On the Quality Requirements of Demand Prediction for Dynamic Public Transport

TL;DR

This study investigates how the accuracy of demand predictions impacts the efficiency of demand-responsive public transport, revealing that skewed errors and large infrequent errors significantly affect performance.

Contribution

It provides an experimental analysis of demand prediction errors' effects on PT operations, highlighting the importance of error distribution shape and dynamic routing benefits.

Findings

Performance improves with non-Gaussian noise compared to Gaussian noise.

Dynamic routing reduces trip time by at least 23%.

Potential annual savings of 809,000 EUR in travel time savings.

Abstract

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 EUR/year in terms of Value of Travel Time Savings for the case study.