Penetration Rates Of Connected Vehicles Research Articles

Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment

With the gradual development of autonomous driving and connected communication technology, urban road networks will be shared by a combination of Human-driven vehicles (HDVs) and Connected vehicles (CVs) for a long era. Besides, as the actual vehicle operator, the driver regulates the vehicle state with an accelerator, gear lever, or brake, whereas the stepwise acceleration process is overlooked in the literature. Motivated by this, comprehensive kinetic difference between the two types of vehicles, we propose a novel heterogeneous multi-phase traffic flow accounting for the HDVs and CVs to close this hole. In the section on linear stability analysis, the reductive perturbation approach was applied to figure out the stability norm of the new model. The findings show that, on the one hand, inhibiting traffic bottlenecks is positively impacted by the penetration rate of CVs and the number of preceding vehicles taken into account; on the other hand, by varying the headway between following vehicles, the multiple-phase transition occurs; consequently, the number of turning points in the optimal velocity function is the sole factor that affects the number of stages in multi-phase transitions. Subsequently, the modified Korteweg-de Vries (mKdV) equation corresponding to the new model is deduced to investigate the nonlinear phenomenon of traffic flow at the vicinity of the critical point, and the kink-antikink soliton wave solution by solving the above characteristic equation can describe the “stop-and-go” phenomenon in real traffic flow. Finally, the conclusion of the numerical experiments is consistent with the above theoretical analysis. This research can provide insight into the dynamic evolution of road traffic flow during the transition from HDVs to semi-CVs and eventually to CVs.

Traffic safety performance evaluation in a connected vehicle environment with queue warning and speed harmonization applications

With the increased adoption of connected vehicle (CV) technologies, safety information is becoming increasingly available to drivers. This study investigates three main questions (1) Do CV-based traffic management applications improve safety on roadways with existing infrastructure-based traffic management systems? (2) Can combining two CV technologies have a greater impact on safety than a single CV technology? and (3) Do geometric and traffic composition factors impact the efficiency of CV technologies? We applied a rarely-used CV dataset and conducted a comprehensive simulation analysis of varying conditions and CV penetration rates that studies have not considered. Two CV applications (queue warning and speed harmonization) implemented in the Intelligent Network Flow Optimization experiment in Seattle, WA were evaluated. Results showed that driver safety performance, based on speed metrics (standard deviation and percentage of extreme values) improved under the CV driving conditions. Combining conventional variable speed limit systems with queue warnings also improved safety for CV drivers. Furthermore, the implementation of a single CV application (queue warning) showed positive changes in the aforementioned speed metrics, congestion mitigation, and reduced conflicts. With the two CV applications combined, no significant differences were observed. Additional tests investigated the impacts of lane changes and roadway attributes on safety in the CV environment.

Heterogeneous lattice hydrodynamic model and jamming transition mixed with connected vehicles and human-driven vehicles

As communication and perception technologies develop, connected vehicles (CVs) advance rapidly. In the process of popularization of CVs, it is bound to lead to the coexistence of CVs and human-driven vehicles (HDVs) on the road network for some time to come, and the previous study assumes that road vehicle is homogeneous, which could not quantify the difference between mixed vehicular flow, based on this, we consider the differences between CAVs and CVs in the way of information acquisition, a heterogeneous lattice hydrodynamics model mixing the HDVs and CVs are presented. Subsequently, by using the small perturbation method, we analyze the linear stability of the proposed model and derive the corresponding stability criteria; when the above stability norm does not hold, in order to investigate the nonlinear phenomenon, nonlinear stability analysis is performed and a modified Korteweg–de Vries equation (mKdV) corresponding to the proposed model is derived. By solving the mKdV equation, we obtain the kink - antikink solitary wave solution which can be used to explain how traffic jams form and propagate. At last, several numerical simulations were conducted to test the impact of the penetration rate of CVs and the bi-directional visual field for both forward-looking and backward-looking on the heterogeneous traffic flow stability, i.e., the first two are positive, while the last is negligible, this conclusion is consistent with the conclusion of theoretical derivation.

Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

AbstractVehicular trajectory data collected by connected automated vehicles (CAVs) is minimal due to the low penetration rates (PRs) of CAVs, and fail to capture the characteristics of traffic flow. This study proposes a fully sampled trajectory reconstruction method for mixed traffic flow with regular vehicles (RVs), connected vehicles (CVs), and CAVs based on car‐following behaviour. Firstly, considering the minimum safety distance constraints between vehicles, an optimization model for minimizing the impact on the acceleration of the known vehicles is developed to obtain the number of inserted RVs. Secondly, the speed of the inserted RVs is estimated based on the traffic flow model. Then, an optimization model is proposed to determine the position of each inserted RV. Finally, numerical simulation is designed to investigate the influence of traffic density and PRs of CAVs and CVs. Results show that the proposed method can better reconstruct the vehicle trajectory on the freeway under the different traffic densities in a congested state. The MAPE of the number and position of inserted RVs is less than 10.7% and 0.37%, respectively. In addition, the proposed method performs well even if the PRs of CAVs and CVs are extremely low.

Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment

Queue length is a crucial measurement of traffic signal control at urban intersections. Conventional queue length estimation methods mostly still rely on fixed detectors. The development of connected vehicles (CV) provides massive amounts of vehicle trajectory data, and the queue length estimation based on CV data has received considerable attention in recent years. However, most existing CV-based methods require the prior knowledge of the penetration rate of CV and vehicle arrivals, but the estimation of these prior distributions has not been well studied. To address this issue, this paper proposes a cycle-based queue length estimation method under partially connected vehicle (CV) environment, with the prior vehicle arrivals being unknown. The empirical Bayes method is adopted to estimate the arrival rate by leveraging the observed queued CV information such as the number and positions. The hyperparameter estimation problem of the prior distribution is solved by the maximum likelihood estimation (MLE) method. To validate the proposed queue length estimation method, a simulation environment with partially connected vehicles is established using VISSIM and Python for data generating. The results in terms of normalized mean absolute errors (NMAE) and normalized root mean square errors (NRMSE) show that the proposed method could produce accurate and reliable estimated queue length under various CV penetration rates.

A Deep Learning Based Traffic State Estimation Method for Mixed Traffic Flow Environment

Traffic state estimation plays a fundamental role in traffic control and management. In the connected vehicles (CVs) environment, more traffic-related data perceived and interacted by CVs can be used to estimate traffic state. However, when there is a low penetration rate of CVs, the data collected from CVs would be inadequate. Meanwhile, the representativeness of the collected data is positively correlated with the penetration rate. This article presents a traffic state estimation method based on a deep learning algorithm under a low and dynamic CVs penetration rate environment. Specifically, we design a K-Nearest Neighbor (KNN) data filling model integrating acceleration data to solve the problem of insufficient data. This method can fuse the time feature of speed by acceleration modification and mine the distribution features of speed by KNN. In addition, to reduce the estimation error caused by penetration rate, we design a Long Short-Term Memory (LSTM) model, which uses penetration rate estimated by Macroscopic Fundamental Diagram (MFD) as one of the input factors. Finally, we use the concept of operational efficiency for reference, dividing traffic state into three categories according to the estimated speed: free flow, optimal flow, and congestion. SUMO is used to simulate traffic cases under different penetration rates to evaluate our scheme. The results suggest that our data filling model can significantly improve filling accuracy under a low penetration rate; there is also a better performance of our estimation model than that of other comparison models in both low and dynamic penetration rates.

