Traveling time prediction in scheduled transportation with journey segments

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• 作者：Avigdor Gal^a ; ^{avigal@ie.technion.ac.il} ; Avishai Mandelbaum^a ; ^{avim@ie.technion.ac.il} ; Franç ; ois Schnitzler^a ; ^{francois@ee.technion.ac.il} ; Arik Senderovich^a ; ^{sariks@tx.technion.ac.il} ; Matthias Weidlich^b ; ^{matthias.weidlich@hu-berlin.de}
• 关键词：Traveling time prediction ; Queue mining ; Machine learning
• 刊名：Information Systems
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：64
• 期：Complete
• 页码：266-280
• 全文大小：1494 K
• 卷排序：64

文摘

Urban mobility impacts urban life to a great extent. To enhance urban mobility, much research was invested in traveling time prediction: given an origin and destination, provide a passenger with an accurate estimation of how long a journey lasts. In this work, we investigate a novel combination of methods from Queueing Theory and Machine Learning in the prediction process. We propose a prediction engine that, given a scheduled bus journey (route) and a ‘source/destination’ pair, provides an estimate for the traveling time, while considering both historical data and real-time streams of information that are transmitted by buses. We propose a model that uses natural segmentation of the data according to bus stops and a set of predictors, some use learning while others are learning-free, to compute traveling time. Our empirical evaluation, using bus data that comes from the bus network in the city of Dublin, demonstrates that the snapshot principle, taken from Queueing Theory, works well yet suffers from outliers. To overcome the outliers problem, we use Machine Learning techniques as a regulator that assists in identifying outliers and propose prediction based on historical data.