journal article·2024-03-21Open access

Evaluating the Impact of V2V Warning Information on Driving Behavior Modification Using Empirical Connected Vehicle Data

Hoseon Kim, Jieun Ko, Aram Jung, Seoungbum Kim

Applied Sciences· Volume 14· Issue 6

Abstract

A connected vehicle (CV) enables vehicles to communicate not only with other vehicles but also the road infrastructure based on wireless communication technologies. A road system with CVs, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides drivers with road and traffic condition information using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed while increasing the spacing or changing lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behavior from a traffic safety point of view. This study seeks to quantitatively evaluate the effect of in-vehicle warning information using per-vehicle data (PVD) collected from freeway C-ITSs. The PVD are reproduced to extract the speed–spacing relationship and are evaluated to determine whether the warning information induces drivers to drive in a conservative way. This study reveals that the in-vehicle warning prompts drivers to increase the spacing while decreasing their speed in the majority of samples. The rate of conservative driving behavior tends to increase during the initial operation period, but no significant changes were observed after this period; that is, the reliability of in-vehicle warning information is not constant in the CV environment.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Traffic and Road Safety · Autonomous Vehicle Technology and Safety · Traffic control and management

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Smart City Engineering, Hanyang University Erica Campus, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea

Department of Transportation and Logistics Engineering, Hanyang University Erica Campus, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Republic of Korea

Department of Urban Engineering/Engineering Research Institute, Gyeongsang National University, 501 Jinju-daero, Jinju-si 52828, Republic of Korea

Funding

Ministry of Land, Infrastructure and Transport

References (35)

Show source-provided references
1. How to Assess the Benefits of Connected Vehicles? A Simulation Framework for the Design of Cooperative Traffic Management Strategies10.1016/j.trc.2016.01.020 ↗
2. Introducing connected vehicles [connected vehicles]
3. Zumpf, S., Gopalakrishna, D., Garcia, V., Ragan, A., English, T., Young, R., Ahmed, M., Kitchener, F., Serulle, N.U., and Hsu, E. (2024, March 18). Connected Vehicle Pilot Deployment Program Phase 1, Application Deployment Plan-ICF/Wyoming, Available online: https://rosap.ntl.bts.gov/view/dot/40121.
4. Talas, M., Bradley, M., Rausch, R., Benevelli, D., Sim, S., Opie, K., Stanley, C., and Whyte, W. (2024, March 18). Connected Vehicle Pilot Deployment Program Phase 1: Comprehensive Deployment Plan: New York City: Volume 1: Technical Application: Part I: Technical and Management Approach, Available online: https://rosap.ntl.bts.gov/view/dot/31730.
5. Cordahi, G., Kamalanathsharma, R., Kolleda, J., Miller, D., Novosad, S., Poling, T., and Sundararajan, S. (2024, March 18). Connected Vehicle Pilot Deployment Program Phase 1: Application Deployment: Tampa (THEA): Final Report, Available online: https://rosap.ntl.bts.gov/view/dot/31734.
6. Kotsi, A., Mitsakis, E., and Tzanis, D. (2020, January 20–23). Overview of C-ITS Deployment Projects in Europe and USA. Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, Rhodes, Greece.10.1109/ITSC45102.2020.9294441 ↗
7. Understanding impacts of aggressive driving on freeway safety and mobility: A multi-agent driving simulation approach10.1016/j.trf.2019.05.017 ↗
8. Variable speed limit: A microscopic analysis in a connected vehicle environment10.1016/j.trc.2015.07.014 ↗
9. Safety and Mobility Evaluation of Cumulative-Anticipative Car-Following Model for Connected Autonomous Vehicles10.3390/smartcities7010021 ↗
10. Influence of connected and autonomous vehicles on traffic flow stability and throughput10.1016/j.trc.2016.07.007 ↗
11. Efficient control of fully automated connected vehicles at freeway merge segments10.1016/j.trc.2017.04.015 ↗
12. Guglielmi, J., Yanagisawa, M., Swanson, E., Stevens, S., and Najm, W. (2024, March 01). Estimation of Safety Benefits for Heavy-Vehicle Crash Warning Applications Based on Vehicle-to-Vehicle Communications, Available online: https://rosap.ntl.bts.gov/view/dot/37005.
13. Safety benefits of arterials’ crash risk under connected and automated vehicles10.1016/j.trc.2019.01.029 ↗
14. Stop sign gap assist application in a connected vehicle simulation environment10.1177/03611981211006111 ↗
15. Miqdady, T., de Oña, R., and de Oña, J. (2023). Traffic Safety Sensitivity Analysis of Parameters Used for Connected and Autonomous Vehicle Calibration. Sustainability, 15.10.3390/su15139990 ↗
16. Atkins, W.S. (2024, March 01). Research on the Impacts of Connected and Autonomous Vehicles (CAVs) on Traffic Flow. Stage 2: Traffic Modelling and Analysis Technical Report, Available online: https://www.gov.uk/government/publications/driverless-vehicles-impacts-on-traffic-flow.
17. Assessing traffic disturbance, efficiency, and safety of the mixed traffic flow of connected vehicles and traditional vehicles by considering human factors10.1016/j.trc.2020.102934 ↗
18. Research on benefits of mixed traffic flow of intelligent connected vehicles
19. Impact of the connected vehicle environment on tunnel entrance zone10.1016/j.aap.2021.106145 ↗
20. Safety impact of connected vehicles on driver behavior in rural work zones under foggy weather conditions10.1177/03611981211049147 ↗
21. The impact of the connected environment on driving behavior and safety: A driving simulator study10.1016/j.aap.2020.105643 ↗
22. Assessment of the safety benefits of vehicles’ advanced driver assistance, connectivity and low level automation systems10.1016/j.aap.2018.04.002 ↗
23. Evaluation of cooperative systems on driver behavior in heavy fog condition based on a driving simulator10.1016/j.aap.2019.04.019 ↗
24. The safety performance of connected vehicles on slippery horizontal curves through enhancing truck drivers’ situational awareness: A driving simulator experiment10.1016/j.trf.2021.04.017 ↗
25. Improving drivers’ merging performance in work zone using an in-vehicle audio warning10.1016/j.trf.2023.04.004 ↗
26. Impact of variable speed limit in a connected vehicle environment on truck driver behavior under adverse weather conditions: Driving simulator study10.1177/0361198119842111 ↗
27. Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving10.1016/j.aap.2019.04.020 ↗
28. Safety assessment of trucks based on GPS and in-vehicle monitoring data10.1016/j.aap.2022.106619 ↗
29. Safety effect analysis of motorcycle V2I collision warning system10.1049/itr2.12124 ↗
30. Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning10.1016/j.trc.2020.102917 ↗
31. Identification of safety benefits by inter-vehicle crash risk analysis using connected vehicle systems data on Korean freeways10.1016/j.aap.2020.105675 ↗
32. Use of real-world connected vehicle data in identifying high-risk locations based on a new surrogate safety measure10.1016/j.aap.2018.07.002 ↗
33. How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data10.1016/j.aap.2019.01.014 ↗
34. A simplified car-following theory: A lower order model10.1016/S0191-2615(00)00044-8 ↗
35. Treiber, M., and Kesting, A. (2013). Elementary Car-Following Models, Springer.10.1007/978-3-642-32460-4_10 ↗