Mapping Bus and Stream Travel Time Using Machine Learning Approaches. (25th April 2022)

• Record Type: Journal Article
• Authors: Nair, Gopikrishnan; Kumar, B. Anil; Vanajaskshi, Lelitha
• Other Names: Chen Mei Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2022(2022)
• Publication Date: 2022-04-25
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2022/9743070
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : Collection of travel time data has always been a strenuous task, especially on Indian roads, due to the highly mixed traffic conditions and the absence of rigid driving characteristics. Travel time data collection methods such as on-board GPS devices and Wi-Fi scanners have their own feasibility issues. The GPS devices cannot be installed on all private vehicles, and Wi-Fi scanners cannot be set up at each and every road corridor. However, many transit agencies of Indian cities are installing GPS units in their public transport buses, making them a rich source of data. These buses travel all over the network and record the travel time and location information of vehicles at a certain interval, which can become the best and reliable source of traffic data under such conditions. The only limitation with this data is that the buses differ greatly from the rest of the vehicles in terms of vehicle characteristics, freedom of driving, effect of bus-stops, etc. So, a viable solution is to model and link the stream and bus travel times. Based on this, the present study proposes a method to map bus travel times with stream travel times using machine learning techniques, namely, Gradient Boosting Method and Support Vector Machines. Preliminary analysis showed that a nonlinear relationship exists between the travel times, and other factors like peak/off-peak hours, day of the week, etc. Results show that the proposed methods can efficiently map bus travel times with stream travel times with an average MAPE of 20% and perform better than existing approaches such as linear regression and artificial neural networks.