Print Email Facebook Twitter Predicting Short-term Bus Ridership with Trip Planner Data: A Machine Learning Approach Title Predicting Short-term Bus Ridership with Trip Planner Data: A Machine Learning Approach Author Wang, Ziyulong (TU Delft Civil Engineering and Geosciences) Contributor Pel, A.J. (mentor) Verma, T. (graduation committee) Krishnakumari, P.K. (graduation committee) van Oort, N. (graduation committee) van Brakel, P. (graduation committee) Degree granting institution Delft University of Technology Date 2020-08-18 Abstract To address the increasing passenger demand in the coming years and make public transport less crowded and delayed, insights into predicted passenger flow are needed. A wide range of studies has used and validated that smart card data can be one of the sound bases for predicting short-term passenger demand. However, it also has several disadvantages, such as the relatively long collection time, the insufficiency to reflect the relationship between passenger behavior and ridership. Trip planner data, which emerged as a type of real-time transit information, could reduce the perceived waiting time of passengers and increase the transit ridership due to the improved satisfaction. Combining these two types of data could potentially cater to the interest of operators in matching the vehicle supply and passenger flow demand at an operational level. Our results show that it is novel and useful to incorporate trip planner data in short-term ridership prediction, however, entirely based on this kind of data would be inaccurate. Random Forest Regression outperforms the other six models that we have selected. The request-related features (variables) can take up 20% of the importance of short-term ridership prediction. Subject Public transportTrip PlannerRidership predictionShort term forecastingMachine Learning To reference this document use: http://resolver.tudelft.nl/uuid:f1e4b495-d2ad-4a1e-803e-13e6c9b39f4a Embargo date 2021-05-31 Part of collection Student theses Document type master thesis Rights © 2020 Ziyulong Wang Files PDF Predicting_Short_Term_Bus ... Report.pdf 16.93 MB Close viewer /islandora/object/uuid:f1e4b495-d2ad-4a1e-803e-13e6c9b39f4a/datastream/OBJ/view