The contribution of connected vehicles to network traffic control: A hierarchical approach

Connected vehicles (CVs) can considerably contribute to traffic control in the near future by providing real-time information. Such information can be exploited to identify the current traffic condition. However, challenges resulting from a high volume of data and lack of perfect knowledge in mixed traffic scenarios (consisting of CVs and conventional vehicles) have led to relatively sparse research in network-wide CV-based traffic control. To deal with such challenges, this paper proposes a hierarchical framework integrating three layers: (i) an intersection controller at individual intersections located in the center of the network, (ii) a network controller to regulate traffic coming into the network, and (iii) a phase controller to optimize the sequence of intersections’ signals. This approach permits the inclusion of information of (a limited number of) CVs to estimate and control queue lengths at individual intersections as well as the overall network load. Simulations carried out in VISSIM validate our approach in comparison with alternative control systems such as the application of (1) a traffic-responsive control, (2) a traffic accumulation regulator, or (3) a network signals coordinator. Moreover, comparisons between the proposed framework and a central system equipped with the information of all vehicles (giving rise to the optimal operation with, however, costly computation) under the consideration of different penetration rates of CVs and network layouts confirm that this framework results in competitive performance indicators, which, owing to the hierarchical structure, can be efficiently employed in real-time applications.

Optimal roadside units location for path flow reconstruction in a connected vehicle environment

The path flow reconstruction problem is used to determine the minimum set of links that must be equipped with traffic monitoring devices to identify vehicle paths in a road network. This study addresses the path flow reconstruction problem in a connected vehicles (CVs) environment. Unlike traditional sensors that can observe both CVs and non-connected vehicles (NCVs), CV-enabled infrastructures, known as roadside units (RSUs), can only identify CVs on roads through vehicle to infrastructure (V2I) communications. They can, however, provide critical traffic information, including traces of the historical trajectories of CVs and possibly the desired path to a destination, thereby inferring partial information on links that are not directly covered by RSUs. RSUs have an “area” rather than a “point” coverage capability. This allows them to simultaneously monitor more than one link. We mathematically developed four variant formulations for the path flow reconstruction problem to optimally locate either a network’s RSU or a mix of the network’s RSUs and automatic vehicle identification (AVI) sensors. The first two models assume 100% market penetration of CVs and the first model determines the links that should be directly covered by RSUs in a road network. While the desired path to a destination is assumed to be unknown. This model determines an upper bound for the number of RSUs required to fully reconstruct path flows by using each RSU to directly cover a link. To consider the coverage and range of the RSU (where RSU can cover more than one link) and to minimize the total cost, Model II optimizes the locations of traditional AVI sensors and RSUs. This allows the model to capitalize on the RSUs’ area and indirect coverage features to fully reconstruct path flow in a road network. Model III considers the gradual deployment of CVs and thus the prevailing mixed traffic environment consisting of both CVs and NCVs. Accordingly, this model relaxes the assumption of a 100% penetration rate of CVs and maximizes the path flow information gain subject to a budget constraint in a mixed traffic environment. Finally, Model IV explores the infrastructure to infrastructure (I2I) communication capability among RSUs, further maximizing the traffic flow information gain of CVs while guaranteeing full path flow reconstruction. The results suggest that fewer RSUs than AVI sensors are required to reach full path flow reconstruction in a road network. The level of unique path flow information obtained from RSUs is also considerably higher than what can be obtained from AVI sensors. It is demonstrated that, in mixed traffic conditions, the coverage range of RSUs and their cost compared to AVI sensors can significantly affect the deployment of either type of sensing devices for maximized path flow information gain.

Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment.

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.

Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment

The connected vehicle (CV) technology can benefit signal coordination with fine-grained spatial and temporal vehicle and infrastructure data via real-time communication. Although CV-based signal coordination systems have been investigated from offline and online strategic perspectives, existing works have yet to address certain coordination performance issues, including the dynamic platoon dispersion effect and low penetration impact. Targeting at resolving these issues, this work proposes a real-time predictive coordination method consisting of a probabilistic single-vehicle-based dynamic platoon dispersion model, an extended link performance function, and a real-time model predictive control (MPC)-based coordination framework. The proposed coordination method was comprehensively investigated by a software-in-loop simulation platform with different practical corridor scenarios in the ACTIVE CV testbed in Canada. Results show the proposed coordination control continuously outperformed existing signal control with lower delays for major streets with different demand profiles and different CV penetration rates, even in low penetration conditions. In conclusion, the proposed CV MPC-based coordination can offer significant potential to further improve the system performance of signal coordination in a low penetration environment; therefore, it has the potential to enhance other CV-based signal control applications in the initial deployment stage of CV technology.

