scholarly journals Microscopic Estimation of Freeway Vehicle Positions from the Behavior of Connected Vehicles

2020 ◽  
Author(s):  
Noah J. Goodall ◽  
Brian L. Smith ◽  
Byungkyu Brian Park
Keyword(s):  
Traffic Signals ◽  
The United States ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Travel Time Prediction ◽  
High Penetration ◽  
Initial Penetration ◽  
Car Following Model ◽  
Status Data

Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicle's acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates.

scholarly journals Review of Usage of Real-World Connected Vehicle Data

2020 ◽  
Vol 2674 (10) ◽  
pp. 939-950
Author(s):  
Yun Zhou ◽  
Raj Bridgelall
Keyword(s):  
Real World ◽  
Large Scale ◽  
The United States ◽  
Connected Vehicles ◽  
United States Department ◽  
Connected Vehicle ◽  
Pilot Model ◽  
Vehicle Data ◽  
World Information ◽  
Vehicle Technologies

GPS loggers and cameras aboard connected vehicles can produce vast amounts of data. Analysts can mine such data to decipher patterns in vehicle trajectories and driver–vehicle interactions. Ability to process such large-scale data in real time can inform strategies to reduce crashes, improve traffic flow, enhance system operational efficiencies, and reduce environmental impacts. However, connected vehicle technologies are in the very early phases of deployment. Therefore, related datasets are extremely scarce, and the utility of such emerging datasets is largely unknown. This paper provides a comprehensive review of studies that used large-scale connected vehicle data from the United States Department of Transportation Connected Vehicle Safety Pilot Model Deployment program. It is the first and only such dataset available to the public. The data contains real-world information about the operation of connected vehicles that organizations are testing. The paper provides a summary of the available datasets and their organization, and the overall structure and other characteristics of the data captured during pilot deployments. Usage of the data is then classified into three categories: driving pattern identification, development of surrogate safety measures, and improvements in the operation of signalized intersections. Finally, some limitations experienced with the existing datasets are identified.

scholarly journals Evaluating Connected Vehicles and Their Applications

2016 ◽  
Vol 138 (12) ◽  
pp. S12-S17 ◽  
Author(s):  
Mohd Azrin Mohd Zulkefli ◽  
Pratik Mukherjee ◽  
Yunli Shao ◽  
Zongxuan Sun
Keyword(s):  
Real Time ◽  
Energy Savings ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
New Paradigm ◽  
Target Vehicle ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Research Platform

This article presents evaluation results of connected vehicles and their applications. Vehicle-to-vehicle communication (V2V) and vehicle-to-infrastructure communication (V2I) can enable a new paradigm of vehicle applications. The connected vehicle applications could significantly improve vehicle safety, mobility, energy savings, and productivity by utilizing real-time vehicle and traffic information. In the foreseeable future, connected vehicles need to operate alongside unconnected vehicles. This makes the evaluation of connected vehicles and their applications challenging. The hardware-in-the-loop (HIL) testbed can be used as a tool to evaluate the connected vehicle applications in a safe, efficient, and economic fashion. The HIL testbed integrates a traffic simulation network with a powertrain research platform in real time. Any target vehicle in the traffic network can be selected so that the powertrain research platform will be operated as if it is propelling the target vehicle. The HIL testbed can also be connected to a living laboratory where actual on-road vehicles can interact with the powertrain research platform.

Novel Cooperative Collision Avoidance Model for Connected Vehicles

2017 ◽  
Vol 2645 (1) ◽  
pp. 144-156 ◽  
Author(s):  
Pangwei Wang ◽  
WenXiang Wu ◽  
Xiaohui Deng ◽  
Lin Xiao ◽  
Li Wang ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Traffic Safety ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Time Interval ◽  
Connected Vehicle ◽  
Emergency Braking ◽  
Acceleration Rate ◽  
Vehicle Platoon ◽  
Connected Vehicle Technology

Connected vehicle technology exchanges real-time vehicle and traffic information through vehicle-to-vehicle and vehicle-to-infrastructure communication. The technology has the potential to improve traffic safety applications such as collision avoidance. In this paper, a novel cooperative collision avoidance (CCA) model that could improve the effectiveness of the collision avoidance system of connected vehicles was developed. Unlike traditional collision avoidance models, which relied mainly on emergency braking, the proposed CCA approach avoided collision through a combination of following vehicle deceleration and leading vehicle acceleration. Through spacing policy theory and nonlinear optimization, the model calculated the desired deceleration rate for the following vehicle and the acceleration rate for the leading vehicle, respectively, at each time interval. The CCA approach was then tested on a scaled platform with hardware-in-the-loop simulation embedded with MATLAB/Simulink and a car simulator package, CarSim. Results show that the proposed model can effectively avoid rear-end collisions in a three-vehicle platoon.

scholarly journals A Dynamic Travel Time Estimation Model Based on Connected Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Daxin Tian ◽  
Yong Yuan ◽  
Honggang Qi ◽  
Yingrong Lu ◽  
Yunpeng Wang ◽  
...  
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Travel Time Estimation ◽  
Route Guidance ◽  
Connected Vehicle ◽  
Estimation Model ◽  
Connected Vehicle Technology

With advances in connected vehicle technology, dynamic vehicle route guidance models gradually become indispensable equipment for drivers. Traditional route guidance models are designed to direct a vehicle along the shortest path from the origin to the destination without considering the dynamic traffic information. In this paper a dynamic travel time estimation model is presented which can collect and distribute traffic data based on the connected vehicles. To estimate the real-time travel time more accurately, a road link dynamic dividing algorithm is proposed. The efficiency of the model is confirmed by simulations, and the experiment results prove the effectiveness of the travel time estimation method.

