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An analytical optimization approach to the joint trajectory and signal optimization problem for connected automated vehicles
Abstract
Traffic conflict points (e.g., intersections, work-zones) cause travel delay, stop-and-go traffic, and excessive energy consumption. Efforts have been taken to improve traffic conflict point performance via trajectory control of connected automated vehicles (CAV) as the CAV technology emerges. One major challenge to these efforts is the complexity in optimization of CAV trajectories, particularly with joint signal timing optimization. This challenge poses barriers to real-time application requirements, scaling them up to address network level problems and drawing analytical insights into problem structures. To overcome this challenge, this paper aims to seek for an efficient and analytical solution to a joint vehicle trajectory and signal timing optimization problem. This problem simultaneously optimizes CAV trajectories and signal timing to minimize travel delay and energy consumption at a conflicting point with two traffic approaches. This study modifies the original complex formulation in two ways. First, the vehicle trajectory shape is simplified into a piece-wise quadratic function with no more than five segments. Second, instead of using the highly non-linear instantaneous fuel consumption function, a simplified macroscopic measure is proposed to approximate fuel consumption as an analytical quadratic function of signal red interval. These simplifications provide elegant theoretical properties that enable solving an analytical exact solution to this complex problem with parsimonious analytical insights. Numerical examples reveal that the proposed model can significantly reduce travel delay and fuel consumption. Moreover, it is demonstrated that the presented algorithm is highly efficient and appropriate for real-world traffic applications.
... For an intersection coordination problem for connected and autonomous vehicles, the vehicles follow the trajectory planned by an intersection controller and arrive at the intersection with a given arrival time and speed (Fayazi and Vahidi, 2018;Feng et al., 2018;Malikopoulos et al., 2018;Soleimaniamiri et al., 2020;Yao et al., 2020). To overcome the computation complexity of the trajectory planning along the road to the intersection, a whole trajectory is always discretized to a few segments, with which the second-order vehicle kinematics, acceleration rate, is fixed (Li et al., 2018;Ma et al., 2017;Zhou et al., 2017). ...
... The trajectory planning method could also be formulated or optimized with an analytical solution based on Pontryagin's minimum principle (PMP) Malikopoulos et al., 2018;Yu et al., 2018). Soleimaniamiri et al. (2020) further jointly optimize the trajectory and arrival time at the intersection based on a Shooting Heuristic (SH) (Li et al., 2018;Ma et al., 2017;Zhou et al., 2017). Overall, the trajectory along the road to the intersection can be formulated and realized when either the arri-val speed or arrival time is predetermined and given to the CAV at upstream of the intersection. ...
... In an AIM environment, the vehicles follow the trajectory planned by an intersection controller and arrive at the intersection with a given arrival time and speed (Fayazi and Vahidi, 2018;Feng et al., 2018;Malikopoulos et al., 2018;Soleimaniamiri et al., 2020;Yao et al., 2020). However, when the traffic condition varies over time, i.e., a priority passing sequence to the intersection is shifted from one earlier coming vehicle to another later coming vehicle, or the traffic demand is increasing sharply from one approach, the vehicle may not follow the planned trajectory and arrive at the intersection with an expected time and speed (Au et al., 2012;Au and Stone, 2010). ...
Rule-based and optimization-based autonomous intersection management (AIM) policies have been evaluated against traditional signal controls in terms of intersection safety, efficiency and emission. As one of AIM policies, reservation-based control has further taken advantage of the benefits of AIM, especially via optimization approaches. This paper presents a time-independent trajectory optimization approach for connected and autonomous vehicles under reservation-based intersection control. The existing approaches assign an arrival time and speed to vehicles ahead of entering the intersection. However, the vehicles may not follow the planned trajectory once the traffic condition varies sharply and thus the trajectory solution becomes infeasible with respect to the assigned arrival time and speed. The proposed approach aims to solve the fail-follow problem by separating the optimization between arrival time, speed, and trajectory planning by optimizing the trajectory without arrival time and speed predetermined. The approach finds the optimal solution in terms of the intersection efficiency meanwhile keeps the feasibility of trajectory planning by formulating the variation of acceleration rate and breaking a whole trajectory into an enlarged set of segments. Two different control strategies, BATCH and ZONE, are also proposed to test the performance of the optimization approach in comparison with another Dynamic Batch strategy. The results validate that the proposed approach can adapt to extremely high traffic demand scenario. Sensitivity analyses also evaluate the performance of the proposed approach under different problem settings in terms of intersection efficiency.
... Because the emerging technology of CAVs brings new opportunities to improve traffic efficiency at intersections, the second stream of literature paid attention to intersection management in the presence of CAVs (Chalaki and Malikopoulos, 2022;Chen et al., 2021;Chalaki and Malikopoulos, 2021;Dresner and Stone, 2004, 2005He et al., 2018;Jiang, 2017;Levin and Rey, 2017). Various AIM policies have been proposed to control the traffic flow at intersections with only CAVs since Dresner and Stone (2004) creatively put forward the imagination of AIM, such as rule-based AIM policies (Dresner and Stone, 2008;Schepperle and Böhm, 2007), optimization-based AIM policies (Soleimaniamiri et al., 2020;Yu et al., 2018), etc. Because intersection-induced conflicts can be resolved by planning each CAV's trajectory directly and promoting cooperation among vehicles in a fully CAV environment, the visual signal of physical traffic lights is not essential anymore. ...
It is widely recognized that human-driven vehicles (HVs) and connected and autonomous vehicles (CAVs) are expected to coexist and share the urban traffic infrastructure in the transportation network for a long time. To fully utilizes CAVs' potential to reduce congestion in the transitional period, this study proposes and addresses the intersection design and signal setting problem in the transportation network with mixed HVs and CAVs. Due to the difference in terms of communication technology and autonomous driving technology for HVs and CAVs, three types of intersections have been proposed to amplify the efficiency-improvement benefit from CAVs by separating CAVs from HVs in a temporal or local-spatial dimension: the conventional signalized intersection, the novel signalized intersection with a dedicated CAV phase and dedicated CAV approaches, and the intelligent signal-free intersection. The problem is to determine the spatial layout of different types of intersections in the transportation network, the cycle time, and green time duration for each phase of signalized intersections that minimize the total travel cost, in which the route choice behavior of heterogeneous travelers has been respected based on the user equilibrium principle. A mixed-integer nonlinear programming model is developed to formulate the proposed intersection design and signal setting problem based on the link-node modeling method, in which the path enumeration is avoided. Then, by employing various linearization techniques (e.g., disjunctive constraints, logarithmic transformation, piecewise linearization with logarithmic-sized binary variables and constraints, outer-approximation technique), the proposed model can be further transformed into a relaxed sub-problem in the form of mixed-integer linear programming. A globally optimal solution algorithm embedding with solving a sequence of relaxed sub-problems and nonlinear mixed complementarity problems is proposed to converge to a global optimum. The results of numerical experiments illustrate that the proposed methodology can significantly improve the performance of the whole network. Moreover, it consistently outperforms the optimization model considering only conventional signalized intersections under various CAV market penetration rates.
