Abstract
Transportation agencies aim to serve the commuters with different transport modes through their transportation network, and they try to predict the demand to fulfil it either by optimizing their operation or by expanding their network and fleet capacity, and the only available data is the daily ridership data that can be captured by their ticketing system, this kind of data will help in optimizing the current operation in order to enhance availability of service and meeting the current demand. Encouraging people to use public transportation requires collecting more data about individuals trips using other modes like cars, taxi, Uber and Careem, and the challenge is more complex when it comes to data related to individuals trips using their own cars, and it requires an intensive efforts to capture this data using surveys or buying it from telecom companies which shows the movement of people. In this project we will focus on data captured by taxi dispatching systems, and it will be analyzed using some algorithms to predict the demand in each location and time into which destination. One of the challenges that should be considered is that taxi service is easier for passengers especially in areas that lack rail and bus services, moreover, it is a non-fixed schedule service, non-fixed route and non-fixed stops, while public transportation is a semi-fixed schedule, and fixed route and stops, and to encourage taxi passengers to use public transportation the transportation authority should consider the potential demand per pick up location and drop off destination and time in order to meet this predicted potential demand. However, it is required also to compare between the taxi demand and public transportation capacity, and this can be used to optimize the public transportation operation and enable the organization to encourage taxi passengers to use the taxi as intermediate mode to get another trip with other public transportation modes. Transportation authorities can rely on this prediction by considering the potential demand of public transportation as potential passenger (public transportation users).
Publication Date
5-2020
Document Type
Master's Project
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Advisor
Sanjay Modak
Advisor/Committee Member
Ioannis Karamitsos
Recommended Citation
Odeh, Mohammad, "Taxi Trips Prediction and Bus Capacity Coverage analysis" (2020). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11182
Campus
RIT Dubai
