Traffic flow simulation under CAV environment using autonomous intersection management (AIM) within a single intersection

• Main Author: Siti Nur Umairah Hashim
• Other Authors: Zhu Feng
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2020
• Subjects: Engineering::Civil engineering::Transportation
• Online Access: https://hdl.handle.net/10356/141916
• Institution: Nanyang Technological University

The ever-progressive Connected and Automated Vehicles (CAVs) space has quickly revolutionised the dynamics of the transportation system. CAVs are prominently recognised for their potential benefits to the transportation system such as the safety aspect and traffic efficiency. The rapid development of CAVs in recent years grew in tandem with the ongoing research into CAVs. One of which is the simulation of traffic flows under CAVs environment. Primarily, traffic flows at intersections as congestions usually happen which may result in unwanted calamitous collisions, posing challenges to traffic flow management. While previous studies have acknowledged the need for a more efficient junction management, more should be investigating the efficiency of intersections under CAVs environment. Autonomous Intersection Management (AIM) is a multiagent systems approach to traffic intersection management (Dresner and Stone, 2008). In AIM, the driver agents communicate ahead of time to the intersection manager in the time space of the intersection to reserve conflict-free trajectories by utilising the wireless communication technology of CAVs. This is contrasting to existing junction managements, utilising stop signs or traffic light signals. In this project, the efficiency of the implementation Autonomous Intersection Management (AIM) within a single intersection under CAVs environment was evaluated by conducting traffic simulation in Simulation of Urban Mobility (SUMO). To study and ascertain the efficiency of the introduction of the First Come First Serve (FCFS) policy of AIM under CAVs environment, simulations were conducted using SUMO, Python and TraCI, with Python being the main programming language of the simulation. Several scenarios were conducted in the simulation, with varying traffic rate generation for the control case using traffic signal controls and thereafter implementing the FCFS-AIM policy. The experimental findings concluded that the implementation of the AIM policy increased the overall efficiency of the junction management under the uncongested traffic flow situations. This was ascertained by analysing 3 attributes of the simulation outputs; the vehicles’ travel time, waiting time and time loss.