Bus Arrival Time Prediction with Limited Data Set using Regression Models

Armands Kviesis, Aleksejs Zacepins, Vitalijs Komasilovs, Marcela Munizaga

2018

Abstract

The increase of population has intensified everyday rush. Traffic congestions are still a problem in cities and are one of the main cause for public transport delays. City residents and visitors have experienced time loss by using public transport buses, because of waiting at the bus stops and not knowing if the bus is delayed or already serviced the stop. Therefore it is valuable for people to know at what time the bus should arrive (or is it already missed) at specific bus stop. Real-time public bus tracking and management system development has been the focus of many researchers, and many studies have been done in this area. This paper focuses on bus travel time prediction comparison between linear regression and support vector regression models (SVR), when using limited data set. Data were limited in a way that only historical GPS (Global Positioning System) coordinates of bus location (recorded each 30 seconds) and driven distance were used, there were no information about arrival/departure times, delays or dwell times. Distance between stops and delay (assumed values based on route observations by authors) were used as inputs for both models. It was concluded that SVR algorithm showed better results, but the difference was not significantly large.

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Paper Citation


in Harvard Style

Kviesis A., Zacepins A., Komasilovs V. and Munizaga M. (2018). Bus Arrival Time Prediction with Limited Data Set using Regression Models.In - RESIST, ISBN , pages 0-0. DOI: 10.5220/0006816306430647


in Bibtex Style

@conference{resist18,
author={Armands Kviesis and Aleksejs Zacepins and Vitalijs Komasilovs and Marcela Munizaga},
title={Bus Arrival Time Prediction with Limited Data Set using Regression Models},
booktitle={ - RESIST,},
year={2018},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006816306430647},
isbn={},
}


in EndNote Style

TY - CONF

JO - - RESIST,
TI - Bus Arrival Time Prediction with Limited Data Set using Regression Models
SN -
AU - Kviesis A.
AU - Zacepins A.
AU - Komasilovs V.
AU - Munizaga M.
PY - 2018
SP - 0
EP - 0
DO - 10.5220/0006816306430647