Advisory Lane-Changing Assistant at Vehicle Incidents with Connected Vehicles

2019 ◽  
Vol 2673 (10) ◽  
pp. 595-609 ◽  
Author(s):  
Hao Yang ◽  
Ken Oguchi
Keyword(s):  
Sensitivity Analysis ◽  
Traffic Congestion ◽  
Vehicle Dynamics ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Lane Changing ◽  
Lane Changes ◽  
Road Congestion ◽  
Driving Assistance

Vehicle incidents on roads result in lane closure and severe traffic congestion, and the frequent mandatory lane changes of the upstream vehicles generate capacity drops ahead of the incidents, which further increase road congestion. With the development of connected vehicles, vehicle incidents can be detected by individual vehicles, and immediate driving assistance can be provided to help them pass the incidents efficiently. This paper proposes a distributed lane-changing assistant (DLCA) system with connected vehicles to advise individual vehicles with the optimal lanes to pass incidents with smaller delays. The system introduces connected vehicles to detect the location and the lane closure information of an incident and broadcast the information to the upstream connected vehicles. To determine the optimal lane for each connected vehicle, a speed index is defined for each lane based on the incident information and the downstream connected vehicle dynamics. The DLCA system is evaluated with a microscopic traffic simulator, INTEGRATION, to illustrate its benefits in improving the performance of individual vehicles and mitigating road congestion. A sensitivity analysis of market penetration rates and demand levels of connected vehicles is also conducted in this paper. The results indicate that the DLCA system can reduce the delay by about 22.1% for the connected vehicles, and it has higher benefits on improving the performance of the entire road at higher market penetration rates. In addition, there exists an optimal demand level to maximize the benefits of the system.

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 Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

2017 ◽  
Vol 2620 (1) ◽  
pp. 54-68 ◽  
Author(s):  
Christopher M. Day ◽  
Howell Li ◽  
Lucy M. Richardson ◽  
James Howard ◽  
Tom Platte ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Goodness Of Fit ◽  
Sampling Period ◽  
Signalized Intersections ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Trajectory Data ◽  
Vehicle Data ◽  
Low Levels ◽  
Vehicle Trajectory

Signal offset optimization recently has been shown to be feasible with vehicle trajectory data at low levels of market penetration. Offset optimization was performed on two corridors with that type of data. A proposed procedure called “virtual detection” was used to process 6 weeks of trajectory splines and create vehicle arrival profiles for two corridors, comprising 25 signalized intersections. After data were processed and filtered, penetration rates between 0.09% and 0.80% were observed, with variations by approach. Then those arrival profiles were compared statistically with those measured with physical detectors, and most approaches showed statistically significant goodness of fit at a 90% confidence level. Finally, the arrival profiles created with virtual detection were used to optimize offsets and compared with a solution derived from arrival profiles obtained with physical detectors. Results demonstrate that virtual detection can produce good-quality offsets with current market penetration rates of probe data. In addition, a sensitivity analysis of the sampling period indicated that 2 weeks may be sufficient for data collection at current penetration rates.

Networkwide Impacts of Vehicle Ecospeed Control in the Vicinity of Traffic Signalized Intersections

2015 ◽  
Vol 2503 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Raj Kishore Kamalanathsharma ◽  
Hesham A. Rakha ◽  
Hao Yang
Keyword(s):  
Traffic Congestion ◽  
Signalized Intersections ◽  
Simulation Software ◽  
Connected Vehicles ◽  
Market Penetration ◽  
Total Delay ◽  
Car Following ◽  
Fuel Savings ◽  
Timing Information ◽  
Vehicle Trajectories

Ecospeed control is an advanced ecodriving or ecovehicle control algorithm that uses signal phasing and timing information from signalized intersections to generate fuel-optimum vehicle trajectories. The proposed algorithm uses connected vehicles technology to communicate between vehicles and the infrastructure. The research presented in this paper integrates the algorithm with state-of-the-art traffic simulation software, in this case the INTEGRATION software, to develop a tool capable of analyzing and evaluating systemwide impacts. The algorithm uses dynamic programming to generate fuel-efficient vehicle trajectories in the vicinity of traffic signalized intersections by controlling the vehicle variable limiting speed (VLS) to minimize fuel consumption while maintaining safe car-following behavior. Ecospeed control uses constraints upstream and downstream of the intersection to generate a longitudinal VLS function. Multiple simulations for levels of congestion (volume-to-capacity ratios) and levels of market penetration suggest that the average fuel savings per vehicle are in the range of 26% when all vehicles are equipped with such systems. Similarly, the average reduction in total delay reaches 65% within the vicinity of traffic signalized intersections. The results also demonstrate that at levels of market penetration less than 50%, the system does not produce systemwide fuel and delay savings. In addition, the savings are higher for lower levels of traffic congestion.

