An aggregation method for dynamic bus arrival time prediction

作者：Zhenliang Ma; Jianping Xing^*; Shihao Yan; Yong Wu; Yubing Wu

来源：Advances in Information Sciences and Service Sciences, 2011, 3(6): 37-48.

DOI：10.4156/aiss.vol3.issue6.5

• Aggregation methods
• Arrival time
• Dynamic algorithm
• Filter
• Filter-based
• Gaussians
• Historical data
• Input datas
• Intelligent transportation systems
• Operations management
• Performance evaluation
• Public transit systems
• Real-time information
• Road section
• Service planning
• Svm
• SVM model
• Travel time
• Two stage
• Weather conditions

Accurate bus arrival time prediction is crucial to the development of intelligent transportation systems and advanced traveler information systems. Automatic stop announcement (ASA) systems have been implemented in various public transit systems to realize automatic stop broadcasting along with other information such as location, travel time, etc. Such information has great potential as input data for a variety of applications including operations management, service planning, and performance evaluation. In this study, an aggregation method is developed for dynamic bus arrival time prediction, using data collected by a real world ASA system. The method implements in two stages, firstly bus baseline estimate travel time along each road section is calculated using SVM with historical trip data at given time-of-day, day-of-week and weather conditions. The second one is a H∞ filter based dynamic algorithm to adjust the arrival time prediction using the most recent trip information and SVM output. The method enables the prediction more accuracy and robust by taking into account of both historical data and real-time information, and there is no assumption of the noise, which is assumed to be Gaussian white by Kalman method. Experiments show that the aggregation method is quite powerful in bus arrival time prediction by comparing with the corresponding SVM model and SVM-KF algorithm.

• 出版日期2011