Loading…
Dynamic Traffic Assignment for regional networks with traffic-dependent trip lengths and regional paths
•Traffic conditions impact the distributions of travel distances.•We develop a framework to determine explicit time-dependent distributions of travel distances.•We discuss a traffic assignment model for regional networks with time-varying travel distances.•We validate our methodology on small and me...
Saved in:
Published in: | Transportation research. Part C, Emerging technologies Emerging technologies, 2021-06, Vol.127, p.103076, Article 103076 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | •Traffic conditions impact the distributions of travel distances.•We develop a framework to determine explicit time-dependent distributions of travel distances.•We discuss a traffic assignment model for regional networks with time-varying travel distances.•We validate our methodology on small and medium-sized city networks, using an MFD simulation environment.•The proposed methodology shows a good performance for estimating traffic-dependent trip lengths.
The estimation of trip lengths has been proven to be a key feature for the application of aggregated traffic models based on the Macroscopic Fundamental Diagram. The paths and distances to be traveled by vehicles in regional networks vary over time, due to changes in the traffic conditions. In this paper, we develop a methodological framework to explicitly determine traffic-dependent regional paths and estimate their travel distances. This framework is incorporated into a dynamic traffic assignment module designed to target the Deterministic and Stochastic User Equilibrium in regional networks. We first discuss how regional paths and their characteristic trip lengths are influenced by changes in the regional traffic dynamics. We then test the proposed methodology for estimating traffic-dependent travel distances on small and medium-sized networks, considering a simulation environment. We show that our methodology provides good estimations of the traffic-dependent trip lengths. Our results also shed light on the importance of how time-dependent trip lengths influence the traffic dynamics in the regions. |
---|---|
ISSN: | 0968-090X 1879-2359 |
DOI: | 10.1016/j.trc.2021.103076 |