The Smartphone-Based Person Travel Survey System: Data Collection, Trip Extraction, and Travel Mode Detection

Travel data is vital for understanding individual travel behaviors and estimating travel demand. Compared with traditional travel surveys in which respondents were asked to recall their trips, smartphones can record GPS trajectories actively at a high level of accuracy in both space and time. However, how to identify trips and detect travel modes from raw GPS data in an efficient way is still challenging. To address these issues, we firstly target high-accuracy data collection through developing a smartphone-based person travel survey system which comprises of three components, i.e., data collector, data processor, and data validator. The data collector is a smartphone app for collecting GPS trajectories; the data processor is a server embedded with rule-based algorithms to extract travel-activity information; and the data validator is a webpage to present the self-extracted travel information for respondents' validation. Secondly, we propose a hybrid random forest and merging algorithm to detect multiple travel modes in complex trips. The algorithm reaches an accuracy of 100% for simple trips and 95.7% for multimodal trips. These results indicate that the proposed hybrid random forest and merging algorithm can help identify travel modes of complex trips more accurately and efficiently.

Automated summary

Travel data is crucial for understanding individual travel behaviors and estimating travel demand. This study proposes a high-accuracy data collection system based on smartphones, and a hybrid random forest and merging algorithm is used to more accurately and efficiently identify travel modes in complex trips.