On the quality requirements of demand prediction for dynamic public transport

On the quality requirements of demand prediction for dynamic public transport

Peled, Inon (Technical University of Denmark)
Lee, Kelvin (Nanyang Technological University)
Jiang, Yu (Technical University of Denmark)
Dauwels, J.H.G. (TU Delft Signal Processing Systems)
Pereira, Francisco C. (Technical University of Denmark)

2021

As Public Transport (PT) becomes more dynamic and demand-responsive, it increasingly depends on predictions of transport demand. But how accurate need such predictions be for effective PT operation? We address this question through an experimental case study of PT trips in Metropolitan Copenhagen, Denmark, which we conduct independently of any specific prediction models. First, we simulate errors in demand prediction through unbiased noise distributions that vary considerably in shape. Using the noisy predictions, we then simulate and optimize demand-responsive PT fleets via a linear programming formulation and measure their performance. Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors. In particular, the optimized performance can improve under non-Gaussian vs. Gaussian noise. We also find that dynamic routing could reduce trip time by at least 23% vs. static routing. This reduction is estimated at 809,000 €/year in terms of Value of Travel Time Savings for the case study.

Dynamic public transport
Demand forecasting
Non-Gaussian noise
Predictive optimization

http://resolver.tudelft.nl/uuid:9980312d-60c2-479c-83ba-37d46068ab7f

https://doi.org/10.1016/j.commtr.2021.100008

2772-4247

Communications in Transportation Research, 1, 1-11

Institutional Repository

journal article

© 2021 Inon Peled, Kelvin Lee, Yu Jiang, J.H.G. Dauwels, Francisco C. Pereira