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.

Modeling Integrated Lane-Changing Behavior

2003 ◽  
Vol 1857 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Tomer Toledo ◽  
Haris N. Koutsopoulos ◽  
Moshe E. Ben-Akiva
Keyword(s):  
Empirical Work ◽  
Flow Characteristics ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Simulation Tools ◽  
Lane Changes ◽  
Trade Offs ◽  
Safety Studies ◽  
Vehicle Trajectory

The lane-changing model is an important component within microscopic traffic simulation tools. Following the emergence of these tools in recent years, interest in the development of more reliable lane-changing models has increased. Lane-changing behavior is also important in several other applications such as capacity analysis and safety studies. Lane-changing behavior is usually modeled in two steps: ( a) the decision to consider a lane change, and ( b) the decision to execute the lane change. In most models, lane changes are classified as either mandatory (MLC) or discretionary (DLC). MLC are performed when the driver must leave the current lane. DLC are performed to improve driving conditions. Gap acceptance models are used to model the execution of lane changes. The classification of lane changes as either mandatory or discretionary prohibits capturing trade-offs between these considerations. The result is a rigid behavioral structure that does not permit, for example, overtaking when mandatory considerations are active. Using these models within a microsimulator may result in unrealistic traffic flow characteristics. In addition, little empirical work has been done to rigorously estimate the parameters of lane-changing models. An integrated lane-changing model, which allows drivers to jointly consider mandatory and discretionary considerations, is presented. Parameters of the model are estimated with detailed vehicle trajectory data.

Lane-Changing Model with Explicit Target Lane Choice

2005 ◽  
Vol 1934 (1) ◽  
pp. 157-165 ◽  
Author(s):  
Tomer Toledo ◽  
Charisma F. Choudhury ◽  
Moshe E. Ben-Akiva
Keyword(s):  
Trajectory Data ◽  
Current Position ◽  
Lane Changing ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Lane Changes ◽  
Proposed Model ◽  
Wide Range ◽  
Vehicle Trajectory ◽  
Better Than

The lane-changing model is an important component of microscopic traffic simulation tools. With the increasing popularity of these tools, a number of lane-changing models have been proposed and implemented in various simulators in recent years. Most of these models are based on the assumption that drivers evaluate the current and adjacent lanes and choose a direction of change (or no change) on the basis of the utilities of these lanes only. The lane choice set is therefore dictated by the current position of the vehicle and in multilane facilities would be restricted to a subset of the available lanes. Thus, existing models lack an explicit tactical choice of a target lane and therefore cannot explain a sequence of lane changes from the current lane to this lane. In this paper, a generalized lane-changing model that explicitly incorporates the choice of target lane is presented. The target lane is the lane that the driver perceives to be the best when a wide range of factors and goals are taken into account. The immediate direction in which a driver changes lanes is determined by the target lane choice. All parameters of the model were jointly estimated with detailed vehicle trajectory data. The model was validated and compared with an existing lane-changing model with the use of a microscopic traffic simulator. The results indicate that the proposed model performs significantly better than the previous model.

Observation-Based Lane-Vehicle Assignment Hierarchy: Microscopic Simulation on Urban Street Network

2000 ◽  
Vol 1710 (1) ◽  
pp. 96-103 ◽  
Author(s):  
Heng Wei ◽  
Joe Lee ◽  
Qiang Li ◽  
Connie J. Li
Keyword(s):  
Traffic Control ◽  
Travel Behavior ◽  
Dynamic Traffic Assignment ◽  
Street Network ◽  
Microscopic Simulation ◽  
Lane Changing ◽  
Urban Street ◽  
Car Following ◽  
Assignment Model ◽  
Vehicle Assignment

A lane-assignment model in a vehicle-based microscopic simulation system describes a vehicle’s position during its journey on an urban street network. In other words, it is used to estimate an individual vehicle’s location, speed, routing plan, lane-choice plan, lane-changing plan, and car-following plan from its entrance to a street network until the end of the trip. From the authors’ observations and study of lanechoice and lane-changing behavior, it is concluded that a vehicle is assigned to a lane in a logical manner depending on the relationship between its route-planned motivation and traffic conditions in the current lane and other lanes. A lane-assignment model consists of three components: lane choice, car following, and lane changing. The lane-changing component is composed of three submodels—a decision model, a lane-changing condition model, and a lane-changing maneuver model. Rules are discussed for lane-choice and lane-changing modeling based on videotaped observations over four-lane urban streets. Then a heuristic structure of a lane-vehicle-assignment model is proposed, which exposes the inherent relationship between vehicle-based travel behavior and lane-vehicle assignment on an urban street network. With the addition of a lane-assignment model derived from observed data, a simulation may be developed to correctly represent travel behavior and dynamic traffic assignment at the lane level and provide a more effective tool for design and evaluation of the performance of strategies for traffic control, traveler information, and congestion alleviation.