... With the development of connected vehicles (CVs) and connected autonomous vehicles (CAVs), it is possible to obtain traffic information (e.g., traffic signal timing, vehicle speed, acceleration), and communicate between vehicle and vehicle/ infrastructure, then help/control vehicles to drive smoothly through advisory speed limit. Numerous speed control studies have been conducted to improve traffic efficiency, safety, and fuel economy, such as Optimal Speed Advisory (OSA) (Mahler and Vahidi 2012;Wan et al. 2016), Green Light Optimal Speed Advisory (GLOSA) (Nguyen et al. 2016), Eco-driving (De Nunzio et al. 2016;Jiang et al. 2017;Huang et al. 2018;Guo et al. 2021), Eco-Cooperative Adaptive Cruise Control (ECACC) (Kamalanathsharma et al. 2013;Yang et al. 2017), Variable Speed Limit (VSL) (Yang et al. 2013;Ubiergo and Jin 2016;Lyu et al. 2017;Yao et al. 2018) and trajectory smoothing (Zhou et al. 2017;Guo et al. 2019;Soleimaniamiri et al. 2020;Yao and Li 2021). ...
Connected vehicles enabled by communication technologies have the potential to improve traffic mobility and enhance roadway safety such that traffic information can be shared among vehicles and infrastructure. Fruitful speed advisory strategies have been proposed to smooth connected vehicle trajectories for better system performance with the help of different car-following models. Yet, there has been no such comparison about the impacts of various car-following models on the advisory strategies. Further, most of the existing studies consider a deterministic vehicle arriving pattern. The resulting model is easy to approach yet not realistic in representing realistic traffic patterns. This study proposes an Individual Variable Speed Limit (IVSL) trajectory planning problem at a signalized intersection and investigates the impacts of three popular car-following models on the IVSL. Both deterministic and stochastic IVSL models are formulated, and their performance is tested with numerical experiments. The results show that, compared to the benchmark (i.e., without speed control), the proposed IVSL strategy with a deterministic arriving pattern achieves significant improvements in both mobility and fuel efficiency across different traffic levels with all three car-following models. The improvement of the IVSL with the Gipps’ model is the most remarkable. When the vehicle arriving patterns are stochastic, the IVSL improves travel time, fuel consumption, and system cost by 8.95%, 19.11%, and 11.37%, respectively, compared to the benchmark without speed control.
... Feng et al. (2018) propose a two-stage optimization problem including a dynamic programming method for both signal timing adjustments and vehicle trajectory designs to minimize vehicle delay. Unified eco-driving control algorithms integrated with signal plan optimizations have also been proposed Long et al., 2020;Soleimaniamiri et al., 2020). To evaluate the benefits of eco-driving, Xin et al. (2019) simulate two city contexts, with adaptive traffic signal control. ...
Efficient operations of traffic signals are of critical importance in urban areas, as signalized intersections prevent the smooth flow of traffic and cause delays. This paper devises an eco-driving algorithm based on connected vehicle technologies, with basic kinematic wave and car-following models. The objectives of the proposed algorithm are to increase the throughput of signal intersections and decrease fuel consumption. Specifically, we focus on a signalized intersection under mixed traffic flows with connected and autonomous vehicles (AVs) and human-driven vehicles (HVs). Through the proposed algorithm, the vehicle speeds at the intersection (i.e., the intersection control speed) and signal timings can be adjusted in response to the real-time traffic conditions. According to the signal timing and the speed at the intersection, the algorithm estimates the time points of each vehicle entering the intersection. An advisory speed limit approach is formulated for each AV, making the vehicle enter the intersection at the allocated timing with the control speed. An onboard alert is set for each HV to stop or pass through. The algorithm is evaluated under various market penetration rates of AVs, different congestion levels, and with signal actuation. The results indicate that the eco-driving algorithm can increase the throughput and average travel speed at signalized intersections in addition to gaining fuel savings.
... the past decade has seen a boom in the research of using cAv information for optimizing signals and timing plans. the focus of these studies has been on different objectives-such as minimizing delays [13], [14], total queue length [15], [16] and green splits [17], [18], [19]. the literature review includes various studies that develop intersection management applications for cAv passing through signalized intersections. ...
... Several studies [2][3][4][5][6] simulated the trajectory of a humandriven vehicle at intersections using the Intelligent Driver Model (IDM). Han et al. and Yao et al. [7,8] used the Gipps CF model to simulate human-driven vehicles crossing the intersection. ...
Many studies have simulated traffic behavior at signalized intersections using various Car-Following (CF) models. However, the performance of which CF Model is superior at signalized intersections has not been thoroughly analyzed and evaluated. In this study, two novel Artificial Neural Network (ANN) CF models, the Convolutional Neural Network—Long Short-term Memory (CNN-LSTM) and the Convolution-LSTM (Conv-LSTM)—are first applied to predict CF behaviors at signalized intersections. Both models can extract spatial and temporal information to address the long-term dependency problem more effectively. Based on the filtered NGSIM dataset, we conduct a comparative empirical study of three conventional CF models and five ANN CF models. The dataset is divided into two categories based on the characteristics of CF behavior at signalized intersections: continuous and discontinuous. The experiments demonstrated that ANN CF models outperformed conventional CF models when the output was the velocity in two categories of traffic flow but only failed to do so when the output was acceleration in discontinuous traffic flow. The proposed models were capable of accurately predicting acceleration, but the traffic fluctuations also existed as time passed. Additionally, it was discovered that while the ANN CF model is preferable for traffic flow simulation, the conventional CF model still cannot be ignored for discontinuous traffic flow simulation, particularly when acceleration is required.
... Such analysis would help decisionmakers and planners study traffic conditions, find traffic patterns, and discover bottlenecks at waterways [Chen et al., 2019]. It also could pave the way for future studies in scheduling vessels, optimizing channel closure time [Kaneria et al., 2019;Rahimikelarijani et al., 2018], estimating vessel arrival time [Wu et al., 2020], and even applying connected vehicles [Soleimaniamiri et al., 2020]. ...
Quantifying waterway traffic characteristics based on Automatic Identification System (AIS) data is beneficial to understand and improve traffic conditions. In this paper, a nested loop algorithm is first presented to segment a waterway and to separate vessel trips, where trip directions are categorized to inbound, outbound, and stop status. Next, to increase computational efficiency, the corresponding vectorized algorithm, which rebuilds the AIS data as an array-based structure, is developed, and traffic features such as travel speed, traffic density, traffic flow, trip attraction, trip generation, and origin-destination (O-D) matrices are extracted. Finally, the methodology is applied to the Houston Ship Channel (HSC) as an implementation instance with one month of AIS data, and the traffic features are quantified for different types of vessels with different widths and drafts at different segments. The vectorized algorithm, along with the trip separation, has considerably decreased processing time compared to the loop-based methods. The results of such analysis can be used for short-term operational planning, such as resource allocation and scheduling, or for long-term waterway projects, such as expansions.
... Additionally, several studies in recent years have analyzed potential applications of trajectory analysis and optimization to improve traffic flow in different scenarios. One example is prediction and planning of Connected Automated Vehicle (CAV) arrivals at intersections followed by optimization of those CAVs' trajectories through the intersection by an intersection controller [21,22]. ...
Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle’s trajectory using sparse optical flow and tracks vehicles in the surrounding environment using convolutional neural networks. Optical flow is used to measure the linear and angular velocity of the host vehicle. The convolutional neural networks are used to measure target vehicles’ positions relative to the host vehicle using image-based detections. Finally, the system fuses host and target vehicle trajectories in the world coordinate system using the velocity of the host vehicle and the target vehicles’ relative positions with the aid of an Extended Kalman Filter (EKF). We implement and test our model quantitatively in simulation and qualitatively on real-world test video. The results show that the algorithm is superior to state-of-the-art sequential state estimation methods such as visual SLAM in performing accurate global localization and trajectory estimation for host and target vehicles.
... Yu et al. (2018) jointly optimized traffic signal and vehicle trajectories under the 100% CAV scenario, which considered all vehicle movements, including left-turn, right-turn, and through traffic. Soleimaniamiri et al. (2020) proposed an analytical joint optimization approach by using simplified approximation functions, and results show significant improvements at a two-phase intersection. ...