Analysis of highway performance under mixed connected and regular vehicle environment

Purpose This study aims to study the connected vehicle (CV) impact on highway operational performance under a mixed CV and regular vehicle (RV) environment. Design/methodology/approach The authors implemented a mixed traffic flow model, along with a CV speed control model, in the simulation environment. According to the different traffic characteristics between CVs and RVs, this research first analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases. A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic. To prove this concept, this study simulated the mixed traffic pattern under various conditions. Findings The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity. Furthermore, a critical CV penetration rate should exist at a specified traffic demand level, which can significantly reduce the speed difference between RVs and CVs. The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance. Originality/value This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations. CV penetration rate (the proportion of CVs in mixed traffic) is the key factor affecting the impacts of CV on freeway operational performance. The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.

Signalised intersection control in a connected vehicle environment: User throughput maximisation strategy

Connected vehicle (CV) technology is expected to bring additional opportunities to share, collect, and exploit various information on vehicles and their occupants. Assuming that CVs are able to transmit on-board users and vehicle data, a user-based signal timing optimisation (UBSTO) strategy is proposed, designed to optimise user throughput for signalised intersections. In the CV environment, the inputs of the proposed algorithm consist of position and speed of CVs, as well as the number of passengers travelling in each vehicle, while the output is the optimum green time duration for each signal phase. In addition, authors' proposed strategy is able to adapt the cycle length to the traffic volume condition. In case of missing users data, the same strategy can also operate in vehicle-based mode, where the objective is vehicle-throughput maximization. The performance of the proposed strategy is compared with a fully actuated controller (FAC) in microscopic simulation, for several scenarios, including different CV penetration rates. Authors' findings show that UBSTO can effectively increase user throughput and decrease average user delay in comparison with FAC, while also prioritising vehicles with higher number of users on-board. These findings have implications for further development of prioritization strategies for public transport and ride-sharing vehicles.

A Gradient-Based Approach for Coordinating Smart Vehicles and Traffic Lights at Intersections

In this letter we consider the coordination of connected and autonomous vehicles (CAVs) with intelligent traffic control signalization to improve the performance of traffic at intersections. We propose a coordination method based on a gradient-based multi-agent system. Specifically, we consider both the traffic light and the approaching vehicles as smart connected agents and we define a suitable edge tension function for each connection between agents. The agents then run a decentralized gradient descent control policy that drives them towards a desirable sequence of intersection crossings. In the literature, the problem of coordinating CAVs at intersections has been widely explored, with different algorithms being proposed. However, the problem of considering both smart traffic lights and smart cars simultaneously is relatively unexplored, especially from a multi-agent system, gradient-based, perspective. The advantages of the edge tension approach lie in its simplicity in terms of computation and in its ability to scale to larger networks. Furthermore, the control framework is applicable under different connected vehicle penetration rates. This flexibility is a desirable advantage given the gradual nature of the implementation of autonomy in real traffic networks. The proposed strategy is evaluated in simulation and significant fuel savings and delay improvements are shown in comparison with fixed-time traffic light control.

A multi-vehicle communication system to assess the safety and mobility of connected and automated vehicles

