Connected Vehicle Applications Research Articles

Using Connected Vehicle Data to Reassess Dilemma Zone Performance of Heavy Vehicles

The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles in the US. Previous studies in the US have found that heavy vehicles are twice as likely to violate a red light compared with passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of connected vehicle (CV) technology to trigger force gap out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from basic safety messages (BSMs) to map-match virtual waypoints located up to 1,050 ft in advance of the stop bar. For a 55 mph approach, field tests determined that using a 6 ft waypoint radius at 50 ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59 s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications.

The Value of Connectivity in the Automotive Sector – a First Look

This study looks at the actual value that connectivity in vehicles can bring to society and the different sectors affected, Connectivity is one of the four critical megatrends facing the automotive industry. These trends are autonomous driving, shared mobility and electrification, yet connectivity is both separate and integrated to these other megatrends. The importance of connectivity is therefore fundamental to the future evolution of the industry. The vehicle is emerging as the next digital platform which may be analogous to the evolution of the smartphone and the opportunities that connectivity provides has incentivized automakers to increasing install embedded connectivity to capture the value from this new ecosystem. Such vehicles are often termed connected vehicles. One estimate from McKinsey Advance Industries (2016) predicts that on-demand mobility and data-driven services from such vehicles could account for approximately $2 trillion (or approximately 30%) of the automotive revenue pool by 2030, with data connectivity services accounting for between $450-$750 billion per year. This growth is built upon advanced mobile telecommunication standards, in particular, cellular standards, which provide the enabling infrastructure for new connectivity-based products and services to emerge in the automotive/mobility sector. Below are highlighted results from the study: • Connectivity is a megatrend that is transforming the automotive industry towards a new mobility sector. In the near future, most new vehicles will be increasingly equipped with embedded connectivity capabilities due to safety regulations, development of V2X functionality, and the potential service revenue. • The growing value of connectivity in the automotive sector is predicated on the development of high performance, open telecommunication standards, such as advanced cellular technology (e.g. 4/5G). • The vehicle is becoming the next big digital platform, generating competition between the existing mobile/smartphone ecosystem and the emerging vehicle ecosystems for control of the value of automotive/mobility services. • Connected vehicle applications are still small but are growing. Current estimates of the revenue from the vehicle ecosystem to automakers were calculated at $670 (US) and $593 (WW) per connected vehicle, based on a subset of existing applications in 2018. • The total revenue from connectivity-enabled products and services in the automotive sector was calculated to grow from $223 billion to $483 billion from 2018-2023 for a subset of existing revenue pools, with forecasts predicting as much as $2 trillion by 2030. • Market revenues do not provide the whole picture, especially when multi-sided business models are deployed. Therefore, the total economic value, including consumer surplus and relevant externalities, is important to bear in mind when determining the value of connectivity in the automotive sector or regulation of the sector.

Towards Efficient NDN Framework for Connected Vehicle Applications

Named Data Networking (NDN) recently arises as a promising technology to support connected vehicle (CV) applications due to the match between their characteristics. However, the fast mobility and the vast number of vehicles raise great challenges in designing a scalable and efficient NDN network for CV applications. Therefore, in this paper, we develop an NDN based CV application framework that handles the challenge through innovations in two aspects. First, we propose a hierarchical hyperbolic NDN backbone architecture (H2NDN). H2NDN exploits the location dependency of CV applications to develop a hierarchical router topology and a hierarchical data/interest namespace. As a result, efficient and scalable data retrieval can be achieved by only configuring static forwarding information base (FIB) on NDN routers. To avoid overloading high-level routers, H2NDN integrates hyperbolic routing into the hierarchical architecture through carefully designed hyperbolic planes. Second, on top of the H2NDN architecture, we further model the optimal data caching problem. Based on the modeling, we propose a distributed adaptive caching strategy that can greatly improve the efficiency of the H2NDN backbone in supporting CV applications. Extensive ndnSIM based experiments with real traffic data in a city prove the efficiency and scalability of the proposed NDN application framework.

