Analysis of Vehicle-Following Behavior in Mixed Traffic Conditions using Vehicle Trajectory Data

2020 ◽  
Vol 2674 (11) ◽  
pp. 842-855
Author(s):  
Madhuri Kashyap N. R. ◽  
Bhargava Rama Chilukuri ◽  
Karthik K. Srinivasan ◽  
Gowri Asaithambi
Keyword(s):  
Mixed Traffic ◽  
Relative Speed ◽  
Linear Regression Models ◽  
Trajectory Data ◽  
Flow Models ◽  
Traffic Conditions ◽  
Traffic Flow Models ◽  
Vehicle Trajectory ◽  
New Variables ◽  
The Relationship

In mixed traffic streams without lane discipline, driving behaviors are complex and difficult to model. However, limited attempts have been made to study the characteristics of these maneuvers using trajectory data. This paper proposes a novel use of vehicle trajectory data to identify car–car and auto–car pairs in the following regime and the regime duration, classify pairs as strict and staggered following, and investigate the factors influencing the following vehicle’s speed under different regimes in mixed traffic. Oblique trajectories and relative speed hysteresis plots are used to identify vehicle pairs in the steady-state following regime. Two new variables, oblique spacing (R) and the angle between the leader and the follower (θ), are proposed. Multiple linear regression models for the follower speed in strict and staggered following regimes are developed. The results show that cars exhibit following behavior more often than other vehicles. Also, while car–car pairs display both left and right staggered following, auto–car pairs predominantly demonstrate left staggered following. Regression analysis shows that the relationship between R and the speed of the following vehicle differs significantly when θ is close to 90° than when it deviates from 90°. The speed of followers is affected by leader and relative speeds. However, the relative speed has a smaller influence in both right and left staggered cases than strict follower cases. Finally, this study provides empirical evidence of qualitative and quantitative differences among following behaviors that can help in developing better microscopic traffic flow models for mixed traffic conditions.

Study of the Response of PW Traffic Flow Model to a Bottleneck on a Circular Road and its Suitability for Heterogeneous Traffic Conditions

2021 ◽  
Vol 9 (4) ◽  
pp. 460-463
Keyword(s):  
Fluid Flow ◽  
Traffic Flow ◽  
Flow Model ◽  
Equation Model ◽  
Heterogeneous Traffic ◽  
Flow Conditions ◽  
Flow Models ◽  
Traffic Conditions ◽  
Model Response ◽  
Traffic Flow Models

The traffic flow conditions in developing countries are predominantly heterogeneous. The early developed traffic flow models have been derived from fluid flow to capture the behavior of the traffic. The very first two-equation model derived from fluid flow is known as the Payne-Whitham or PW Model. Along with the traffic flow, this model also captures the traffic acceleration. However, the PW model adopts a constant driver behavior which cannot be ignored, especially in the situation of heterogeneous traffic.This research focuses on testing the PW model and its suitability for heterogeneous traffic conditions by observing the model response to a bottleneck on a circular road. The PW model is mathematically approximated using the Roe Decomposition and then the performance of the model is observed using simulations.

Calibrating Wiedemann-99 Model Parameters to Trajectory Data of Mixed Vehicular Traffic

2021 ◽  
pp. 036119812110375
Author(s):  
Ankit Anil Chaudhari ◽  
Karthik K. Srinivasan ◽  
Bhargava Rama Chilukuri ◽  
Martin Treiber ◽  
Ostap Okhrin
Keyword(s):  
Goodness Of Fit ◽  
Simulation Models ◽  
Microscopic Level ◽  
Model Parameters ◽  
Mixed Traffic ◽  
Relative Speed ◽  
Trajectory Data ◽  
Conventional Vehicle ◽  
Car Following ◽  
Emergency Braking

