Signal Phase And Timing Research Articles

Optimization of Vehicle Trajectories Considering Uncertainty in Actuated Traffic Signal Timings

This paper introduces a robust green light optimal speed advisory (GLOSA) system for fixed and actuated traffic signals considering a probability distribution. These distributions represent the domain of possible switching times from the signal phasing and timing (SPaT) messages. The system finds the least-cost (minimum fuel consumption) vehicle trajectory using a computationally efficient A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^\ast$</tex-math> </inline-formula> algorithm incorporated within a dynamic programming (DP) procedure to minimize the vehicle’s total fuel consumption. Constraints are introduced to ensure that vehicles do not collide with other vehicles, run red indications, or exceed a maximum vehicular jerk for passenger comfort. Results of simulation scenarios are evaluated against empirical comparable trajectories of uninformed drivers to compute fuel consumption savings. The proposed approach produced significant fuel savings compared to an uninformed driver behavior amounting to 37% on average for deterministic SPaT and 30% for stochastic SPaT data. A sensitivity analysis was performed to understand how the degree of uncertainty in SPaT predictions affects the optimal trajectory’s fuel consumption. The results present the required levels of confidence in these predictions to achieve savings in fuel consumption. Specifically, the study demonstrates that the proposed system can be within 85% of the maximum savings if the timing error is ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 3.3 seconds) at a 95% confidence level. Results also emphasize the importance of more reliable SPaT predictions as the time to green decreases relative to the time the vehicle is expected to reach the intersection given its current speed.

Evaluation of the driving performance and user acceptance of a predictive eco-driving assistance system for electric vehicles

In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles (BEVs) while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), namely reference-tracking MPC and car-following MPC, that use the information from onboard sensors, signal phase and timing (SPaT) messages from traffic light infrastructure, and geographical information of the driving route to compute an energy-optimal driving speed. An optimal speed suggestion and informative advice are indicated to the driver using a visual feedback. Moreover, the warning alerts during unsafe car-following situations are provided through an auditory feedback. pEDAS provides continuous feedback and encourages the drivers to perform energy-efficient car-following while tracking a preceding vehicle, travel at safe speeds at turns and curved roads, drive at energy-optimal speed determined using dynamic programming in freeway scenarios, and travel with a green-wave optimal speed to cross the signalized intersections at a green phase whenever possible. Furthermore, to evaluate the efficacy of the proposed eco-driving assistance system, user studies were conducted with 41 participants on a dynamic driving simulator. The objective analysis revealed that the drivers achieved mean energy savings up to 10%, reduced the speed limit violations, and avoided unnecessary stops at signalized intersections by using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated using the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The results showed an overall positive attitude of users and that the perceived usefulness and perceived behavioral control were found to be the significant factors in influencing the behavioral intention to use pEDAS.

Energy-Optimal Speed Control for Autonomous Electric Vehicles Up- and Downstream of a Signalized Intersection

Signalized intersections can increase the vehicle stops and consequently increase the energy consumption by forcing stop-and-go dynamics on vehicles. Eco-driving with the help of connectivity is a solution that could avoid multiple stops and improve energy efficiency. In this paper, an eco-driving framework is developed, which finds the energy-efficient speed profile both up- and downstream of a signalized intersection in free-flow situations (eco-FF). The proposed framework utilizes the signal phasing and timing (SPaT) data that are communicated to the vehicle. The energy consumption model used in this framework is a combination of vehicle dynamics and time-dependent auxiliary consumption, which implicitly incorporates the travel time into the function and is validated with real-world test data. It is shown that, by using the proposed eco-FF framework, the vehicle’s energy consumption is notably reduced.

Energy-Efficient Driving in Connected Corridors via Minimum Principle Control: Vehicle-in-the-Loop Experimental Verification in Mixed Fleets

Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin's Minimum Principle in a feedback manner. Urban conditions are then imposed from exogeneous traffic comprised of a mixture of human-driven vehicles (HVs) - as well as other CAVs. As such, safe disturbance compensation is achieved by implementing a model predictive controller (MPC) to anticipate and avoid collisions by issuing braking commands as necessary. The control strategy is experimentally vetted through vehicle-in-the-loop (VIL) of a prototype CAV that is embedded into a virtual traffic corridor realized through microsimulation. Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without. Additionally, the passive energy benefits realizable for human drivers when driving behind downstream CAVs are measured, showing up to 22% fuel savings in a HV when driving behind a small penetration of connectivity-enabled automated vehicles.