A Double Obfuscation Approach for Protecting the Privacy of IoT Location Based Applications

Connected vehicles and smart cars have become highly reliant on location-based services (i.e. LBS) to provide accurate, personalized and intelligent services. However, location-based services have endangered its users to considerable risks concerning the privacy and security of users' personal data. Although existing research provides a myriad of methods to improve and protect user privacy in LBS applications, most of these methods are concerned with handling static queries and non-mobile objects only. Moreover, various issues and challenges still persist with regards to the need to trust third parties, overloading of the user, and low accuracy of the returned results. This paper contributes a Double Obfuscation Approach (referred to as DOA) that applies two phases of obfuscation consecutively whilst integrating two differing privacy protection approaches, namely Obfuscation and Trusted Third Party, and two techniques, namely fog caching technology and mix zone. In essence, the DOA obfuscates and hides the identity and location of its users using the fog nodes, which operate as a trusted third party (TTP), and without the need to reveal the identity of the users or trust the cooperating nodes. Moreover, this paper presents a DOA algorithm that improves the overall user privacy and system performance using the fog nodes, which split the responses of each query into five parts, thus reducing the processing time of the results by the user and enhancing the overall accuracy where the user directly selects the most suitable parts based on his current location. Overall, the hybrid DOA approach empowers the users of connected vehicle applications to protect their privacy through an algorithm that caters for the dynamic nature of user queries and mobility of objects. The results of our comparative simulations against well-known hybrid privacy protection methods demonstrate the superiority of the proposed Double Obfuscation Approach especially with respect to user privacy whilst maintaining a nominal overhead on the user, reduced response time and high accuracy of the obtained results.

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computation-intensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges.

Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The safety, mobility, environmental, energy, and economic benefits of transportation systems, which are the focus of recent connected vehicle (CV) programs, are potentially dramatic. However, realization of these benefits largely hinges on the timely integration of digital technology into upcoming as well as existing transportation infrastructure. CVs must be enabled to broadcast and receive data to and from other CVs [vehicle-to-vehicle (V2V) communication], to and from infrastructure [vehicle-to-infrastructure (V2I) communication], and to and from other road users, such as bicyclists or pedestrians (vehicle-to-other road users communication). Further, the infrastructure and transportation agencies that manage V2I-focused applications must be able to collect, process, distribute, and archive these data quickly, reliably, and securely. This paper focuses on V2I applications and investigates current digital roadway infrastructure initiatives. It highlights the importance of including digital infrastructure investment alongside investment in more traditional transportation infrastructure to keep up with the auto industry push toward increasing intervehicular communication. By studying current CV testbeds and smart-city initiatives, this paper identifies digital infrastructure being used by public agencies. It also examines public agencies limited budgeting for digital infrastructure and finds that current expenditure is inadequate for realizing the potential benefits of V2I applications. Finally, the paper presents a set of recommendations, based on a review of current practices and future needs, designed to guide agencies responsible for transportation infrastructure. It stresses the importance of collaboration for establishing national and international platforms for the planning, deployment, and management of digital infrastructure to support connected transportation systems.

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.

Connected Vehicle-Based Lane Selection Assistance Application

Connected vehicle (CV) technology has great potential to improve the performance of today’s advanced driver assistance systems in terms of safety, energy efficiency, and driving comfort. The aim of this paper is to develop a specific CV application that assists with lane selection, i.e., finding the best travel lane in terms of travel time based on predicted lane-level traffic states. In this paper, a spatial-temporal model (ST-model) was developed, which utilizes spatial and temporal information of road cells to predict future traffic states. This information was used by the proposed lane selection assistance application to select an optimal lane sequence for the application-equipped vehicle. A comprehensive simulation-based evaluation was then conducted under various scenarios, e.g., with different traffic volumes, penetration rates of communication-capable vehicles, and information update cycles. The evaluation results reveal several interesting findings, including: 1) the proposed ST-model outperforms the basic estimation model in terms of traffic state prediction accuracy; 2) travel times of application-equipped vehicles can be reduced by up to 8% with the use of the proposed lane selection assistance application when compared with the baseline, under various traffic scenarios; 3) the application can be effective in the early deployment stage of CV technology, where the penetration rate of communication-capable vehicles is still low; and 4) the potential conflict risk of application-equipped vehicles is reduced, although the application is mainly designed for mobility benefits, due to the more strategic and informed lane changes suggested by the proposed application.

Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving

In response to developing and/or diminishing foggy conditions, the variable speed limit application in a connected vehicle environment (CV-VSL) can estimate and deliver recommended travel speeds to individual drivers, which can help to reduce crashes when visibility conditions change. This study aims to quantify the effectiveness of the CV-VSL application by exploring drivers’ reactions to warnings (e.g., recommended travel speeds). In order to analyze the effectiveness of the CV-VSL application, a connected vehicle testing platform was established based on a driving simulator, and characteristics of the drivers’ speed adjustments after receiving warnings were analyzed with respect to different levels of visibility (i.e., no fog, slight fog, and heavy fog). This study also examined the effect of warnings on drivers in different impact zones (i.e., clear zone, transition zone, and fog zone). Three indicators were identified: 1) speed at the end of the clear zone, 2) maximum deceleration rate in the transition zone, and 3) average speed reduction in the fog zone. Throughout the experiment, the relationship between speed adjustments and the level of visibility was explored. The results indicated that the CV-VSL application is effective in making drivers reduce travel speeds in all three types of zones. Furthermore, it appeared that the CV-VSL application could help manage travel speeds prior to vehicles entering the transition zone, and influence drivers’ braking decisions upon encountering reduced visibility. It was also found that the CV-VSL application was more effective in heavy fog conditions than in light fog conditions. The connected vehicle testing platform based on the driving simulator provided a new method for evaluating the effectiveness of in-vehicle messaging generated by connected vehicle applications.

Impact of Variable Speed Limit in a Connected Vehicle Environment on Truck Driver Behavior under Adverse Weather Conditions: Driving Simulator Study

In 2015, the U.S. Department of Transportation (U.S. DOT) selected Wyoming as one of three sites to develop, test, and deploy a suite of connected vehicle (CV) applications on a 402-mi Interstate 80 corridor. One of the Wyoming’s key CV applications is the variable speed limit (VSL) warning, which aimed to provide commercial truck drivers with real-time regulatory and advisory speed limits to help in better managing speeds under adverse weather conditions, and reducing potential speed variances that may cause traffic collisions. This paper developed a driving simulator testbed to assess the impact of the Wyoming’s CV-based VSL (CV-VSL) application on truck drivers’ behavior under adverse weather conditions. A total of 18 professional truck drivers were recruited to participate in the driving simulator experiment. Participants’ instantaneous speeds at various locations were collected to reveal the impact of the CV-VSL warnings on their driving behavior. Simulation results showed that when the advisory speed limits were lower than 55 mph, participants generally followed the VSLs displayed on the CV human–machine interface (HMI). In addition, traffic flows utilizing CV-VSL technology tend to exhibit lower average speeds and speed variances compared with baseline scenarios. These effects of CV-VSL warnings can bring potential safety benefits, as reduction in average speeds and speed variances are effective surrogate measures of safety, that is, lower risk of crashes, under adverse weather conditions.

A Real-time Traffic Signal Control Strategy Under Partially Connected Vehicle Environment

The performance of a traffic system tends to improve as the percentage of connected vehicles (CV) in total flow increases. However, due to low CV penetration in the current vehicle market, improving the traffic signal operation remains a challenging task. In an effort to improve the performance of CV applications at low penetration rates, the authors develop a new method to estimate the speeds and positions of non-connected vehicles (NCV) along a signalized intersection. The algorithm uses CV information and initial speeds and positions of the NCVs from loop detectors and estimates the forward movements of the NCVs using the Gipps’ car-following model. Calibration parameters of the Gipps’ model were determined using a solver optimization tool. The estimation algorithm was applied to a previously developed connected vehicle signal control (CVSC) strategy on two different isolated intersections. Simulations in VISSIM showed the estimation accuracy higher for the intersection with less lanes. Estimation error increased with the decrease in CV penetration and decreased with the decrease in traffic demand. The CVSC strategy with 40% and higher CV penetration (for Intersection 1) and with 20% and higher CV penetration (for Intersection 2) showed better performance in reducing travel time delay and number of stops than the EPICS adaptive control.

