Over the past decade, the popularity of regional bus services has grown in large North American cities owing to more people living in suburban areas and commuting to the Central Business District to work every day. Estimating journey time for regional buses is challenging because of the low frequencies and long commuting distances that typically characterize such services. This research project developed a mathematical model to estimate regional bus travel time using artificial neural networks (ANN). ANN outperformed other forecasting methods, namely historical average and linear regression, by an average of 35 and 26 seconds respectively. The ANN results showed, however, overestimation by 40% to 60%, which can lead to travellers missing the bus. An operational strategy is integrated into the model to minimize stakeholders’ costs when the model’s forecast time is later than the scheduled bus departure time. This operational strategy should be varied as the commuting distance decreases.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/26531 |
| Date | 30 March 2011 |
| Creators | Wong, Andrew Chun Kit |
| Contributors | Shalaby, Amer Saïd, Abdulhai, Baher |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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