scholarly journals Modeling and simulation of vehicle group lane-changing behaviors in upstream segment of ramp areas under a connected vehicle environment

2019 ◽  
Vol 1 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Haijian Li ◽  
Zhufei Huang ◽  
Lingqiao Qin ◽  
Shuo Zheng ◽  
Yanfang Yang
Keyword(s):  
Traffic Congestion ◽  
Road Safety ◽  
Design Methodology ◽  
Vehicle Group ◽  
Connected Vehicle ◽  
Lane Changing ◽  
Content Type ◽  
Traffic Capacity ◽  
Simulation Results ◽  
Vehicle Technologies

Purpose The purpose of this study is to effectively optimize vehicle lane-changing behavior and alleviate traffic congestion in ramp area through the study of vehicle lane-changing behaviors in upstream segment of ramp areas. Design/methodology/approach In the upstream segment of ramp areas under a connected vehicle environment, different strategies of vehicle group lane-changing behaviors are modeled to obtain the best group lane-changing strategy. The traffic capacity of roads can be improved by controlling group lane-changing behavior and continuously optimizing lane-changing strategy through connected vehicle technologies. This paper constructs vehicle group lane-changing strategies in upstream segment of ramp areas under a connected vehicle environment. The proposed strategies are simulated by VISSIM. Findings The results show that different lane-changing strategies are modeled through vehicle group in the upstream segment of ramp areas, which can greatly reduce the delay of ramp areas. Originality/value The simulation results verify the validity and rationality of the corresponding vehicle group lane-changing behavior model strategies, effectively standardize the driver's lane-changing behavior, and improve road safety and capacity.

scholarly journals VISSIM-Based Simulation and Analysis of Upstream Segments in Ramp Areas for Optimizing Vehicle Group Lane-Changing Behaviors

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Haijian Li ◽  
Zhufei Huang ◽  
Xiaofang Zou ◽  
Shuo Zheng ◽  
Yanfang Yang
Keyword(s):  
Traffic Congestion ◽  
Road Safety ◽  
Vehicle Group ◽  
Connected Vehicle ◽  
Lane Changing ◽  
The Road ◽  
Road Capacity ◽  
Traffic Capacity ◽  
Vehicle Technologies ◽  
And Control

The traffic congestion in ramp areas is becoming increasingly prominent. In the upstream segments of ramp areas, effective management and control of lane-changing behaviors can improve the road capacity and make full use of the existing road resource. With the continuous development and application of connected vehicle technologies, lane-changing behaviors can be performed by vehicle groups. Under a connected vehicle environment, the lane-changing behaviors by vehicle groups are controlled in the upstream segment in a ramp area, and the lane-changing behaviors can be completed prior to entering the ramp area. Finally, lane-changing strategies are optimized and identified. VISSIM simulates these proposed strategies. This paper considers the delay as the output index for analyzing and comparing various strategies. The results demonstrate that the delays of different lane-changing strategies are also different. If the delays of ramp areas are to be substantially reduced, it is necessary to continuously optimize the lane-changing strategies by vehicle groups in the upstream segments. This optimization of lane-changing strategies will effectively regulate drivers’ lane-changing behaviors, improve road safety, and increase traffic capacity.

scholarly journals Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities: A Review, Open Issues, and Future Perspectives

Telecom ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 108-140
Author(s):  
Paulo Álvares ◽  
Lion Silva ◽  
Naercio Magaia
Keyword(s):  
Traffic Congestion ◽  
Resource Constraints ◽  
Smart Cities ◽  
The Internet ◽  
Connected Vehicle ◽  
Sensitive Data ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Security Properties ◽  
Vehicle Networks

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

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 Safety improvements by intelligent connected vehicle technologies: A meta-analysis considering market penetration rates

2021 ◽  
Vol 159 ◽  
pp. 106234
Author(s):  
Guiming Xiao ◽  
Jaeyoung Lee ◽  
Qianshan Jiang ◽  
Helai Huang ◽  
Mohamed Abdel-Aty ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Vehicle Technologies

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.

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