NDLI: An adaptive transportation prediction model for the informal public transport sector in Africa

Content Provider	IEEE Xplore Digital Library
Author	Ndibatya, I. Booysen, M.J. Quinn, J.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Stellenbosch Univ., Stellenbosch, South Africa (Ndibatya, I.; Booysen, M.J.) \|\| Dept. of Comput. Sci., Makerere Univ., Makerere, Uganda (Quinn, J.)
Abstract	The informal public transport sector in Sub-Saharan Africa is responsible for transporting the overwhelming majority of the workforce. Often, passengers have to wait for hours for taxis to coincidentally pass by to pick them up, making the transport mode notoriously inefficient. Despite its relevance and impact, the sector is afforded little attention in terms of regulation, development and organization, giving rise to a complex and inefficient system that affects millions of people. In fact, little is known about the industry. To advance understanding of this system, minibus taxis were equipped with tracking devices in this study. Tracking data was then used to develop a model that describes the transport network - essentially finding patterns in the apparent chaos for the potential benefit of its users. The adaptive model uses unsupervised learning to predict the informal stages in the city and provide travelers with intelligence on the best time and place to get transport, thereby reducing the waiting time at the taxi rank and the informal roadside stops. Experimental results show 70.4% model accuracy in dynamically learning the taxi behavior and accurately predicting the best places to get taxis at a given time of the day.
Starting Page	2572
Ending Page	2577
File Size	529405
Page Count	6
File Format	PDF
ISBN	9781479960781
DOI	10.1109/ITSC.2014.6958102
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Data models Predictive models Clustering algorithms Cities and towns Roads Africa Intelligent Transport Systems Adaptive Model Informal Transport Sector Transport Prediction
Content Type	Text
Resource Type	Article

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