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.

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.

scholarly journals A Multiclass, Multimodal Dynamic Traffic Assignment Model with Departure Time

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Meng Meng ◽  
Chunfu Shao ◽  
Yiik Diew Wong ◽  
Jie Zhang
Keyword(s):  
Traffic Control ◽  
Dynamic Traffic Assignment ◽  
Route Guidance ◽  
Traffic Equilibrium ◽  
Dynamic Traffic ◽  
Schedule Delay ◽  
Assignment Model ◽  
Departure Time ◽  
Departure Time Choice ◽  
Dynamic Route

The paper develops a multiclass, multimodal dynamic traffic equilibrium model with consideration of the departure time choice problem. Travelers choose the departure time and the route simultaneously with a Logit-based structure. The route travel cost is a summation of travel time and schedule delay which is associated with arrival time at destination. In addition, the travelers are classified into three groups according to their value of time. A variational inequality (VI) formulation is proposed based on the equilibrium conditions. Two examples are given to testify the effectiveness of the model and the solution algorithm. The model can give the optimal travel route as well as the best departure time, which would contribute to traffic control and dynamic route guidance.

An optimal downsampling procedure for microscopic simulation modeling of transportation networks: a proof-of-concept study

2007 ◽  
Vol 34 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Ciprian Alecsandru ◽  
Sherif Ishak ◽  
Yan Zhang
Keyword(s):  
Traffic Control ◽  
Transformation Process ◽  
General Motors ◽  
Proof Of Concept ◽  
Simulation Environment ◽  
Microscopic Simulation ◽  
Car Following ◽  
Real Time Traffic ◽  
Behavioral Parameters ◽  
Management Functions

This paper investigates and demonstrates the concept of scalability of microscopic traffic simulation systems as a means of reducing the associated computational requirements and maximizing their potential support for real-time traffic control and management functions. The primary goal of this research is to examine the feasibility of transforming the original simulation environment into a downsampled simulation environment, where fewer representative entities are simulated. This must be achieved, however, while retaining maximum fidelity to microscopic simulation properties and preserving most of the macroscopic characteristics. The methodology is presented as an optimization problem whose objective is to minimize the errors resulting from the transformation process and to seek optimal values of the behavioral parameters in the downsampled environment. In this proof-of-concept stage, experimental analysis was conducted on a homogeneous freeway segment using one of the well-known, and arguably sufficiently calibrated, car-following models developed by General Motors Laboratories (GM3). The results were promising and showed that optimal relationships between the behavioral parameters in both environments can be established to minimize the information loss associated with the transformation process.Key words: microscopic simulation, scalability, computational efficiency, downsampling, car-following models.

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

2021 ◽  
pp. 036119812199938
Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Vehicle Body ◽  
Lane Change ◽  
Lane Changing ◽  
Car Following ◽  
Pilot Model ◽  
Naturalistic Driving

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

Estimating the Effects of Urban Street Incidents on Capacity

2017 ◽  
Vol 2615 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Aidin Massahi ◽  
Mohammed Hadi ◽  
Maria Adriana Cutillo ◽  
Yan Xiao
Keyword(s):  
Network Performance ◽  
Field Measurements ◽  
Signalized Intersection ◽  
Limited Information ◽  
Microscopic Simulation ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Urban Street ◽  
Incident Duration

The effect of incidents on capacity is the most critical parameter in estimating the influence of incidents on network performance. The Highway Capacity Manual 2010 (HCM 2010) provides estimates of the drop in capacity resulting from incidents as a function of the number of blocked lanes and the total number of lanes in the freeway section. However, there is limited information on the effects of incidents on the capacity of urban streets. This study investigated the effects on capacity of the interaction between the drop in capacity below demand at a midblock urban street segment location and upstream and downstream of signalized intersection operations. A model was developed to estimate the drop in capacity at the incident location as a function of the number of blocked lanes, the distance from the downstream intersection, and the green time–to–cycle length (g:C) ratio of the downstream signal. A second model was developed to estimate the reduction in the upstream intersection capacity resulting from the drop in capacity at the midblock incident location as estimated by the first model. The second model estimated the drop in capacity of the upstream links feeding the incident locations as a function of incident duration time, the volume-to-capacity (V/C) ratio at the incident location, and distance from an upstream signalized intersection. The models were developed on the basis of data generated with the use of a microscopic simulation model calibrated by comparison with parameters suggested in HCM 2010 for incident and no-incident conditions and by comparison with field measurements.

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