We propose a new methodology for calibrating Wiedemann-99 vehicle-following parameters for mixed traffic (different conventional vehicle classes) based on trajectory data. The existing acceleration equations of the Wiedemann model are modified to represent more realistic driving behavior. Exploratory analysis of simulation data revealed that different Wiedemann-99 model parameters could lead to similar macroscopic behavior, highlighting the importance of calibration at the microscopic level. Therefore, the proposed methodology is based on optimizing performance measures at the microscopic level (acceleration, speed, and trajectory profiles) to estimate suitable calibration parameters. Further, the goodness of fit for the observed data is sensitive to the numerical integration method used to compute vehicles’ velocity and position. We found that the calibrated parameters using the proposed methodology perform better than other approaches for calibrating mixed traffic. The results reveal that the calibrated parameter values and, consequently, the thresholds that delineate closing, following, emergency braking, and opening regimes, vary between two-wheelers and cars. The window (in the relative speed versus gap plot) for the unconscious following is larger for cars while the free-flow regime is more extensive for two-wheelers. Moreover, under the same relative speed and gap stimulus, two-wheelers and cars may be in different regimes and display different acceleration responses. Thus, accurate calibration of each vehicle’s parameters is essential for developing micro-simulation models for mixed traffic. The calibration analysis results of strict and overlapping staggered car following signify an impact of staggered car following compared with strict car following which demands separate calibration for strict and staggered following.

Analysis and Comparison of Microscopic Traffic Flow Models with Real Traffic Microscopic Data

2005 ◽  
Vol 1934 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Vincenzo Punzo ◽  
Fulvio Simonelli
Keyword(s):  
Traffic Flow ◽  
Simulation Models ◽  
Microscopic Traffic Simulation ◽  
Flow Models ◽  
Traffic Conditions ◽  
Measure Model ◽  
Road Systems ◽  
Traffic Flow Models ◽  
Real Traffic ◽  
Better Than

The evermore widespread use of microscopic traffic simulation in the analysis of road systems has refocused attention on submodels, including car-following models. The difficulties of microscopic-level simulation models in the accurate reproduction of real traffic phenomena stem not only from the complexity of calibration and validation operations but also from the structural inadequacies of the submodels themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty with the gathering of accurate field data. In this study, the use of kinematic differential Global Positioning System instruments allowed the trajectories of four vehicles in a platoon to be accurately monitored under real traffic conditions on both urban and extraurban roads. Some of these data were used to analyze the behaviors of four microscopic traffic flow models that differed greatly in both approach and complexity. The effect of the choice of performance measures on the model calibration results was first investigated, and intervehicle spacing was shown to be the most reliable measure. Model calibrations showed results similar to those obtained in other studies that used test track data. Instead, validations resulted in higher deviations compared with those from previous studies (with peaks in cross validations between urban and extraurban experiments). This confirms the need for real traffic data. On comparison of the models, all models showed similar performances (i.e., similar deviations in validation). Surprisingly, however, the simplest model performed on average better than the others, but the most complex one was the most robust, never reaching particularly high deviations.

Parametric study of microscopic two-dimensional traffic flow models: A literature review

2018 ◽  
Vol 45 (11) ◽  
pp. 909-921 ◽  
Author(s):  
Geetimukta Mahapatra ◽  
Akhilesh Kumar Maurya ◽  
Partha Chakroborty
Keyword(s):  
Simulation Models ◽  
Lateral Position ◽  
Two Dimensional ◽  
Mixed Traffic ◽  
Lane Changing ◽  
The Real ◽  
Flow Models ◽  
Traffic Stream ◽  
Traffic Flow Models ◽  
Lane Based

Indian traffic is highly heterogeneous consisting of all-inclusive vehicle characteristics, occupying any lateral position over the entire road width which results in vehicles continuous interaction with the neighbouring vehicles (in both longitudinal and lateral directions), indicating two-dimensional (2D) traffic manoeuvre, opposite to the traditional one-dimensional (1D) interaction of vehicles in lane based traffic. Certain modifications were made in the existing 1D models to describe the overtaking and lane changing manoeuvre of the mixed traffic stream. However, the continuous lateral manoeuvre of the no-lane based mixed traffic cannot be described by these parameters. This paper initially provides a brief review of different 2D behavioural models, which describe the longitudinal and lateral movements simultaneously. Also, the various existing commercially available traffic micro-simulation frameworks developed for representing the real traffic are reviewed. Different microscopic traffic parameters used in the existing simulation models to mimic the real-world traffic are identified, which can be used to understand the 2D traffic stream.

