A Simulation Study on Automated Transport Mode Detection in Near-Real Time using a Neural Network

Das, R.D., Ronald, N., and Winter. S. (2015), A Simulation Study on Automated Transport Mode Detection in Near-Real Time using a Neural Network. In Proceedings of Research@Locate’15, Brisbane, Australia.

Detecting transport modes in near-real time is important for various context-aware location based services and understanding urban dynamics. In this paper we present a simulated study on detecting transport modes in near-real time using a neural network. We have shown how detection accuracy will vary with different temporal window sizes and different combination of modes. Since in urban environment transport modes move slowly due to traffic, considering movement attributes or kinematics alone for mode detection is not sufficient. That is why we investigated how spatial information can improve mode detection accuracy. The model has achieved 82%-95% accuracy and proves its efficacy over other detection models.

This paper is available online in the Research@Locate proceedings.