A bi-Level programming method for SPaT estimation at fixed-time controlled intersections using license plate recognition data

Signal phase and timing (SPaT) information is a necessary input for traffic performance evaluation. However, current SPaT estimation studies mainly focus on estimation of cycle length or green time of a certain movement, and are realized mostly by floating car data whose data quality significantly affects the estimation accuracy. As license plate recognition (LPR) systems are becoming a widely implemented and reliable data source in China, in this study, a SPaT estimation method is proposed using the LPR data for fixed-time controlled intersections. The SPaT estimation problem is formulated as a bi-level programming model to find the optimal match between the phase boundaries and the LPR passing time series in the study period. Evaluation is done with an empirical case and compared with an existing method, results show that the estimation accuracies of the phase duration can reach 90.0%, outperforming the existing method and demonstrating great potential for practical application.

Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence

Information and communication technology has many promising benefits including improvement the traffic network capacity, efficiency, and stability. However, to date, most of the improvements in signal management and interactions in connected vehicle environments focus solely on the vehicular side. This has led to a massive gap for non-motorized users and vulnerable road users. Specifically, deficit perception capability, inconsistent dissemination, obsolescent acquisition techniques, and ignorance of equality make the current experience of the active non-motorized users inconvenient and risky, especially for those with disabilities. To serve the users in an unbiased and automated way, a novel cooperated signal phase and timing (SPaT) services infrastructure — Vision Enhanced Non-motorized Users Services (VENUS) smart node is proposed. With customized up-to-date computer vision algorithms and artificial intelligence pipelines on the edge, VENUS smart node can collect necessary active-user information (including location, class, pose direction and mobility status), and generate directional crossing request for every pedestrian and cyclist in real time. Meanwhile, the improved communication system makes the VENUS node a reliable information hub to share the SPaT messages and carry interactions to/from the signal controller, connected vehicles and user personal information devices (i.e., cell phones, wearable devices) through various protocols. Based on extensive experimentation, 1076 testing users from six intersections, the VENUS sensing achieves 90.24% accuracy on directional-aware crossing trigger generation and 89.87% accuracy on mobility status estimation for normal users and four types of disabled persons. Furthermore, the VENUS smart node is fully compatible with the connected vehicles environment, and improves the signal system at low cost, mainly due to its flexibility and adaptability with existing infrastructure. The VENUS smart node is the first connected infrastructure architecture that integrates traffic sensing, data processing and information dissemination together for the self-operating indistinguishable signal services based on edge computing.

A Fast Optimal Speed Planning System in Arterial Roads for Intelligent and Connected Vehicles

Speed planning system is generally equipped for intelligent and connected vehicles (ICVs). Under the circumstances of autonomous driving, an energy-optimal speed trajectory is usually desired, particularly on urban arterial roads with complex traffic conditions involved. However, the existing speed planning solutions in the literature have not dealt with the problem of time consuming. Also, the ego vehicle could not perfectly track a precalculated speed reference because of the dynamically varying traffic. Thus, optimal speed planning cannot always be guaranteed. In this article, a fast optimal speed planning system for complex urban driving situations is established through an adaptive hierarchical control framework. In the planning layer, dynamic programming (DP) and the interior-point optimizer are jointly used to compute the global speed trajectory with access to signal phase and timing (SPaT) information. The computational burden is greatly alleviated based on a weighted orientation graph assumption and problem decomposition. The following layer utilizes the Informer, which is a transformer-based model, to predict preceding vehicle speed. Then, a target-switching model-predictive controller (MPC) is adopted for global speed trajectory following and adaption. The proposed approach significantly reduces speed planning computation time compared to previous solutions. Simulation results based on real road traffic scenes manifest that 22.0% of energy is saved compared with human driving.

