Tag Archives: transport mode detection

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

R. Das and S. Winter, “Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory,” ISPRS International Journal of Geo-Information, vol. 5, no. 11, 2016 (open access).

Abstract:

Transport mode information is essential for understanding people’s movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other’s limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection.

A Neuro-Fuzzy based Hybrid Intelligent Framework for Transport Mode Detection

R. Das and S. Winter, “A Neuro-Fuzzy based Hybrid Intelligent Framework for Transport Mode Detection,” in Proceedings of the 6th International Workshop on Mobile Entity
Localization, Tracking and Analysis (MELT)
, 2016.

Abstract:

Understanding transport mode detection, is important to transport planning and movement behavior analysis. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classi cation. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format with more exibility, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel multi-layered hybrid intelligent framework is developed by integrating a fuzzy logic and a machine learning approach to complement each other’s limitations. Thus in this paper an intelligent fuzzy logic-based near-real time mode detection framework is presented that can perform comparable to state-of-the-art machine learning approaches by bridging the trade-off between explanatory power and prediction accuracy of the models. The results demonstrate that the proposed framework has achieved the aim in the context of near-real time transport mode detection.