Bimodal Distribution Function with Fuzzy Regression in Predicting Random Truckload Patterns
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9, Issue 1
Abstract
A bimodal distribution function with statistical parameters obtained using fuzzy regression is presented for predicting random truckload patterns that include all load ranges including overloads. Overload trucks often appear as a sizable portion of truck populations on highways. In applications when damage estimation of transportation facilities such as pavements and bridges is desired, theoretical models providing a reasonable representation of truckload populations including overloads will be useful. Load populations mostly exhibit inconsistent patterns, often with two or more distinct peaks. This is because of a combination of loaded and empty trucks as well as overloads in the population. As such, a mixed distribution model instead of a simple statistical distribution is used to portray a realistic representation of truckload populations. In this paper, using the bimodal model, theoretical distributions are developed to (1) represent the entire truckload population with weigh-in-motion (WIM) data, and (2) predict the population with limited data. If no truckload population is available, the bimodal distribution model can still be used, with certain assumptions, based on fuzzy regression using certain parameters pertaining to the traffic data such as the average daily truck traffic and an overall estimate of the percentage of overload data in the population. This paper represents new directions in modeling truckload patterns with capabilities to offer the continuity as well as the bimodal feature of the limited load data.
Practical Applications
Overload trucks, which refers to truck weights in excess of the 356 kN (80 kips), in the US often appear as a sizable portion of truck populations on highways. As truck populations grow, there is a potential for increase in the frequency of overloads as well. In applications when damage estimation of transportation facilities such as pavements and bridges are desired, theoretical models providing a reasonable representation of truck load populations including overloads will be useful. Load populations mostly exhibit an inconsistent pattern, often with two or more distinct peaks. The reason is attributed to the variety of loads in the population, and in most cases, it can be because of a combination of loaded and empty trucks as well as overloads appearing in the data. Mixed probability distribution models consisting of two functions (bimodal models) have been tried in the past and appear to offer a better solution for truck load populations. In this study, using the bimodal model, theoretical distributions are developed to (1) represent the entire truck load population with WIM data, and (2) predict the population with certain assumptions based on fuzzy regression using limited traffic pattern information available for the roadway.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors wish to acknowledge the Illinois and Michigan Departments of Transportation for their cooperation and support of the research in providing the WIM data.
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Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9 • Issue 1 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 10, 2022
Accepted: Oct 10, 2022
Published online: Nov 26, 2022
Published in print: Mar 1, 2023
Discussion open until: Apr 26, 2023
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Design (by type)
- Distribution functions
- Dynamic loads
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Fuzzy logic
- Highway transportation
- Infrastructure
- Load factors
- Mathematical functions
- Mathematics
- Models (by type)
- Parameters (statistics)
- Regression analysis
- Solid mechanics
- Statistical analysis (by type)
- Statistics
- Structural design
- Structural dynamics
- Traffic models
- Transportation engineering
- Trucks
- Vehicle loads
- Vehicles
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