Header

UZH-Logo

Maintenance Infos

A fuzzy logic based transport mode detection framework in urban environment


Das, Rahul Deb; Winter, Stephan (2018). A fuzzy logic based transport mode detection framework in urban environment. Journal of Intelligent Transportation Systems, 22(6):478-489.

Abstract

Transport mode detection is an emerging research area in different domains such as urban planning, context-aware mobile computing, and intelligent transportation systems. Current approaches are mostly data-driven, based on machine learning approaches. However, machine learning models require substantial training data and cannot explain the reasoning procedure. Data-driven approaches also fall short while interpreting trajectories where ground truth information is limited. Therefore, this paper develops a novel knowledge-based approach for interpreting smartphone global positioning system trajectories by detecting various transport modes used during travel. The proposed model is based on an expert system that can work without any training, based solely on expert knowledge. Core is a fuzzy multiple-input multiple-output expert system using kinematic and spatial information with a well explained fuzzy reasoning scheme through a fuzzy rule base. The model can provide alternate predictions with varied certainty factors. Different membership function combinations have been evaluated in terms of accuracy and ambiguity, and the result demonstrates that the model performs best using a Gaussian–Gaussian combination, comparable to the existing machine learning approaches.

Abstract

Transport mode detection is an emerging research area in different domains such as urban planning, context-aware mobile computing, and intelligent transportation systems. Current approaches are mostly data-driven, based on machine learning approaches. However, machine learning models require substantial training data and cannot explain the reasoning procedure. Data-driven approaches also fall short while interpreting trajectories where ground truth information is limited. Therefore, this paper develops a novel knowledge-based approach for interpreting smartphone global positioning system trajectories by detecting various transport modes used during travel. The proposed model is based on an expert system that can work without any training, based solely on expert knowledge. Core is a fuzzy multiple-input multiple-output expert system using kinematic and spatial information with a well explained fuzzy reasoning scheme through a fuzzy rule base. The model can provide alternate predictions with varied certainty factors. Different membership function combinations have been evaluated in terms of accuracy and ambiguity, and the result demonstrates that the model performs best using a Gaussian–Gaussian combination, comparable to the existing machine learning approaches.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

4 downloads since deposited on 30 Jan 2019
4 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Uncontrolled Keywords:Control and Systems Engineering, Software, Applied Mathematics, Automotive Engineering, Information Systems, Aerospace Engineering, Computer Science Applications
Language:English
Date:2 November 2018
Deposited On:30 Jan 2019 13:17
Last Modified:07 May 2019 13:15
Publisher:Taylor & Francis
ISSN:1547-2442
OA Status:Closed
Publisher DOI:https://doi.org/10.1080/15472450.2018.1436968

Download