Connected Vehicle Environment Research Articles

Smart Signal Control System for Accident Prevention and Arterial Speed Harmonization under Connected Vehicle Environment

The intent of this paper is to develop a system that can integrate connected vehicle (CV) data and traffic sensor information to concurrently address the need to improve urban arterial safety and mobility. Under the mixed traffic pattern of CVs and human-driven vehicles (HVs), the system aims to achieve three primary objectives: proactively preventing rear-end collision, reactively protecting side-street traffic from red-light-running vehicles, and effectively facilitating speed harmonization along local arterials. The embedded safety function will integrate CV and roadside sensor data to compute the distribution of dilemma zones for vehicles of different approaching speeds in real-time. Such data fusion will enable the proposed system to offer the advice of either “stop” or “go” to both CVs and HVs so as to prevent rear-end collisions and side-angled crashes. Given the locations and speeds of CVs, and the number of vehicles monitored by sensors, the proposed system can further compute the time-varying intersection queue length. Then the embedded mobility function will optimize the arterial signal plan in real-time and produce the speed advisory for approaching vehicles to facilitate their progression through intersections. Results from extensive simulation experiments confirm the effectiveness of the proposed system in both reducing potential intersection crash rates and improving arterial progression efficiency. The proposed control framework also proves the effectiveness of using dilemma zone protection sensors for traffic mobility improvement.

Platoon Priority Visualization Modeling and Optimization for Signal Coordination in the Connected Vehicle Environment

Although connected vehicle (CV)-based signal coordination has some proposed prototypes and has been investigated by several different strategies, the existing works have issues that require attention, including additional CV-based system uncertainties and platoon priority request considerations. Thus, a generalized framework consisting of a platoon-based bicyclic coordination diagram (Bi-PCD), a new probabilistic surrogate quantification, and a platoon priority-based offset optimization in the CV environment is proposed to improve the coordination performance. The proposed Bi-PCD extends the scope of the current Purdue coordination diagram (PCD) and its variants by covering extra practical and emerged variables. A prototype and field tests in a CV test pilot for general scenarios were implemented in a typical arterial road to verify performances of the proposed Bi-PCD and the offset optimization method. Field results demonstrated that using Bi-PCD could obtain explicit platoon features in both limited and full CV penetration conditions. The comprehensive analysis showed that the proposed Bi-PCD and the platoon priority-based offset optimization could further improve performances of the signal coordination.

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.

Preceding Vehicle Identification for Cooperative Adaptive Cruise Control Platoon Forming

Cooperative adaptive cruise control (CACC) has shown great potential in enhancing traffic efficiency and sustainability. While past research efforts focused on the development of CACC systems and their demonstrations, very few of them considered in detail how to form a CACC platoon in real traffic, where the proper identification of preceding vehicle is required. To ensure safe and reliable CACC operations, the following vehicle needs to establish the correct connection with its preceding vehicle. Although this can be done by matching information shared by surrounding vehicles with the ego-vehicle’s radar measurements, the existence of sensor/global positioning system (GPS) errors makes it a challenging task. Considering possible sensor/GPS errors in real traffic, this paper proposes a procedure of identifying preceding vehicle under fully connected vehicle environment and evaluates three preceding vehicle identification systems (PVIS), namely, location-based PVIS, distance-based PVIS, and integrated PVIS combining both location and distance information. The mathematical models of PVISs are developed. The performance evaluation of the PVISs is conducted based on real vehicle trajectory data from the Next Generation Simulation (NGSIM) program, which reflects how vehicles’ relative positions change in a high-density segment of highway. The feasibility, performance, and potential of the three PVISs are compared. The results show that location-based PVIS requires a relative positioning accuracy below 1.1 m to ensure an acceptable identification time with zero failure rate. The integrated PVIS has the best performance, providing 99% confidence in identifying preceding vehicle within 1.3 s under typical sensor error settings.

A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control

Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.

Effects of on-Board Unit on Driving Behavior in Connected Vehicle Traffic Flow

Connected vehicle technology has potentials to increase traffic safety, reduce traffic pollution, and ease traffic congestion. In the connected vehicle environment, the information interaction among people, cars, roads, and the environment is significantly enhanced, and driver behavior will change accordingly due to increased external stimulation. This paper designed a Vehicle-to-Vehicle (V2V) on-board unit (OBU) based on driving demand. In addition, a simulation platform for the interconnection and communication between the OBU and simulator was built. Thirty-one test drivers were investigated to drive an instrumented vehicle in four scenarios, with and without the OBU under two different traffic states. Collected trajectory data of the subject vehicle and the vehicle in front, as well as sociodemographic characteristics of the test drivers were used to evaluate the potential impact of such OBUs on driving behavior and traffic safety. Car-following behavior is an essential component of microsimulation models. This paper also investigated the impacts of the V2V OBU on car-following behaviors. Considering the car-following related indicators, the k-Means algorithm was used to categorize different car-following modes. The results show that the OBU has a positive impact on drivers in terms of speed, front distance, and the time to stable regime. Furthermore, drivers’ opinions show that the system is acceptable and useful in general.

