several methodological issues itself, i.e. to cope with periods of lost signals, uncertainty of the received coordinates, as well as problems to match the data to a digital map. The choice situation can be explained by the relative weighting of the different attributes. The best overall deterministic utility function can be estimated by a similar approach, but where the fit from each possible combination of coefficients are compared to the entire set of routes for each person. Finally, variation between persons can be investigated and estimated by comparing the results for all participants. The method was used on the AKTA GPS-data. The map matching was done on a detailed digital map, which included 350,000 links. The links were classified in 25 classes (from motorway, to driveways and gravel road), and included variables concerning length, free flow travel time, congested time in different time-intervals during the day, and road pricing (for those parts of the experiment where the participants faced road-pricing). The utility functions included the same variables. Each person travelled between 250 and 1,000 trips during the experiment (determined on their travel pattern), and 500 persons participated. They were selected after a factorial design based on income and commuting pattern (between different areas of Copenhagen). The paper presents the estimated utilities functions, and discusses the intra and inter person variations of the coefficients as well as the size of the error term. The participants participated in a SP-experiment before the main driving experiment. The results of this prior estimation are compared with the results from the GPS-data to see if the two approaches provide the same or different results. For the covering abstract see ITRD E126595.
Abstract