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Title:

Performance Comparison of Bus Travel Time Prediction Models across Indian Cities

Accession Number:

01668796

Record Type:

Component

Availability:

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Order URL: http://worldcat.org/issn/03611981

Abstract:

Road traffic congestion has become a global worry in recent years. In many countries congestion is a major factor, causing noticeable loss to both economy and time. The rapid increase in vehicle ownership accompanied by slow growth of infrastructure has resulted in space constraints in almost all major cities in India. To mitigate this issue, authorities have shifted to more sustainable management solutions like Intelligent Transport System (ITS). Advanced Public Transportation System (APTS) is an important area in ITS which could considerably offset the growing ownership of private vehicles as public transport holds a noticeable mode share in several major cities in India. Getting access to real-time information about public transport would certainly attract more users. In this regard, this work aims at developing a reliable structure for predicting arrival/travel time of various public transport systems under heterogeneous traffic conditions existing in India. The data used for the study is collected from three cities—Surat, Mysore, and Chennai. The data is analyzed across space and time to extract patterns which are further utilized in prediction models. The models examined in this paper are k-NN classifier, Kalman Filter and Auto-Regressive Integrated Moving Average (ARIMA) techniques. The performance of each model is evaluated and compared to understand which methods are suitable for different cities with varying characteristics.

Report/Paper Numbers:

18-04897

Language:

English

Authors:

R, Jairam
Kumar, B. Anil
Arkatkar, Shriniwas S
Vanajakshi, Lelitha

Pagination:

pp 87-98

Publication Date:

2018

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2672
Issue Number: 31
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Print

Features:

Figures (8) ; References (21) ; Tables (1)

Subject Areas:

Data and Information Technology; Planning and Forecasting; Public Transportation

Files:

TRIS, TRB, ATRI

Created Date:

Dec 22 2017 10:38AM

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