Development of a binary logistic lane change model and its validation using empirical freeway data

An effective macroscopic lane change (LC) model is required to facilitate active and dynamic lane management to develop cell-based multilane macroscopic traffic models. Existing logistic regression LC models do not undertake model classification of lane change; do not consider performance measures in the validation of field data and ignore movement between lanes during time-varying traffic. Models that consider the direction of LC are, however, biased in their prediction of left LC (LLC) and right LC (RLC) direction. This study proposed a simplified macroscopic cell-based binary logistic LC (BLLC) model describing the LC probability using fewer explanatory variables; in this model, the direction of LC is considered as a new variable. Considering the model performance measures, the results show that there exists substantial difference in LC behaviour in both directions. The present model also achieved a smaller difference in the percentage of accurate prediction (0.9%) between the LLC and RLC.

[1]  F. Hsieh,et al.  Sample size tables for logistic regression. , 1989, Statistics in medicine.

[2]  Tomoki Taniguchi,et al.  Simulating Lane-changing Dynamics towards Lane-flow Equilibrium Based on Multi-lane First Order Traffic Flow Model , 2015 .

[3]  Jing Shi,et al.  A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model , 2019, J. Intell. Transp. Syst..

[4]  Dirk Helbing,et al.  General Lane-Changing Model MOBIL for Car-Following Models , 2007 .

[5]  Gang-Len Chang,et al.  AN EMPIRICAL INVESTIGATION OF MACROSCOPIC LANE-CHANGING CHARACTERISTICS ON UNCONGESTED MULTILANE FREEWAYS , 1991 .

[6]  Danyang Li,et al.  Spatiotemporal Traffic Flow Prediction with KNN and LSTM , 2019, Journal of Advanced Transportation.

[7]  Takayuki Morikawa,et al.  An optimal mandatory lane change decision model for autonomous vehicles in urban arterials , 2017, J. Intell. Transp. Syst..

[8]  Nazmul Haque,et al.  Adaptive neuro-fuzzy approach for modeling equilibrium speed–density relationship , 2018 .

[9]  Hui-xuan Li,et al.  Cellular Automata Approach for Modeling Lane Changing Execution , 2016, J. Cell. Autom..

[10]  P. M. E. Altham,et al.  Improving the Precision of Estimation by Fitting a Model , 1984 .

[11]  Zheng Chen,et al.  A Novel Lane Change Decision-Making Model of Autonomous Vehicle Based on Support Vector Machine , 2019, IEEE Access.

[12]  Tony Z. Qiu,et al.  A Macroscopic Lane-changing Model for Freeway Considering Different Incentives and Traffic States , 2017 .

[13]  Kaan Ozbay,et al.  Robust Calibration of Macroscopic Traffic Simulation Models Using Stochastic Collocation , 2015 .

[14]  Motohiro Fujita,et al.  Predicting driver's lane-changing decisions using a neural network model , 2014, Simul. Model. Pract. Theory.

[15]  Carlos F. Daganzo,et al.  Lane-changing in traffic streams , 2006 .

[16]  Agachai Sumalee,et al.  Multiclass multilane model for freeway traffic mixed with connected automated vehicles and regular human-piloted vehicles , 2019, Transportmetrica A: Transport Science.

[17]  Carlos F. Daganzo,et al.  In Traffic Flow, Cellular Automata = Kinematic Waves , 2004 .

[18]  P. Hidas.  Evaluation and Further Development of Car Following Models in Microscopic Traffic Simulation , 2006 .

[19]  Ding Zhao,et al.  Accelerated Evaluation of Automated Vehicles. , 2016 .

[20]  Peter Hidas,et al.  MODELLING LANE CHANGING AND MERGING IN MICROSCOPIC TRAFFIC SIMULATION , 2002 .

[21]  Michael J. Cassidy,et al.  Increasing Capacity of an Isolated Merge by Metering its On-Ramp , 2004 .

[22]  Stefan Jakubek,et al.  Macroscopic traffic model for large scale urban traffic network design , 2018, Simul. Model. Pract. Theory.

[23]  R. Bertini,et al.  Empirical Study of Traffic Features at a Freeway Lane Drop , 2005 .

[24]  Pierluigi Pisu,et al.  Coordination strategy for vehicles passing multiple signalized intersections: A connected vehicle penetration rate study , 2017, 2017 American Control Conference (ACC).

[25]  M. Iinuma,et al.  Reduction of membrane fluidity by antibacterial sophoraflavanone G isolated from Sophora exigua. , 2000, Phytomedicine : international journal of phytotherapy and phytopharmacology.

[26]  Bart van Arem,et al.  Improving Traffic Flow Efficiency by In-Car Advice on Lane, Speed, and Headway , 2014, IEEE Transactions on Intelligent Transportation Systems.

[27]  Kitae Jang,et al.  Logistic regression model for discretionary lane changing under congested traffic , 2015 .

[28]  Agachai Sumalee,et al.  Modeling the impacts of mandatory and discretionary lane-changing maneuvers , 2016 .

[29]  Chouki Sentouh,et al.  Driver-Automation Cooperative Approach for Shared Steering Control Under Multiple System Constraints: Design and Experiments , 2017, IEEE Transactions on Industrial Electronics.

[30]  Serge P. Hoogendoorn,et al.  Macroscopic traffic state estimation using relative flows from stationary and moving observers , 2018, Transportation Research Part B: Methodological.

[31]  A. Skabardonis,et al.  Understanding stop-and-go traffic in view of asymmetric traffic theory , 2009 .

[32]  Yong Wang,et al.  Evaluating the Impacts of Bus Stop Design and Bus Dwelling on Operations of Multitype Road Users , 2018, Journal of Advanced Transportation.

