Lane-free Artificial-Fluid Concept for Vehicular Traffic

A novel paradigm for vehicular traffic in the era of connected and automated vehicles (CAVs) is proposed, which includes two combined principles: lane-free traffic and vehicle nudging, whereby vehicles are "pushing" (from a distance, using communication or sensors) other vehicles in front of them. This traffic paradigm features several advantages, including: smoother and safer driving; increase of roadway capacity; and no need for the anisotropy restriction. The proposed concept provides, for the first time since the automobile invention, the possibility to actively design (rather than describe) the traffic flow characteristics in an optimal way, i.e. to engineer the future CAV traffic flow as an efficient artificial fluid. Options, features, application domains and required research topics are discussed. Preliminary simulation results illustrate some basic features of the concept.

[1]  Norman I. Badler,et al.  Virtual Crowds: Methods, Simulation, and Control , 2008, Virtual Crowds: Methods, Simulation, and Control.

[2]  Serge P. Hoogendoorn,et al.  State-of-the-art crowd motion simulation models , 2013 .

[3]  Markos Papageorgiou,et al.  Motorway Path Planning for Automated Road Vehicles Based on Optimal Control Methods , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[4]  Ge Guo,et al.  Cooperative Spacing Control for Interconnected Vehicle Systems With Input Delays , 2017, IEEE Transactions on Vehicular Technology.

[5]  Nico Kaempchen,et al.  Highly Automated Driving on Freeways in Real Traffic Using a Probabilistic Framework , 2012, IEEE Transactions on Intelligent Transportation Systems.

[6]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[7]  Mashrur Chowdhury,et al.  Review of Microscopic Lane-Changing Models and Future Research Opportunities , 2013, IEEE Transactions on Intelligent Transportation Systems.

[8]  Jérôme Härri,et al.  Modeling and Analysis of Mixed Flow of Cars and Powered Two Wheelers , 2018 .

[9]  D. Helbing,et al.  On the controversy around Daganzo’s requiem for and Aw-Rascle’s resurrection of second-order traffic flow models , 2008, 0805.3402.

[10]  Ella M. Atkins,et al.  Distributed multi‐vehicle coordinated control via local information exchange , 2007 .

[11]  Petros A. Ioannou,et al.  Using front and back information for tight vehicle following maneuvers , 1999 .

[12]  Markos Papageorgiou,et al.  SOME REMARKS ON MACROSCOPIC TRAFFIC FLOW MODELLING , 1998 .

[13]  Tom V. Mathew,et al.  Towards Behavioral Modeling of Drivers in Mixed Traffic Conditions , 2016 .

[14]  Majid Sarvi,et al.  Crowd behaviour and motion: Empirical methods , 2018 .

[15]  Tomer Toledo,et al.  Driving Behaviors: Models and Challenges for Non-Lane Based Mixed Traffic , 2016 .

[16]  D. Manjunath,et al.  A Microscopic Model for Lane-Less Traffic , 2019, IEEE Transactions on Control of Network Systems.

[17]  Wang,et al.  Review of road traffic control strategies , 2003, Proceedings of the IEEE.

[18]  H. M. Zhang,et al.  Anisotropic property revisited––does it hold in multi-lane traffic? , 2003 .

[19]  B. K. Bhavathrathan,et al.  Evolution of macroscopic models for modeling the heterogeneous traffic: an Indian perspective , 2012 .

[20]  Haiying Li,et al.  Social force models for pedestrian traffic – state of the art , 2018 .

[21]  C. Daganzo THE CELL TRANSMISSION MODEL.. , 1994 .

[22]  Prabir Barooah,et al.  Stability and robustness of large platoons of vehicles with double‐integrator models and nearest neighbor interaction , 2013 .

[23]  Narendra Ahuja,et al.  A potential field approach to path planning , 1992, IEEE Trans. Robotics Autom..

[24]  Washington Y. Ochieng,et al.  Modelling shared space users via rule-based social force model , 2015 .

[25]  Fei-Yue Wang,et al.  A framework for artificial transportation systems: from computer simulations to computational experiments , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[26]  Mike McDonald,et al.  ITS and Traffic Management , 2007 .

[27]  Guohui Zhang,et al.  Force-Driven Traffic Simulation for a Future Connected Autonomous Vehicle-Enabled Smart Transportation System , 2018, IEEE Transactions on Intelligent Transportation Systems.

[28]  Takayoshi Yoshimura,et al.  Efficient Driving on Multilane Roads Under a Connected Vehicle Environment , 2016, IEEE Transactions on Intelligent Transportation Systems.

[29]  Tom V. Mathew,et al.  Passenger car units and saturation flow models for highly heterogeneous traffic at urban signalised intersections , 2011 .

[30]  Werner Huber,et al.  Experience, Results and Lessons Learned from Automated Driving on Germany's Highways , 2015, IEEE Intelligent Transportation Systems Magazine.