Urban Bus Arrival Time Prediction: A Review of Computational Models
Mehmet Altinkaya1, Metin Zontul2

1Mehmet Altinkaya, Department of Computer Engineering, Istanbul Aydin University, Istanbul, Turkey.
2Dr. Metin Zontul, Asst. Prof., Department of Software Engineering, Istanbul Aydin University, Istanbul, Turkey.

Manuscript received on 21 September 2013 | Revised Manuscript received on 28 September 2013 | Manuscript published on 30 September 2013 | PP: 164-169 | Volume-2 Issue-4, September 2013 | Retrieval Number: D0823092413/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Traffic flow in major urban roads is affected by several factors. It is often interrupted by stochastic conditions, such as traffic lights, road conditions, number of vehicles on the road, time of travel, weather conditions, driving style of vehicles. The provision of timely and accurate travel time information of transit vehicles is valuable for both operators and passengers, especially when dispatching is based on estimation of potential passengers waiting along the route rather than the predefined time schedule. Operators manage their dispatches in real time, and passengers can form travel preferences dynamically. Arrival time estimation for time scheduled public transport busses have been studied by many researchers using various paradigms. However, dynamic prediction on some type of transit vehicles, which do not follow any dispatch time schedule, or stop station constrains introduces extra complexities. In this paper, a survey on the recent studies using historical data, statistical methods, Kalman Filters and Artificial Neural Networks (ANN) have been applied to GPS data collected from transit vehicles, are collected with an emphasis on their model and architecture.
Keywords: Bus Travel Time Prediction, Intelligent Transportation Systems (ITS), Advanced Traveller Transportation Systems (AITS), Kalman Filtering, Machine Learning, Artificial Neural Networks (ANN).

Scope of the Article: Machine Learning