A New Algorithm for Mode Detection in Travel Surveys

Abstract

New technologies offer various opportunities for advancing travel surveys. This chapter presents a new approach for automated identification of trip stages and travel modes as the core outcome from travel surveys and a key requirement for subsequent steps, such as the automated assignment of trips. Mode prediction of eight modes of transport is realized by two multinomial logistic regression models, based on only nine features from GPS and acceleration data. The algorithm achieved an overall detection rate of 79 percent. The authors found that motorcycle and moped, railway, bicycle, and pedestrian obtained better results, whereas urban public transport caused some difficulties in detection.