The emerging connected and automated vehicle (CAV) technologies offer new opportunities for urban signalized intersection management. Through wireless communication and advanced sensing capabilities, CAVs can detect the surrounding traffic environment and share real-time vehicular information with each other and the infrastructure, and individual trajectories of CAVs can be precisely controlled. This paper proposes a real-time learning and control framework for signalized intersection management, which includes both signal optimization and CAV trajectory control. The proposed framework integrates perception, prediction, planning, and optimization components and aims at improving efficiency mixed connected automated traffic in terms of traffic throughput and delay. This framework applies the Long Short Term Memory (LSTM) networks to implicitly learn traffic patterns and driver behavior and then estimate and predict the microscopic traffic conditions that are only partially observable. Then it utilizes deep reinforcement learning (DRL) to solve signal optimization problems by learning from the dynamic interactions between vehicles and the traffic environment. Under the proposed framework, the vehicular trajectories of CAVs can be controlled to maximize the utilization of the green time and reduce the start-up lost time by using a highly efficient trajectory planning algorithm. The CAV platooning operation, in coordination with traffic signals, is also implemented such that CAVs can pass the intersection efficiently. Simulations are performed at a signalized intersection with multi-lane approaches, high traffic demand, and standard ring-barrier control, and results show that the proposed DRL-TP3 framework can significantly improve the throughput and reduce the average delay across different CAV market penetration rates (MPRs). We also investigate the impacts of different sensor capabilities of unobservable vehicle estimation and implementation of a lane change prohibition zone under the DRL-TP3 framework.
... Yu et al. (2018) jointly optimized traffic signal and vehicle trajectories under the 100% CAV scenario, which considered all vehicle movements, including left-turn, right-turn, and through traffic. Soleimaniamiri et al. (2020) proposed an analytical joint optimization approach by using simplified approximation functions, and results show significant improvements at a two-phase intersection. ...
The emerging connected and automated vehicle (CAV) technologies offer new opportunities for urban signalized intersection management. Through wireless communication and advanced sensing capabilities, CAVs can detect the surrounding traffic environment and share real-time vehicular information with each other and the infrastructure, and individual trajectories of CAVs can be precisely controlled. This paper proposes a real-time learning and control framework for signalized intersection management, which includes both signal optimization and CAV trajectory control. The proposed framework integrates perception, prediction, planning, and optimization components and aims at improving the efficiency of mixed connected automated traffic. This framework applies the Long Short Term Memory (LSTM) networks to implicitly learn traffic patterns and driver behavior and then estimate and predict the microscopic traffic conditions that are only partially observable. Then it utilizes deep reinforcement learning (DRL) to solve signal optimization problems by learning from the dynamic interactions between vehicles and the traffic environment. Under the proposed framework, the vehicular trajectories of CAVs can be controlled to maximize the utilization of the green time and reduce the start-up lost time by using a highly efficient trajectory planning algorithm. The CAV platooning operation, in coordination with traffic signals, is also implemented such that CAVs can pass the intersection efficiently. Simulations are performed at a realistic signalized intersection, and results show that the proposed DRL-TP3 framework can significantly improve the throughput and reduce the average delay across different CAV market penetration rates (MPRs). We also investigate the impacts of different sensor capabilities of unobservable vehicle estimation and implementation of a lane change prohibition zone under the DRL-TP3 framework.
The advancement of the Intelligent Transport Systems (ITS) and the emerging Connected and Automated Vehicles (CAVs) technology are acknowledged to hold a great potential to mitigate challenging problems in the current transportation networks. Particularly, a proper traffic control strategy with a precise vehicular movement control scheme can alleviate the congestion and improve the safety and efficiency of the traffic. This paper proposes a novel bi-level control framework that combines a design of traffic signal timings at a network level, and a detailed trajectory control policy for individual vehicles at a link-level within a network of CAVs. We develop a group-based longitudinal trajectory planning scheme to coordinate vehicular movements at the lower level of our framework while abiding by the signal operations along with end-to-end vehicle routing decisions from the upper network level optimization. This joint and mutual interaction between the two different control strategies in the urban signalized corridors is complex and can significantly affect the overall network’s performance, nevertheless has not been explored previously in the literature. The proposed framework enables such studies where we derive an efficient algorithm that iteratively solves the mixed-integer linear programming (MILP) and linear programming models in each link at the lower and over the network at the upper levels, respectively. Numerical results show the effectiveness of the proposed joint control framework in network performance regarding the average travel time, queue formation and dissipation across the network.
Connected and Autonomous Vehicles (CAVs) are a promising technology that is ready to be deployed in the near future to improve the traffic efficiency and safety as well as environment. Extensive studies have been done to investigate the potential performance of CAVs on freeways, at roundabouts, and conventional intersections. Nevertheless, innovative intersections, as an important component of today’s transportation infrastructure, have been seldom investigated in relation to the performance of CAVs. Hence, this research is designed to examine how CAV technologies can influence the performance of a superstreet, one of the popular innovative intersection designs. In this research, the car-following model, platooning, trajectory planning, and adaptive signal control are specified for CAVs and signal controllers in a superstreet. An equivalent conventional intersection with the same lane configurations is also constructed in the simulation environment to make a fair comparison and gain important insights. More importantly, the findings from this research may provide references for studies on other innovative intersections which share similar design characteristics.
This study introduces a new virtual track-based framework and open-source tools for modeling partially schedulable connected and automated mobility (CAM) systems on layered networks considering the emerging trend of CAM system deployment. First, a coupled network representation is developed for macroscopic, mesoscopic, and microscopic CAM system modeling with tight inherent consistencies. This enables the behaviorally sound modeling of demand-supply interactions in hierarchical CAM systems from a layer decomposition perspective such that different levels of tasks can be performed in proper layers to achieve a balance between representation details and computational efficiency. A spatial-discrete virtual track-based microscopic network representation is designed for both high-fidelity vehicle dynamics modeling and maintaining consistency with high-level routing decisions in CAM applications to enable individualized active traffic management. Second, based on the proposed layered network structure, we examine effective methods of traffic simulation, optimization, and operation of CAM systems, with a special focus on different degrees of system schedulability. Third, two open-source packages, osm2gmns and CAMLite, are introduced to support open-source ecosystems and the research community for CAM system modeling. Representative numerical experiments are performed to demonstrate the effectiveness of the proposed methodologies and open-source tools.
Vehicle trajectory data derived from automatic vehicle location (AVL) and automatic vehicle identification (AVI) systems provide critical support for intelligent transportation systems. However, the field-obtained vehicle trajectories are usually incomplete due to sensor malfunction or communication issues. To recover the incomplete data, the existing reconstruction methods have to impose strong assumptions on driver route choice behaviors (network level) and/or traffic dynamics (link level). With the tremendous data available, leveraging data-driven approaches to address the vehicle trajectory reconstruction problem with minimal assumptions is promising. This paper proposes a general dynamic sequential learning framework to reconstruct vehicle trajectory points for both AVL and AVI data. First, an Isolation Forest based ensemble learning model is developed to extract trajectory sequences attributed to different trips in an entire trip chain of a vehicle. Second, the dynamic recurrent neural network (dynamic RNN) is tailored to learn the underlying patterns from the complete AVL and AVI trajectories, respectively. Third, a sequential prediction scheme is customized to reconstruct AVL and AVI trajectories based on the trained networks. To validate the proposed method, two experiments are conducted. One is a simulation experiment with AVL data gathered from a well-calibrated simulation model. The other is a field experiment with AVI data collected from a real-world automatic license plate recognition (ALPR) system. The results show that the proposed method achieves superior performance for both AVL and AVI data compared with the traditional methods. The impacts of different sampling rates and traffic conditions on the model performance are also discussed.
