Field Evaluation of Vehicle to Infrastructure Communication-Based Eco-Driving Guidance and Eco-Signal System
Abstract
In urban areas, the main factor causing excessive fuel consumption is stop-and-go, which is prevalent at urban intersections. To decrease fuel consumption at the intersections, various methods have been proposed and evaluated using simulation models and driving simulators. However, it is not known how reliable the driving simulator results would be when compared with those of the field test. In this paper, eco-driving guidance and eco-signal system based on vehicle-to-infrastructure communication are developed and tested in the field in a multi-vehicle environment. They were also tested using a driving simulator and the results were compared with those of the field test. This was intended to assess the reliability of driving simulator-based study. The field test results at an isolated intersection indicated that the eco-driving guidance could save the fuel consumption around 20%–40% and the eco-signal system could reduce the fuel consumption up to 45%. Although the absolute fuel consumptions between the driving simulator test and the field test did not match, the results from both tests confirmed better performance with eco-driving guidance and eco-signal system when compared with the base scenarios. This indicates that a driving simulator is an effective tool for assessing relative benefits of eco-driving guidance and eco-signal system.
Get full access to this article
View all access and purchase options for this article.
References
1. US Energy Information Administration. https://www.eia.gov/totalenergy/data/monthly/#consumption. Accessed July 17, 2016.
2. Li M., Boriboonsomsin K., Wu G., Zhang W. B., Barth M. Traffic Energy and Emission Reductions at Signalized Intersections: A Study of the Benefits of Advanced Driver Information. International Journal of ITS Research, Vol. 7, No. 1, 2009, pp 49–58.
3. Saboohi Y., Farzaneh H. Model for Developing an Eco-driving Strategy of a Passenger Vehicle Based on the Least Fuel Consumption. Applied Energy, Vol. 86, No. 10, 2009, pp. 1925–1932.
4. Barth M., Mandava S., Boriboonsomsin K., Xia H. Dynamic ECO-driving for arterial corridors. IEEE Forum on Integrated and Sustainable Transportation System (FISTS), Vienna, Austria, 2011.
5. Rakha H., Kamalanathsharma R. K. Eco-driving at Signalized Intersections Using V2I Communication. 2011 14th International IEEE Conference on Intelligent Transportation Systems. Washington, D.C., 2011.
6. Qian G., Chung E. Evaluating Effects of Eco-driving at Traffic Intersections Based on Traffic Micro-simulation. Transport Research Forum 2011 Proceedings, Adelaide, Australia, 2011.
7. Katwijk R. Using I2V Communication to Reduce Traffic Emissions at Urban Signalized Intersections. Procedia - Social and Behavioral Sciences, Vol. 48, 2012, pp. 2385–2392.
8. Xia H., Boriboonsomsin K., Barth M. Dynamic Eco-driving for Signalized Arterial Corridors and its Indirect Network-Wide Energy/Emissions Benefits. Journal of Intelligent Transportation Systems, Vol. 17, No. 1, 2013, pp 31–41.
9. CIECA. International Project on ‘Eco-driving’ in Category B Driver Training and the Driving Test. Final Report, Brussels, 2007.
10. Stillwater T., Kurani K. Field Test of Energy Information Feedback: Driver Responses and Behavioral Theory. Presented at 90th Annual Meeting of the Transportation Research Board, Washington, D.C., 2011.
11. Tulusan J., Steggers H., Fleisch E., Staake T. Supporting Eco-driving with Eco-feedback Technologies: Recommendations Targeted at Improving Corporate Car Drivers’ Intrinsic Motivation to Drive More Sustainable. Proc., 14th ACM International Conference on Ubiquitous Computing. Pittsburgh, PA, 2012.
12. Barth M., Boriboonsomsin K. Energy and Emissions Impacts of a Freeway-based Dynamic Eco-driving System. Transportation Research Part D: Transport and Environment, Vol. 14, No. 6, 2009, pp. 400–410.
13. Kurani K. S., Stillwater T., Jones M. Ecodrive I-80: A Large Sample Fuel Economy Feedback Field Test. Final Report, ITS-RR-13-15. Oak Ridge National Laboratory, Oak Ridge, TN, 2013.
14. Kirsten D., Nina S., Richard H., Philippus F., Bart A. Roadside versus In-Car Speed Support for Green Wave: A Driving Simulator Study. Presented at 87th Annual Meeting of the Transportation Research Board, Washington, D.C., 2008.
