Effect of Pulse‐and‐Glide Strategy on Traffic Flow for a Platoon of Mixed Automated and Manually Driven Vehicles

The fuel consumption of ground vehicles is significantly affected by how they are driven. The fuel-optimized vehicular automation technique can improve fuel economy for the host vehicle, but their effectiveness on a platoon of vehicles is still unknown. This article studies the performance of a well-known fuel-optimized vehicle automation strategy, i.e., Pulse-and-Glide (PnG) operation, on traffic smoothness and fuel economy in a mixed traffic flow. The mixed traffic flow is assumed to be a single-lane highway on flat road consisting of both driverless and manually driven vehicles. The driverless vehicles are equipped with fuel economy-oriented automated controller using the PnG strategy. The manually driven vehicles are simulated using the Intelligent Driver Models (IDM) to mimic the average car-following behavior of human drivers in naturalistic traffics. A series of simulations are conducted with three scenarios, i.e., a single car, a car section, and a car platoon. The simulation results show that the PnG strategy can significantly improve the fuel economy of individual vehicles. For traffic flows, the fuel economy and traffic smoothness vary significantly under the PnG strategy.

[1]  G. J. Fleer,et al.  Stationary dynamics approach to analytical approximations for polymer coexistence curves. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Huei Peng,et al.  Strategies to minimize the fuel consumption of passenger cars during car-following scenarios , 2012 .

[3]  Erik Hellström,et al.  Look-ahead Control for Heavy Trucks to minimize Trip Time and Fuel Consumption , 2007 .

[4]  I. Iervolino,et al.  Computer Aided Civil and Infrastructure Engineering , 2009 .

[5]  Dirk Helbing,et al.  Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  R. E. Wilson,et al.  Multianticipative Nonlocal Macroscopic Traffic Model , 2014, Comput. Aided Civ. Infrastructure Eng..

[7]  Davis Lc.  Effect of adaptive cruise control systems on traffic flow. , 2004 .

[8]  Hojjat Adeli,et al.  Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting , 2005 .

[9]  John R. Wagner,et al.  Nonlinear control of a continuously variable transmission (CVT) , 2003, IEEE Trans. Control. Syst. Technol..

[10]  Hojjat Adeli,et al.  Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures , 2008 .

[11]  Hojjat Adeli,et al.  Neural Network-Wavelet Microsimulation Model for Delay and Queue Length Estimation at Freeway Work Zones , 2006 .

[12]  Suzana Kahn Ribeiro,et al.  Energy efficiency technologies for road vehicles , 2009 .

[13]  Hojjat Adeli,et al.  An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection , 2000 .

[14]  Swaroop Darbha,et al.  Intelligent Cruise Control Systems And Traffic Flow Stability , 1998 .

[15]  L. C. Davis.  Effect of adaptive cruise control systems on mixed traffic flow near an on-ramp , 2005 .

[16]  Steven Broekx,et al.  Using on-board logging devices to study the longer-term impact of an eco-driving course , 2009 .

[17]  Jianqiang Wang,et al.  Minimum Fuel Control Strategy in Automated Car-Following Scenarios , 2012, IEEE Transactions on Vehicular Technology.

[18]  J Van Mierlo,et al.  Driving style and traffic measures-influence on vehicle emissions and fuel consumption , 2004 .

[19]  Jianqiang Wang,et al.  Model Predictive Multi-Objective Vehicular Adaptive Cruise Control , 2011, IEEE Transactions on Control Systems Technology.

[20]  Hojjat Adeli,et al.  Mesoscopic-Wavelet Freeway Work Zone Flow and Congestion Feature Extraction Model , 2004 .

[21]  Enrique F. Castillo,et al.  On the Probabilistic and Physical Consistency of Traffic Random Variables and Models , 2014, Comput. Aided Civ. Infrastructure Eng..

[22]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[23]  Sean N. Brennan,et al.  Adaptive Cruise Control: Towards higher traffic flows, at the cost of increased susceptibility to congestion , 2010 .

[24]  R. E. Wilson,et al.  Mechanisms for spatio-temporal pattern formation in highway traffic models , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  D. Helbing,et al.  Theoretical vs. empirical classification and prediction of congested traffic states , 2009, 0903.0929.

[26]  Martin Treiber,et al.  How Reaction Time, Update Time, and Adaptation Time Influence the Stability of Traffic Flow , 2008, Comput. Aided Civ. Infrastructure Eng..

[27]  P. Ioannou,et al.  Longitudinal control of heavy trucks: environmental and fuel economy considerations , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[28]  L. Davis.  Effect of adaptive cruise control systems on traffic flow. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Maria Zarkadoula,et al.  Training urban bus drivers to promote smart driving: A note on a Greek eco-driving pilot program , 2007 .

[30]  Hojjat Adeli,et al.  Wavelet Packet‐Autocorrelation Function Method for Traffic Flow Pattern Analysis , 2004 .

[31]  Hojjat Adeli,et al.  Neural network model for rapid forecasting of freeway link travel time , 2003 .

[32]  Chris Manzie,et al.  Fuel economy improvements for urban driving : Hybrid vs. intelligent vehicles , 2007 .

[33]  Carlos González,et al.  A transferable belief model applied to LIDAR perception for autonomous vehicles , 2013, Integr. Comput. Aided Eng..

[34]  R. Eddie Wilson,et al.  Criteria for convective versus absolute string instability in car-following models , 2011, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.