This paper presents a novel method for estimating the number of vehicles traveling along signalized approaches using probe vehicle data only. The proposed method uses the Kalman Filtering technique to produce reliable vehicle count estimates using real-time probe vehicle estimates of the expected travel times. The proposed method introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of probe vehicles. The proposed method is evaluated using empirical and simulated data, the former of which were collected along a signalized roadway in downtown Blacksburg, VA. Results indicate that vehicle count estimates produced by the proposed method are accurate. The paper also examines the model's accuracy when installing a single stationary sensor (e.g., loop detector), producing slight improvements especially when the probe vehicle market penetration rate is low. Finally, the paper investigates the sensitivity of the estimation model to traffic demand levels, showing that the model works better at higher demand levels given that more probe vehicles exist for the same market penetration rate.
This paper addresses the problem of finding the optimal Eco-Driving (ED) speed profile of an electric Connected and Automated Vehicle (CAV) in an isolated urban un-signalized intersection. The problem is formulated as a single-level optimization and solved using Pontryagin's Minimum Principle (PMP). Analytical solutions are presented for various conflicts that occur at an intersection. Cooperation is introduced amongst CAVs as the ability to share intentions. Two levels of cooperation, namely the Cooperative ED (C-ED) and Non-Cooperative (NC-ED) algorithms are evaluated, in a simulation enviro…
This paper presents a method based on precise modeling of traffic flow using a fuzzy-real algorithm for optimal control of a signalized intersection. By improving social indicators such as security, well-fare and economy, intercity transportation has increased and traffic congestion developed. Today, the plight of traffic congestion is the cause of waste of time, capital, and health risks. Although the construction of new streets, expressways, and highways can improve this situation, when inadequate space and limited budget is available, it is not practical. Optimization of signalized intersec…
Consecutive traffic signalized intersections can increase vehicle stops, producing vehicle accelerations on arterial roads and potentially increasing vehicle fuel consumption levels. Eco-driving systems are one method to improve vehicle energy efficiency with the help of vehicle connectivity. In this paper, an eco-driving system is developed that computes a fuel-optimized vehicle trajectory while traversing more than one signalized intersection. The system is designed in a modular and scalable fashion allowing it to be implemented in large networks without significantly increasing the computat…
Digital Twin, as an emerging technology related to Cyber-Physical Systems (CPS) and Internet of Things (IoT), has attracted increasing attentions during the past decade. Conceptually, a Digital Twin is a digital replica of a physical entity in the real world, and this technology is leveraged in this study to design a cooperative driving system at non-signalized intersections, allowing connected vehicles to cooperate with each other to cross intersections without any full stops. Within the proposed Digital Twin framework, we developed an enhanced first-in-first-out (FIFO) slot reservation algor…
This paper explores a novel data-driven approach based on recent developments in Koopman operator theory and dynamic mode decomposition (DMD) for modeling signalized intersections. Vehicular flow and queue formation on signalized intersections have complex nonlinear dynamics, making system identification, modeling, and controller design tasks challenging. We employ a Koopman theoretic approach to transform the original nonlinear dynamics into locally linear infinite-dimensional dynamics. The data-driven approach relies entirely on spatio-temporal snapshots of the traffic data. We investigate s…
Intersection management with mixed cooperative and non-cooperative vehicles is crucial in next-generation transportation systems. For fully non-cooperative systems, a minimax scheduling framework was established, while it is inefficient in mixed systems as the benefit of cooperation is not exploited. This letter focuses on the efficient scheduling in mixed systems and proposes a two-stage policy that makes full use of the cooperation relation. Specifically, a long-horizon self-organization policy is first developed to optimize the passing order of cooperative vehicles in a distributed manner, …
The emergence of Connected and Automated Vehicles (CAVs) promises better traffic mobility for future transportation systems. Existing research mostly focused on fully-autonomous scenarios, while the potential of CAV control at a mixed traffic intersection where human-driven vehicles (HDVs) also exist has been less explored. This paper proposes a notion of "1+n" mixed platoon, consisting of one leading CAV and n following HDVs, and formulates a platoon-based optimal control framework for CAV control at a signalized intersection. Based on the linearized dynamics model of the "1+n" mixed platoon,…
A Right-of-Way Based Strategy to Implement Safe and Efficient Driving at Non-Signalized Intersections for Automated Vehicles
Graduate School at Shenzhen, Tsinghua University（清华大学深圳研究生院）；Department of Automation, BNRist, Tsinghua University（清华大学自动化系，北京理工大学）；traffic management solution division, traffic throughput through simulation experiments（交通管理解决方案部）
Trajectory planning for connected and automated vehicles (CAVs) has the potential to improve operational efficiency and vehicle fuel economy in traffic systems. Despite abundant studies in this research area, most of them only consider trajectory planning in the longitudinal dimension or assume the fully CAV environment. This study proposes an approach to the decentralized planning of CAV trajectories at an isolated signalized intersection under the mixed traffic environment, which consists of connected and human-driven vehicles (CHVs) and CAVs. A bi-level optimization model is formulated base…
University of California at Riverside（加州大学河滨分校）；Beijing Jiaotong University（北京交通大学）
Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL…
MIT（麻省理工学院）；Laboratory for Information & Decision Systems（信息与决策系统实验室）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Civil and Environmental Engineering（土木与环境工程系）；Institute of Data Systems and Society（数据系统与社会研究所）
