Predictive car-following scheme for improving traffic flows on urban road networks
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Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections. This paper proposes a car-following scheme in a model predictive control (MPC) framework to improve the traffic flow behavior, particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle (CV) environment. Using information received through vehicle-to-vehicle (V2V) communication, the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon. The objective function is to minimize the weighted costs due to speed deviation, control input, and unsafe gaps. The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision. The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections. The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.
KeywordsCar-following scheme model predictive control vehicle string connected vehicle environment distributed control
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- U. S. EIA. Annual Energy Outlook 2019: with Projections to 2050. 2019: https://doi.org/www.eia.gov/outlooks/aeo/.
- U. S. EPA. Inventory of U. S. Greenhouse Gas Emissions and Sinks: 1990–2017. 2019: https://doi.org/www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2015.
- European Commission. Accompanying the White Paper on Transport: Roadmap to a Single European Transport Area — Towards a Competitive and Resource Efficient Transport System. 2011: https://doi.org/www.eurosfaire.prd.fr/7pc/bibliotheque/consulter.php?id=2383.
- H. Xia, K. Boriboonsomsin, F. Schweizer, et al. Field operational testing of eco-approach technology at a fixed-time signalized intersection. Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, AK: IEEE, 2012: 188–193.Google Scholar
- M. Barth, J. Collins, G. Scora, et al. Measuring and modeling emissions from extremely low-emitting vehicles. Transportation Research Record, Washington, D.C.: National Academy of Sciences, 2006: 21–31.Google Scholar
- J. Bishop, A. Nedungadi, G. Ostrowski, et al. An Engine Start/Stop System for Improved Fuel Economy. SAE Technical Paper. 2007: https://doi.org/10.4271/2007-01-1777.
- I. M. Berry. The Effects of Driving Style and Vehicle Performance on the Real-world Fuel Consumption of US Light-duty Vehicles. Ph.D. dissertation. Cambridge: Massachusetts Institute of Technology, 2010.Google Scholar
- M. A. S. Kamal, M. Masakazu, J. Murata, et al. Influence of eco-driving on fuel consumption of vehicles in the following traffic. International Journal of Automotive Engineering, 2013, 4(3): 55–61.Google Scholar
- H. Rakha, R. K. Kamalanathsharma. Eco-driving at signalized intersections using V2I communication. Proceedins of the 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, D.C.: IEEE, 2011: 341–346.Google Scholar
- B. Van Arem, C. M. Tampere, K. Malone. Modelling traffic flows with intelligent cars and intelligent roads. Proceedings of the 4th Intelligent Vehicles Symposium. Columbus: IEEE, 2003: 456–461.Google Scholar
- S. Jones, A. Huss, E. Kural, et al. Development of predictive vehicle and drivetrain operating strategies based upon advanced information and communication technologies. Energy and Environment, Hoboken: Wiley, 2014: 137–150.Google Scholar