The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

AN INTEGRATED CONTROL MODEL FOR FREEWAY INTERCHANGES

This dissertation proposes an integrated control framework to deal with traffic congestion at freeway interchanges. In the neighborhood of freeway interchanges, there are six potential problems that could cause severe congestion, namely lane-blockage, link-blockage, green time starvation, on-ramp queue spillback to the upstream arterial, off-ramp queue spillback to the upstream freeway segments, and freeway mainline queue spillback to the upstream interchange. The congestion problem around freeway interchanges cannot be solved separately either on the freeways or on the arterials side. To eliminate this congestion, we should balance the delays of freeways and arterials and improve the overall system performance instead of individual subsystem performance. This dissertation proposes an integrated framework which handles interchange congestion according to its severity level with different models. These models can generate effective control strategies to achieve near optimal system performance by balancing the freeway and arterial delays. The following key contributions were made in this dissertation: 1. Formulated the lane-blockage problem between the movements of an arterial intersection approach as an linear program with the proposed sub-cell concept, and proposed an arterial signal optimization model under oversaturated traffic conditions; 2. Formulated the traffic dynamics of a freeway segment with cell-transmission concept, while considering the exit queue effects on its neighboring through lane traffic with the proposed capacity model, which is able to take the lateral friction into account; 3. Developed an integrated control model for multiple freeway interchanges, which can capture the off-ramp spillback, freeway mainline spillback, and arterial lane and link blockage simultaneously; 4. Explored the effectiveness of different solution algorithms (GA, SA, and SA-GA) for the proposed integrated control models, and conducted a statistical goodness check for the proposed algorithms, which has demonstrated the advantages of the proposed model; 5. Conducted intensive numerical experiments for the proposed control models, and compared the performance of the optimized signal timings from the proposed models with those from Transyt-7F by CORSIM simulations. These comparisons have demonstrated the advantages of the proposed models, especially under oversaturated traffic conditions

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness