|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Author(s):||Abidin, Ahmad Faisal|
|Title:||Towards Improved Vehicle Arrival Time Prediction in Public Transportation: Integrating SUMO and Kalman Filter Models|
|Citation:||Abidin AF & Kolberg M (2015) Towards Improved Vehicle Arrival Time Prediction in Public Transportation: Integrating SUMO and Kalman Filter Models In: 2015 17th UKSIM-AMSS International Conference on Modelling and Simulation. 17th Intl Conference on Modelling and Simulation, UKSim 2015, IEEE, March 2015, Washington DC, USA, 25.03.2015-27.03.2015. Washington DC, USA: IEEE Computer Society, pp. 147-152. http://uksim2015.info/.|
|Conference Name:||17th Intl Conference on Modelling and Simulation, UKSim 2015, IEEE, March 2015|
|Conference Dates:||2015-03-25 - 2015-03-27|
|Abstract:||Accurate bus arrival time prediction is a key component for improving the attractiveness of public transport. In this research, a model of bus arrival time prediction, which aims to improve arrival time accuracy, is proposed. The arrival time will be predicted using a Kalman Filter (KF) model, by utilising information acquired from social networks. Social Networks feed road traffic information into the model, based on information provided by people who have witnessed events and then updated their social media accordingly. In order to accurately assess the efficiency of KF model, we simulate realistic road scenarios using the traffic simulator Simulation in Urban Mobility (SUMO). SUMO is capable of simulating real world road traffic using digital maps and realistic traffic models. This paper discusses modelling a road journey using Kalman Filters and verifying the results with a corresponding SUMO simulation. As a second step, SUMO based measures are used to inform the KF model. Integrating the SUMO measures with the KF model can be seen as an initial step to verifying our premise that realtime data from social networks can eventually be used to improve the accuracy of the KF prediction. Furthermore, it demonstrates an integrated experimental environment.|
|Status:||AM - Accepted Manuscript|
|Rights:||© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|UKSIM2015-010315-mko.pdf||Fulltext - Accepted Version||1.8 MB||Adobe PDF||View/Open|
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