Modeling Speed Disturbance Absorption following Current State–Control Action–Expected State Chains

2005 ◽  
Vol 1934 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Ruihua Tao ◽  
Heng Wei ◽  
Yinhai Wang ◽  
Virginia P. Sisiopiku
Keyword(s):  
Recovery Period ◽  
Control Action ◽  
State Control ◽  
Trajectory Data ◽  
Lane Changing ◽  
Car Following ◽  
Current State ◽  
Speed Advantage ◽  
Vehicle Trajectory ◽  
A Current

This paper explores driver behavior in a paired car-following mode in response to a speed disturbance from a front vehicle. A current state– control action–expected state (SAS) chain is developed to provide a framework for modeling of the hierarchy of expected actions incurred during the need for speed disturbance absorption. Three car-following scenarios and one lane-changing scenario are identified with defined perceptual informative variables to describe the process of speed disturbance absorption. Those variables include dynamic spacing versus the follower's speed, disturbance-effecting and -ending spacing, headway, acceleration– deceleration, speed recovery period, speed advantage, and lane-changing duration. A significant improvement in car-following modeling introduced in the paper is the integration of car-following and lane-changing behaviors in the SAS chain. Moreover, critical values of perceptual informative variables are statistically developed as a function of the follower's speed by using observed vehicle trajectory data. Furthermore, models that determine the probability of a lane change in response to a speed disturbance and models for acceptable lane-changing decision-making conditions at the adjacent lanes are developed on the basis of the analysis of observed vehicle trajectory data. The work presented in this paper provides an analysis of speed disturbance and speed absorption phenomena and car-following and lane-changing behaviors at the microscopic level. This work establishes the foundation for further research on multiple speed disturbance absorption and its impact on traffic stabilities at the macroscopic analysis level.

Characterizing Lane Changes via Digitized Infrastructure and Low-Cost GPS

2019 ◽  
Vol 2673 (8) ◽  
pp. 298-309 ◽  
Author(s):  
Ishtiak Ahmed ◽  
Alan Karr ◽  
Nagui M. Rouphail ◽  
Gyounghoon Chun ◽  
Shams Tanvir
Keyword(s):  
Autonomous Vehicles ◽  
Lateral Displacement ◽  
Low Cost ◽  
Detection Algorithm ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Lane Changes ◽  
Detection Systems ◽  
Level Information

With the expected increase in the availability of trajectory-level information from connected and autonomous vehicles, issues of lane changing behavior that were difficult to assess with traditional freeway detection systems can now begin to be addressed. This study presents the development and application of a lane change detection algorithm that uses trajectory data from a low-cost GPS-equipped fleet, supplemented with digitized lane markings. The proposed algorithm minimizes the effect of GPS errors by constraining the temporal duration and lateral displacement of a lane change detected using preliminary lane positioning. The algorithm was applied to 637 naturalistic trajectories traversing a long weaving segment and validated through a series of controlled lane change experiments. Analysis of naturalistic trajectory data revealed that ramp-to-freeway trips had the highest number of discretionary lane changes in excess of 1 lane change/vehicle. Overall, excessive lane change rates were highest between the two middle freeway lanes at 0.86 lane changes/vehicle. These results indicate that extreme lane changing behavior may significantly contribute to the peak-hour congestion at the site. The average lateral speed during lane change was 2.7 fps, consistent with the literature, with several freeway–freeway and ramp–ramp trajectories showing speeds up to 7.7 fps. All ramp-to-freeway vehicles executed their first mandatory lane change within 62.5% of the total weaving length, although other weaving lane changes were spread over the entire segment. These findings can be useful for implementing strategies to lessen abrupt and excessive lane changes through better lane pre-positioning.

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.

Modeling Anticipation and Relaxation of Lane Changing Behavior Using Deep Learning

2021 ◽  
pp. 036119812110286
Author(s):  
Kequan Chen ◽  
Pan Liu ◽  
Zhibin Li ◽  
Yuxuan Wang ◽  
Yunxue Lu
Keyword(s):  
Short Term Memory ◽  
Trajectory Data ◽  
Lane Changing ◽  
Factors Affecting ◽  
Video Footage ◽  
Input Variables ◽  
Vehicle Trajectory ◽  
Lstm Network ◽  
Two Stages ◽  
The Impact

Modeling lane changing driving behavior has attracted significant attention recently. Most of the existing models are homogeneous and do not recognize the anticipation and relaxation phenomena occurring during the maneuver. To fill this gap, we adopted long short-term memory (LSTM) network and used large quantities of trajectory data extracted from video footage collected by an unmanned automated vehicle in Nanjing, China. Then, we divided complete lane changing behavior into two stages, that is, anticipation and relaxation. Description analysis of lane changing behavior revealed that the factors affecting the two stages are significantly different. In this context, two LSTM models with different input variables were proposed to predict the anticipation and the relaxation during the lane changing activity, respectively. The vehicle trajectory data were further divided into an anticipation dataset and a relaxation dataset to train the two LSTM models. Then we applied numerical tests to compare our models with two baseline models using real trajectory data of lane changing behavior. The results suggest that our models achieved the best performance for trajectory prediction in both lateral and longitudinal positions. Moreover, the simulation results show that the proposed models can precisely replicate the impact of the anticipation phenomenon on the target lane, and the relationship between the speed and spacing of the lane changing vehicle during the relaxation process can be reproduced with reasonable accuracy.

