Characteristics of Heavy Vehicle Discretionary Lane Changing Based on Trajectory Data

2021 ◽  
pp. 036119812110513
Author(s):  
Gen Li ◽  
Jianxiao Ma ◽  
Zhen Yang
Keyword(s):  
Heavy Vehicle ◽  
Heavy Vehicles ◽  
Gap Acceptance ◽  
Trajectory Data ◽  
Data Set ◽  
Lane Changing ◽  
Substantial Impact ◽  
Lane Changes ◽  
Rich Data ◽  
The Right

A comprehensive analysis of the motivations, gap acceptance, duration, and speed adjustment of heavy vehicle lane changes (LC) is conducted in this paper. An rich data set containing 433 discretionary LC trajectories of heavy vehicles is used in this study and the data set is divided into two data sets based on the LC direction (LC to the left lane [LCLL] and LC to the right lane [LCRL]) for comparison. It is seen that LCLL and LCRL have significantly different motivations, which also results in different gap acceptance behavior. However, the LC direction does not significantly influence the LC duration. The navigation speed significantly influences the LC duration of heavy vehicles and the LC duration will decrease with the increase of speed, indicating the substantial impact of traffic conditions on LC duration. An obvious speed synchronization phenomenon is found in the process of LCLL, which is not the case in LCRL. The results of this study highlight the distinct characteristics of the LC of heavy vehicles and produce a better understanding of the lane-changing behaviors of heavy vehicles. The fitted distributions of LC duration and further investigation into gap acceptance behaviors may be used for microscopic traffic simulation and auto driving.

Time Gap Evaluation due to the Existence of Heavy Vehicles on Urban Expressways

2015 ◽  
Vol 802 ◽  
pp. 375-380
Author(s):  
Wardati Hashim ◽  
Ahmad Kamil Arshad ◽  
Masria Mustafa ◽  
Noor Azreena Kamaluddin
Keyword(s):  
Statistical Analysis ◽  
Video Recording ◽  
Road User ◽  
Heavy Vehicle ◽  
Heavy Vehicles ◽  
Gap Acceptance ◽  
Lane Changing ◽  
Average Speed ◽  
Critical Gap ◽  
Time Gap

Time gap is important for road user to make decision relative to the lad vehicle at a roadway segment. Theoretically, if the gap is larger than reaction time, drivers would maintain the safe following distance from the vehicle in front or else the probability of vehicle collusion is considerably high. In expressways, gap is important for the purpose of lane changing and overtaking. Due to high allowable speed on expressways, time gap might be affected, especially with the consideration of heavy vehicle existence. This paper attempts to statistically justify any significance correlation between speed and time gap in relative to critical gap acceptance pertaining to the heavy vehicles and cars interaction on urban expressways. Extensive data was collected through video recording before being abstracted and processed by utilizing the TRAIS software. Then, statistical analysis in relative to the speed and time gap for various vehicles interactions are presented. The results showed there is a significant correlation between speed and time gap for all vehicles interaction. When cars following other cars at allowable average speed, the time gap is relatively low leading to a lower critical gap acceptance as compared to the situation with the existence of heavy vehicles.

Analysing the Performance of a Fuzzy Lane Changing Model Using Data Mining

2015 ◽  
pp. 1540-1566
Author(s):  
Sara Moridpour
Keyword(s):  
Traffic Safety ◽  
Traffic Simulation ◽  
Heavy Traffic ◽  
Heavy Vehicle ◽  
Heavy Vehicles ◽  
Passenger Cars ◽  
Lane Changing ◽  
Microscopic Traffic Simulation ◽  
Traffic Characteristics ◽  
Using Data

Heavy vehicles have substantial impact on traffic flow particularly during heavy traffic conditions. Large amount of heavy vehicle lane changing manoeuvres may increase the number of traffic accidents and therefore reduce the freeway safety. Improving road capacity and enhancing traffic safety on freeways has been the motivation to establish heavy vehicle lane restriction strategies to reduce the interaction between heavy vehicles and passenger cars. In previous studies, different heavy vehicle lane restriction strategies have been evaluated using microscopic traffic simulation packages. Microscopic traffic simulation packages generally use a common model to estimate the lane changing of heavy vehicles and passenger cars. The common lane changing models ignore the differences exist in the lane changing behaviour of heavy vehicle and passenger car drivers. An exclusive fuzzy lane changing model for heavy vehicles is developed and presented in this chapter. This fuzzy model can increase the accuracy of simulation models in estimating the macroscopic and microscopic traffic characteristics. The results of this chapter shows that using an exclusive lane changing model for heavy vehicles, results in more reliable evaluation of lane restriction strategies.

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.

Analysing the Performance of a Fuzzy Lane Changing Model Using Data Mining

2013 ◽  
pp. 289-315 ◽  
Author(s):  
Sara Moridpour
Keyword(s):  
Traffic Safety ◽  
Traffic Simulation ◽  
Heavy Traffic ◽  
Heavy Vehicle ◽  
Heavy Vehicles ◽  
Passenger Cars ◽  
Lane Changing ◽  
Microscopic Traffic Simulation ◽  
Traffic Characteristics ◽  
Using Data

Heavy vehicles have substantial impact on traffic flow particularly during heavy traffic conditions. Large amount of heavy vehicle lane changing manoeuvres may increase the number of traffic accidents and therefore reduce the freeway safety. Improving road capacity and enhancing traffic safety on freeways has been the motivation to establish heavy vehicle lane restriction strategies to reduce the interaction between heavy vehicles and passenger cars. In previous studies, different heavy vehicle lane restriction strategies have been evaluated using microscopic traffic simulation packages. Microscopic traffic simulation packages generally use a common model to estimate the lane changing of heavy vehicles and passenger cars. The common lane changing models ignore the differences exist in the lane changing behaviour of heavy vehicle and passenger car drivers. An exclusive fuzzy lane changing model for heavy vehicles is developed and presented in this chapter. This fuzzy model can increase the accuracy of simulation models in estimating the macroscopic and microscopic traffic characteristics. The results of this chapter shows that using an exclusive lane changing model for heavy vehicles, results in more reliable evaluation of lane restriction strategies.

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.

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.

Proposed Heavy Vehicle Encroachment Adjustment Factor

2015 ◽  
Vol 2521 (1) ◽  
pp. 103-110
Author(s):  
Christine E. Carrigan ◽  
Malcolm H. Ray
Keyword(s):  
Probability Model ◽  
Large Data ◽  
Segment Length ◽  
Heavy Vehicle ◽  
Heavy Vehicles ◽  
Adjustment Factor ◽  
Probability Models ◽  
Data Set ◽  
Vehicle Type ◽  
Passenger Vehicles

Encroachment probability models such as the Roadside Safety Analysis Program (RSAP) have traditionally assumed that heavy vehicles and passenger vehicles share the same encroachment characteristics. This assumption was reviewed in developing bridge railing selection guidelines in NCHRP 22-12(03), where an examination of a specific highway and a national sample of data indicated that trucks encroached at a different rate than passenger vehicles. This paper describes the development of a new vehicle-type encroachment adjustment factor (EAF). The results confirmed previous findings, but this analysis controlled for traffic volumes, highway type, percentage of heavy vehicles [i.e., percentage of trucks (PT)], and segment length. The result was a more robust model that was valid over a wider range of average annual daily traffic and PTs. The large data set included 635,464 segments of data from the states of Ohio and Washington. The proposed EAF was recommended for inclusion in RSAPv3. Ideally, encroachment data would be collected for heavy vehicles to determine the frequency of heavy vehicles encroaching onto the roadside and the trajectories heavy vehicles took during encroachment, but this process proved to be financially challenging. The study used crash data to carry out a comprehensive analysis of traffic volume, heavy vehicle mix, highway type, and segment length. A vehicle-type EAF was developed for divided and undivided roadways. The results provided some indication of how best to incorporate heavy vehicles in the encroachment probability model used in RSAP.

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.

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.

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