Crowd-Sensing Road Surface Quality Using Connected Vehicle Data

This work presents an approach for collecting road surface data using connected vehicles. Road surface readings from multiple production vehicles were collected and aggregated to estimate road roughness measured by the International Roughness Index (IRI). The analysis compared multiple instances of connected vehicle data with high speed pavement profile vehicle (Class 1 profiler) data. A separate analysis compared multiple instances of connected vehicle data to an advanced walking profiler. Results demonstrate the feasibility of harvesting road surface data from the existing connected vehicles to support continuous road surface monitoring applications. Benefits include more timely acquisition of pavement data, broader coverage of the road network, and potential for aiding existing survey fleet in targeting early signs of pavement degradation. Collected roughness measurements were found to be closely aligned with reference devices that were employed as part of this study. A regional experiment in the Detroit Metropolitan area that covered 64 mi of roadways found that the connected vehicle data was highly correlated with Class 1 profiler data where 83% of traveled miles had a 0.8 or higher correlation. Moreover, 85% of the measurements had small absolute errors less than 50 in./mi and half of the measurements had absolute errors less than 20 in./mi. A test track experiment at Virginia Tech Transportation Institute Smart Road facility compared the connected vehicle data to the advanced walking profiler and showed that the correlations for repeatability and reproducibility are 0.90 and 0.91, respectively, which are very close to the standard requirement for certified profilers.

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Review of Usage of Real-World Connected Vehicle Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120940996 ◽

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.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211017914 ◽

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.

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

Sensors ◽

10.3390/s21113864 ◽

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.

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

Journal of Advanced Transportation ◽

10.1155/2020/1456207 ◽

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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Value in vehicles: economic assessment of automotive data

Digital Policy Regulation and Governance ◽

10.1108/dprg-05-2018-0025 ◽

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.

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Supporting road maintenance with in-vehicle data: Results from a field trial on road surface condition monitoring

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) ◽

10.1109/itsc.2016.7795917 ◽

2016 ◽

Cited By ~ 2

Author(s):

Richard Brunauer ◽

Karl Rehrl

Keyword(s):

Condition Monitoring ◽

Field Trial ◽

Surface Condition ◽

Road Surface ◽

Road Maintenance ◽

Vehicle Data

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On the estimation of connected vehicle penetration rate based on single-source connected vehicle data

Transportation Research Part B Methodological ◽

10.1016/j.trb.2019.06.003 ◽

2019 ◽

Vol 126 ◽

pp. 169-191 ◽

Cited By ~ 6

Author(s):

Wai Wong ◽

Shengyin Shen ◽

Yan Zhao ◽

Henry X. Liu

Keyword(s):

Penetration Rate ◽

Connected Vehicle ◽

Single Source ◽

Vehicle Data

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118758050 ◽

2018 ◽

Vol 2672 (16) ◽

pp. 1-13 ◽

Cited By ~ 2

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.

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