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

Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles

With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag

Coordination and Analysis of Connected and Autonomous Vehicles in Freeway On-Ramp Merging Areas

Freeway on-ramps are typical bottlenecks in the freeway network, where the merging maneuvers of ramp vehicles impose frequent disturbances on the traffic flow and cause negative impacts on traffic safety and efficiency. The emerging Connected and Autonomous Vehicles (CAVs) hold the potential for regulating the behaviors of each individual vehicle and are expected to substantially improve the traffic operation at freeway on-ramps. The aim of this research is to explore the possibilities of optimally facilitating freeway on-ramp merging operation through the coordination of CAVs, and to discuss the impacts of CAVs on the traffic performance at on-ramp merging.In view of the existing research efforts and gaps in the field of CAV on-ramp merging operation, a novel CAV merging coordination strategy is proposed by creating large gaps on the main road and directing the ramp vehicles into the created gaps in the form of platoon. The combination of gap creation and platoon merging jointly facilitates the mainline and ramp traffic and targets at the optimal performance at the traffic flow level. The coordination consists of three components: (1) mainline vehicles proactively decelerate to create large merging gaps; (2) ramp vehicles form platoons before entering the main road; (3) the gaps created on the main road and the platoons formed on the ramp are coordinated with each other in terms of size, speed, and arrival time. The coordination is analytically formulated as an optimization problem, incorporating the macroscopic and microscopic traffic flow models. The model uses traffic state parameters as inputs and determines the optimal coordination plan adaptive to real-time traffic conditions.The impacts of CAV coordination strategies on traffic efficiency are investigated through illustrative case studies conducted on microscopic traffic simulation platforms. The results show substantial improvements in merging efficiency, throughput, and traffic flow stability. In addition, the safety benefits of CAVs in the absence of specially designed cooperation strategies are investigated to reveal the CAV’s ability to eliminate critical human factors in the ramp merging process

A Q-LEARNING BASED INTEGRATED VARIABLE SPEED LIMIT AND HARD SHOULDER RUNNING CONTROL TO REDUCE TRAVEL TIME AT FREEWAY BOTTLENECK

To increase traffic mobility and safety, several types of active traffic management (ATM) strategies, such as variable speed limit (VSL) and hard shoulder running (HSR), are implemented in many countries. While all kinds of ATM strategies show promise in releasing traffic congestion, many studies indicate that stand-alone strategies have very limited capability. This paper proposes an integrated VSL and HSR control strategy based on a reinforcement learning (RL) technique, Q-learning (QL). The proposed strategy bridges a direct connection between the traffic flow data and the ATM control strategies via intensive self-learning processes thus reduces the need for human knowledge. A typical congested interstate highway, I-270 in Maryland, U.S. is simulated using a dynamic traffic assignment (DTA) model to evaluate the proposed strategy. Simulation results indicated that the integrated strategy outperforms the stand-alone strategies and traditional feedback-based VSL strategy in mitigating congestions and reducing travel time on the freeway corridor

Literature Review of Advancements in Adaptive Ramp Metering

Over a period spanning more than 30 years, several ramp metering algorithms have been developed to improve the operation of freeways. Many of these algorithms were deployed in several regions of the world, and field evaluations have shown their significance to improve traffic conditions on freeways and ramps. Previous reviews of ramp metering algorithms focused more on the research outcomes and evaluations of traditional metering algorithms developed in the early stage of ramp metering research. The purpose of this paper is to cover the more recent developments in ramp metering in relation to the traditional metering strategies. Several local and coordinated ramp metering algorithms were reviewed. In summary, Asservissement Linéaire d’Entrée Autoroutière (ALINEA) was found to be the most widely deployed local ramp metering strategy. The algorithm is simple and implementation costs less than other strategies. It also guarantees the targeted performance goals provided that the on-ramp has sufficient storage. Several extensions were proposed in the literature to fine-tune its performance. Among the coordinated metering strategies, zone based metering is simple to implement and easy to re-configure. System-wide adaptive ramp metering (SWARM) algorithm is more sensitive to calibrate for accurate prediction of traffic states. Heuristic ramp-metering coordination (HERO) algorithm can be useful if both local and coordinated control are desired particularly if the local control is using ALINEA. Fuzzy logic based algorithms are gaining popularity because of the simplicity and the fast re-configuration capability. Advanced real time metering system (ARMS) seems theoretically promising because of its proactive nature to prevent congestion; however, its performance is highly dependent upon accurate predictions. Finally, some guidelines were proposed for future research to develop new proposals and to extend the existing algorithms for guaranteed performance solutions.Scopu

Field Implementation of Freeway Control

This dissertation presents a series of traffic management studies including freeway and intersection traffic modeling, estimation, control methodologies and field implementation tests. First, a traffic flow prediction method that combines the most recent traffic data with historical traffic data is studied. An autoregressive moving average with exogenous input (ARMAX) model is estimated on-line based on the most recent vehicle detector station (VDS) data. Results obtained using empirical freeway mainline and on-ramp data show that this method outperforms methods that rely only on the historical average of the data to perform a prediction, especially during days with unusual traffic flow demands. Second, two freeway control strategies: coordinated ramp metering (CRM) and variable speed advisory (VSA) are investigated and implemented in field tests. In the control of CRM study, the freeway is modeled by the cell transmission model (CTM) and the control problem is solved by the model predictive control (MPC) scheme. The proposed CRM is deployed in a segment of California State Route 99 Northbound (SR-99N) for a five-week field test. The test results shows that the freeway efficiency can be improved by 7.25% for morning peak hours. In the VSA control study, an advisory speed limit control is designed by using traffic flow stabilization of the Lighthill-Whitham-Richards (LWR) model. The proposed VSA is deployed in a segment of California State Route 78 Eastbound (SR-78E) for a five-week field test. The test results shows that the freeway efficiency can be improved by 8.71% for morning peak hours. Both control strategies indicate freeway efficiency improvement in congested traffic. Third, the large-scale signalized intersection traffic network control by offset optimization is also studied. The traffic network is described by a directed graph and the traffic dynamic is represented by continuous-time fluid queue model with sinusoidal arrival and departure rate assumption. The original non-convex offset optimization problem can be relaxed into a semidefinite program (SDP). The Burer-Monteiro (BM) method is used for solving the large SDP to avoid conic constraints. Two real-world traffic simulation networks, respectively in Manhattan, NY and in Pasadena, CA are constructed for demonstrating the BM method. Numerical simulation results indicate that BM method has good scalability and it can efficiently recover optimal solutions of the SDP

Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events

Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents