Predicting Short-term Bus Ridership with Trip Planner Data: A Machine Learning Approach

Predicting Short-term Bus Ridership with Trip Planner Data: A Machine Learning Approach

Wang, Ziyulong (TU Delft Civil Engineering and Geosciences)

Pel, A.J. (mentor)
Verma, T. (graduation committee)
Krishnakumari, P.K. (graduation committee)
van Oort, N. (graduation committee)
van Brakel, P. (graduation committee)

Delft University of Technology

2020-08-18

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.

Public transport
Trip Planner
Ridership prediction
Short term forecasting
Machine Learning

http://resolver.tudelft.nl/uuid:f1e4b495-d2ad-4a1e-803e-13e6c9b39f4a

2021-05-31

Student theses

master thesis

© 2020 Ziyulong Wang