2013 Session: 666

• Application of Naturalistic Driving Data to Modeling of Driver Car-Following Behavior
  Abstract: The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. This paper seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car-following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models.
  Authors: Sangster, John; Rakha, Hesham; Du, Jianhe
  Authors: Sangster, John; Rakha, Hesham; Du, Jianhe
  Year: 2013
  Document Type: Paper
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-0594
• Parameter Value Changes and Model Performance of Intelligent Driver Model and Macroscopic Consequences in Case of an Emergency Situation
  Abstract: Emergency situations (e.g., evacuation following a disaster) have been shown to have a substantial impact on traffic flow operations. However it was not yet clear how the adaptation effects in longitudinal driving behavior underlying this impact can best be modeled. Furthermore it was not yet clear what the macroscopic consequences are of the adaptation effects in longitudinal driving behavior. To this end in this contribution we report the results of the estimation of pa- rameter values and model performance of the Intelligent Driver Model using the data obtained through a driving simulator study. Furthermore we show the results of a case study using a microscopic simulation program and the parameter values obtained through the estimation of the Intelligent Driver Model. We show that e,regency situations have a substantial influence on parameter values and model performance of the Intelligent Driver Model. Furthermore we show that the adaptation effects represented in parameter values and model performance have a substantial influence on macroscopic flow characteristics. This contribution finishes with a discussion section and recommendations for future research.
  Authors: Hoogendoorn, Raymond Gerard; Hoogendoorn, Serge; van Arem, Bart; Brookhuis, Karel A.
  Authors: Hoogendoorn, Raymond Gerard; Hoogendoorn, Serge; van Arem, Bart; Brookhuis, Karel A.
  Year: 2013
  Document Type: Paper
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-3662
• Applying Task-Capability-Interface Model to Intelligent Driver Model in Relation to Complexity
  Abstract: Due to technological innovations the driving task is becoming increasingly complex. Complexity of the driving task is however not only determined by an increased amount of information directed at road users, but also by other external conditions (e.g., the road design, weather and interactions with other road users). In this contribution we assume that the complexity of the driving tasks leads to adaptation effects in longitudinal driving behavior, in which we distin- guish between compensation effects and performance effects. However, it was not yet clear how these effects can best be modeled. We argue that current models, such as the Intelligent Driver Model insufficiently incorporate human factors and are therefore less adequate in describing and predicting effects due to changes in the complexity of the driving task. To this end in this contribution we introduce a new theoretical framework and as an example implement this framework into the Intelligent Driver Model. Through two case studies using the microscopic simulation software package MOTUS we show that the model performs relatively well and clear shows the effect of a changing balance between the demand of the driving task and the capability of the driver. In this sense we show that an increase and relaxation in the capability of the driver provides a relatively good explanation for the capacity funnel phenomenon. Furthermore we show the effect of the provision of an ”optimal” amount of information versus ”information overload” on individual driving behavior and traffic flow operations. The contribution finishes with a discussion section as well as recommendations for future research.
  Authors: Hoogendoorn, Raymond Gerard; van Arem, Bart; Hoogendoorn, Serge; Brookhuis, Karel A.
  Authors: Hoogendoorn, Raymond Gerard; van Arem, Bart; Hoogendoorn, Serge; Brookhuis, Karel A.
  Year: 2013
  Document Type: Paper
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-3666
• Empirical Analysis of Speed Synchronization of Merge Vehicle from Entrance Ramp
  Abstract: Exploring the lane change preparation, termed as synchronization, with a new integrated view may trigger the understanding of the complex lane change behavior and help microscopic traffic flow modeling. This paper reports a fundamental work from various aspects to study the speed synchronization behavior of the merging vehicle by tracking their trajectories on the merge-related lanes. By classifying the merging vehicles into ¡°Original Gap¡± type and ¡°Overtaking¡± type, the existence of the speed synchronization during the lane change preparation stage is proved by comparing the speed difference between the merging vehicle and PL/PF at different locations. After this, a synchronization rule of the merging vehicles is constructed. The merging vehicles tend to maintain a speed which is 5~7 m/s higher than the speed of PL (putative leader) to overtake unsatisfied current gap on the adjacent main lane. When they meet an acceptable gap, they would take a two-step strategy to merge into main lane. Then, the effect of the speed difference between merge vehicles and PL/PF (putative follower) on the gap selection are concluded, which is that higher speed difference leads to gap rejection. Finally, the absolute speed difference between merging vehicle and PL/PF are modeled using multi-regression method. The merging vehicles' speed synchronization direction (acceleration or deceleration), the speed difference between PL and PF, the time headways and the distance from merging vehicles¡¯ current location to the end of the auxiliary lane are found to have significant effects on the speed difference tolerance.
  Authors: Wan, Xia; Jin, Jing; Zheng, Liang; Cheng, Yang; Ran, Bin
  Authors: Wan, Xia; Jin, Jing; Zheng, Liang; Cheng, Yang; Ran, Bin
  Year: 2013
  Document Type: Paper
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-4546
• Comparison of Data-Fitted First-Order Traffic Models and Their Second-Order Generalizations via Trajectory and Sensor Data
  Abstract: The Aw-Rascle-Zhang (ARZ) model can be interpreted as a generalization of the first order Lighthill-Whitham-Richards (LWR) model, possessing a family of fundamental diagram curves, rather than a single one. We investigate to which extent this generalization increases the predictive accuracy of the models. To that end, a systematic comparison of two types of data-fitted LWR models and their second order ARZ counterparts is conducted, via a version of the three-detector problem test. The parameter functions of the models are constructed using historic fundamental diagram data. The model comparisons are then carried out using time-dependent data, of two very different types: vehicle trajectory data, and single-loop sensor data. The study of these PDE models is carried out in a macroscopic sense, i.e., continuous field quantities are constructed from the discrete data, and discretization effects are kept negligibly small.
  Authors: Fan, Shimao; Seibold, Benjamin
  Authors: Fan, Shimao; Seibold, Benjamin
  Year: 2013
  Document Type: Paper
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-4853
• Application of Naturalistic Driving Data to Modeling of Driver Car-Following Behavior
  Authors: Sangster, John
  Authors: Sangster, John
  Year: 2013
  Document Type: Presentation
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-0594
• Parameter Value Changes and Model Performance of Intelligent Driver Model and Macroscopic Consequences in Case of an Emergency Situation
  Authors: Hoogendoorn, Raymond
  Authors: Hoogendoorn, Raymond
  Year: 2013
  Document Type: Presentation
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-3662
• Applying Task-Capability-Interface Model to Intelligent Driver Model in Relation to Complexity
  Authors: Hoogendoorn, Raymond
  Authors: Hoogendoorn, Raymond
  Year: 2013
  Document Type: Presentation
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-3666
• Comparison of Data-Fitted First-Order Traffic Models and Their Second-Order Generalizations via Trajectory and Sensor Data
  Authors: Seibold, Benjamin
  Authors: Seibold, Benjamin
  Year: 2013
  Document Type: Presentation
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-4853
• Empirical Analysis of Speed Synchronization of Merge Vehicle from Entrance Ramp
  Authors: Wan, Xia
  Authors: Wan, Xia
  Year: 2013
  Document Type: Presentation
  Subject: Operations and Traffic Management
  Session: 666
  Paper Number: 13-4546