01042531

Component

https://doi.org/10.3141/2024-02

http://scholar.google.com...tsky&publication_year=2007

Knowledge of the normal traffic flow pattern is required for a number of transportation applications. Traditionally, the simple historic average has been considered as the best way to derive the traffic pattern. However, this method may often be significantly biased by the presence of incidents. One solution to avoid this bias is through visual inspection of the data by experts. The experts could identify anomalies caused by incidents and thereby identify the underlying normal traffic patterns. Three main challenges of this approach are (a) the bias introduced because of subjectivity, (b) the additional time required to analyze the data manually, and (c) the increasing sizes of the available traffic data sets. To address these challenges and also to exploit the potential of information technology, new data analysis tools are essential. In this research, a new tool, the quantum-frequency algorithm, was developed. This algorithm can aid in the automated identification of traffic flow patterns from large data sets. The paper presents the algorithm along with its theoretical basis. Finally, in the case study presented in the paper, the algorithm was able to identify a reasonable traffic pattern automatically from a large set of archived data. When compared with the historic average, it was found that the pattern identified by the quantum-frequency algorithm resulted in 39% lower cumulative deviation from the pattern identified manually by experts.

01088321

English

pp 8-17

2007

Transportation Research Record: Journal of the Transportation Research Board

9780309104517

Print

Figures (8) ; References (24) ; Tables (1)

Algorithms; Data files; Quantum mechanics; Traffic data; Traffic flow; Traffic flow theory; Traffic incidents; Travel patterns

Automated identification systems

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Feb 8 2007 6:38PM

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• Decision Support System for Predicting Traffic Diversion Impact Across Transportation Networks Using Support Vector Regression
• Evaluating Highway Capacity Investments Using a Geographic Information Systems-Based Tool: Trip-Based Full Marginal Cost Approach
• Forecasting of Short-Term Freeway Volume with v-Support Vector Machines
• Fuzzy Optimization Approach: Comparison with the Classical Optimization Method Using the Problem of Timing a Traffic Signal
• Information Technology Used to Evaluate Nontraditional Traffic Monitoring Systems
• Methodology for Selection and Development of TransXML Schemas
• Transportation Asset Management and Visualization Using Semantic Models and Google Earth
• Visualizing and Analyzing Activities in an Integrated Space-Time Environment: Temporal Geographic Information System Design and Implementation
• Wavelet-Based Pavement Distress Image Edge Detection with À Trous Algorithm