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2…
Tsinghua University（清华大学）；China Agricultural University（中国农业大学）；National University of Singapore（新加坡国立大学）
Intersections are quite challenging among various driving scenes wherein the interaction of signal lights and distinct traffic actors poses great difficulty to learn a wise and robust driving policy. Current research rarely considers the diversity of intersections and stochastic behaviors of traffic participants. For practical applications, the randomness usually leads to some devastating events, which should be the focus of autonomous driving. This paper introduces an adversarial learning paradigm to boost the intelligence and robustness of driving policy for signalized intersections with den…
Coordinating intersections in arterial networks is critical to the performance of urban transportation systems. Deep reinforcement learning (RL) has gained traction in traffic control research along with data-driven approaches for traffic control systems. To date, proposed deep RL-based traffic schemes control phase activation or duration. Yet, such approaches may bypass low volume links for several cycles in order to optimize the network-level traffic flow. Here, we propose a deep RL framework that dynamically adjusts offsets based on traffic states and preserves the planned phase timings and…
Southeast University（东南大学）；Tibet University（西藏大学）；Nanjing Les Information Technology Co.Ltd（南京莱斯信息技术股份有限公司）
The upgrading and updating of vehicles have accelerated in the past decades. Out of the need for environmental friendliness and intelligence, electric vehicles (EVs) and connected and automated vehicles (CAVs) have become new components of transportation systems. This paper develops a reinforcement learning framework to implement adaptive control for an electric platoon composed of CAVs and human-driven vehicles (HDVs) at a signalized intersection. Firstly, a Markov Decision Process (MDP) model is proposed to describe the decision process of the mixed platoon. Novel state representation and re…
An automated vehicle refers to a vehicle that can achieve a safe movement on a roadway facility without the influence of a human driver. With emerging trend of the connected vehicle concept over the past decade, numerous state-of-the-art applications focusing on automated vehicle-based intersection control have been proposed. The main purpose of this study is to estimate and evaluate impact of designated lanes for automated vehicles and recommend some viable lane configuration scenarios for signalized urban arterials. The automated driving was simulated in PTV Vissim using trajectory-driven co…
Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency. Most of the existing research on unsignalized intersection cooperation considers fixed lane direction, where only specific turning behavior of vehicles is allowed on each lane. Given that traffic volume and the proportion of vehicles with different turning expectation may change with time, fixed lane direction may lead to inefficiency at intersections. This paper proposes a multi-lane unsignalized intersection cooperat…
In recent years, the development of connected and automated vehicle (CAV) technology has inspired numerous advanced applications targeted at improving existing transportation systems. As one of the widely studied applications of CAV technology, connected eco-driving takes advantage of Signal Phase and Timing (SPaT) information from traffic signals to enable CAVs to approach and depart from signalized intersections in an energy-efficient manner. However the majority of the connected eco-driving studies have been numerical or microscopic traffic simulations. Only few studies have implemented the…
Speed advisory systems for connected vehicles rely on the estimation of green (or red) light duration at signalized intersections. A particular challenge is to predict the signal phases of semi- and fully-actuated traffic lights. In this paper, we introduce an algorithm that processes traffic measurement data collected from advanced detectors on road links and assigns "PASS"/"WAIT" labels to connected vehicles according to their predicted ability to go through the upcoming signalized intersection within the current phase. Additional computations provide an estimate for the duration of the curr…
Developments in sensor technologies, especially emerging connected and autonomous vehicles, facilitate better queue length (QL) measurements on signalized intersection approaches in real time. Currently there are very limited methods that utilize QL information in real-time to enhance the performance of signalized intersections. In this paper we present methods for QL estimation and a control algorithm that adjusts maximum green times in actuated signals at each cycle based on QLs. The proposed method is implemented at a single intersection with random and platoon arrivals, and evaluated in VI…
In this paper, we establish a decentralized optimal control framework for connected and automated vehicles (CAVs) crossing multiple adjacent, multi-lane signal-free intersections to minimize energy consumption and improve traffic throughput. Our framework consists of two layers of planning. In the upper-level planning, each CAV computes its optimal arrival time at each intersection recursively along with the optimal lane to improve the traffic throughput. In the low-level planning, we formulate an energy-optimal control problem with interior-point constraints, the solution of which yields the …
Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the patterns in real-time in order to reduce congestion. Real-time prediction of queue lengths can be used to adjust the phasing and timings for different movements at an intersection with ASCS. The accuracy of the prediction varies based on the factors, such as the stochastic nature of the vehicle arrival rates, time of the day, weather and drive…
We propose a novel framework to differentiate between vehicle trajectories originating from human and non-human drivers by constructing a data-driven boundary using parametric signal temporal logic (STL). Such construction allows us to evaluate the trajectories, detect rare-events, and reduce the uncertainty of driver behaviors when it assumes the form of a disturbance in control synthesis and evaluation problems. We train a classifier that separates admissible (i.e. human) examples - which arise from real-world demonstrations - and inadmissible (i.e. non-human) examples that are generated by …