Autonomous vehicles that travel without considering the lane marks and utilizing all road width have an opportunity to maximize the use of vehicles’ performance. By taking advantage of the entire width of curvy roads and the cooperative behavior of connected autonomous vehicles, new options for path planning can be implemented while utilizing the existing infrastructure. The proposed cooperative controller uses a nonlinear model predictive control (NMPC) approach for dozens of autonomous vehicles without considering lane marks. This controller maximizes vehicles’ progress on the road with minimal control efforts while complying with design constraints imposed by road geometry, distances between vehicles, and vehicle dynamics. The controller is tested in two simulation case studies. The first examines the performance under two different plant (reality) models. The second considers dozens of vehicles and compares the traffic flow characteristics between the lane-free concept and the lane-based concept within different vehicle densities. The simulation results show that the lane-free concept can improve the traffic flow performance compared with the lane-based road concept, i.e. reducing passengers’ time on the road, reducing energy consumption, and increasing road capacity. These improvements depend on the road density and track layout. In order to demonstrate the proposed controller, three laboratory experiments with several homogeneous and heterogeneous robots were conducted.
Signal offset for coordinated traffic signal control is traditionally optimized based on posted speed limit, free-flow speed, or average speed among intersections, without considering the variations of travel speed. Variation in travel speed caused by interference on arterials may lead to inaccurate offset estimation, reducing the efficiency of coordination control. Therefore, this study develops an arterial offset optimization method for traffic signal coordination control using real-time speed collected from high-resolution crowdsourced data. The objective of the proposed method is to minimize the average delay on the corridor. The optimization problem is formulated as integer programming, and a genetic algorithm (GA) is utilized to search for the best offset solution. The proposed method is evaluated on a major arterial (Speedway Boulevard) in Tucson, Arizona. In the numerical exercise, the effectiveness and performance of the proposed method are evaluated in various scenarios, including a scenario with non-recurring congestion. The results show that using high-resolution real-time speed data can reduce travel delay time in a coordinated direction by 32.5% and 17.6% when compared with methods using speed limit and free-flow speed, respectively, and the proposed method is more reliable and robust for handling traffic conditions with varying volume and speed.
Purpose
This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs).
Design/methodology/approach
The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control.
Findings
It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies.
Originality/value
In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.
Using automatic identification system (AIS) data, this article first has extended the definition of three widely used roadway congestion indices to maritime transportation systems (MTS), traffic speed index (TSI), traffic rate index (TRI), and dwell time index (DTI). Next, a methodology is developed to measure the indices based on AIS data, considering various factors, including path geometry, time of day, and the type and size of vessels, and finally the method has been applied to the AIS data of the Houston Ship Channel (HSC) to evaluate the applicability in real cases. The results show that although average TSI and TRI cannot represent waterway congestion, the real-time values (rather than the average) at the micro level can help finding location, time, and severity of traffic congestion. Besides, while TSI and TRI have shortcomings, both average and real-time dwell time index (DTI) can quantify traffic congestion and highlight severity in different waterway segments for different types of vessels. When congestion happens at some narrow waterways, vessels need to wait at sea buoy or docks, thus dwell time index (DTI) can quantify traffic congestion better than in-transit indices such as travel speed, TSI. According to HSC DTI, most tankers experience long waiting times at the sea buoy and Galveston Bay, while cargo vessels experience delays at Bayport and Barbour's Cut terminals. This paper helps the decision-makers quantify congestion in different sections of a waterway and provides measures to compare congestion for national competing projects at different waterways.
The application of connected and automated vehicles can significantly reduce traffic congestion, fuel consumption, and transportation emissions. Most existing studies on connected and automated vehicles focus on improving traffic efficiency; the impact on fuel consumption and transportation emissions is not concerned. This study evaluates the influence of connected and automated vehicles on fuel consumption and emissions of mixed traffic flow on the expressway. Firstly, fuel consumption and transportation emissions models are introduced. Secondly, three car-following models are employed to capture the car-following behaviors in the mixed traffic flow. Then, a numerical simulation is designed to investigate the influence of connected and automated vehicles on fuel consumption and transportation emissions of mixed traffic flow. Finally, some factors that impacted fuel consumption and transportation emissions of mixed traffic flow are discussed. The simulation results show that connected automated vehicles can significantly reduce fuel consumption and transportation emissions. The maximum reduction percentages of HC, NOx, CO, and fuel consumption are 24.33%, 27.06%, 37.53%, and 40.58%, respectively, at 100% penetration rate of connected automated vehicles. Moreover, the parameters of the car-following models have a significant influence on fuel consumption and transportation emissions. The result indicates that the design of more minor expected headway would significantly improve the energy-saving and emissions reduction effect of connected automated vehicles.
Connected and automated vehicles (CAVs) trajectories not only provide more real-time information by vehicles to infrastructure but also can be controlled and optimized, to further save travel time and gasoline consumption. This paper proposes a two-level model for traffic signal timing and trajectories planning of multiple connected automated vehicles considering the random arrival of vehicles. The proposed method contains two levels, i.e., CAVs’ arrival time and traffic signals optimization, and multiple CAVs trajectories planning. The former optimizes CAVs’ arrival time and traffic signals in a random environment, to minimize the average vehicle’s delay. The latter designs multiple CAVs trajectories considering average gasoline consumption. The dynamic programming (DP) and the General Pseudospectral Optimal Control Software (GPOPS) are applied to solve the two-level optimization problem. Numerical simulation is conducted to compare the proposed method with a fixed-time traffic signal. Results show that the proposed method reduces both average vehicle’s delay and gasoline consumption under different traffic demand significantly. The average reduction of vehicle’s delay and gasoline consumption are 26.91% and 10.38%, respectively, for a two-phase signalized intersection. In addition, sensitivity analysis indicates that the minimum green time and free-flow speed have a noticeable effect on the average vehicle’s delay and gasoline consumption.
With wireless communication and autonomous vehicle control capabilities, automated vehicle technology has the potential to improve the performance of an intersection. The objective of this research was to develop an intersection control algorithm that can jointly optimize the system performance and the trajectory of every single vehicle. An optimization algorithm was developed for a four-approach intersection with the consideration of turning movements and a full set of possible phases under a 100% automated vehicle environment. The intersection controller makes decisions on the vehicle passing sequence using a genetic algorithm–based optimization method, and at the same time it calculates the optimal vehicle trajectories. The optimization process repeats over a time horizon to process continually arriving vehicles. The performance of the proposed algorithm was assessed in various scenario-based simulation experiments and the results were compared with the actuated signal control. It was concluded that the proposed algorithm is able to reduce the intersection average travel time delay by 16.3% to 79.3%, depending on the demand scenario.
Signalized intersections play an important role in transportation efficiency and vehicle fuel economy in urban areas. This paper proposes a cooperative method of traffic signal control and vehicle speed optimization for connected automated vehicles, which optimizes the traffic signal timing and vehicles' speed trajectories at the same time. The method consists of two levels, i.e., roadside traffic signal optimization and onboard vehicle speed control. The former calculates the optimal traffic signal timing and vehicles' arrival time to minimize the total travel time of all vehicles; the latter optimizes the engine power and brake force to minimize the fuel consumption of individual vehicles. The enumeration method and the pseudospectral method are applied in roadside and onboard optimization, respectively. Simulation studies are conducted to compare the proposed method with benchmark methods. The results show significant improvement of transportation efficiency and fuel economy by the cooperation method.