The effects of warning characteristics on driver behavior in connected vehicles systems with missed warnings

With emerging new technologies, the vehicles in the future with connected vehicle systems (CVS) will be equipped with the ability to communicate with each other and aim to provide drivers with information in a timely and reliable way to improve driver safety. This study was designed to investigate the interaction effects of warning lead time (2.5 s vs. 4.5 s), warning reliability (73% vs. 89%), and speech warning style (command vs. notification) on driver performance and subjective evaluation of warnings in CVS. A driving simulator study with thirty-two participants was designed to simulate a connected vehicle environment with missed warnings due to failures in the communication network of the CVS. With regard to the response types, the results showed that notification warnings led to a lower probability of braking response and a higher probability of braking and steering response compared with command warnings. The results showed command warnings led to a smaller collision rate compared to notification warnings with the warning lead time of 2.5 s, whereas no significant difference of collision rates was found between two warning styles when the warning lead time is 4.5 s. These results suggest notification warnings should be selected when the warning lead time is longer and the warning systems are highly reliable, which resulted in higher safety benefits and higher subjective rating. Command warnings could be selected when the warning lead time is shorter since they led to more safety benefits, but such selection has to be made with caution since command warnings may limit drivers’ response type and were perceived as less helpful than notification warnings.

Effect of dynamic route guidance on urban traffic network under Connected Vehicle environment


 
 
 Although Connected Vehicle technology is developing rapidly, connected vehicles (CV) are going to mix with the traditional vehicles (i.e., non-connected vehicles) for a long time. The effects of deploying CV on urban traffic systems are actually not clear. The main objective of this study is to evaluate the potential effects of route guidance under connected vehicle environment on an urban traffic network in terms of traffic mobility and safety. Microscopic simulation approach is used to conduct CV environment simulation and the rolling horizon approach is used for information updating among the connected vehicles. Meanwhile, driving behavior is modeled through aggressiveness and awareness of drivers. Traffic mobility for the road network was measured by average trip time and average vehicle trip speed. A surrogate measure, i.e., the time-to-collision involved incident rate for one kilometer driven, was used to assess the safety of the road network. Based on a real urban traffic network, the impacts of market penetration levels of connected vehicles and information updating intervals were studied. Simulation results showed that market penetration level of connected vehicles has little impact on the mobility and safety of road network. In addition, according to the simulation conducted in this paper, shorter updating interval is shown to be likely to lead to better mobility, while the safety of road network is likely to decline, under the assumptions embraced in the simulation. By contrast, the simulation also showed that longer updating interval is likely to lead to better safety and decreased mobility.
 
 

A Speed Control Method at Successive Signalized Intersections Under Connected Vehicles Environment

A growing body of speed control methods for signalized intersections have been developed using V2I and V2V technology. However, limited efforts are made to reduce the vehicle fuel consumption and emissions during the driving process at successive signalized intersections. To fill the gaps, this paper proposed a novel speed control method for the successive signalized intersections under connected vehicles environment. Using the upcoming traffic signal phasing, timing information and the vehicle queues at the signalized intersection, vehicle speed was optimized to reduce the vehicle fuel consumption and emissions. To validate its effectiveness, a real-time simulation framework embodied the characteristics of the connected vehicles environment is conducted using the multi-agent technology. In different traffic flows, the fuel consumption, CO2 emissions and travel time based on the speed control method are compared with that of without speed control. The effectiveness of the proposed speed control method in different signal timing plan was also tested. Simulation results show that the proposed speed control method could reduce more than 18% fuel consumption and CO2 emissions and 9% travel time in the medium density of traffic flow, which is the best control effect.

