📆 Tuesday, December 17, 2024

🕐 12:00 pm CET

📍 ETSECCPB, Sala Tesines C1-002 (C/Jordi Girona 1-3, mòdul C1, Campus Nord, Barcelona)

ABSTRACT

With the constant advancements in autonomous drive technologies and the growing popularity of tactical urbanism in street reorganization, it is essential to conduct research to evaluate the effects of these new forms of mobility on transportation networks. This research thesis explores their effects on different variables of transportation systems, such as congestion pricing and parking pricing during peak commute times. It also investigates how super blocks and autonomous vehicles impact traffic flow on a larger scale. Furthermore, it evaluates the effectiveness of public transportation by integrating shared autonomous vehicles to reduce congestion at critical locations. This thesis uses Vickery's bottleneck model to calculate the total travel cost for dynamic traffic patterns during morning and evening commutes in the presence of automated vehicles. To achieve the system's optimum, this research employs a time-dependent congestion toll and social parking pricing strategy. This thesis applies the formulated model to a numerical study with an assumed network link. The numerical case study's conclusions clarified the importance of AVs in the network to reduce congestion toll and parking pricing. This thesis proposes the super block model as a way to make the streets liveable in the presence of AVs. The research employs a genetic algorithm to implement the super block model in a network with a grid structure. It addresses the dynamic user equilibrium assignment for demand and topology segmentation. In this context, the thesis analyses the performance of a network with super blocks and AVs through a macroscopic fundamental diagram. Furthermore, in terms of sustainable public transport planning, AVs also play an important role in increasing the system's performance. Therefore, this thesis formulated a methodology for multimodal transport that combines public transport and shared AVs to improve network efficiency. In this context, the research measures public transport performance metrics, such as passengers' total travel time (which includes walking, waiting, and vehicle time), after implementing the optimal multimodal transport strategy. The results suggest that integrating shared AVs at critical locations of public transport improves the overall efficiency and connectivity of the network. Finally, the numerical studies presented in this thesis add value to the current state of the art in terms of optimal toll pricing, parking pricing, super block model, shared AVs, and public transport performance in the presence of emerging technologies such as “Autonomous Vehicles.” The methodologies designed in this thesis can be used by other researchers, mobility planners, and policymakers to achieve the optimal congestion tolls, to optimize the network having super blocks, and to improve the public transport network performance.

Committee

• President: Dr. José Manuel Vasallo
• Secretary: Dr. Miguel Angel Estrada
• Member: Dr. Antonio David Masegosa
  
  
• PhD advisor: Dr. Sergi Saurí

PHD CANDIDATE

Ms. Samra Sarwar is a PhD Urban Mobility researcher at CIMNE's Innovation Unit in Transport, CENIT. Her research focuses on explaining and understanding the traffic flow impacts of automated driving, external disturbances in traffic (impacts on congestion cost, tolling strategies, etc.) and to offer solutions to mitigate negative effects of disturbances through traffic management. She completed her Masters in Transportation Engineering as a Stipendium Hungaricum Scholar from Budapest University of Technology and Economics, at the faculty of transportation and vehicle engineering from 2017 to 2019. She was a teaching fellow at University of Engineering and Technology, Lahore, at the faculty of civil engineering. At professional level she has worked in The Urban Sector Planning & Management Services Unit, a public sector company, as Research Associate-Transport.