In this paper, a control algorithm is presented that integrates connected vehicles in the feedback loop of traffic signal control, which results in highly flexible, signal-group based signalization and speed adaptation of vehicles. The method is based on Model Predictive Control and incorporates a mutual optimization of both traffic signal timings and vehicle trajectories. In light of emerging communication technology, connected vehicles are expected to deliver more detailed data about the current traffic flow compared to stationary detection. This data can be used to influence the signal timing. By capitalizing on the possibility of providing information to connected vehicles, a second means of influence is enabled: Information about future signal timings can be provided to the drivers and hence, further reductions in the number of stops and an increase of traffic flow at the beginning of the green time can be achieved. The complexity increases when both ways of influence are combined, which is often omitted in previous research. This combination is addressed in this paper by introducing an optimized signal control with an integrated speed advisory system. The presented algorithm features an innovative functionality to adjust the predictability of signal timings to account for the reliability of speed advisory messages. A simulation study is carried out as a proof of concept and to evaluate the trade-off between optimality and predictability of the traffic signal control algorithm.
Recently there has been significant research on environment-focused Connected Vehicle (CV) applications that involve determining optimal speed profiles for vehicles traveling through signalized intersections and conveying this information to drivers via driver-vehicle interfaces (DVI's). However, findings from previous studies indicate that drivers may not be able to precisely follow the recommended speed profiles, resulting in degraded effectiveness of the applications. Moreover, the DVI could be distracting, which may compromise safety. As an alternative, partial automation can play an important role in ensuring that the benefits of these CV applications are fully realized. In this study, a partially automated vehicle system with an eco-approach and departure feature (called the GlidePath Prototype), which can receive dedicated short range communication (DSRC) message sets from the intersection and automatically follow recommended speed profiles, was developed, demonstrated, and evaluated. The results revealed that compared to manually following the recommended speed profiles, the GlidePath Prototype reduced fuel consumption by 17% on average. In some cases, the fuel savings are greater than 40% while the travel time is shortened by up to 64%. Furthermore, the system potentially improved the driving comfort since it would smooth out the speed profiles.
It is a common vision that connected and automated vehicles (CAVs) will increasingly appear on the road in the near future and share roads with traditional vehicles. Through sharing real-time locations and receiving guidance from infrastructure, a CAV's arrival and request for green light at intersections can be approximately predicted along their routes. When many CAVs from multiple approaches at intersections place such requests, a central challenge is how to develop an intersection automation policy (IAP) to capture complex traffic dynamics and schedule resources (green lights) to serve both CAV requests (interpreted as request for green lights on a particular signal phase at time t) and traditional vehicles. To represent heterogeneous vehicle movements and dynamic signal timing plans, we first formulate the IAP optimization as a special case of machine scheduling problem using a mixed integer linear programming formulation. Then we develop a novel phase-time-traffic (PTR) hypernetwork model to represent heterogeneous traffic propagation under traffic signal operations. Since the IAP optimization, by nature, is a special sequential decision process, we also develop sequential branch-and-bound search algorithms over time to IAP optimization considering both CAVs and traditional vehicles in the PTR hypernetwork. As the critical part of the branch-and-bound search, special dominance and bounding rules are also developed to reduce the search space and find the exact optimum efficiently. Multiple numerical experiments are conducted to examine the performance of the proposed IAP optimization approach.
The projected rapid growth of the market penetration of connected and autonomous vehicle technologies (CAV) highlights the need for preparing sufficient highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). This study proposes an analytical capacity model for highway mixed traffic based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. We identify sufficient and necessary conditions for the mixed traffic capacity to increase (or decrease) with CAV market penetration rate and platooning intensity. These theoretical results caution scholars not to take CAVs as a sure means of increasing highway capacity for granted but rather to quantitatively analyze the actual headway settings before drawing any qualitative conclusion. This analytical framework further enables us to build a compact lane management model to efficiently determine the optimal number of dedicated CAV lanes to maximize mixed traffic throughput of a multi-lane highway segment. This optimization model addresses varying demand levels, market penetration rates, platooning intensities and technology scenarios. The model structure is examined from a theoretical perspective and an analytical approach is identified to solve the the optimal CAV lane number at certain common headway settings. Numerical analyses illustrate the application of this lane management model and draw insights into how the key parameters affect the optimal CAV lane solution and the corresponding optimal capacity. This model can serve as a useful and simple decision tool for near future CAV lane management.
Automated vehicles, or AVs (i.e. those that have the ability to operate without a driver and can communicate with the infrastructure) may transform the transportation system. This study develops and simulates an algorithm that can optimize signal control simultaneously with the AV trajectories under undersaturated traffic flow of AV and conventional vehicles. This proposed Intelligent Intersection Control System (IICS) operates based on real-time collected arrival data at detection ranges around the center of intersection. Parallel to detecting arrivals, the optimized trajectories and signal control parameters will be transmitted to AVs and the signal controller to be implemented. Simulation experiments using the proposed IICS algorithm successfully prevented queue formation up to undersaturated condition. Comparison of the algorithm to operations with conventional actuated control shows 38-52% reduction in average travel time compared to conventional signal control.
This paper formulates a simplified traffic smoothing model for guiding movements of connected automated vehicles on a general one-lane highway segment. Adapted from the shooting heuristic proposed by Zhou et al. (2017) and Ma et al. (2017) , this model confines each vehicle's trajectory as a piecewise quadratic function with no more than five pieces and lets all trajectories in the same platoon share identical acceleration and deceleration rates. Similar to the shooting heuristic, the proposed simplified model is able to control the overall smoothness of a platoon of connected automated vehicles and approximately optimize traffic performance in terms of fuel efficiency and driving comfort. While the shooting heuristic relies on numerical meta-heuristic algorithms that cannot ensure solution optimality, we discover a set of elegant theoretical properties for the general objective function and the associated constraints in the proposed simplified model, and consequentially propose an efficient analytical algorithm for solving this problem to the exact optimum. Interestingly, this exact algorithm has intuitive physical interpretations, i.e., stretching the transitional parts of the trajectories (i.e., parts with acceleration and deceleration adjustments) as far as they reach the upstream end of the investigated segment, and then balancing the acceleration and deceleration magnitudes as close as possible. Numerical examples reveal that this exact algorithm has much more efficient computational performance and the same or better solution quality compared with the previously proposed shooting heuristic. These examples also illustrate how to apply this model to CAV control problems on signalized segments and at non-stop intersections. Further, we study a homogeneous special case of this model and analytically formulate the relationship between queue propagation and trajectory smoothing. One counter-intuitive finding is that trajectory smoothing may not always cause longer queue propagation but instead may mitigate queue propagation with appropriate settings. This theoretical finding has valuable implications to joint optimization of queuing management and traffic smoothing in complex transportation networks.
The vision of intelligent vehicles traveling in road networks has prompted numerous concepts to control future traffic flow, one of which is the in-vehicle actuation of traffic control commands. The key of this concept is using intelligent vehicles as actuators for traffic control systems. Under this concept, we design and test a control system that connects a traffic controller with in-vehicle controllers via Vehicle-to-Infrastructure communication. The link-level traffic controller regulates traffic speeds through variable speed limits (VSL) gantries to resolve stop-and-go waves, while intelligent vehicles control accelerations through vehicle propulsion and brake systems to optimize their local situations. It is assumed that each intelligent vehicle receives VSL commands from the traffic controller and uses them as variable parameters for the local vehicle controller. Feasibility and effectiveness of the connected control paradigm are tested with simulation on a two-lane freeway stretch with intelligent vehicles randomly distributed among human-driven vehicles. Simulation shows that the connected VSL and vehicle control system improves traffic efficiency and sustainability, i.e. total time spent in the network and average fuel consumption rate are reduced compared to (uncontrolled and controlled) scenarios with 100% human drivers and to uncontrolled scenarios with the same intelligent vehicle penetration rates.
