Lara Montini

Trip Purpose Identification from GPS Tracks

Travel surveys are increasingly taking advantage of GPS data, which offer precise route and time observations and a potentially reduced response burden. In these surveys, travel diaries are usually constructed automatically where research on the employed procedures has been focused on mode identification. The goal of the research reported here was to improve trip purpose identification. The analysis used random forests, a machine-learning approach that had been successfully applied to mode identification. The analysis was based on GPS tracks and accelerometer data collected by 156 participants who took part in a 1-week travel survey in Switzerland that was completed in 2012. The results show that random forests provide robust trip purpose classification. For ensemble runs, the share of correct predictions was between 80% and 85%. Different setups of the classifier were possible and sometimes required by the application context. The training set and its input variables (feature set) of the classifier were defined in various ways. Four relevant setups were tested for this study.

An agent-based cellular automaton cruising-for-parking simulation

Abstract This paper reports on the development of an agent-based cruising-for-parking simulation using the cellular automaton (CA) approach. The software was tested on a small-scale scenario, and a first verification step was performed for a real-world scenario for the town center of Zürich. Approaches to integrating the simulation into MATSim, a multi-agent transport simulation program, are discussed. The software is open source and can be downloaded from a free software repository. Empirical data that may be valuable for future model calibration are currently being surveyed in a global positioning system (GPS) study at the authors’ institute.