Bus-Car Mode Identification: Traffic Condition–Based Random-Forests Method

Abstract

Travel mode identification is one of the key issues in travel behavior analysis. A number of algorithms have been proposed to detect travel modes particularly by using global positioning system (GPS) data, whereas most algorithms rarely consider traffic conditions. To fill the gap, this paper distinguishes two representative travel modes, i.e., bus and car, by using the random-forests method, of which the corresponding feature variables are examined under various traffic conditions. Local congestion variables are defined to reduce uncertainties between bus and car. The results indicate that the overall detection accuracy of the not-in-congestion trips is as high as 94.0%, and that of in-congestion trips is 91.1%, demonstrating that distinguishing traffic conditions using random forests can reliably improve travel modes detection accuracy. It is found that distinguishing local traffic conditions can further improve accuracy. The paper contributes to travel behavior analysis and modeling, and the proposed method is ready for a wide range of transportation practices, including traffic planning and management.