2333. Minimizing Vehicle Delay Time in a Port Area Through Simulation Optimization of Traffic Light Control
Invited abstract in session WC-1: Port and shipping logistics, stream Mobility, Transportation, and Traffic.
Wednesday, 13:30-15:00Room: Audimax
Authors (first author is the speaker)
| 1. | Bruna Helena Pedroso Fabrin
|
| Institute for Operations Research, Universität Hamburg | |
| 2. | Wolfgang Brüggemann
|
| Inst. f. Operations Research, Universität Hamburg | |
| 3. | Larissa Timm
|
| HPA | |
| 4. | Alwin Brehde
|
| Hamburg Port Consulting | |
| 5. | Carsten Eckert
|
| Hamburg Port Consulting | |
| 6. | Ralf Sahling
|
| Hamburg Port Consulting | |
| 7. | Justin Wilckens
|
| HPC, TUHH | |
| 8. | Julia Hertel
|
| University of Hamburg | |
| 9. | Ulrich Baldauf
|
| R&D, Hamburg Port Consulting | |
| 10. | Leif-Erik Gorris
|
| Hamburg Port Consulting |
Abstract
In a port area, traffic demand can greatly vary depending on the hour of the day and travel direction. For example, more traffic can be expected in certain hours of the day in the direction of a terminal in order to unload or load cargo. This can lead to congestion and delays. Thus, drivers are more susceptible of being irritated or making mistakes due to staying too long in traffic. One way of working on this issue is by optimizing the traffic light control, so that traffic flow is considerably improved. This study is conducted within the IHATEC’s HafenPlanZen project, which was developed to aid port planners in strategic decision making, such as traffic planning and adaptation.
An important measure of traffic is Average Delay Time (ADT), which is defined as the extra time that vehicles need to complete their journey due to disruptions, such as congestion or traffic lights, in comparison to driving on a free path. This study had the goal of robustly minimizing the maximum ADT in a port area, because this represents the worst case of the rush hour traffic. To achieve this, we use a simulation optimization methodology and implement the Downhill Simplex Method. Results shows large improvements in travel times with maximum delay being about 5 times smaller than that of the initial solution considered.
Keywords
- Transportation
- Robust Optimization
- Mobility
Status: accepted
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