01622559

Component

https://doi.org/10.3141/2645-11

Identifying and classifying traffic and congestion patterns are essential parts of modern traffic management underpinned by the emerging intelligent transport systems. This paper explores the potential of using a combination of image processing methods to identify and classify regions of congestion within spatiotemporal traffic (speed, flow) contour maps. The underlying idea is to use these regions as (archetype) shapes that in many combinations can make up a wide variety of larger-scale traffic patterns. In this paper, use of a so-called statistical shape model is proposed as a low-dimensional representation of the archetype shape, and an active shape model algorithm coupled with linear classification is developed to classify the patterns of interest. Application of the proposed method is demonstrated with a preliminary set of speed contour maps reconstructed from loop detector data in the Netherlands. The results show that the extended active shape model can be used as a multiclass classifier. In particular, 70% of the traffic patterns in the test data were correctly classified with use of only two archetype shapes and simple logistic classifiers. The results point to the importance of use of expert knowledge by means of (a priori) manual classification of the training examples. This work opens many research directions, including semiautomated searches through traffic databases, automatic detection, and classification of new traffic patterns.

01628860

17-01621

English

pp 94–103

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309460408

Digital/other

Figures (6) ; References (21) ; Tables (2)

Classification; Contours; Image processing; Pattern recognition systems; Shape; Traffic congestion

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

TRIS, TRB, ATRI

Dec 8 2016 10:32AM

• 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
• Multicontextual Machine-Learning Approach to Modeling Traffic Impact of Urban Highway Work Zones
• 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
• Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data
• Vehicle Trajectory Analysis System via Mutual Information and Sparse Reconstruction