Public Thesis defense : BAMDAD MEHRABANI
lab | Louvain-la-Neuve, Bruxelles Saint-Gilles, Tournai
Modeling the resilience of the road network in the presence of connected automated vehicles by Behzad BAMDAD MEHRABANI
Pour l’obtention du grade académique de Doctorat en sciences de l’ingénieur et technologie
The advent of Connected Automated Vehicles (CAVs) has led to significant transformations in the transportation industry, with potential effects on road capacity and safety. By utilizing real-time data, CAVs have the potential to mitigate disruptions caused by incidents and reroute themselves to improve network resilience. Additionally, CAVs may select more optimal routes that benefit society as a whole. Consequently, evaluating the impact of CAVs on road network resilience is of paramount importance. However, few studies have assessed the resilience of large-scale road networks, including redundancy, robustness, and recovery speed. This investigation aims to quantify the resilience of Belgium's road network under CAV circumstances. To do so, simulation-based models of road network resilience is employed. This study establishes a base-case model of Belgium's road network, followed by examining differences in route selection between CAVs and Human Driven Vehicles (HDVs) by solving the multiclass traffic assignment problem. Finally, the resilience of Belgium's road network is assessed under various road disruption and CAV penetration rate scenarios, utilizing a novel performance indicator to create a "resilience triangle" that captures all aspects of resilience. This research's results could be of great assistance to decision-makers and policymakers in utilizing the advantages of CAVs.
Jury members :
- Prof. Luca Sgambi (UCLouvain), supervisor
- Prof. Sergio Altomonte (UCLouvain), chairperson
- Prof. Bart Jourquin (UCLouvain), secretary
- Prof. Maaike Snelder (TU Delft, Netherlands)
- Dr. Konstantinos Gkoumas (European Research Executive Agency, Belgium)
Pay attention :
The public defense of Behzad Bamdad Mehrabani scheduled for Thursday 01 February at 02:00 p.m will also take place in the form of a video conference
Meeting ID: 335 730 577 13
Passcode: FRHdKK