This paper studies a problem of controlling trajectories of a platoon of
vehicles on a highway segment with connected and automated vehicles. This
problem is complex because each vehicle trajectory is an infinite-dimensional
object and neighboring trajectories have complex interactions (e.g.,
car-following behavior). A parsimonious shooting heuristic algorithm is
proposed to construct vehicle trajectories on a signalized highway segment that
comply with boundary conditions for vehicle arrivals, vehicle mechanical
limits, traffic lights and vehicle following safety. This algorithm breaks each
vehicle trajectory into a few sections and each is analytically solvable. This
decomposes the original hard trajectory control problem to a simple
constructive heuristic. Then we slightly adapt this shooting heuristic
algorithm to efficiently solve a leading vehicle problem on an uninterrupted
freeway. To study theoretical properties of the proposed algorithms, the time
geography theory is generalized by considering finite accelerations. With this
generalized theory, it is found that under mild conditions, these algorithms
can always obtain a feasible solution to the original complex trajectory
control problem. Further, we discover that the shooting heuristic solution is a
generalization of the solution to the classic kinematic wave theory by
incorporating finite accelerations. We identify the theoretical bounds to the
difference between the shooting heuristic solution and the kinematic wave
solution. Numerical experiments are conducted to verify the theoretical results
and to draw additional insights into the potential of trajectory control in
improving traffic performance. Building upon this foundation, an optimization
framework will be presented in a following paper as Part II of this study.
The problem of eco-driving is analyzed for an urban traffic network in presence of signalized intersections. It
is assumed that the traffic light timings are known and available to the vehicles via infrastructure-to-vehicle
communication. This work provides a solution to the energy consumption minimization while traveling
through a sequence of signalized intersections and always catching a green light. The optimal- control problem
is non-convex because of the constraints coming from the traffic lights; therefore, a sub-optimal strategy
to restore the convexity and solve the problem is proposed. Firstly, a pruning algorithm aims at reducing the
optimization domain by considering only the portions of the traffic light’s green phases that allow to drive in
compliance with the city speed limits. Then, a graph is created in the feasible region in order to approximate
the energy consumption associated with each available path in the driving horizon. Lastly, after the problem
convexity is recovered, a simple optimization problem is solved on the selected path to calculate the optimal
crossing times at each intersection. The optimal speeds are then suggested to the driver. The proposed
sub-optimal strategy is compared with the optimal solution provided by dynamic programming for validation
purposes. It is also shown that the low computational load of the presented approach enables robustness
properties and results very appealing for online use.
Vehicle speed trajectory significantly impacts fuel consumption and greenhouse gas emissions, especially for trips on signalized arterials. Although a large amount of research has been conducted aiming at providing optimal speed advisory to drivers, impacts from queues at intersections are not considered. Ignoring the constraints induced by queues could result in suboptimal or infeasible solutions. In this study, a multi-stage optimal control formulation is proposed to obtain the optimal vehicle trajectory on signalized arterials, where both vehicle queue and traffic light status are considered. To facilitate the real-time update of the optimal speed trajectory, a constrained optimization model is proposed as an approximation approach, which can be solved much quicker. Numerical examples demonstrate the effectiveness of the proposed optimal control model and the solution efficiency of the proposed approach.
This paper addresses the problem of simultaneous route guidance and traffic signal optimization problem (RGTSO) where each vehicle in a traffic network is guided on a path and the traffic signals servicing these vehicles are set to minimize their travel times. The network is modeled as a space-phase-time (SPT) hyper-network to explicitly represent the traffic signal control phases and time-dependent vehicle paths. A Lagrangian-relaxation-based optimization framework is proposed to decouple the RGTSO problem into two subproblems: the Route Guidance (RG) problem for multiple vehicles with given origins and destinations and the Traffic Signal Optimization (TSO) problem. In the RG subproblem, the route of each vehicle is provided subject to time-dependent link capacities imposed by the solution of the TSO problem, while the traffic signal timings are optimized according to the respective link travel demands aggregated from the vehicle trajectories. The dual prices of the RG subproblem indicate search directions for optimization of the traffic signal phase sequences and durations in the TSO subproblem. Both RG and TSO subproblems can be solved using a computationally efficient finite-horizon dynamic programming framework, enhanced by parallel computing techniques. Two numerical experiments demonstrated that the system optimum of the RGTSO problem can be quickly reached with relatively small duality gap for medium-size urban networks.
Enhancements were provided to a previously developed genetic algorithm (GA) for traffic signal optimization for oversaturated traffic conditions. A broader range of optimization strategies was provided to include modified delay minimization with a penalty function and throughput maximization. These were added to the initial delay minimization strategy and were further extended to cover all operating conditions. The enhanced program was evaluated at different intersection spacings. The optimization strategies were evaluated and compared with their counterpart from TRANSYT-7F, version 8.1. A microscopic stochastic simulation program, CORSIM, was used as the unbiased evaluator. Hypothesis testing indicated that the GA-based program with average delay minimization produced a superior signal-timing plan compared with those produced by other GA strategies and the TRANSYT-7F program in terms of queue time. It was also found from the experiments that TRANSYT-7F tended to select longer cycle lengths than the GA program to reduce random plus oversaturation delay.
In the last few years, there has been a significant increase of interest related to vehicle automation, even though the fundamental building blocks for automating vehicles have been developed over the last several decades. In parallel, there has been a big push to make vehicles more energy efficient and less polluting, through the development of advanced powertrains, the development and promotion of alternative lower-carbon fuels, better managing vehicle miles traveled, and improving traffic operations. One of the key questions is how can vehicle automation contribute to energy efficiency and reducing emissions. In this paper, we outline some of these potential impacts, examining issues such as vehicle design, vehicle and traffic operations, and even potential changes in activity patterns.
An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results.
The operation of traffic signals is currently limited by the data available from traditional point sensors. Point detectors can provide only limited vehicle information at a fixed location. The most advanced adaptive control strategies are often not implemented in the field because of their operational complexity and high-resolution detection requirements. However, a new initiative known as connected vehicles allows the wireless transmission of the positions, headings, and speeds of vehicles for use by the traffic controller. A new traffic control algorithm, the predictive microscopic simulation algorithm, which uses these new, more robust data, was developed. The decentralized, fully adaptive traffic control algorithm uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future. The objective function uses either delay only or a combination of delay, stops, and decelerations. To measure the objective function, the algorithm uses a microscopic simulation driven by present vehicle positions, headings, and speeds. The algorithm is relatively simple, does not require point detectors or signal-to-signal communication, and is completely responsive to immediate vehicle demands. To ensure drivers’ privacy, the algorithm does not store individual or aggregate vehicle locations. Results from a simulation showed that the algorithm maintained or improved performance compared with that of a state-of the-practice coordinated actuated timing plan optimized by Synchro at low and midlevel volumes, but that performance worsened under saturated and oversaturated conditions. Testing also showed that the algorithm had improved performance during periods of unexpected high demand and the ability to respond automatically to year-to-year growth without retiming.
