Impact of Trip Distance Distribution Time Dependency and Aggregation Levels in Bathtub Models -- A Comparative Simulation Analysis

TL;DR

This study compares accumulation-based and trip-based bathtub traffic models under various trip distance distribution scenarios, highlighting how time dependency and aggregation levels affect their accuracy in simulating urban traffic.

Contribution

It provides a systematic simulation comparison of bathtub models with different TDD scenarios and network properties, revealing factors influencing their robustness and accuracy.

Findings

Time dependency of TDD increases model errors

Aggregation levels significantly affect model performance during demand transitions

Network state transition speed influences model accuracy

Abstract

Bathtub models are used to study urban traffic within a certain area. They do not require to take into account the detailed network topology. The emergence of different bathtub models has raised the question of which model can provide more robust and accurate results under different demand scenarios and network properties. This paper presents a comparative simulation analysis of the accumulation-based model and trip-based models under static and dynamic trip distance distribution (TDD) scenarios. Network accumulation was used to validate and compare the performance of the bathtub models with results from the macroscopic traffic simulation with dynamic traffic assignment. Three networks were built to explore the effect of network properties on the accuracy of bathtub models. Two are from the network of Delft, the Netherlands, and one is a reference toy network. The findings show that the time dependency of TDD can increase the errors in bathtub models. Using TDD in different aggregation levels can significantly influence the performance of bathtub models during demand transition periods. The state transition speed of networks is also found to be influential. Future research could explore the effects of dynamic TDD under congested situations and develop enhanced bathtub models that can better account for different network state transition speeds.