—Travel delays and the ever increasing traffic queues negatively affect the everyday lives of commuters. Users of public transit systems, such as the TTC, are left unaware of the length of the delay they might experience on their journey. One of the most important factors in an Intelligent Transportation System (ITS) is the ability to predict the bus’s arrival time accurately which not only increases the current customers’ satisfaction, but also attracts new customers for the system. The objectives of this paper are (1) to predict the bus travel times using GPS data, and (2) to develop a parametric optimization model to improve bus arrival times based on the speed distribution. The bus travel time optimization model proposed in this research explicitly includes arrival time, dwell times, schedule adherence, and traffic congestion, all critical factors for accurate bus arrival times. In this paper, a test bus route was used in downtown Toronto, Canada. The optimization model performed significantly better than the historical data-based models. The proposed model identified the relationship between travel times and the independent variables, leading to superior results.
—Optimization model, bus arrival time, road safety, public transportation.
Udai Hassein and Lisa Kadoury are with the Department of Civil Engineering Ryerson University, Toronto, Ontario, Canada (e-mail: firstname.lastname@example.org).
Kaarman Raahemifar is with the Department. of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada (e-mail: email@example.com).
Cite: Udai Hassein, Kaarman Raahemifar, and Lisa Kadoury, "Improving Travel Information Systems with the Assistance of GPS," International Journal of Information and Electronics Engineering vol. 6, no. 1, pp. 49-53, 2016.