Transit Priority Signal Control Scheme Considering the Coordinated Phase for Single-Ring Sequential Phasing Under Connected Vehicle Environment

Real-time transit signal priority (TSP) controls is affected by coordination phase and deprive non-transit traffic benefits. In this paper, fully considering the migration states of coordinated phases and queuing states of non-transit vehicles, a transit signal priority controlling method is proposed for the single-ring sequential phasing under the connected vehicle (CV) environment. The queue length of the non-transit phase is estimated using real-time parking position of the probe vehicles in CV environment, and then the compression capability of phase can be confirmed. Under the premise of not destroying the green waveband of the intersection group, three models are proposed, including the green time extension, red time truncation, and phase insertion models. In the proposed model, the maximum priority effect and the minimum time deviations for the non-transit phase are taken as the optimization object. The maximum/minimum green time, total cycle length, and the backward/forward migration time of coordinated phases are taken as the constraints. Through the analysis of experimental results with examples, the transit priority control model proposed in this paper effectively improves the transit priority efficiency, and minimizes the impact on the non-transit traffic benefits while considering the coordinated phase state and not destroying the coordination effect.

Safety Evaluation of the Advanced Stop Assist System in Connected Vehicle Environment

Although traffic signals are installed to reduce the overall number of collisions at intersections, certain types, in particular, rear-end collisions are increasing due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposes and evaluates an Advanced Stop Assist System (ASAS) at signalized intersections by using Vehicle-to-Infrastructure (V2I) communication. The proposed system utilizes communication data, received from roadside equipment, to provide approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking at a yellow or red signal. A simulation test bed was modeled using VISSIM, a microscopic simulation software, to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that a penetration rate of at least 60% is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections.

Full Bayesian conflict-based models for real time safety evaluation of signalized intersections

Existing advanced traffic management and emerging connected vehicles (CVs) technology can generate considerable amount of data on vehicle positions and trajectories. This data can be used for real-time safety optimization of intersections. To achieve this, it is essential to first understand how changes in signal control affect safety in real-time. This paper develops conflict-based safety performance functions (SPFs) of signalized intersections at the cycle level using multiple traffic conflict indicators. The developed SPFs relate various dynamic traffic parameters to the number of rear-end conflicts at the signal cycle. The traffic parameters included: queue length, shock wave speed and area, and the platoon ratio. The Time-to-Collision, the Modified-Time-to-Collision, and the Deceleration Rate to Avoid the Crash were used as traffic conflict indicators. Traffic video-data collected from six signalized intersections was used in the analysis. The SPFs were developed using the Full Bayesian approach to address the unobserved heterogeneity and the variation among different sites. Overall, the results showed that all the developed SPFs have good fit with all explanatory variables being statistically significant. Also, the highest conflict frequency was noticed at the beginning of the green time, while the highest conflict severity was noticed at the beginning of the red time. Lastly, the results can be used most beneficially in real-time safety optimization of signalized intersection.

Effects of Warning Characteristics on Driver Performance in Connected Vehicle Systems with Missing Warnings

The connected vehicle systems (CVS) aim to provide drivers with information in a timely and reliable way to improve transportation safety. With the emerging wireless communication technologies, the vehicles will be equipped with the ability to communicate with each other about the surrounding traffic situations by exchanging vehicle status and motion data via Dedicated Short-Range Communications (DSRC) network (Kenney, 2011). With the assistance of the cooperative collision warnings, the impact of designed warning parameters on driver performance is increasingly important. Existing empirical studies have studied the warning timing and warning reliability in determining the effectiveness of the collision warning systems in advanced driver assistance systems (ADAS). In terms of warning timing, the studies in reached consistent conclusions that early warnings induced more timely braking and longer braking process, resulted in higher trust of the warning systems, and reduced collision rates (for example, Abe & Richardson, 2006a; Lee, McGehee, Brown, & Reyes, 2002; Yan, Xue, Ma, & Xu, 2014; Yan, Zhang, & Ma, 2015; Wan, Wu, and Zhang, 2016). In terms of the warning reliability, research has shown that warnings with a higher reliability increased driver’s trust of the warning systems, led to higher frequency in warning responses, and reduced crash rates (for example, Abe, Itoh, & Yamamura, 2009; Bliss & Acton, 2003; Maltz & Shinar, 2007; Sullivan, Tsimhoni, & Bogard, 2008). However, the interaction effects of warning lead time and warning reliability on driver performance was not examined especially under the connected vehicle settings. The current research investigated the interaction effects of warning lead time (2.5s vs. 4.5s), warning reliability (73% vs. 89%), and speech warning style (command vs. notification) on driver performance and subjective evaluation of warnings in CVS. A driving simulator study with thirty-two participants was conducted to simulate a connected vehicle environment with missing warnings due to the failures in the data transmission within the communication network of the CVS. The results showed command warnings led to a smaller collision rate compared to notification warnings with the warning lead time of 2.5s, whereas notification warnings resulted in a smaller collision rate compared to command warning with the warning lead time of 4.5s. These results suggested notification warnings should be selected when warning lead time is longer and warning reliability is higher, which resulted in higher safety benefits and higher subjective ratings. Command warnings could be selected when warning lead time is shorter since they led to more safety benefits. However, such selection has to be made with caution since command warnings may limit drivers’ response type and were perceived as less helpful than notification warnings.