A Wireless Localization Algorithm With BPNN-MEA-QPSO for Connected Vehicle

As one of key technologies of connected vehicles (CVs) applications, wireless localization can provide accurate and reliable vehicle location for high occupancy tolling and safety critical vehicle applications, such as collision avoidance. Several artificial intelligence methods, such as back propagation neural network (BPNN) and particle swarm optimization (PSO) method, have been employed to optimize the pass-loss model and to improve the accuracy of wireless localization algorithm. However, in view of the stochasticity of initial weights and thresholds in BPNN, it is difficult to reach the global convergence. In this study, a novel double-layer architecture for wireless localization algorithm is proposed based on the optimization of initial weights and thresholds in BPNN and the refinement of search direction and step in PSO algorithm. Based on the architecture, the wireless localization algorithm integrating BPNN with mind evolutionary algorithm (MEA) and quantum-behaved PSO (QPSO) method is proposed and validated using the experiment data in field environment. The validation results show that the proposed localization algorithm has better localization accuracy by comparison with the other localization algorithms, which the average error of the proposed localization algorithm in field environment is about 19 meters. In addition, the localization accuracy shows an improving tendency with the increasing of the number of base stations connected to moving vehicle. The location error is less than 10 meters when the number of base stations connected to the moving vehicle is greater than 7. For the wireless localization in field environment, the accuracy is acceptable for CVs applications.

A Preliminary Investigation into the Impact of Connected Vehicle Human-Machine Interface on Driving Behavior

Abstract This paper developed a driving simulator testbed, which includes three simulation scenarios that can represent different real-world driving conditions, to investigate the impact of Connected Vehicle (CV) Human-Machine Interface (HMI) on driving behavior. A total number of 23 professional drivers were invited to participate in the driving simulator test; a comprehensive questionnaire was employed to collect the participant’s feedback of CV HMI. It was found that 96 percent of the participants stated that CV HMI provided them with improved road condition information and increased their safety while driving; 65 percent of the participants stated the CV applications and the display unit did not introduce significant distraction from the main driving task.

Cybersecurity Attacks in Vehicle-to-Infrastructure Applications and Their Prevention

A connected vehicle (CV) environment is comprised of diverse computing infrastructure, data communication and dissemination, and data collection systems that are vulnerable to the same cyberattacks as all traditional computing environments. Cyberattacks can jeopardize the expected safety, mobility, energy, and environmental benefits from CV applications. As cyberattacks can lead to severe consequences such as traffic incidents, it has become one of the primary concerns in CV applications. In this paper, we evaluate the impact of cyberattacks on the vehicle-to-infrastructure (V2I) network from a V2I application point of view. Then, we develop a novel V2I cybersecurity architecture, named CVGuard, which can detect and prevent cyberattacks on the V2I applications. In designing CVGuard, key challenges, such as scalability, resiliency and future usability were considered. A case study using a distributed denial of service (DDoS) attack on a V2I application, “Stop Sign Gap Assist (SSGA)” application, shows that CVGuard was effective in mitigating the adverse safety effects created by a DDoS attack. In our case study, because of the DDoS attack, conflicts between the minor and major road vehicles occurred at an unsignalized intersection, which could have caused crashes. A reduction of conflicts between vehicles occurred because CVGuard was in operation. The reduction of conflicts was compared based on the number of conflicts before and after the implementation and operation of the CVGuard security platform. Analysis revealed that the strategies adopted by CVGuard were successful in reducing the conflicts by 60% where a DDoS attack compromised the SSGA application at an unsignalized intersection.

Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed

The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehicles and infrastructure (e.g., traffic signals, centers). Real-world CV deployments, including pilot deployments, help solve technical issues and observe potential benefits, both of which support the broader adoption of the CV system. This study focused on the Clemson University Connected Vehicle Testbed (CU-CVT) with the goal of sharing the lessons learned from the CU-CVT deployment. The motivation of this study was to enhance early CV deployments with the objective of depicting the lessons learned from the CU-CVT testbed, which includes unique features to support multiple CV applications running simultaneously. The lessons learned in the CU-CVT testbed are described at three different levels: (i) the development of system architecture and prototyping in a controlled environment, (ii) the deployment of the CU-CVT testbed, and (iii) the validation of the CV application experiments in the CU-CVT. Field experiments with CV applications validated the functionalities needed for running multiple diverse CV applications simultaneously using heterogeneous wireless networking, and meeting real-time and non-real-time application requirements. The unique deployment experiences, related to heterogeneous wireless networks, real-time data aggregation, data dissemination and processing using a broker system, and data archiving with big data management tools, gained from the CU-CVT testbed, could be used to advance CV research and guide public and private agencies for the deployment of CVs in the real world.

A Joint Multi-Criteria Utility-Based Network Selection Approach for Vehicle-to-Infrastructure Networking

The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users’ preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.

Blind Corner Propagation Model for IEEE 802.11p Communication in Network Simulators

Vehicular Ad Hoc Network (VANET) has been developed to enhance quality of road transportation. The development of safety applications could reduce number of road accidents. IEEE 802.11p is a promising standard for intervehicular communication, which would enable the connected-vehicle applications. However, in the well-known network simulators such as NS3 and Omnet, there is no propagation model that can simulate the IEEE 802.11p communication at blind corner realistically. Thus, in this paper, we conducted the real-world experiments of IEEE 802.11p in order to construct the model to describe the characteristics of the IEEE 802.11p communication at the blind corners. According to the experimental results, we observe that the minimum distance between the vehicle and the corner can effectively be represented as the key parameter in the model. Moreover, we have a variable parameter for adjusting the impact of the obstruction which could be different at each type of blind corners. The simulation results using our proposed model are compared with those using the existing obstacle model. The results showed that our proposed model is much more closely aligned with the real experimental results.

3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data

High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.

Vehicle Reidentification in a Connected Vehicle Environment using Machine Learning Algorithms

Deployment of connected vehicles will become possible for most American cities in the next 10 to 20 years. Connected vehicle (CV) applications (e.g., mobility, safety, environment) are constantly receiving vehicle data. The current ID protection mechanism assumes a vehicle’s ID changes every 5 minutes, so the topic of rematching vehicles is of interest in privacy protection and performance measure research. This paper explores the possibility of rematching connected vehicles’ IDs using popular machine learning techniques, including logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), linear and nonlinear support vector machine (SVM) and nearest neighbor algorithms. An experiment is conducted using a microscopic traffic simulation model through a software-in-the-loop technique. The best average mismatching rate is 14%. To assess potential factors’ effects on matching accuracy, a Poisson mixed regression model is analyzed under the Bayesian inference framework. Findings are: different matching algorithms vary in matching performance and the linear SVM, the QDA and the LDA have the best accuracy results; traffic volume and market penetration rate have little impact on matching results; location and number of vehicles to be matched are considered significant. The results make the performance measurement of future CV applications feasible and also suggest that more secure mechanisms are needed to protect the public.

Safety analysis of the eco-approach and departure application at a signalized corridor

Various intelligent transportation systems and strategies for reducing vehicle fuel consumption and emissions have been developed. Energy and emissions could be reduced with the compromise of travel time in some environment-focused Connected Vehicle (CV) applications, which highlighted performance measures in terms of mobility and sustainability. Nevertheless, few studies have focused on safety assessment of such CV applications. In this study, a CV-based eco-driving application, namely, Eco-Approach and Departure (EAD), is selected as an example. A microscopic safety analysis tool, Surrogate Safety Assessment Model (SSAM), is utilized to assess the safety influence of EAD application in multiple scenarios. Further analysis is performed from two perspectives: (1) application users, i.e., EAD-equipped vehicles versus unequipped vehicles; and (2) traffic operators, i.e., overall traffic performance with and without the introduction of EAD. For each perspective, conflict statistics (e.g., frequency, time-to-collision distribution), overall and by type, are analyzed. Results indicate EAD is beneficial in improving the safety performance of equipped vehicles. The influence of EAD on overall traffic is scenario dependent, and a high penetration rate shows positive effects on network-wide safety benefits for most scenarios.