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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...

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Related Experiment Video

Updated: Jul 25, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Clustering and pedestrian crashes prediction modelling: Amman case.

¹Hussein Technical University, Amman, Jordan.

International Journal of Injury Control and Safety Promotion

|June 25, 2023

Summary

Pedestrian casualties are a significant issue. This study found that road network length and housing permits best predict pedestrian deaths, with spatial patterns showing dispersion rather than clustering.

Keywords:

K-mean Moran’s I Pedestrian crashes general linear model spatial analysis spatial regression

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Area of Science:

Traffic safety research
Urban planning
Geographic information systems (GIS)

Background:

Pedestrian casualties represent a critical domestic and international safety concern.
Understanding the spatial distribution of these incidents is vital for effective prevention strategies.

Purpose of the Study:

To analyze the spatial distribution of pedestrian casualties.
To identify key contributing factors influencing these incidents.
To develop predictive models for pedestrian casualty events.

Main Methods:

Analysis of three years of crash data integrated with demographic, land use, and road network information.
Application of Kernel Density Estimation (KDE) to identify casualty hotspots.
Utilized spatial autocorrelation (Moran's I), cluster K-Means, spatial regression, and generalized linear regressions (GLM) for in-depth analysis.

Main Results:

Kernel Density Estimation identified a pedestrian death cluster within a 1250-meter radius.
Spatial autocorrelation analysis indicated that 17 out of 22 attributes related to casualties, road networks, demographics, and land use exhibited positive spatial autocorrelation.
The spatial pattern of pedestrian casualties was found to be random and insignificant over time, with a tendency towards dispersion.
Generalized Linear Models (GLM) with a Poisson distribution revealed that road network length, optionally combined with housing permits, were the most effective predictors of pedestrian casualties.
Spatial dimensions did not significantly enhance classic regression models, and autoregressive coefficients were not significant.

Conclusions:

Road network length and housing permits are key predictors for pedestrian casualty incidents.
The spatial distribution of pedestrian casualties tends towards dispersion, influenced by surrounding attributes.
Standard regression models, including spatial approaches, showed limited improvement in prediction accuracy compared to simpler GLM models.