Mixed-Integer Linear Programming for Optimal Scheduling of Autonomous Vehicle Intersection Crossing

We propose an urban traffic management scheme for an all connected vehicle environment. If all the vehicles are autonomous, for example, in smart city projects or future's dense city centers, then such an environment does not need a physical traffic signal. Instead, an intersection control server processes data streams from approaching vehicles, periodically solves an optimization problem, and assigns to each vehicle an optimal arrival time that ensures safety while significantly reducing number of stops and intersection delays. The scheduling problem is formulated as a mixed-integer linear program (MILP), and is solved by IBM CPLEX optimization package. The optimization outputs (scheduled access/arrival times) are sent to all approaching vehicles. The autonomous vehicles adjust their speed accordingly by a proposed trajectory planning algorithm with the aim of accessing the intersection at their scheduled times. A customized traffic microsimulation environment is developed to determine the potentials of the proposed solution in comparison to two baseline scenarios. In addition, the proposed MILP-based intersection control scheme is modified and simulated for a mixed traffic consisting of autonomous and human-controlled vehicles, all connected through a wireless communication to the intersection controller of a signalized intersection.

Signal Priority Request Delay Modeling and Mitigation for Emergency Vehicles in Connected Vehicle Environment

Connected vehicle-based signal priority control is widely regarded as an advanced method for improving travel efficiency of an emergency vehicle when passing through intersections. However, the vehicle-to-everything (V2X) communication delay is a critical factor affecting the performance of signal request and has rarely been considered in existing studies. This paper conducted a comprehensive delay analysis of the preemption signal request and its influence on practical preemption control for the emergency vehicle. First of all, a general end-to-end delay decomposition model is formulated to analyze significant delay uncertainties from different sources. Then, a compensated distance strategy is adopted for cooperative preemption control to ensure the reliability of preemption control and minimize impacts on performance caused by communication delay. Based on the analysis of field data and numerical results, the proposed model is able to reveal characteristics of communication delay for multimodal traffic signal control with priority. The proposed communication delay compensation strategy shows clear benefits in improving the performance of signal preemption control priority for an emergency vehicle at intersections and therefore has potential to enhance V2X applications in a connected vehicle environment.

Predicting Imminent Crash Risk with Simulated Traffic from Distant Sensors

The aim of this research was to investigate the performance of simulated traffic data for real-time crash prediction when loop detector stations are distant from the actual crash location. Nearly all contemporary real-time crash prediction models use traffic data from physical detector stations; however, the distance between a crash location and its nearest detector station can vary considerably from site to site, creating inconsistency in detector data retrieval and subsequent crash prediction. Moreover, large distances between crash locations and detector stations imply that traffic data from these stations may not truly reflect crash-prone conditions. Crash and noncrash events were identified for a freeway section on I-94 EB in Wisconsin. The cell transmission model (CTM), a macroscopic simulation model, was applied in this study to instrument segments with virtual detector stations when physical stations were not available near the crash location. Traffic data produced from the virtual stations were used to develop crash prediction models. A comparison revealed that the predictive accuracy of models developed with virtual station data was comparable to those developed with physical station data. The finding demonstrates that simulated traffic data are a viable option for real-time crash prediction given distant detector stations. The proposed approach can be used in the real-time crash detection system or in a connected vehicle environment with different settings.

Queue Spillover Management in a Connected Vehicle Environment

To alleviate the queue spillovers at intersections of urban roads during rush hours, a solution to the cross-spill problem based on vehicle networking technologies is proposed. This involves using connected vehicle technology, to realize the interactive information on vehicle and intersection signal control. The maximum control distance between intersections is determined by how vehicles are controlled and would travel in that connected environment. A method of calculating overflow tendency towards intersection queuing is also proposed, based on the maximum phase control distance. By this method, the intersection overflow is identified, and then the signal phases are re-optimized according to the requirements of different phases. Finally, overflow prevention control was also performed in this study. The VISSIM simulation results show that the method can better prevent the overflow of queues at intersections.

