On the quality requirements of demand prediction for dynamic public transport

Inon Peled; Kelvin Lee; Yu Jiang; Justin Dauwels; Francisco C. Pereira

doi:10.1016/j.commtr.2021.100008

Communications in Transportation Research 2021, 1(1): 100008 https://doi.org/10.1016/j.commtr.2021.100008

Research Article |

Open Access | Issue | Published: 06 November 2021

On the quality requirements of demand prediction for dynamic public transport

Show Author's Information Hide Author's Information Inon Peled^{^a}(

), Kelvin Lee^{^b}, Yu Jiang^{^a}(

), Justin Dauwels^{^c}, Francisco C. Pereira^{^a}

Danmarks Tekniske Universitet (DTU), Technology, Management and Economics Dept., Kgs. Lyngby, 2800, Denmark

Nanyang Technological University (NTU), Graduate College, 50 Nanyang Avenue, Singapore, 637553

Delft University of Technology (TU Delft), Microelectronics Dept., 2600, Netherlands

Keywords:

Dynamic public transport, Demand forecasting, Non-Gaussian noise, Predictive optimization

Cite this article:

Peled I, Lee K, Jiang Y, et al. On the quality requirements of demand prediction for dynamic public transport. Communications in Transportation Research, 2021, 1(1): 100008. https://doi.org/10.1016/j.commtr.2021.100008

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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.

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About this article

On the quality requirements of demand prediction for dynamic public transport

Show Author's information Hide Author's Information Inon Peled^{^a}(

), Kelvin Lee^{^b}, Yu Jiang^{^a}(

), Justin Dauwels^{^c}, Francisco C. Pereira^{^a}

Danmarks Tekniske Universitet (DTU), Technology, Management and Economics Dept., Kgs. Lyngby, 2800, Denmark

Nanyang Technological University (NTU), Graduate College, 50 Nanyang Avenue, Singapore, 637553

Delft University of Technology (TU Delft), Microelectronics Dept., 2600, Netherlands

Abstract

Keywords: Dynamic public transport, Demand forecasting, Non-Gaussian noise, Predictive optimization

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Publication history

Received: 25 August 2021

Revised: 16 October 2021

Accepted: 16 October 2021

Published: 06 November 2021

Issue date: December 2021

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Acknowledgements

Data for this research was obtained by kind permission of the Danish Transport, Construction and Housing Authority, Movia Transit Agency, the Danish National Rail Company and the Danish Metro Company.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).