Green time usage metrics on signalized intersections and arterials using high-resolution traffic data

The performance of traffic signal phasing and timing (SPaT) plans are directly related to temporal fluctuations in traffic volumes and their distribution across competing movements at the intersection. A well-timed signal plan generally allocates green times in proportion to the observed volumes. With the advance of data collection techniques and technology, it is possible to obtain and use high-resolution traffic data using detectors at intersections to measure the real-time performance of the SPaT plan to better achieve this goal. This study introduces novel green time metrics and methods to evaluate the efficiency of green time allocation along arterials and at individual intersections using automated, real-time, and high-resolution traffic data. An empirical application is presented for four intersections along a major radial arterial in Salt Lake City, Utah and a detailed analysis of the phases and movements for a single intersection located on that arterial. The proposed methods and results can help identify opportunities to improve the signal timing plan for the entire corridor or movements within secondary approaches by identifying locations and/or time periods where signal timing does not align well with observed traffic volumes. These metrics can be automated to provide traffic engineers with an alert as to when a particular intersection should be examined as a candidate for signal retiming, and they can also help identify what changes need to be made with the current SPaT plans.

Hierarchical Control of Heavy-Duty Trucks Through Signalized Intersections With Non-Deterministic Signal Timing

This paper presents a hierarchical Green-Light Approach Speed (h-GLAS) strategy for controlling heavy-duty trucks traveling through urban/suburban environments, where future intersection signal phase and timing (SPaT) information is non-deterministic. Through vehicle-to-infrastructure communication, past SPaT information is sent to the vehicle and is used to forecast future predictions of the signal timing using a Gaussian Process (GP) model. The h-GLAS strategy uses the predicted SPaT information to generate an efficient desired velocity profile that navigates through intersections where the probability of a green light is maximized. This velocity profile is tracked by a convex model predictive controller (MPC) that simultaneously minimizes mechanical energy expenditure and braking effort over its prediction horizon. Downstream from the MPC, we implement a command governor (CG) that adjusts the MPC output by the minimum amount necessary to maintain safe vehicle following and ensure that the vehicle stops at all red lights. Using a proprietary medium-fidelity Simulink model provided by Volvo Trucks, we characterize the h-GLAS strategy’s performance over a real suburban route, consisting of 10 actuated signalized intersections, using actual SPaT information provided by the NC Department of Traffic (DOT). Simulation results demonstrate a 30-43% reduction in fuel consumption, as compared to a baseline control strategy, which is attributable primarily to avoiding the massive energy losses associated with braking. Furthermore, we evaluate the computational efficiency of our approach by assessing average simulation execution times.

A detection method for cyber-attack on connected signal phase and timing information

This paper proposes a method to detect cyber-attacks by cross-checking Signal Phase and Timing (SPaT) information and connected vehicle trajectories data. The proposed method is only applicable to cyber-attacks that involve information tempering. The proposed V2I cyber-attacks detection algorithm includes the following features: i) effective in detecting the authenticity of broad-casted SPaT information; ii) feasible to identify the occurrence of cyber-attacks when the defensive function fails; iii) functional in the environment of partially connected and automated traffic; iv) leveraging transportation knowledge for cybersecurity assurance. Furthermore, a sensitivity analysis on the connected vehicle penetration rate is conducted. The best performance is observed when the penetration rate is 100%. Under this penetration rate, the experimental result demonstrates that the accuracy rate of the proposed method reached 94%.

