01659763

Component

https://doi.org/10.1177/0361198118799166

https://doi.org/10.1177/0361198118799166

In this study, a two-regime, steady-state, traffic stream model is developed by applying the macroscopic Gazis–Herman–Rothery model to fixed sensor data on freeways. The uncongested and congested regimes are modeled discontinuously with an overlap range defined in terms of density. The overlap is important as various phenomena related to the change in traffic state can be modeled by introducing this overlap. Two empirical tools for removing non-stationary, mixed-state, and erroneous observations are applied at different stages of the model development process. Three constraints justified by the Highway Capacity Manual (HCM) were applied to fit the model so that the fitted parameters have reasonable and physically interpretable values. The proposed model is applied to one year of data (2013) obtained from fixed sensors located at five freeway basic segments near Raleigh, North Carolina. The resulting fundamental diagrams show that the fitted models reasonably represent the steady-state observations. Two forms of the freeway flow model described in the HCM were applied to the same observations to provide a continuous model comparison. Two statistical performance measures, mean squared error of flow rate and Bayesian Information Criterion, verify that the proposed model is preferable to the HCM models both in terms of fit alone and when considering the tradeoff between fit and model complexity. It is expected that the proposed discontinuous steady-state model will be useful to researchers and practitioners to study various site-specific freeway traffic stream characteristics.

18-06577

English

pp 51-62

2018

Transportation Research Record: Journal of the Transportation Research Board

Print

Figures (6) ; References (25) ; Tables (2)

Freeways; Macroscopic traffic flow; Sensors; Statistical analysis; Traffic data

Highway Capacity Manual

Raleigh (North Carolina)

Highways; Planning and Forecasting

TRIS, TRB, ATRI

Jan 8 2018 11:42AM

• A Macroscopic Multi-Lane Traffic Flow Model for ACC/CACC Traffic Dynamics
• A New Control Strategy Integrated into the Desired Safety Margin Car-Following Model Considering the Disturbance Level
• A Platooning Strategy for Automated Vehicles in the Presence of Speed Limit Fluctuations
• A Simple Example of a Two-Dimensional Model for Traffic: Discussion about Assumptions and Numerical Methods
• Control of Connected and Autonomous Vehicles with Cut-in Movement using Spring Mass Damper System
• Evaluating Recurring Traffic Congestion using Change Point Regression and Random Variation Markov Structured Model
• Extended Desired Safety Margin Car-Following Model That Considers Variation of Historical Perceived Risk and Acceptable Risk
• Improved Numerical Method for Aw-Rascle Type Continuum Traffic Flow Models
• Introducing a Re-Sampling Methodology for the Estimation of Empirical Macroscopic Fundamental Diagrams
• Investigating Transfer Flow between Urban Networks Based on a Macroscopic Fundamental Diagram
• Model for Planning and Sizing Curbside Parking Lanes in Urban Networks
• Modeling and Evaluating Traffic Flow at Sag Curves When Imposing Variable Speed Limits on Connected Vehicles
• Modeling Driver Merging Behavior: A Repeated Game Theoretical Approach
• Multi-Thread Optimization for the Calibration of Microscopic Traffic Simulation Model
• Off-Ramp Coupling Conditions Devoid of Spurious Blocking and Re-Routing
• Optimizing Transit Signal Priority Implementation along an Arterial
• Perimeter Control as an Alternative to Dedicated Bus Lanes: A Case Study
• Real-Time Estimation of Lane Change Risks Based on the Analysis of Individual Vehicle Interactions
• Resurrecting the Lost Vehicle Trajectories of Treiterer and Myers with New Insights into a Controversial Hysteresis
• Some Indications on How to Calibrate the Social Force Model of Pedestrian Dynamics
• Traffic Flow of Merging Pedestrian Crowds: How Architectural Design Affects Collective Movement Efficiency
• Traffic Shockwave Detection in a Connected Environment using the Speed Distribution of Individual Vehicles