Traffic Volume Estimate Based on Low Penetration Connected Vehicle Data at Signalized Intersections: A Bayesian Deduction Approach

2021 ◽  
pp. 1-14
Author(s):  
Zhao Zhang ◽  
Siyao Zhang ◽  
Lei Mo ◽  
Mengdi Guo ◽  
Feng Liu ◽  
...  
Keyword(s):  
Traffic Volume ◽  
Signalized Intersections ◽  
Connected Vehicle ◽  
Volume Estimate ◽  
Vehicle Data

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.

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.

Value in vehicles: economic assessment of automotive data

2018 ◽  
Vol 20 (6) ◽  
pp. 513-527
Author(s):  
Alexander M. Soley ◽  
Joshua E. Siegel ◽  
Dajiang Suo ◽  
Sanjay E. Sarma
Keyword(s):  
Model Performance ◽  
Economic Assessment ◽  
Connected Vehicle ◽  
Data Generation ◽  
Content Type ◽  
Monetary Value ◽  
Vehicle Data ◽  
Data Market ◽  
Data Stewardship ◽  
Value Model

Purpose The purpose of this paper is to develop a model to estimate the value of information generated by and stored within vehicles to help people, businesses and researchers. Design/methodology/approach The authors provide a taxonomy for data within connected vehicles, as well as for actors that value such data. The authors create a monetary value model for different data generation scenarios from the perspective of multiple actors. Findings Actors value data differently depending on whether the information is kept within the vehicle or on peripheral devices. The model shows the US connected vehicle data market is worth between US$11.6bn and US$92.6bn. Research limitations/implications This model estimates the value of vehicle data, but a lack of academic references for individual inputs makes finding reliable inputs difficult. The model performance is limited by the accuracy of the authors’ assumptions. Practical implications The proposed model demonstrates that connected vehicle data has higher value than people and companies are aware of, and therefore we must secure these data and establish comprehensive rules pertaining to data ownership and stewardship. Social implications Estimating the value of data of vehicle data will help companies understand the importance of responsible data stewardship, as well as drive individuals to become more responsible digital citizens. Originality/value This is the first paper to propose a model for computing the monetary value of connected vehicle data, as well as the first paper to provide an estimate of this value.

Application of LiDAR and Connected Vehicle Data to Evaluate the Impact of Work Zone Geometry on Freeway Traffic Operations

2018 ◽  
Vol 2672 (16) ◽  
pp. 1-13 ◽  
Author(s):  
Michelle M. Mekker ◽  
Yun-Jou Lin ◽  
Magdy K. I. Elbahnasawy ◽  
Tamer S. A. Shamseldin ◽  
Howell Li ◽  
...  
Keyword(s):  
Case Studies ◽  
Work Zone ◽  
Extensive Literature ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Traffic Operations ◽  
Work Zones ◽  
Geometric Data ◽  
Vehicle Data ◽  
The Impact

Extensive literature exists regarding recommendations for lane widths, merging tapers, and work zone geometry to provide safe and efficient traffic operations. However, it is often infeasible or unsafe for inspectors to check these geometric features in a freeway work zone. This paper discusses the integration of LiDAR (Light Detection And Ranging)-generated geometric data with connected vehicle speed data to evaluate the impact of work zone geometry on traffic operations. Connected vehicle speed data can be used at both a system-wide (statewide) or segment-level view to identify periods of congestion and queueing. Examples of regional trends, localized incidents, and recurring bottlenecks are shown in the data in this paper. A LiDAR-mounted vehicle was deployed to a variety of work zones where recurring bottlenecks were identified to collect geometric data. In total, 350 directional miles were covered, resulting in approximately 360 GB of data. Two case studies, where geometric anomalies were identified, are discussed in this paper: a short segment with a narrow lane width of 10–10.5 feet and a merging taper that was about 200 feet shorter than recommended by the Manual on Uniform Traffic Control Devices. In both case studies, these work zone features did not conform to project specifications but were difficult to assess safely by an inspector in the field because of the high volume of traffic. The paper concludes by recommending the use of connected vehicle data to systematically identify work zones with recurring congestion and the use of LiDAR to assess work zone geometrics.

scholarly journals Connected-Vehicle Data Exchanges and Positioning Computing Based on the Publish-Subscribe Paradigm

2019 ◽  
Vol 07 (10) ◽  
pp. 82-93
Author(s):  
Kachane Sonklin ◽  
Charles Wang ◽  
Dhammika Jayalath ◽  
Yanming Feng
Keyword(s):  
Connected Vehicle ◽  
Vehicle Data

scholarly journals PENGARUH KEMACETAN DI SIMPANG SABILILLAH TERHADAP IMPLEMENTASI CITRA LALU LINTAS DI SIMPANG PROGRESIF

2015 ◽  
Vol 9 (2) ◽  
pp. 114
Author(s):  
Supiyono, Dwi Ratnaningsih, Rudy Ariyanto
Keyword(s):  
Road Traffic ◽  
Flame Length ◽  
Traffic Volume ◽  
Signalized Intersections ◽  
Degree Of Saturation ◽  
The Road ◽  
Road Section ◽  
On The Road ◽  
High Traffic Volume

Progress of a country in line with the progress of traffic (transport). Fluency in traffic is determined by the smoothness of traffic on the road. Problems often arise on the highway is congestion at the intersection. Neither was signalized intersections and signalized intersections. Problems at the intersection is less accuracy green flame at the intersection with the number of vehicles in a segment. A road with high traffic volume vehicle green flame low while other road traffic volume small green flame length. So in a long queue roads, while other roads are deserted while still green flame.     This study aims to minimize the occurrence of conflic at the intersection of green flame. Research will make iterations in the intersection, where a road section which will be nominated densely green flame, the flame of the green according to the volume of traffic on these roads. Each road will be a green flame in accordance with the volume of traffic, without having to change any program there is a change in traffic volume.The degree of saturation of the calculation obtained by ....Keywords: roads, hight traffic, progressive intersection, degree saturation

scholarly journals Roundabout Performance Analysis Using Connected Vehicle Data

2022 ◽  
Vol 12 (01) ◽  
pp. 42-58
Author(s):  
Enrique Saldivar-Carranza ◽  
Jijo K. Mathew ◽  
Howell Li ◽  
Darcy M. Bullock
Keyword(s):  
Performance Analysis ◽  
Connected Vehicle ◽  
Vehicle Data
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