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Related Concept Videos

Design Consideration01:22

Design Consideration

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Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
The factor of safety is another key...
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PD Controller: Design01:26

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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Related Experiment Video

Updated: Dec 11, 2025

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Developing Bicycle-Vehicle Crash-Specific Safety Performance Functions in Alabama Using Different Techniques.

Niloufar Shirani-Bidabadi1, Naveen Mallipaddi1, Kirolos Haleem2

  • 1Department of Civil and Environmental Engineering University of Alabama in Huntsville, Huntsville, AL, 35899.

Accident; Analysis and Prevention
|August 25, 2020
PubMed
Summary

This study developed bicycle-vehicle safety performance functions (SPFs) using Conway-Maxwell-Poisson and machine learning models. Multivariate adaptive regression splines (MARS) proved superior for predicting bicycle-vehicle crashes across various road facilities.

Keywords:
Bicycle-Vehicle CrashesCOM-PoissonConway-Maxwell-PoissonMARSMultivariate Adaptive Regression SplinesSafety Performance Functions

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Area of Science:

  • Transportation Engineering
  • Traffic Safety Research
  • Road Safety Analysis

Background:

  • Bicycle-vehicle crashes pose a significant safety concern on roadways.
  • Existing safety performance functions (SPFs) may not fully capture the complexities of bicycle-vehicle interactions.
  • The Highway Safety Manual (HSM) requires updated SPFs for various road facilities.

Purpose of the Study:

  • To develop and compare bicycle-vehicle safety performance functions (SPFs) for five key roadway facilities.
  • To evaluate the effectiveness of the Conway-Maxwell-Poisson (COM-Poisson) model and multivariate adaptive regression splines (MARS) for crash prediction.
  • To identify significant factors influencing bicycle-vehicle crash occurrence.

Main Methods:

  • Utilized a dataset of 1,311 bicycle-vehicle crashes in Alabama (2011-2015).
  • Applied the COM-Poisson model to account for crash under-dispersion.
  • Employed the MARS machine learning technique, known for handling non-linear relationships and interactions.
  • Developed SPFs for urban two-lane undivided (U2U) segments, urban four-lane divided/undivided (U4DU) segments, rural two-lane undivided (R2U) segments, urban signalized (USG) intersections, and urban stop-controlled (UST) intersections.

Main Results:

  • For roadway segments (U2U), factors like curves, grades, and heavy traffic flow influenced crash frequency.
  • At urban signalized intersections (USG), the absence of right-turn lanes, bus stops, and increased major road AADT correlated with more crashes.
  • Divided medians on major approaches reduced crashes at both signalized (USG) and stop-controlled (UST) intersections.
  • MARS models demonstrated superior performance over COM-Poisson models across all five facilities based on MAD, MSPE, and generalized R-square.

Conclusions:

  • MARS is a highly effective and promising technique for predicting bicycle-vehicle crashes.
  • The findings provide valuable insights for improving road safety for cyclists on various facility types.
  • Updated SPFs are crucial for effective traffic safety management and infrastructure design.