Urban transportation is a key issue worldwide, especially in Sub-Saharan Africa where there has been a rapid increase in population and at the same time, a lack of infrastructure which include railways, airways and roads. People’s mobility in these African nations is mostly provided by bus services and a large-scale informal public transportation system known as paratransit (for example, car rapides in Senegal, Tro Tros in Ghana, taxis in Uganda and Ethiopia, and Matatus in Kenya). This brings up the need for transport demand estimation, which is a challenging task, particularly in developing countries. The main reason for the challenge is that the estimation methods usually require large datasets which can be quite difficult, costly, and time-consuming to collect. When it comes to demand estimation, important factors include the accuracy and transparency of data. Accurate data can help us identify trends in passenger demand so that better informed decisions about future investments in infrastructure and capacity can be made. In this chapter, we discuss how passenger demand for public transportation services can be estimated using mobile phone network data. Based on the inferred travel demand, strategic locations for public transportation services such as paratransit and taxi stands can then be suggested accordingly. This chapter is inspired by our original research work done by Demissie et al. (IEEE Trans Intell Transp Syst. 2016;17(9):2466–78; 13th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON). IEEE; 2016).
Inferring Passenger Travel Demand Using Mobile Phone CDR Data
Urban Informatics Using Mobile Network Data ; Kapitel : 2 ; 17-43
2022-11-30
27 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Call detail records , Trip generation inference , Paratransit , Travel demand , Public transport , Transit route Computer Science , Data Structures and Information Theory , Artificial Intelligence , Data Mining and Knowledge Discovery , Statistics, general , Transportation Technology and Traffic Engineering , Computer Applications
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