Sequential Optimization of an Emergency Response Vehicle’s Intra-Link Movement in a Partially Connected Vehicle Environment

2021 ◽  
pp. 036119812110179
Author(s):  
Gaby Joe Hannoun ◽  
Pamela Murray-Tuite ◽  
Kevin Heaslip ◽  
Thidapat Chantem
Keyword(s):  
Emergency Response ◽  
Lateral Position ◽  
Automated System ◽  
Sequential Optimization ◽  
Connected Vehicles ◽  
Linear Programs ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Short Term ◽  
New Approach

This paper introduces a semi-automated system that facilitates emergency response vehicle (ERV) movement through a transportation link by providing instructions to downstream non-ERVs. The proposed system adapts to information from non-ERVs that are nearby and downstream of the ERV. As the ERV passes stopped non-ERVs, new non-ERVs are considered. The proposed system sequentially executes integer linear programs (ILPs) on transportation link segments with information transferred between optimizations to ensure ERV movement continuity. This paper extends a previously developed mathematical program that was limited to a single short segment. The new approach limits runtime overhead without sacrificing effectiveness and is more suitable to dynamic systems. It also accommodates partial market penetration of connected vehicles using a heuristic reservation approach, making the proposed system beneficial in the short-term future. The proposed system can also assign the ERV to a specific lateral position at the end of the link, a useful capability when next entering an intersection. Experiments were conducted to develop recommendations to reduce computation times without compromising efficiency. When compared with the current practice of moving to the nearest edge, the system reduces ERV travel time an average of 3.26 s per 0.1 mi and decreases vehicle interactions.

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

scholarly journals Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shan Fang ◽  
Lan Yang ◽  
Tianqi Wang ◽  
Shoucai Jing
Keyword(s):  
Fuel Consumption ◽  
Trajectory Planning ◽  
Signalized Intersections ◽  
Planning Method ◽  
Signalized Intersection ◽  
Driver Model ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Traffic Lights ◽  
Trigonometric Model

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

Identifying Patterns of Key Factors in Sun Glare-Related Traffic Crashes

2021 ◽  
pp. 036119812110378
Author(s):  
Subasish Das ◽  
Xiaoduan Sun ◽  
Bahar Dadashova ◽  
M. Ashifur Rahman ◽  
Ming Sun
Keyword(s):  
Environmental Issues ◽  
The United States ◽  
Traffic Information ◽  
Key Factors ◽  
Contributing Factors ◽  
Additional Insight ◽  
Traffic Crashes ◽  
Traffic Crash ◽  
Single Vehicle ◽  
Open Country

Sun glare is one of the major environmental issues contributing to traffic crashes. Every year, many traffic crashes in the United States are attributed to sun glare. However, quantitative analysis of the influence of sun glare on traffic crashes has not been widely undertaken. This study used traffic crash narrative data for 7 years (2010–2016) from Louisiana to identify crash reports that provided evidence of drivers indicating sun glare as the primary contributing factor of the crashes. Additional geometry and traffic information was collected to identify the list of key crash-contributing factors. This study used cluster correspondence analysis to perform the data analysis. After performing several iterations, six clusters were identified that provided additional insight in relation to sun glare-related crashes. The six clusters are associated with mixed (business and residential) localities, intersection-related crashes on U.S. roadways, single-vehicle crashes on residential two-lane undivided roadways, curve-related crashes on parish roadways in residential localities, interstate-related crashes in open country localities, and curve-related crashes in open country localities. The findings of the current study can add insights to the ongoing safety analysis on sun glare-related crashes.

Heterogeneous traffic flow model under connected vehicles environment considering cyberattacks

2021 ◽  
Author(s):  
Lizhen Lin ◽  
Hongxia Ge ◽  
Rongjun Cheng
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Heterogeneous Traffic ◽  
Connected Vehicles ◽  
Vehicle To Vehicle ◽  
Car Following Model ◽  
Time Extension ◽  
Potential Impact ◽  
The Stability ◽  
Traffic Behavior

Under the Vehicle-to-Vehicle (V2V) environment, connected vehicles (CVs) can share the traveling information with each other to keep the traffic flow stable. However, the open network cooperation environment makes CVs vulnerable to cyberattacks, which leads to changes in driving behavior. The existing theories divide cyberattacks into three types: bogus information, replay/delay and collusion cyberattacks. In addition, the mixed flow consisting of truck and car is a common form of road traffic. In order to clarify the potential impact of cyberattacks on mixed traffic flow, this paper proposes an extended car-following model considering cyberattacks under CVs environment. Subsequently, the stability of the model is analyzed theoretically, and the stability condition of the model is obtained. The numerical simulation is carried out and the result shows that the cyberattacks lead to different degrees of traffic behavior hazards such as queue time extension, congestion and even rear end collision. Among them, cooperative attack is the most serious.

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