Affiliations: [a] Education Technology and Network Center, Chang’an University, Xi’an 710064, China | [b] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China | [c] School of Highway, Chang’an University, Xi’an 710064, China
Abstract: In this paper, a hybrid model composed of the correlation ratio analysis and the Support Vector Machine (SVM) is proposed for trip mode recognition. The correlation ratio analysis algorithm is applied to determine the optimal time window of the input attributes so as to optimize the input parameters. The SVM is applied to carry out the trip mode recognition for the whole trip. On this basis, the influence of data sampling frequency on trip mode recognition is further evaluated using large-scale field test data. The results show that: (1) the correlation ratio analysis and the SVM hybrid model attains the best performance for trip mode recognition, the average mode recognition precision reaches 89.8% at the sampling frequency of 1 s; (2) the data sampling frequency significantly affects the trip mode recognition effect; when the sampling frequency is high (less than 5 s), the mode recognition precision is above 70%, however, when the sampling frequency is relatively low (more than 30 s), the bus and car mode recognition precision falls rapidly below 37%. These results provide a reference for using the smartphone sensor datasets to supplement or even replace household travel surveys in transportation planning in the future.