Road-Speed Profile for Enhanced Perception of Traffic Conditions in a Partially Connected Vehicle Environment

Anticipative driving of vehicles, considering the microscopic dynamics of the preceding traffic, can bring great improvement in the traffic flows in a connected vehicle environment (CVE), where 100% vehicles frequently share their states using vehicle-to-vehicle communication technology. This paper addresses a partially CVE, where unconnected (manually driven) vehicles are mixed in the traffic, and proposes a novel method of comprehending traffic conditions that can be used for realizing highly anticipative driving. More specifically, we refer to anticipative driving as the predictive control of a host vehicle considering its preceding traffic conditions in an extended view. For enhanced perception of the traffic conditions on the road, a road-speed profile is proposed that concisely describes the mean speed in each small segment or cell of the road by effectively extracting information from traffic big data, i.e., broadcasted data from all surrounding vehicles to the host vehicle. This process of dynamically approximating the road-speed profile is performed in two steps. First, a conditional persistence prediction model is proposed to estimate the future states of the connected vehicles. Such predicted states can sufficiently compensate missing data from any unconnected vehicles. Second, the predicted time, position, and speed of the vehicles are then mapped onto the road cells, and subsequently, the corresponding cell speed is tuned by employing the weighted-moving average technique. Accuracy of both the persistence prediction model and the road-speed profile are empirically evaluated for different penetration of the connected vehicles. Finally, an anticipative driving scheme in a model predictive control framework is developed by incorporating the proposed road-speed profile, and performance of the anticipative driving is compared with the existing scheme that needs the CVE.

Queue Estimation in a Connected Vehicle Environment: A Convex Approach

This paper proposes a convex optimization based algorithm for queue profile estimation in a connected vehicle environment, which can also be used for trajectory reconstruction, delay evaluation, etc. This algorithm generalizes the widely-adopted assumption of a linear back of queue (BoQ) curve to a piecewise linear BoQ curve to consider more practical scenarios. The piecewise linear BoQ curve is estimated via a convex optimization model, ensuring efficient computation. Moreover, this paper explicitly handles cases with low penetration rates and low sampling rates, as well as measurement noises. In addition, the proposed methodology is extended to an urban arterial, reusing the estimated departure information from the upstream intersections to further improve the estimation accuracy. Finally, two online implementation approaches are presented to perform real-time queue estimation. The proposed methodology is tested with two datasets: the Lankershim data set in the NGSIM project and the simulated dataset of Wehntalerstrasse, Zurich, Switzerland. Results show that the error is less than 1.5 cars in undersaturated scenarios and 5.2 cars in oversaturated scenarios if the penetration rates are larger than 0.1 and sampling rates are higher than 0.05 s−1. It is demonstrated that by considering a piecewise linear BoQ curve, the estimation accuracy can be improved by up to 16%. Incorporating flow successfully can also reduce the estimation error by up to 16%. Results further show that the proposed methodology is robust to measurement errors. It is finally shown that the proposed framework can be solved within a reasonable time (0.8 s), which is sufficient for most real-time applications.

Real-time detection of drivers’ texting and eating behavior based on vehicle dynamics

With rapid advancement in cellphones and intelligent in-vehicle technologies along with driver’s inclination to multitasking, crashes due to distracted driving had become a growing safety concern in our road network. Some previous studies attempted to detect distracted driving behaviors in real-time to mitigate their adverse consequences. However, these studies mainly focused on detecting either visual or cognitive distractions only, while most of the real-life distracting tasks involve driver’s visual, cognitive, and physical workload, simultaneously. Additionally, previous studies frequently used eye, head, or face tracking data, although current vehicles are not commonly equipped with technologies to acquire such data. Also those data are comparatively difficult to acquire in real-time during traffic monitoring operations. To address the above issues, this study focused on developing algorithms for detecting distraction tasks that involve simultaneous visual, cognitive, and physical workload using only vehicle dynamics data. Specifically, algorithms were developed to detect driving behaviors under two distraction tasks – texting and eating. Experiment was designed to include the two distracted driving scenarios and a control with multiple runs for each. A medium fidelity driving simulator was used for acquiring vehicle dynamics data for each scenario and each run. Several data mining techniques were explored in this study to investigate their performance in detecting distraction. Among them, the performance of two linear (linear discriminant analysis and logistic regression) and two nonlinear models (support vector machines and random forests) is reported in this article. Random forests algorithms had the best performance, which detected texting and eating distraction with an accuracy of 85.38% and 81.26%, respectively. This study may provide useful guidance to successful development and implementation of distracted driver detection algorithms in connected vehicle environment, as well as to auto manufacturers interested in integrating distraction detection systems in their vehicles.