Co-optimization method of speed planning and energy management for fuel cell vehicles through signalized intersections

Network of vehicles technology can help vehicles obtain more traffic information during their journey. In this paper, a co-optimization method of speed planning and energy management is proposed for fuel cell vehicles (FCVs) while passing through multiple continuous signalized intersections. Based on the off-line dynamic programming (DP) algorithm and the signal phase and timing (SPaT), the constant-speed hydrogen consumption map and the weighted orientation graph of the FCV are established. Afterwards, a bi-level DP method is introduced to solve the speed planning problem online. In the meantime, a modified alternating direction method of multipliers (ADMM) algorithm, which includes a cyclic constraint inspection strategy to constrain the power change rate of fuel cell system (FCS), is used to solve the energy management problem. The simulation results demonstrate that the proposed method can generate an optimal speed reference trajectories for FCV through 4 and 8 signalized intersections in 3 s and 5 s respectively, which is practically implementable on real vehicles. Simulation results show that our proposed method is able to achieve similar fuel saving performance with off-line DP but a in real time framework, which is over 20% better than the improved intelligent driving model (IDM) with ADMM. • Co-optimization method of speed planning and energy management is investigated. • A bi-level DP method is presented to solve the speed planning problem. • The modified ADMM is applied to energy management system of fuel cell vehicles.

Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-network-based energy consumption model

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi-level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation.

A suggestion-based fuel efficient control framework for connected and automated vehicles in heterogeneous urban traffic

This paper proposes a novel robust suggestion-based control framework based on model predictive control (MPC) to optimize the fuel efficiency of connected and automated vehicles (CAVs) in heterogeneous urban traffic (i.e., including both CAVs and human-driven vehicles (HDVs)). In this suggestion-based control framework, the CAVs are considered to provide suggestion commands to the HDVs to follow. These suggestion commands are the velocities the CAVs want the HDVs to follow to improve their own, as well as the group’s fuel economy. The host CAV thus needs to find its own fuel-efficient control solution, as well as the suggestion commands to be provided to its preceding HDV. We assume the CAVs can communicate with the HDVs via Vehicle to Vehicle (V2V) communication, and the Signal Phase and Timing (SPaT) information are available to the CAVs through Vehicle to Infrastructure (V2I) communication. The suggested velocity commands are held constant for a predefined time to allow the driver in the HDV to reach the suggested velocity. We also consider that the suggested velocity commands are non binding, i.e., a driver can choose not to follow the suggested velocity. For this control framework to function, we present a velocity prediction model based on experimental data that captures the response of a HDV to different suggested-commands, and a robust approach to ensure collision avoidance. The velocity prediction’s accuracy is also validated with the experimental data (on a table-top drive simulator), and the results are presented in this paper. Simulation studies show the proposed control strategy’s efficacy compared with existing baseline methods.

Study on the Feasibility of Vehicle-to-Infrastructure Applications Supported through Advanced Wireless Communications

Connected and automated vehicle (CAV) technology has the potential to improve transportation systems. CAV technology has been shown to bring benefits in transportation mobility, safety, and the environment. The signal phase and timing (SPaT) message is a fundamental and critical CAV message, as it enables connectivity between vehicles and infrastructure. Because SPaT messages can be transmitted using different wireless communication technologies, it is necessary to study the latency and coverage of SPaT messages in such cases. This study investigates performances of SPaT messages transmitted using two popular communication technologies in CAVs: dedicated short-range communications (DSRC) and cellular 3rd generation partnership project (3GPP)/4th generation long-term evolution (4G/LTE). To provide a robust evaluation, SPaT data transmitted by DSRC and cellular networks were collected in the field at various intersections in Northern Virginia and used to compare performance in terms of latency and distance coverage. The results showed that the latency experienced by SPaT messages over cellular networks is well below the 100 ms required by most infrastructure applications, implying that cellular communications may be used for many vehicle-to-infrastructure (V2I) safety and mobility applications. The feasibility of several CAV applications was investigated based on the network performance observed in this study. Specifically, several safety, mobility, and environmental applications were tested to determine whether DSRC and cellular networks could satisfy their requirements for network performance. The study also provides conclusive remarks on whether the wireless communication technologies are capable of supporting safety, mobility, and environmental applications.