A new freeway work zone traffic delay and cost optimization model is presented in terms of two variables: the length of the work zone segment, and the starting time of the work zone using average hourly traffic data. The total work zone cost defined as the sum of user delay, accident, and maintenance costs is minimized and the number of lane closures, darkness factor, and seasonal variation travel demand normally ignored in prior research are included. To find the global optimum solution, a Boltzmann-simulated annealing neural network is developed to solve the resulting mixed real variable-integer cost optimization problem for short-term work zones. The new model can be used as an intelligent decision support system to (1) find the optimum work zone segment length and optimum starting time; (2) study the impact of various factors such as number of lane closures and darkness; and (3) quickly observe the relation between the total work zone cost and the work zone segment length and starting time in a quantitative and rational way.
Numerous fast heuristic algorithms, including shooting heuristics (SH), have been developed for real-time trajectory optimization, although their optimality has not yet been quantified. This paper compares the performance between fast heuristics and exact optimization models. We investigate a core trajectory optimization problem as a building block for numerous trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a one-lane highway when the arrival and departure times and velocity are given. To apply the SH algorithm to this problem, we adapt it to a fast-simplified shooting heuristic (FSSH) model to solve the trajectory smoothing problems with different arrival and departure velocities. An exact trajectory optimization (ETO) model is formulated that takes the vehicle position and velocity as the decision variables, and the fuel consumption and driving comfort as the objective function. The constraints of the model are based on the limits and safety of the vehicle dynamics between consecutive vehicles. We demonstrate the convexity of the ETO objective function, ensuring the solvability of the ETO model at the true optimum using gradient descent algorithms supplied by the MATLAB optimization toolbox. Six groups of numerical experiments using different input parameters and one experiment using real Next Generation Simulation (NGSIM) data are conducted. ETO can improve the objective values by a few to tens of percentage points. However, FSSH achieves a greater solution efficiency with an average solution time of less than 0.1 s compared to ~450 s for ETO.
Reservation-based methods with simple policies such as first-come-first-service (FCFS) have been proposed in the literature to manage connected and automated vehicles (CAVs) at isolated intersections. However, a comprehensive analysis of intersection capacity and vehicle delay under FCFS-based control is missing, especially under high traffic demand. To address this problem, this study adopts queueing theory and analytically shows that such method is incapable of handling high demand with multiple conflicting traffic streams. Furthermore, an optimization model is proposed to optimally serve CAVs arriving at an intersection for delay minimization. This study then compares the performance of the proposed optimization-based control with reservation-based control as well as conventional vehicle-actuated control at different demand levels. Simulation results show that the proposed optimization-based control performs best and it has noticeable advantages over the other two control methods. The advantages of reservation-based control are insignificant compared with vehicle-actuated control under high demand.
Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field.
Trajectory optimization, a critical problem in connected autonomous vehicle control, has been intensively studied recently. A number of fast heuristic algorithms, such as shooting heuristics (SH) (1), have been developed to meet required time efficiency for real-time applications, but the optimality of their solutions is yet to be quantified. This paper aims to bridge this gap and compare the performance between fast heuristics and exact optimization models. For comparison purposes, we investigate a core trajectory optimization problem as a building block for a variety of trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a general one-lane highway segment when the arrival and departure time and velocity of each vehicle are given. To enable the SH algorithm applicable to this general core problem, we adapt it to a fast-simplified shooting heuristic (FSSH) model that can solve the trajectory smoothing problems with different arrival and departure velocities. Then an exact trajectory optimization (ETO) model is formulated as a nonlinear programming problem, which takes vehicle position and velocity profiles as the decision variables, and the fuel consumption and the driving comfort of the whole platoon as the objective function. The constraints of the model are constructed based on vehicle dynamics limits and safety between consecutive vehicles. We prove the convexity of the ETO objective function, which ensures that the ETO model can be solved to the true optimum with the gradient descent algorithms supplied by the Matlab optimization toolbox. Further, 11 groups of numerical experiments with different input parameters are conducted. It is found that compared with FSSH, ETO can improve the objective values by a magnitude ranging from a few percent to tens of percent. However, FSSH far outperforms ETO in solution efficiency: the average solution time of FSSH is less than 1 second yet that of ETO is around 500 seconds.
We address the problem of optimally controlling connected and automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling, so as to minimize energy consumption subject to a throughput maximization requirement. We show that the solution of the throughput maximization problem depends only on the hard safety constraints imposed on CAVs and its structure enables a decentralized optimal control problem formulation for energy minimization. We present a complete analytical solution of these decentralized problems and derive conditions under which feasible solutions satisfying all safety constraints always exist. The effectiveness of the proposed solution is illustrated through simulation which shows substantial dual benefits of the proposed decentralized framework by allowing CAVs to conserve momentum and fuel while also improving travel time.
Existing traffic signal control systems only allocate green time to different phases to avoid conflicting vehicle movements. With advances in connected and automated vehicle (CAV) technologies, CAV trajectories not only provide more information than existing infrastructure-based detection systems, but also can be controlled to further improve mobility and sustainability. This paper presents a mixed integer linear programming (MILP) model to optimize vehicle trajectories and traffic signals in a unified framework at isolated signalized intersections in a CAV environment. A new planning horizon strategy is applied to conduct the optimization. All vehicle movements such as left-turning, right-turning and through are considered. Phase sequences, green start and duration of each phase, and cycle lengths are optimized together with vehicle lane-changing behaviors and vehicle arrival times for delay minimization. Vehicles are split into platoons and are guaranteed to pass through the intersection at desired speeds and avoid stops at stop bars. Exact vehicle trajectories are determined based on optimized vehicle arrival times. For the trajectory planning of platoon leading vehicles, an optimal control model is implemented to minimize fuel consumption/emission. For following vehicles in a platoon, Newell's car-following model is applied. Simulation results validate the advantages of the proposed control method over vehicle-actuated control in terms of intersection capacity, vehicle delays, and CO2 emissions. A sensitivity analysis is conducted to show the potential benefits of a short minimum green duration as well as the impacts of no-changing zones on the optimality of the proposed model.
Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection.
Connected and automated vehicles (CAVs) have the potential to improve safety by reducing and mitigating traffic accidents. They can also provide opportunities to reduce transportation energy consumption and emissions by improving traffic flow. Vehicle communication with traffic structures and traffic lights can allow individual vehicles to optimize their operation and account for unpredictable changes. This paper summarizes the developments and the research trends in coordination with the CAVs that have been reported in the literature to date. Remaining challenges and potential future research directions are also discussed.
Advanced connected and automated vehicle technologies enable us to modify driving behavior and control vehicle trajectories, which have been greatly constrained by human limits in existing manually-driven highway traffic. In order to maximize benefits from these technologies on highway traffic management, vehicle trajectories need to be not only controlled at the individual level but also coordinated collectively for a stream of traffic. As one of the pioneering attempts to highway traffic trajectory control, Part I of this study (Zhou et al., 2016) proposed a parsimonious shooting heuristic (SH) algorithm for constructing feasible trajectories for a stream of vehicles considering realistic constraints including vehicle kinematic limits, traffic arrival patterns, car-following safety, and signal operations. Based on the algorithmic and theoretical developments in the preceding paper, this paper proposes a holistic optimization framework for identifying a stream of vehicle trajectories that yield the optimum traffic performance measures on mobility, environment and safety. The computational complexity and mobility optimality of SH is theoretically analyzed, and verifies superior computational performance and high solution quality of SH. A numerical sub-gradient-based algorithm with SH as a subroutine (NG-SH) is proposed to simultaneously optimize travel time, a surrogate safety measure, and fuel consumption for a stream of vehicles on a signalized highway section. Numerical examples are conducted to illustrate computational and theoretical findings. They show that vehicle trajectories generated from NG-SH significantly outperform the benchmark case with all human drivers at all measures for all experimental scenarios. This study reveals a great potential of transformative trajectory optimization approaches in transportation engineering applications. It lays a solid foundation for developing holistic cooperative control strategies on a general transportation network with emerging technologies.