Characterizing and Modeling Observed Lane-Changing Behavior: Lane-Vehicle-Based Microscopic Simulation on Urban Street Network

2000 ◽  
Vol 1710 (1) ◽  
pp. 104-113 ◽  
Author(s):  
Heng Wei ◽  
Eric Meyer ◽  
Joe Lee ◽  
Chuen Feng
Keyword(s):  
Trajectory Data ◽  
Street Network ◽  
Microscopic Simulation ◽  
Lane Changing ◽  
Urban Street ◽  
Lane Changes ◽  
Condition Model ◽  
Video Observations ◽  
Vehicle Trajectory ◽  
The Relationship

Key findings are discussed regarding characteristics of lane-changing behavior based on observations of an urban street network. An in-depth exploration of observed lane-changing behavior and its modeling were conducted using vehicle trajectory data extracted from video observations using VEVID, a software package developed by the authors, integrated with a video-capture system. As a result, rules for modeling lane-changing behavior are proposed with respect to various types of lane changes. A lane-changing model consists of three components: a decision model, a condition model, and a maneuver model. Drivers’ decisions to change lanes depend on travel maneuver plans, the current lane type (i.e., the relationship between the current lane and the driver’s planned route), and traffic conditions in the current and adjacent lanes. A lane-changing condition model is the description of acceptable conditions for different types of lane changes. A lane-changing maneuver model describes a vehicle’s speed and duration when a certain type of lane change occurs. All of these models are established in a heuristic structure.

Application of Trajectory Data for Investigating Vehicle Behavior in Mixed Traffic Environment

2018 ◽  
Vol 2672 (43) ◽  
pp. 122-133 ◽  
Author(s):  
Narayana Raju ◽  
Pallav Kumar ◽  
Aayush Jain ◽  
Shriniwas S. Arkatkar ◽  
Gaurang Joshi
Keyword(s):  
Traffic Flow ◽  
Research Work ◽  
Driving Behavior ◽  
Video Data ◽  
Hysteresis Phenomenon ◽  
Mixed Traffic ◽  
Trajectory Data ◽  
Traffic Conditions ◽  
Micro Level ◽  
Flow States

The research work reported here investigates driving behavior under mixed traffic conditions on high-speed, multilane highways. With the involvement of multiple vehicle classes, high-resolution trajectory data is necessary for exploring vehicle-following, lateral movement, and seeping behavior under varying traffic flow states. An access-controlled, mid-block road section was selected for video data collection under varying traffic flow conditions. Using a semi-automated image processing tool, vehicular trajectory data was developed for three different traffic states. Micro-level behavior such as lateral placement of vehicles as a function of speed, instant responses, vehicle-following behavior, and hysteresis phenomenon were evaluated under different traffic flow states. It was found that lane-wise behavior degraded with increase in traffic volume and vehicles showed a propensity to move towards the median at low flow and towards the curb-side at moderate and heavy flows. Further, vehicle-following behavior was also investigated and it was found that with increase in flow level, vehicles are more inclined to mimic the leader vehicle’s behavior. In addition to following time, perceiving time of subject vehicle for different leading vehicles was also evaluated for different vehicle classes. From the analysis, it was inferred that smaller vehicles are switching their leader vehicles more often to escape from delay, resulting in less following and perceiving time and aggressive gap acceptance. The present research work reveals the need for high-quality, micro-level data for calibrating driving behavior models under mixed traffic conditions.

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