Identifying travel mode with GPS data

ABSTRACT

Travel mode identification is an essential step in travel information detection with global positioning system (GPS) survey data. This paper presents a hybrid procedure for mode identification using large-scale GPS survey data collected in Beijing in 2010. In a first step, subway trips were detected by applying a GPS/geographic information system (GIS) algorithm and a multinomial logit model. A comparison of the identification results reveals that the GPS/GIS method provides higher accuracy. Then, the modes of walking, bicycle, car and bus were determined using a nested logit model. The combined success rate of the hybrid procedure was 86%. These findings can be used to identify travel modes based on GPS survey data, which will significantly improve the efficiency and accuracy of travel surveys and data analysis. By providing crucial travel information, the results also contribute to modeling and analyzing travel behaviors and are readily applicable to a wide range of transportation practices.

KEYWORDS:

• Travel mode identification
• GPS
• GIS
• travel survey
• nested logit model
• hybrid procedure

Acknowledgements

Many thanks to Dr Xu Yingjun of Jilin University for help in data collection.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research is funded by the National Natural Science Foundation of China (50908099), the Humanity and Social Science Youth Foundation of Ministry of Education of China (14YJC630225) and the China Postdoctoral Science Foundation (2014M551191).