Machine-Learning Approaches to Identify Travel Modes Using Smartphone-Assisted Survey and Map Application Programming Interface

Abstract

Travel mode choice prediction is essential for transportation planning and travel demand prediction. One of the conventional travel survey methods is collecting data over landline telephones, which lacks efficiency because of financial and time resource needs. In this regard, smartphone-assisted travel surveys can be applied to overcome the mentioned deficiencies. Smartphone-assisted travel surveys allow respondents to record GPS data, travel purpose, and travel mode via an application, simplifying the survey process. With various sensors equipped, the precision of data is ensured. Based on the survey results, varied approaches have been seen to travel mode identification. For this study, a travel survey was conducted in Hangzhou, China, supported by the smartphone application TraceRecord integrated with online mapping services. Several steps were undertaken to recognize different kinds of travel modes. First, preprocessing was adopted to screen out defective logs. With the employment of A-Map Application Programming Interface (API), trajectory segmentation was substantially boosted. Then, separately, features related to velocity, acceleration, and heading were extracted from the survey data. To achieve better accuracy and efficacy, two classification algorithms—support vector machine (SVM) and gradient boosting decision tree (GBDT)—were applied to model the travel mode identification problem. Compared with the SVM, GBDT produced a higher prediction accuracy of 90.16%. Further analysis was implemented based on the results of the GBDT model, and velocity-related features contributed the most to the identification problem. The study explores the possibility of applying travel mode recognition in real-world conditions and discusses further mining of the survey data.