PhD, University of Cincinnati, 2024, Engineering and Applied Science: Civil Engineering

Ramp metering is a freeway traffic management technique designed to regulate the vehicle flow merging onto the freeway, thereby improving the freeway throughput and minimizing disruptions. However, determining dynamic metering rate is always dependent upon accurate identification of mainline traffic and on-ramp arrival demand, all of which are not always accurately captured by traditional detection technologies (such as fixed-point loops). On the other hand, if an on-ramp experiences high arrival demand, queuing vehicles on the on-ramp may cause back propagation to the upstream local street, leading to traffic congestion across the local street network. To address these issues, this research proposes to create a coordinated control strategy to synchronize the ramp metering control schemes with the signalized control system at the upstream intersection. The implementation of such an integrated system necessitates robust intercommunication of detailed traffic state information between different roadway segments, thereby imposing more requirements on data acquisition. To solve this problem, recent research has begun to explore the potential of using data from Connected Vehicles (CVs) and/or Automated Vehicles (AVs), or simply termed CAVs in the research, to support the implementation of the integrated strategy. However, these previous studies often assume a 100% market penetration rate (MPR) of CAVs and global communication capabilities, which simplifies the analysis of CAV driving behavior and overlooks the complexities of traffic data dissemination. Hence, these optimal solutions cannot be guaranteed effective under a real-world mixed automated traffic environment, which should be a more common scenario in recent years. Moreover, due to the inherent complexity of this issue, existing research typically concentrates on either adaptive intersection signal control or ramp metering separately. The coordinated control between these two elements has not been consid (open full item for complete abstract)

Committee: Heng Wei Ph.D. (Committee Chair); John Ash Ph.D. (Committee Member); Zhixia Li Ph.D. (Committee Member); Xuefu Zhou Ph.D. (Committee Member) Subjects: Transportation

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Civil Engineering

Connected and automated (CAV) technologies offer new opportunities that can transmit real-time vehicular information, and their trajectories can be precisely controlled, which eliminates the barriers of conventional control framework. However, before the CAVs are prevalent in the traffic stream, a mixed-autonomy environment will last a long period with gradually increasing CAV market penetration. This indicates that traffic management under mixed-autonomy environment is essential in the transition from conventional traffic management to full-autonomy traffic control. To signalized intersection, vehicular trajectory control and signal optimization based on CAV technologies are two approaches that have significant potential to mitigate congestion, lessen the risk of crashes, reduce fuel consumption, and decrease emissions at intersections. Therefore, these two approaches should be integrated into a unified traffic management framework such that both aspects can be optimized simultaneously to achieve maximum benefits. This dissertation proposes a mixed-autonomy traffic management framework to integrate the signal control and trajectory control systematically. The framework architecture consists of six layers, including sensing, information, planning, optimization, control, and evaluation, and each layer has its own scope and responsibility. The proposed framework is a flexible and compatible framework for joint optimization of vehicle trajectory and signal control, and it can be applied for both full-autonomy and mixed-autonomy environments. The development details of major components are also described. A dynamic programming (DP) framework with trajectory planning with piecewise polynomials (TP3) as a subroutine (DP-TP3) is presented to solve the joint optimization of signal control and vehicle trajectory control considering conflicts of the four movements. The proposed TP3 algorithm provides an analytically solvable operation for vehicular trajectory const (open full item for complete abstract)

Committee: Jiaqi Ma Ph.D. (Committee Chair); Na Chen Ph.D. (Committee Member); Kelly Cohen Ph.D. (Committee Member); Nabil Nassif Ph.D. (Committee Member) Subjects: Civil Engineering

Master of Science, The Ohio State University, 2021, Mechanical Engineering

With ongoing research in intelligent transport systems and connected and automated vehicles, enabled by advancements in artificial intelligence, the large-scale advanced simulation has become an important part of product/software development for the automotive industry. Nowadays, traffic simulations are used to mimic real-world environment scenarios for connected vehicle technologies. The focus of this thesis lies in the development of microscopic traffic simulation calibration and enhance traffic signal control systems This thesis makes the following major contributions. First, a calibration framework is proposed which harnesses the exiting data set of OSU campus shuttles (CABS) to determine the traffic state and create a microscopic traffic simulation. The traffic simulation is implemented for a section of the OSU campus(“Woody Hayes Drive") which can be extended to the entire OSU campus. The second contribution is an investigation of an intelligent traffic signal control system. The signal control operation is formulated as a decision-making process where each controller or control component is modeled as an intelligent agent. The agents make decisions based on traffic conditions and their past knowledge of the environment. A state estimation method and an adaptive control scheme by reinforcement learning (RL) are introduced to implement such an intelligent system. Simulation experiments ii have been performed to verify the improvements of intelligent traffic control systems and compare them with the existing control policy. The third contribution summarises the initial integration work for the co-simulation framework completed by dSpace ASM and SUMO to create a complete real-time simulation of urban environments for ADAS testing. The demo scenario is the OSU campus with traffic demand generated using the calibrated model from the first part of the thesis.

