01631169

Component

http://amonline.trb.org/

Impact assessments of highway construction work zones (CWZs) are mandated for all federally funded highway infrastructure improvement projects. However, most existing approaches are ad hoc or project specific, so they are incapable of being benchmarked for any particular spatial region. A novel multicontextual approach to modeling the traffic impact of urban highway CWZs is proposed and tested in this paper. The proposed approach is unique because it models the impact of CWZ operations through a multicontextual quantitative method using big data for improved accuracy. In this study, a machine-learning technique was adopted to predict long-term traffic flow rates and the corresponding truck percentages. With the use of these predicted values, stereotypical patterns of traffic volume-to-capacity ratios were created for typical urban nighttime closures. Third-order curve-fitting models to achieve potential work zone travel time delays in heavily trafficked large urban cores were then developed and validated. This study will greatly help state and local governments and the general traveling public in major cities know the potential traffic flow resulting from construction and thereby facilitate progress on highway improvement projects with the better-informed work zone traffic flow and thus improve safety and mobility in and between CWZs.

01628860

17-05425

English

pp 184–194

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309460408

Print

Figures (5) ; References (43) ; Tables (3)

Mathematical prediction; Neural networks; Night; Traffic delays; Traffic flow; Traffic models; Travel time; Urban highways; Work zones

Construction; Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting

TRIS, TRB, ATRI

Dec 8 2016 12:08PM

• Application of Dempster–Shafer Data Fusion Technique in Support of Decision Making with Big Data
• Automated Vehicle Recognition with Deep Convolutional Neural Networks
• Data Processing Framework for Development of Driving Cycles with Data from SHRP 2 Naturalistic Driving Study
• Data-Based Sorting Algorithm for Variable Message Sign Location: Case Study of Beijing
• Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments
• Detection of Crack Growth in Asphalt Pavement Through Use of Infrared Imaging
• Development and Application of an Ecodriving Support Platform Based on Internet+: Case Study in Beijing Taxicabs
• Economic Feasibility Study for Pavement Monitoring Using Synthetic Aperture Radar-Based Satellite Remote Sensing: Cost–Benefit Analysis
• Enhancing Relative Positioning Accuracy in Vehicle-to-Vehicle Safety: Multivehicle Kinematics and Nonlinear Particle Filtering Approach
• Evaluating the Promotion of Innovative Mobility Options with an Agent-Based Modeling and Simulation Approach
• Hidden Markov Approach to Dynamically Modeling Car Ownership Behavior
• Hunt for Perfect Detection of Parking Occupancy: Evaluation of On-Street Technology and Its Ability to Address Urban Challenges
• Investigating Impacts of Communication Loss on Signal Performance with Use of Event-Based Data
• Methodology for Calculating Latency of GPS Probe Data
• Novel Cooperative Collision Avoidance Model for Connected Vehicles
• Optimized Video Tracking for Automated Vehicle Turning Movement Counts
• Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests
• Traffic Congestion Pattern Classification Using Multiclass Active Shape Models
• Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data
• Vehicle Trajectory Analysis System via Mutual Information and Sparse Reconstruction