Print Email Facebook Twitter Centralized nonlinear model predictive control for highway traffic throughput maximization: Case study of on-ramp merging Title Centralized nonlinear model predictive control for highway traffic throughput maximization: Case study of on-ramp merging Author Khalid, Omer (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control) Contributor Steur, E. (mentor) Wang, M. (mentor) De Schutter, B.H.K. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Systems and Control Date 2020-10-12 Abstract Traffic congestion on highways is a multi-sectoral phenomenon affecting society, the economy and the environment. It often takes place at specific locations such as on and off-ramps, weaving segments and intersections. The on-ramp merging procedure is considered as one of the main factors that causes traffic congestion on highways. The studies in literature show that the merging procedure can result in adverse traffic scenarios such as the buildup of the vehicles on the ramp which causes a downstream drop in capacity and subsequent blockage of upstream off-ramp traffic flow. Moreover, the on-ramp vehicles need to take the actions of leading and following mainlane vehicles into account during the merging process. On highly congested roads, this merging process becomes even more tedious and undesirable stop-and-go traffic behavior becomes unavoidable. Connected and autonomous vehicles (CAVs) that can provide safe gaps between vehicles along with identifying appropriate merging speed profiles have the potential to reduce traffic accidents and improve traffic efficiency. This thesis introduces a nonlinear model predictive control (NMPC) strategy for autonomous merging control based on a cost function that tracks the desired inter-vehicular gaps for on-ramp and mainlane vehicles, and thus intends to fully exploit the capacity of the road in order to maximize the traffic throughput. The proposed controller aims to optimize both acceleration and steering rate profiles of vehicles, and to guide on-ramp vehicles to merge efficiently, without frequent slowdown or wait for merging gaps at the end of the ramp along with minimal disruption to the mainlane traffic flow. The controller is evaluated under different initial conditions, ranging from low to high traffic conditions. The performance of the controller is compared to that of a baseline scenario, and the results show that the proposed controller increases travel times in the range of 2.46% and 4.17% for different traffic conditions, without disrupting the mainline traffic operation. Additionally, average speed of vehicles is improved in the range of 8.2% and 4.5% under different traffic conditions. Subject Model Predictive ControlRamp MergingTraffic Control To reference this document use: http://resolver.tudelft.nl/uuid:f67cbf26-31ec-4c94-98c7-706548410d15 Part of collection Student theses Document type master thesis Rights © 2020 Omer Khalid Files PDF MSc_Report.pdf 7.37 MB Close viewer /islandora/object/uuid:f67cbf26-31ec-4c94-98c7-706548410d15/datastream/OBJ/view