Travel path-related information plays a key role in understanding the route choice behavior of users in metropolitan cities. In this paper, the authors investigated the application of smartphone inertial sensors such as accelerometer, gyroscope and magnetometer for collecting the travelled path information of a trip maker. A framework was proposed to predict the actual travelled path that uses the smartphone's inertial sensor data collected from the trip makers and a detailed road network database. The proposed framework was tested on an academic campus road network with three different commuting vehicles. A total of 100 trips were collected using three different vehicles, namely, bus, car and Erickshaw, out of which 22 trips were used for training the decision tree algorithm. The algorithm is able to predict the path features with 95.6% accuracy. The effectiveness of the proposed framework was evaluated with 77 trips corresponding to E-rickshaw. The results show that the proposed framework was able to predict the actual travelled path with 79.22 % accuracy, and the travelled path could be predicted within the top ten alternative routes with 100% accuracy. Thus, the framework presented in this study shows a huge potential for predicting the actual travelled path without using the location sensor data.
Travel Path Tracking Using Smartphone Inertial Sensors: An Experimental Study on an Academic Campus Road Network
Lecture Notes in Civil Engineering
International Conference on Traffic and Granular Flow ; 2022 ; Delhi, India October 08, 2022 - October 10, 2022
2024-05-26
8 pages
Article/Chapter (Book)
Electronic Resource
English
Travel path , Inertial sensors , Machine learning , Map-matching , Road network database etc Physics , Soft and Granular Matter, Complex Fluids and Microfluidics , Transportation Technology and Traffic Engineering , Mathematical Physics , Theoretical, Mathematical and Computational Physics , Engineering
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