Connected and automated vehicles (CAVs) are expected to improve both traffic safety and efficiency by reducing the human driver errors. Recently, many researchers have focused on the simulation-based studies in order to evaluate the benefits of CAVs due to the lack of real-world data. However, none of the previous studies have attempted to differentiate the benefits of CAVs over automated vehicles (AVs) by incorporating multiple preceding vehicle information (i.e., acceleration, position, etc.). This paper aims to fill the existing gap by utilizing separate car-following models for both CAVs and AVs in order to approximate their driving behavior in the Aimsun Next simulation platform. Additionally, a different car-following model is used for the connected vehicles (CVs) without automation by addressing the human driver compliance factor. This study also utilizes mixed penetration of CAV and CV with no automation. A well calibrated and validated simulation testbed is developed for SR417 in Orlando, Florida which is the base scenario in this study. To this end, the impact of CAVs, AVs, and CVs are evaluated based on both traffic efficiency (i.e., travel time) and safety (i.e., traffic conflicts) under various market penetration rates (MPRs). The traffic efficiency results show that travel time is significantly reduced for any MPRs of CAVs, AVs, and the mixture of CVs and CAVs compared to the base scenario. A generalized estimating equation (GEE) model is developed to quantify the travel time improvement for CAVs, AVs, and the mixture of CVs and CAVs. The results suggest at least 20% penetration is required for CAVs to get travel time improvement while 40% penetration is needed for AVs. Also, CAV significantly outperforms AV for the same MPRs. For the safety assessment, traffic conflicts are estimated by using different surrogate measures i.e., time-to-collision (TTC) and time exposed time-to-collision (TET). The results imply that crash risk is significantly reduced for CAVs, AVs, and the mixture of CVs and CAVs scenarios compared to the base condition. A Bayesian zero-inflated negative binomial model is developed in order to model the number of traffic conflicts as a function of MPRs of CAVs, AVs, mixture of CVs and CAVs, and traffic parameters. The results confirm that CAVs are more efficient in reducing crash risk compared to AVs for the same MPR. The mix penetration rate of CV (60%) and CAV (20%) shows almost similar reduction of crash risk with the 80% MPR of AV. Also, crash risk analysis based on different vehicle types shows that CAVs driving behavior is safer compared to the AVs. Finally, the results of this study indicate a significant improvement of both traffic efficiency and safety by implementing CAV with multivehicle communication system on the freeway segments.

Analysis of queue estimation process at signalized intersections under low connected vehicle penetration rates

This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of connected vehicles under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the accuracy of the queue estimates is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed potential to help as an assessment tool to evaluate the obtained queue estimates.

An Integrated Microsimulation Approach for Safety Performance Assessment of the Wyoming Connected Vehicle Pilot Deployment Program

The 402-mile of Interstate 80 in Wyoming was selected by the U.S. Department of Transportation to develop, test, and deploy a suite of Connected Vehicle (CV) applications (WYDOT CV Pilot). It is expected that after full deployment of CV technology, the pilot will improve safety and mobility under adverse weather conditions by creating new ways to communicate road and travel information to both drivers and fleet managers. In this regard, this research employed an integrated microsimulation modeling approach to assess the safety performance of the WYDOT CV Pilot. A 23-mile representative I-80 corridor was selected for developing the microsimulation models. Traffic flow and driving behavior data under winter snowy weather condition were collected to calibrate the baseline microsimulation model. A driving simulator experiment was conducted to quantitatively investigate the impacts of CV technology on driving behavior; accordingly, the driving behavior data under CV environment were employed to properly update the calibrated CV microsimulation models. The safety effectiveness of the WYDOT CV Pilot were assessed for various demand levels and CV penetration rates. It was concluded that WYDOT CV applications increased drivers’ situation awareness under adverse weather conditions, and thus reduced the crash risk. The reductions in conflicts displayed a decreasing trend with the increase of CV penetration rates, but the reduction was not significant when CV penetration was lower than 10 percent. The maximum reduction in conflicts was 85 percent when all trucks were equipped with CV technology.

Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

<div class="section abstract"><div class="htmlview paragraph">Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.</div></div>

Integrating safety and mobility for pathfinding using big data generated by connected vehicles