[33]  Victor L. Knoop,et al.  Lane Determination With GPS Precise Point Positioning , 2017, IEEE Transactions on Intelligent Transportation Systems.

[34]  Jinwoo Lee,et al.  A probability model for discretionary lane changes in highways , 2016 .

[35]  Serge P. Hoogendoorn,et al.  Quantifying the Number of Lane Changes in Traffic , 2012 .

[36]  Kai Nagel,et al.  Two-lane traffic rules for cellular automata: A systematic approach , 1997, cond-mat/9712196.

[37]  Wen-Long Jin.  A kinematic wave theory of lane-changing traffic flow , 2005 .

[38]  Dali Wei,et al.  An Enhanced Microscopic Traffic Simulation Model for Application to Connected Automated Vehicles , 2017 .

[39]  Antonella Ferrara,et al.  Freeway Traffic Modelling and Control , 2018 .

[40]  Dirk Helbing.  Empirical traffic data and their implications for traffic modeling , 1997 .

[41]  Ruey Long Cheu,et al.  A Cell Transmission Model with Lane Changing and Vehicle Tracking for Port of Entry Simulations , 2009 .

[42]  Haris N. Koutsopoulos,et al.  Integrated driving behavior modeling , 2007 .

[43]  W. Knospe,et al.  A realistic two-lane traffic model for highway traffic , 2002, cond-mat/0203346.

[44]  Peter M. Atkinson,et al.  Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy , 2011 .

[45]  Hideyuki Kita,et al.  A merging–giveway interaction model of cars in a merging section: a game theoretic analysis , 1999 .

[46]  H. Shahabi,et al.  Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models , 2014 .

[47]  Jun Gao,et al.  Multivariate time series prediction of lane changing behavior using deep neural network , 2018, Applied Intelligence.

[48]  Lars Schmidt-Thieme,et al.  Analyzing cooperative lane change models for connected vehicles , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).

[49]  Junqiang Xi,et al.  Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics , 2017, IEEE Transactions on Intelligent Transportation Systems.

[50]  Victor L. Knoop,et al.  Lane distribution of traffic near merging zones influence of variable speed limits , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[51]  Federico Rupi,et al.  Lane-Distribution Models and Related Effects on the Capacity for a Three-Lane Freeway Section: Case Study in Italy , 2017 .

[52]  Ichiro Sakata,et al.  Using advanced adaptive cruise control systems to reduce congestion at sags: An evaluation based on microscopic traffic simulation , 2019, Transportation Research Part C: Emerging Technologies.

[53]  Jinming You,et al.  Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study , 2018 .

[54]  Tomer Toledo,et al.  Modeling Duration of Lane Changes , 2007 .

[55]  Carl-Johan Hoel,et al.  Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[56]  Jonas Fredriksson,et al.  If, When, and How to Perform Lane Change Maneuvers on Highways , 2016, IEEE Intelligent Transportation Systems Magazine.

[57]  Kun Cao,et al.  A dynamic automated lane change maneuver based on vehicle-to-vehicle communication , 2016 .

[58]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[59]  Zuduo Zheng,et al.  A game theory-based approach for modelling mandatory lane-changing behaviour in a connected environment , 2019, Transportation Research Part C: Emerging Technologies.

[60]  Haris N. Koutsopoulos,et al.  Modeling Integrated Lane-Changing Behavior , 2003 .

[61]  Ruey Long Cheu,et al.  A binary decision model for discretionary lane changing move based on fuzzy inference system , 2016 .

[62]  Fang Liu,et al.  Lane-changes prediction based on adaptive fuzzy neural network , 2018, Expert Syst. Appl..

[63]  Avishai Ceder.  Transportation and traffic theory: proceedings of the 14th International Symposium on Transportation and Traffic Theory, Jerusalem, Israel, 20-23 July, 1999 , 1999 .

[64]  Markos Papageorgiou,et al.  Traffic flow optimisation in presence of vehicle automation and communication systems – Part I: A first-order multi-lane model for motorway traffic , 2015 .

[65]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[66]  Jui-Yi Ho,et al.  Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression , 2017 .

[67]  Xinkai Wu,et al.  A lane-change trajectory model from drivers’ vision view , 2017 .

[68]  Hai-Jun Huang,et al.  Macroscopic modeling of lane‐changing for two‐lane traffic flow , 2009 .

[69]  Rodney X. Sturdivant,et al.  Applied Logistic Regression: Hosmer/Applied Logistic Regression , 2005 .

[70]  A. Shakoor,et al.  A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses , 2010 .

[71]  Cheol Oh,et al.  Estimation of rear-end crash potential using vehicle trajectory data. , 2010, Accident; analysis and prevention.

[72]  A. Brenning.  Spatial prediction models for landslide hazards: review, comparison and evaluation , 2005 .

[73]  Malachy Carey,et al.  Extending the Cell Transmission Model to Multiple Lanes and Lane-Changing , 2015 .

[74]  Jorge A. Laval,et al.  Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model , 2008 .

[75]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .

[76]  Jian Jiao,et al.  A state dependent mandatory lane-changing model for urban arterials with hidden markov model method , 2019 .

[77]  Moshe Ben-Akiva,et al.  Lane-Changing Model with Explicit Target Lane Choice , 2005 .

[78]  P. G. Gipps,et al.  A MODEL FOR THE STRUCTURE OF LANE-CHANGING DECISIONS , 1986 .

[79]  Benjamin Coifman,et al.  Lane-Change Maneuvers Consuming Freeway Capacity , 2005 .

[80]  Ruey Long Cheu,et al.  Comparisons of mandatory and discretionary lane changing behavior on freeways , 2018, International Journal of Transportation Science and Technology.

[81]  Martin Treiber,et al.  Modeling Lane-Changing Decisions with MOBIL , 2009 .