Evaluating the Impact of Heavy Vehicles on Lane-changing Decisions of Car Drivers : A Neural-network-based Methodology

From common driving experiences, car drivers are unwilling to follow heavy vehicles owing to speed and visibility obstructions, and most of them choose to change lanes to mitigate such obstructions. In literature, however, the impact of heavy vehicles on lane-changing decisions of following car drivers has rarely been investigated. In this study, a methodology for shedding light on such impact is proposed, which is based on two neural network models-the lane-changing model and the vehicle conversion model. Estimated by the large-scale trajectory data, the models both acquire high accuracy. By converting the nearest lead vehicles to heavy vehicles and detecting driving decisions of the following car drivers, the percentage of changed driving decisions under traffic conditions with the different proportion of heavy vehicles is clearly given, and the number of increased lane changes caused by heavy vehicles is quantitatively provided, as well.

[1]  Takashi Oguchi.  ANALYSIS OF BOTTLENECK PHENOMENA AT BASIC FREEWAY SEGMENTS , 2000 .

[2]  J. G. Hunt,et al.  Modelling dual carriageway lane changing using neural networks , 1994 .

[3]  I. Flood,et al.  Neural networks in civil engineering: a review , 2001 .

[4]  Ronghui Liu,et al.  The principles of calibrating traffic microsimulation models , 2008 .

[5]  J W Stuster,et al.  THE UNSAFE DRIVING ACTS OF MOTORISTS IN THE VICINITY OF LARGE TRUCKS , 1999 .

[6]  Majid Sarvi,et al.  Modelling the heavy vehicle drivers’ lane changing decision under heavy traffic conditions , 2009 .

[7]  Majid Sarvi,et al.  Effect of Surrounding Traffic Characteristics on Lane Changing Behavior , 2010 .

[8]  Majid Sarvi,et al.  Enhanced Evaluation of Heavy Vehicle Lane Restriction Strategies in Microscopic Traffic Simulation , 2012 .

[9]  Eleni I. Vlahogianni,et al.  Statistical methods versus neural networks in transportation research: Differences, similarities and some insights , 2011 .

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

[11]  Yibing Wang,et al.  Comparing Heavy Vehicle and Passenger Car Lane-Changing Maneuvers on Arterial Roads and Freeways , 2011 .

[12]  Lasse Rosendahl,et al.  Methods to improve prediction performance of ANN models , 2003, Simul. Model. Pract. Theory.

[13]  Lily Elefteriadou,et al.  Lane‐Changing Behavior on Urban Streets: An “In‐Vehicle” Field Experiment‐Based Study , 2012, Comput. Aided Civ. Infrastructure Eng..

[14]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[15]  Motohiro Fujita,et al.  Evaluation of Car-following Models Using Trajectory Data from Real Traffic , 2012 .

[16]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

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

[18]  S. Figen Kalyoncuoglu,et al.  An alternative approach for modelling and simulation of traffic data: artificial neural networks , 2004, Simul. Model. Pract. Theory.

[19]  Motohiro Fujita,et al.  MODELING ACCELERATION AND DECELERATION BEHAVIOR IN HOV LANE BY USING DISCRETE CHOICE THEORY , 2012 .

[20]  Ahmed Al-Kaisy,et al.  Developing passenger car equivalents for heavy vehicles on freeways during queue discharge flow , 2002 .

[21]  Hesham A Rakha,et al.  Developing Passenger Car Equivalency Factors for Heavy Vehicles During Congestion , 2005 .

[22]  Pengcheng Zhang,et al.  Behavior-based analysis of freeway car-truck interactions and related mitigation strategies , 2005 .

[23]  Vincenzo Punzo,et al.  On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data , 2011 .

[24]  Herbert Yoo,et al.  DRIVER BEHAVIOR WHILE FOLLOWING CARS, TRUCKS, AND BUSES , 1999 .

[25]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[26]  Martin T. Hagan,et al.  Neural network design , 1995 .

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