Some of the currently available estimation techniques are introduced, and the assumptions on which they are based are examined. In general, these assumptions are unrealistic; so accurate delay estimates are not really possible. The difficulties are particularly acute when the arrival flows approach capacity. Some of the procedures avoid the worst of the problems at high flows by methods that, though they involve considerable mathematics, are based on modeling that is essentially qualitative rather than quantitative. Such methods abandon the quest for accuracy in favor of reasonableness. If accuracy is desired, however, a new generation of models that take more account of variations in travel demand over time is needed.
Freeway on-ramp metering is widely used and studied in many big cities of foreign countries. According to the metering range and its complexity, the metering methodologies can be classified as pretimed metering, local responsive metering and coordinated responsive metering. In China, there have been so far no large-scale studies in this field and no practical cases available. A review and analysis are made of the essential freeway on-ramp metering methodologies and their advantages and disadvantages. In addition, some perspectives on the development of concerned research are brought up.
An efficient, reliable and futuristic Air Transportation System (ATS) is the key to handle the anticipated rapid changes in the aviation industry over the next few decades. Trajectory Based Operations (TBO) could be considered as one of the most significant solutions to cater for these problems. This paper discusses the development of a 4D optimal flight trajectory based on aircraft performance, weather forecasts, Air Traffic Control (ATC) database and aircraft operational data. Dynamic Programming is utilized in the optimization process for its qualities of avoiding iterative calculations, predicting the required amount of calculations and feasibility of applying inequality constraint conditions. Optimal flight trajectories were generated for a single aircraft for its climb, cruise and descent phases in a provided 4D grid platform based on a busy domestic flight route, between Tokyo (Haneda) and Fukuoka. Flight data were recorded on a commercial GPS data logger inside an airliner cabin and used to compare with optimum solutions obtained from the model. Consumed fuel consumption and flight time were evaluated to understand the benefits gained by the optimization.
Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.
Current traffic control systems which customarily give no direct information to the driver could benefit by the addition of dynamic advisory speed signs. Such signs, first introduced in the 1950s, can enable drivers to pass through successive green signals and give reduced fuel consumption, stops, travel time, emissions, noise and accidents. To attempt to quantify these reductions, a discrete vehicle simulation using program MULTSIM has been run on a 6 km idealized road and also on 2.3 km of the multi-lane arterial, Military Road, Sydney. Typically, with all drivers complying, fuel savings of the order of 10% and a halving of stops have been predicted. Travel time reductions and fuel savings depend on the coordination speed. Higher coordination speeds favour travel time savings over fuel consumption reductions, whilst lower coordination speeds favour fuel savings. Operation near the minimum fuel consumption speed is desirable. Traffic management could benefit from the change in shape of platoons. This should allow more stringent signal timing in adaptively controlled networks, giving greater flexibility for operational designs.
Since the introduction of the vehicle infrastructure integration (VII) and connected vehicle (CV) initiatives in the United States, numerous in-vehicle technologies based on wireless communications are currently being deployed. One of these technologies is cooperative adaptive cruise control (CACC) systems, which provide better connectivity, safety, and mobility by allowing vehicles to travel in denser platoons through vehicle-to-vehicle (V2V) communication. Accordingly, the research presented in this article develops a simulation/optimization tool that optimizes the movement of CACC-equipped vehicles as a replacement for traditional intersection control. This system, which is named iCACC, assumes that the intersection controller receives vehicle requests to travel through an intersection and advises each vehicle on the optimum course of action ensuring no crashes occur while at the same time minimizing the intersection delay. Four intersection control scenarios are compared, namely: a traffic signal, an all-way stop control (AWSC), a roundabout, and the iCACC controller. The results show that the proposed iCACC system significantly reduces the average intersection delay and fuel consumption level by 90 and 45%, respectively. Additionally, the article investigates the impact of vehicle dynamics, weather conditions, and level of market penetration of equipped vehicles on the future of automated vehicle control.
The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.
Researchers have attempted to compute a fuel-optimal vehicle trajectory by receiving traffic signal phasing and timing information. This problem, however, is complex when microscopic models are used to compute the objective function. This paper suggests use of a multi-stage dynamic programming tool that not only provides outputs that are closer to optimum, but are also computationally much faster. It uses a recursive trajectory generation that is similar to least-cost path-finding algorithms that optimizes the upstream profile while comparing discretized downstream cases. Since dynamic programming is faster than traditional computational methods, the algorithm can afford to use microscopic models and thereby be sensitive to a multitude of inputs such as grade, weather etc. Agent-based simulations suggest fuel savings in the range of 19 percent and travel-time savings of 32 percent in the vicinity of intersections. This research also showed potential benefits to vehicles following a vehicle that uses the proposed logic.
Traffic signals at intersections are an integral component of the existing transportation system and can significantly contribute to vehicular delay along urban streets. The current emphasis on the development of automated (i.e., driverless and with the ability to communicate with the infrastructure) vehicles brings at the forefront several questions related to the functionality and optimization of signal control in order to take advantage of automated vehicle capabilities. The objective of this research is to develop a signal control algorithm that allows for vehicle paths and signal control to be jointly optimized based on advanced communication technology between approaching vehicles and signal controller. The algorithm assumes that vehicle trajectories can be fully optimized, i.e., vehicles will follow the optimized paths specified by the signal controller. An optimization algorithm was developed assuming a simple intersection with two single-lane through approaches. A rolling horizon scheme was developed to implement the algorithm and to continually process newly arriving vehicles. The algorithm was coded in MATLAB and results were compared against traditional actuated signal control for a variety of demand scenarios. It was concluded that the proposed signal control optimization algorithm could reduce the ATTD by 16.2-36.9% and increase throughput by 2.7-20.2%, depending on the demand scenario.
To reduce fuel consumption in the transportation sector research focuses mainly on the development of more efficient drive train technologies and alternative drive train designs. Another and immidiately applicable way found to reduce fuel consumption in road vehicles is to change vehicle operation such that system efficiency is maximized. The concept of Eco- driving refers to the change of driver behavior in a fuel saving way or more generally in an energy saving way. In this paper system efficiency of a vehicle is optimized using a dynamic programming optimization approach. Given a drive cycle a so called 'eco-drive cycle' is identified in which a vehicle performs the same distance with the same stops in equivalent time, while consuming less fuel.
EPA is developing a new mobile source emissions model that is comprehensive in source category, pollutant and analysis scale. A primary impetus for this effort is the National Research Council's review of EPA's mobile source modeling program, published in 2000, which recommended a) the development of a modeling system more capable of supporting smaller-scale analyses; b) improved characterization of emissions from high-emitting vehicles, heavy-duty vehicles, and off-road sources; c) improved characterization of particulate matter and toxic emissions; d) improved model evaluation and uncertainty assessments; and e) a long-term planning effort coordinated with other governmental entities engaged in emissions modeling. A cross-agency team representing OTAQ, OAQPS, ORD and Regional offices published an initial proposal to address these recommendations in April 2001. Since then, work has progressed on developing a conceptual design and software framework for the new modeling system. This design has been informed by recent advances in subregional emissions modeling and considers how the new mobile source emissions model should interact with new generation transportation and air quality models. Other important considerations of the design are how to capitalize on methods for improved characterization of in-use emissions, such as on-board emissions measurement, and how to incorporate model validation and uncertainty evaluation into the model structure. We present the latest information on a design and implementation plan for the model.