15. Hiraoka T., Terakado Y., Matsumoto S., Yamade S. Quantitative evaluation of Eco-driving on fuel consumption based on driving simulator experiments. Proc., 16th World Congress on Intelligent Transport Systems, Stockholm, Sweden, 2009.
16. Krause M., Bengler K. Traffic Light Assistant – Driven in a Simulator. Proc., 2012 International IEEE Intelligent Vehicle Symposium (IV-2012), Madrid, Spain, 2012.
17. Niu D., Sun J. Eco-driving Versus Green Wave Speed Guidance for Signalized Highway Traffic: A Multi-Vehicle Driving Simulator Study. Social and Behavioral Sciences, Vol. 96, 2013, pp. 1079–1090.
18. Zhao Y., Wagh A., Hou Y., Hulme K., Qiao C., Sadek A. W. Integrated Traffic-Driving-Networking Simulator for the Design of Connected Vehicle Applications: Eco-Signal Case Study. Journal of Intelligent Transportation Systems, Vol. 20, No. 1, 2014, pp. 75–87.
19. Cui L., Park B. Assessing Eco-driving Behaviors Using Driving Simulator. Presented at 96th Annual Meeting of the Transportation Research Board, Washington, D.C., 2017.
20. SAE International. J2735 Dedicated Short Range Communications (DSRC) Message Set Dictionary. 2016.
21. Barth M., Mandava S., Boriboonsomsin K., Xia H. Dynamic ECO-driving for Arterial Corridors. IEEE Forum on Integrated and Sustainable Transportation System (FISTS), Vienna, Austria, 2011.
22. Jung H., Choi S., Park B. B., Son S. H. Bi-Level Optimization for Eco-Traffic Signal System. Proc., 5th International Conference on Connected Vehicles and Expo (ICCVE), Seattle, WA, 2016.
23. Ko B., Choi S., Park B. B., Son S. H. Field Implementation of Eco-driving and Eco-signal System. Proc., 3rd International Conference on Vehicle Technology and Intelligent Transport Systems, Porto, Portugal, 2017
24. Wikipedia. https://en.wikipedia.org/wiki/Braking_distance. Accessed July 18, 2017.
25. Taoka G. T. Brake Reaction Times of Unalerted Drivers. ITE Journal, Vol. 59, No. 3, 1989, pp. 19–21.
26. TASS International, TNO. PreScan R7.2.0 Manual – A Simulation & Verification Environment for Intelligent Vehicle Systems, September 2015.
Cite article
Cite article
Cite article
OR
Download to reference manager
If you have citation software installed, you can download article citation data to the citation manager of your choice
Information, rights and permissions
Information
Published In
Article first published online: October 12, 2018
Issue published: December 2018
Authors
Author Contributions
The authors confirm contribution to the paper as follows: study conception and design: Park, Choi, Ko, and Cui; data collection: Ko, Cui, and Ryu; analysis and interpretation of results: all authors; draft manuscript preparation: Ko and Cui. All authors reviewed the results and approved the final version of the manuscript.
Metrics and citations
Metrics
Journals metrics
This article was published in Transportation Research Record: Journal of the Transportation Research Board.
VIEW ALL JOURNAL METRICSArticle usage*
Total views and downloads: 195
*Article usage tracking started in December 2016
Altmetric
See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores
Articles citing this one
Receive email alerts when this article is cited
Web of Science: 0
Crossref: 9
- Review of Energy Management Strategies in Plug-in Hybrid-Electric Vehi...
- Eco-Driving: A Scientometric and Bibliometric Analysis
- Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicl...
- Environmental Effects of Eco-Driving on Courier Delivery
- How Many Trajectories Are Needed to Develop Facility- and Speed-Specif...
- Method for Evaluating Eco-Driving Behaviors Based on Vehicle Specific ...
- Energy management strategies of connected HEVs and PHEVs: Recent progr...
- How much vehicle activity data is needed to develop robust vehicle spe...
- Survey on Disaster Management using VANET
Figures and tables
Figures & Media
Tables
View Options
Get access
Access options
If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:
loading institutional access options
Alternatively, view purchase options below:
Purchase 24 hour online access to view and download content.
Access journal content via a DeepDyve subscription or find out more about this option.
