Optimizing capacity of signalized road network with reversible lanes
This paper studies the network capacity problem on signalized road network with reversible lanes. A Mixed Network Design Problem (MDNP) is formulated to describe the problem where the upper-level problem is a mixed integer non-linear program designed to maximize the network capacity by optimizing th...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Vilnius Gediminas Technical University
2018-01-01
|
| Series: | Transport |
| Subjects: | |
| Online Access: | https://journals.vgtu.lt/index.php/Transport/article/view/121 |
| Summary: | This paper studies the network capacity problem on signalized road network with reversible lanes. A Mixed Network Design Problem (MDNP) is formulated to describe the problem where the upper-level problem is a mixed integer non-linear program designed to maximize the network capacity by optimizing the input parameters (e.g. the signal splits, circles, reassigned number of lanes and O–D demands), while the lower-level problem is the common Deterministic User Equilibrium (DUE) assignment problem formulated to model the drivers’ route choices. According to whether one way strategy is permitted in practice, two strategies for implementing reversible roadway are considered. In the first strategy, not all lanes are reversible and the reversible roadways always hold its ability to accommodate the two-way traffic flow. In the second strategy, one-way road is allowed, which means that all the lanes are reversible and could be assigned to one flow direction if the traffic flow in both directions is severally unsymmetrical. Genetic Algorithm (GA) is detailedly presented to solve the bi-level network capacity problem. The application of the proposed method on a numerical example denotes that Strategy 2 can make more use of the physical capacity of key links (signal controlled links), thus, the corresponding network capacity outperforms it is of Strategy 1 considerably.
First published online 14 January 2015 |
|---|---|
| ISSN: | 1648-4142 1648-3480 |