With the emergence of the internet of things, pathfinding problems have recently received a significant amount of attention. Various commercial applications provide automated routing by considering travel time, travel distance, fuel consumption, complexity of the road, etc. However, many of these prospective applications do not consider route safety. Emergence of high-resolution big data generated by connected vehicles (CV) helps us to integrate safety into routing problem. The goal of this study is to address safety aspects in pathfinding problems by developing a methodological framework that simultaneously considers safety and mobility. To reach this goal, the concept of volatility is utilized as a surrogate safety performance measure to quantify route safety and driver behavior. The proposed framework uses CV big data and real-time traffic data to calculate safety indices and travel times. Measured safety indices include 5-year crash history, route speed and acceleration volatility, and driver volatility. Travel time and safety shape a cost function called “route impedance.” The algorithm has the flexibility for the user to predefine the weight for safety consideration. It also uses driver volatility to automatically increase safety weight for volatile drivers. To illustrate the algorithm, a numerical example is provided using an origin-destination pair in Ann Arbor, MI, and more than 42 million CV observations from around 2,500 CVs from the Safety Pilot Model Deployment (SPMD) were analyzed. The sensitivity analysis is performed to discuss the impact of penetration rate of CVs and time of the trip on the results. Finally, this paper shows suggested routes for multiple scenarios to demonstrate the outcome of the study. The results revealed that the algorithm might suggest different routes when considering safety indices and not just travel time.

An equitable traffic signal control scheme at isolated signalized intersections using Connected Vehicle technology

This paper extends a real-time, Connected Vehicle-based traffic signal control algorithm at isolated intersections to balance between two competing intersection objectives: efficiency and equity. In this approach, a central controller is used to collect real-time locations of connected vehicles at regular intervals, which can then be used to also identify the existence of some of the non-connected vehicles. The control algorithm then aims to optimize the discharge sequence of naturally occurring platoons of vehicles based on their proximity. Specifically, the strategy selects the platoon discharge sequence—and associated signal phase and timing plan—that minimizes average vehicle delay (measure of efficiency) while limiting the maximum delay any individual vehicle may experience (measure of equity). The latter objective is only possible with detailed vehicle-level information available from connected vehicles. The results show that without the threshold on maximum individual vehicle delay, average delay is often minimized at the expense of very large delays imposed onto some vehicles. By implementing a threshold, both the maximum vehicle delay and the distribution of individual vehicle delays—as measured by the standard deviation and a common measure of population inequity, the Gini coefficient—can be improved, often with only negligible impacts to intersection efficiency. However, the tradeoff between equity and efficiency becomes more significant as the maximum vehicle delay threshold decreases. Sensitivity tests show that this control algorithm works well for different total traffic demands and different demand patterns. The proposed algorithm is also effective under imperfect connected vehicle penetration rates when the connected vehicles make up more than 40% of the traffic stream. The results suggest that the proposed strategy can help significantly reduce long delays and inequitable treatment of vehicles at an intersection when vehicle-level information is available to a signal controller.

Performance evaluation framework of Wyoming connected vehicle pilot deployment program: summary of Phase 2 pre-deployment efforts and lessons learned

Purpose This paper aims to present a summary of the performance measurement and evaluation plan of the Wyoming connected vehicle (CV) Pilot Deployment Program (WYDOT Pilot). Design/methodology/approach This paper identified 21 specific performance measures as well as approaches to measure the benefits of the WYDOT Pilot. An overview of the expected challenges that might introduce confounding factors to the evaluation effort was outlined in the performance management plan to guide the collection of system performance data. Findings This paper presented the data collection approaches and analytical methods that have been established for the real-life deployment of the WYDOT CV applications. Five methodologies for assessing 21 specific performance measures contained within eight performance categories for the operational and safety-related aspects. Analyses were conducted on data collected during the baseline period, and pre-deployment conditions were established for 1 performance measures. Additionally, microsimulation modeling was recommended to aid in evaluating the mobility and safety benefits of the WYDOT CV system, particularly when evaluating system performance under various CV penetration rates and/or CV strategies. Practical implications The proposed performance evaluation framework can guide other researchers and practitioners identifying the best performance measures and evaluation methodologies when conducting similar research activities. Originality/value To the best of the authors’ knowledge, this is the first research that develops performance measures and evaluation plan for low-volume rural freeway CV system under adverse weather conditions. This paper raised some early insights into how CV technology might achieve the goal of improving safety and mobility and has the potential to guide similar research activities conducted by other agencies.