Categorizing Merging and Diverging Strategies of Truck Drivers at Motorway Ramps and Weaving Sections using a Trajectory Dataset

2020 ◽  
Vol 2674 (9) ◽  
pp. 855-866
Author(s):  
Salil Sharma ◽  
Maaike Snelder ◽  
Lóránt Tavasszy ◽  
Hans van Lint
Keyword(s):  
Finite Mixture Models ◽  
Research Area ◽  
Truck Drivers ◽  
Lane Change ◽  
Trajectory Data ◽  
Microscopic Simulation ◽  
Data Set ◽  
Lane Changing ◽  
Change Behavior ◽  
Essential Components

Lane-changing models are essential components for microscopic simulation. Although the literature recognizes that different classes of vehicles have different ways of performing lane-change maneuvers, lane change behavior of truck drivers is an overlooked research area. We propose that truck drivers are heterogeneous in their lane change behavior too and that inter-driver differences within truck drivers exist. We explore lane changing behavior of truck drivers using a trajectory data set collected around motorway bottlenecks in the Netherlands which include on-ramp, off-ramp, and weaving sections. Finite mixture models are used to categorize truck drivers with respect to their merging and diverging maneuvers. Indicator variables include spatial, temporal, kinematic, and gap acceptance characteristics of lane-changing maneuvers. The results suggest that truck drivers can be categorized into two and three categories with respect to their merging and diverging behaviors, respectively. The majority of truck drivers show a tendency to merge or diverge at the earliest possible opportunity; this type of behavior leads to most of the lane change activity at the beginning of motorway bottlenecks, thus contributing to the raised level of turbulence. By incorporating heterogeneity within the lane-changing component, the accuracy and realism of existing microscopic simulation packages can be improved for traffic and safety-related assessments.

Lane-changing gap acceptance model for freeway merging in simulation

2008 ◽  
Vol 35 (3) ◽  
pp. 301-311 ◽  
Author(s):  
Jin-Tae Kim ◽  
Joonhyon Kim ◽  
Myungsoon Chang
Keyword(s):  
Field Data ◽  
Gap Acceptance ◽  
Microscopic Simulation ◽  
Lane Changing ◽  
Lane Changes ◽  
Critical Gap ◽  
Proposed Model ◽  
Simulation Based ◽  
Single Vehicle ◽  
Acceptance Model

Existing techniques for microscopic simulation of lane changes utilize a single critical gap for a single vehicle. Freeway merging areas have been among the most difficult aspects of simulations due to the wide variety of merging behaviors in these areas. This paper proposes a gap acceptance model developed to update the size of the critical trailing gap for a merging vehicle during simulation based on the location of the vehicle in an acceleration lane. It also considers the relative speed and critical leading gap. Sets of critical trailing gap values for various situations are computed. The outputs from the microscopic simulations utilizing the proposed model were compared with field data, producing strong statistical evidence that the simulation results and field data were significantly comparable.

scholarly journals Comparison of Microscopic Drivers' Probabilistic Lane-changing Models With Real Traffic Microscopic Data

2012 ◽  
Vol 23 (4) ◽  
pp. 241-251
Author(s):  
Seyyed Mohammad Sadat Hoseini
Keyword(s):  
Probabilistic Models ◽  
Simulation Models ◽  
Real Data ◽  
Microscopic Level ◽  
Trajectory Data ◽  
Lane Changing ◽  
Traffic Models ◽  
Decision Factors ◽  
Vehicle Trajectory ◽  
Real Traffic

The difficulties of microscopic-level simulation models to accurately reproduce real traffic phenomena stem not only from the complexity of calibration and validation operations, but also from the structural inadequacies of the sub-models themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty in gathering accurate field data. This paper studies the traffic behaviour of individual drivers utilizing vehicle trajectory data extracted from digital images collected from freeways in Iran. These data are used to evaluate the four proposed microscopic traffic models. One of the models is based on the traffic regulations in Iran and the three others are probabilistic models that use a decision factor for calculating the probability of choosing a position on the freeway by a driver. The decision factors for three probabilistic models are increasing speed, decreasing risk of collision, and increasing speed combined with decreasing risk of collision. The models are simulated by a cellular automata simulator and compared with the real data. It is shown that the model based on driving regulations is not valid, but that other models appear useful for predicting the driver’s behaviour on freeway segments in Iran during noncongested conditions.

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