Investigating the operational performance of connected and autonomous vehicles on signalized superstreets

ABSTRACT With the development of artificial intelligence and wireless communication technology, connected and autonomous vehicles (CAVs) have been treated as a promising strategy to increase road capacity and mitigate traffic congestion. Besides the technology of CAVs, innovative intersection design was also originally introduced as a countermeasure for dealing with traffic congestion at intersections. Though many studies have been conducted to explore the benefits of CAVs under various transportation scenarios, few have been implemented to explore the impact of CAVs on traffic flow at innovative intersections. Hence, to achieve a better understanding of the impacts of CAVs on existing transportation infrastructure, this study conducts a simulation-based study to investigate the operational performance of CAVs with available Signal Phase and Timing (SPaT) information in the environment of typical innovative intersection design, i.e. superstreets. The impact of CAVs with different market penetration rates on the operational performance of a superstreet is identified. The operational performance of the superstreet increases as the market penetration rate increases overall. Average speed and average traffic delay for vehicles in the superstreet system can be improved with the increase of market penetration rates.

In-Vehicle Test Results for Advanced Propulsion and Vehicle System Controls Using Connected and Automated Vehicle Information

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This paper documents an extensive simulation and experimental campaign that provides a systematic quantification of fuel economy and mobility benefits resulting from leveraging Vehicle-to-Everything (V2X) connectivity, longitudinal (Level 1+) automation, and advanced cylinder deactivation to improve the propulsion system and vehicle efficiency. A Vehicle Dynamics and Powertrain (VD&amp;amp;PT) optimization framework, developed jointly by The Ohio State University and BorgWarner, utilizes advanced route information available from the navigation system and GPS, and Vehicle-to-Infrastructure (V2I) communication when available to minimize the cumulative fuel consumption over a driver-selected route.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;To assess the benefits of these technologies in real-world conditions, a Monte Carlo simulation framework was developed to quantify the impact of Signal Phase and Timing (SPaT), variability in traffic conditions, and behavior of different drivers on the fuel consumption and vehicle travel time. Further, verification through in-vehicle testing was conducted by reconstructing a subset of these scenarios on a proving ground.&lt;/div&gt;&lt;/div&gt;

A Spatial Searching Method for Planning Under Time-Dependent Constraints for Eco-Driving in Signalized Traffic Intersection

Typical numerical solutions to planning problems under time-dependent constraints (like traffic signals) involve searching in time plus state space. We consider a sampled discrete spatial formulation of the vehicle dynamics. This allows us to propose an optimal planning algorithm with much reduced search-space and time complexity, for vehicles moving across signalized intersections with full knowledge of the traffic Signal, Phasing and Timing (SPaT) information. Then we extend these results to partial knowledge by casting the problem as a Markov Decision Process (MDP). The proposed algorithms are demonstrated through numerical simulations that show a five-fold improvement in runtime compared with a standard time-state formulation, while providing comparable improvements in fuel economy with no vehicle dynamic constraints or traffic rules violated.

Estimating Signal Timing of Actuated Signal Control Using Pattern Recognition under Connected Vehicle Environment

The Signal Phase and Timing (SPaT) message is an important input for research and applications of Connected Vehicles (CVs). However, the actuated signal controllers are not able to directly give the SPaT information since the SPaT is influenced by both signal control logic and real-time traffic demand. This study elaborates an estimation method which is proposed according to the idea that an actuated signal controller would provide similar signal timing for similar traffic states. Thus, the quantitative description of traffic states is important. The traffic flow at each approaching lane has been compared to fluids. The state of fluids can be indicated by state parameters, e.g. speed or height, and its energy, which includes kinetic energy and potential energy. Similar to the fluids, this paper has proposed an energy model for traffic flow, and it has also added the queue length as an additional state parameter. Based on that, the traffic state of intersections can be descripted. Then, a pattern recognition algorithm was developed to identify the most similar historical states and also their corresponding SPaTs, whose average is the estimated SPaT of this second. The result shows that the average error is 3.1 seconds.

Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions

For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project.

Battery Electric Vehicle Eco-Cooperative Adaptive Cruise Control in the Vicinity of Signalized Intersections

This study develops a connected eco-driving controller for battery electric vehicles (BEVs), the BEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory is formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location, and signal timing, defined by vehicle characteristics and signal phase and timing (SPaT) data shared under a connected vehicle environment. The optimal speed trajectory is computed in real-time by the proposed BEV eco-CACC-I controller, so that a BEV can follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate its performance under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays.