Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Manipulation and Analysis01:21

Manipulation and Analysis

61
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
61
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.4K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.4K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.6K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.6K
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

14.3K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
14.3K
Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

13.1K
An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
13.1K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

69
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
69

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Dual-Functional Luminescent MOF Sensor for Phenylmethanol Molecule and Tb<sup>3+</sup> Cation.

Inorganic chemistry·2018
Same author

The Development and Test of a Sensor for Measurement of the Working Level of Gas-Liquid Two-Phase Flow in a Coalbed Methane Wellbore Annulus.

Sensors (Basel, Switzerland)·2018
Same author

A Thioflavin T-induced G-Quadruplex Fluorescent Biosensor for Target DNA Detection.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2018
Same author

A metal-organic framework derived hierarchical nickel-cobalt sulfide nanosheet array on Ni foam with enhanced electrochemical performance for supercapacitors.

Dalton transactions (Cambridge, England : 2003)·2018
Same author

Functional analysis of a type 2C protein phosphatase gene from Ammopiptanthus mongolicus.

Gene·2018
Same author

[Inversive LISS plate in treating intertrochanteric and subtrochanteric fractures combined with femoral shaft fractures].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2018

Related Experiment Video

Updated: Sep 13, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

Intersection crash analysis considering longitudinal and lateral risky driving behavior from connected vehicle data:

Lei Han1, Mohamed Abdel-Aty1

  • 1Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, United States.

Accident; Analysis and Prevention
|July 31, 2025
PubMed
Summary

Connected vehicle data reveals that analyzing lateral turning behaviors, like hard left and right turns, significantly improves intersection crash prediction accuracy. This approach accounts for spatial variations and nonlinear effects in driving patterns.

Keywords:
Connected vehicle dataIntersection safetyRear-end crashesRisky driving behaviorSpatial heterogeneityTurn crashes

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.5K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.4K

Related Experiment Videos

Last Updated: Sep 13, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.5K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.4K

Area of Science:

  • Transportation Engineering
  • Traffic Safety Analysis
  • Machine Learning Applications

Background:

  • Traditional intersection safety studies focus on macro-level data, neglecting micro-level driving behaviors.
  • Connected Vehicle (CV) technology enables extraction of detailed driving dynamics.
  • Lateral turning behaviors at intersections are critical for safety but have been understudied.

Purpose of the Study:

  • To comprehensively analyze intersection driving dynamics by including both longitudinal and lateral behaviors.
  • To address spatial heterogeneity and nonlinear effects in crash frequency prediction.
  • To improve the accuracy of intersection crash prediction models.

Main Methods:

  • Extraction of driving behavior features, including longitudinal movements and lateral turns, from CV data.
  • Development of a novel spatial Machine Learning (ML) framework integrating nonlinear ML models (LightGBM) with geographically weighted regression.
  • Training both global and localized ML models to capture average estimations and spatial heterogeneity.

Main Results:

  • Inclusion of lateral turning behaviors significantly enhanced intersection crash frequency prediction accuracy.
  • The proposed spatial ML framework integrating LightGBM outperformed traditional models (Random Forest, XGBoost, LightGBM, MLP) in RMSE, MAE, and R².
  • Driving features exhibit nonlinear impacts and spatial heterogeneity; hard braking and acceleration influence rear-end crashes differently in urban vs. downtown areas, while hard left turns impact sideswipe and left-turn crashes in suburban areas.

Conclusions:

  • Lateral turning behaviors are crucial predictors of intersection safety.
  • Spatial ML frameworks effectively capture localized driving patterns and improve crash prediction.
  • Understanding the context-specific (e.g., downtown, suburban) nonlinear impacts of driving behaviors is vital for targeted safety interventions.