Committee: Punit Tulpule Dr. (Advisor); Qadeer Ahmed Dr. (Committee Member); Shawn Midlam-Mohler Dr. (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2020, Engineering and Applied Science: Civil Engineering

With the increasing demand for real-time control logic, infrastructure-enabled vehicle detector data is being considered for state of art traffic signal control strategies. The conventional detection methods are usually point detection that cannot directly measure vehicle speed and location. This has been the biggest challenge to design a robust traffic control system. Connected Vehicles (CVs) due to the advancements in wireless communication technology are a potential solution to overcome this challenge. The emerging CV technology provides an opportunity to formulate an ambulant data platform that allows the actual data transfer among multiple vehicles as well as with the infrastructure. More significantly, the CV's capability of serving as the mobile trajectory sensors could help us to reduce the dependencies on conventional infrastructure-based vehicle detectors. The connected vehicles can provide increased opportunities and enforce more challenges for the signal control of urban traffic. These include vehicle to infrastructure (V2I), vehicle to vehicle (V2V), and vehicle to something else's(V2X). The core objective of this study is to create a framework in which algorithms, modeling methods, and testing schemes for the optimization of urban traffic signal under mixed traffic conditions are included (coexistence of conventional vehicles and CVs). For isolated intersections or multiple intersections along a corridor, this framework can improve traffic signal timing. Precisely, the major assignments of this research include: 1.Thorough testing in traffic simulation to reinforce the proposed methods. This research evaluated the CCACSTO algorithm at four different penetration rates of CAVs for three different traffic conditions (light traffic, mild traffic, and heavy traffic). The simulation test results show that average vehicle delay and queue length with CCACSTO algorithm reduced by 46.04% and 56.15% respectively under 50% penetration rate of CAVs.

Committee: Heng Wei Ph.D. (Committee Chair); Jiaqi Ma Ph.D. (Committee Member); Nick Yeretzian MS Civil Engineering (Committee Member) Subjects: Transportation

MS, University of Cincinnati, 2016, Engineering and Applied Science: Computer Science

As the technology is advancing day by day, the intelligent transportation industry is also experiencing a advancement in vehicle communication technology. The future for the automotive industry are the self-driving vehicles. Next-generation cars and other automobiles are getting equipped with unique electronics sensors like LIDAR, ultrasonic sensors, radar sensors. These sensors monitor different aspects of vehicle movements such as vehicle's speed, position, longitudinal and lateral acceleration. The vehicle communication technology exists, but vehicles rarely communicate their information with the road side infrastructures. The connected vehicle initiative and the deployment of wireless communication techniques will help in improving vehicle safety and also reduce traffic congestion.The traffic signal control timing plans are designed in such way that they can minimize the vehicle travel delay based on conditions such as historical traffic volumes. In-pavement induction loop detectors and video detectors make small adjustments to signal timings, but they are unreliable and limited in terms of range. With the connected vehicle initiative, vehicles can communicate with the roadside infrastructure such as traffic signal control within 300 meters of an intersection through communication techniques like the Dedicated Short Range Communication (DSRC). A unique algorithm is proposed which uses a concept of vehicle platooning as the vehicle control model. Vehicle platooning helps in increasing the throughput of a particular road. The vehicle control is based on Cooperative Adaptive Cruise Control (CACC) mechanism. The proposed algorithm also uses a global nature inspired optimization algorithm known as Multi-objective Bat Algorithm. This algorithm takes into consideration different input such as the queue length of the intersection roads and actual flow rate and give out the optimized value of the green signal time for the next phase signal to be implemented. (open full item for complete abstract)

Committee: Dharma Agrawal D.Sc. (Committee Chair); Rui Dai Ph.D. (Committee Member); Chia Han Ph.D. (Committee Member) Subjects: Computer Science