Monte Carlo Method and Applications in Urban Population and Traffic Simulations

Monte Carlo simulation provides a powerful computational framework for spatial analysis and has become increasingly popular with rising computing power. Some applications include data disaggregation, designing a statistical significance test and modeling individual behaviors. This chapter demonstrat...

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Bibliographic Details
Published inComputational Methods and GIS Applications in Social Science - Lab Manual pp. 220 - 238
Main Authors Liu, Lingbo, Wang, Fahui
Format Book Chapter
LanguageEnglish
Published United Kingdom CRC Press 2024
Taylor & Francis Group
Edition1
Subjects
Column Selection
Traffic Simulation
Joiner Node
CMSA
Bottom Input
Census Tract Centroids
Result Table
Case Study 12A
Case Study 12B
CSV File
Aggregation Method
Python Script
Left Table
Road Network Dataset
Origin Id
Top Input
File Path
Destination Id
Group Column
Census Tracts
Id Column
Census Blocks
Groups Tab
Shortest Network Paths
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Summary:Monte Carlo simulation provides a powerful computational framework for spatial analysis and has become increasingly popular with rising computing power. Some applications include data disaggregation, designing a statistical significance test and modeling individual behaviors. This chapter demonstrates the value of Monte Carlo technique in spatial analysis in two case studies. One simulates urban population density patterns across uniform area units, and another simulates urban traffic patterns. Monte Carlo simulation provides a powerful computational framework for spatial analysis and has become increasingly popular with rising computing power. Some applications include data disaggregation, designing a statistical significance test and modeling individual behaviors. This chapter demonstrates the value of Monte Carlo technique in spatial analysis in two case studies. One simulates urban population density patterns across uniform area units, and another simulates urban traffic patterns. Main data sources include the 2010 Census data and the 2010 Land Use Inventory data. Python script file ProbSampling.py based on the np.random function in the Numpy package is used to implement a Monte Carlo simulation of OD traffic volumes according to the distribution of Trip.
ISBN:9781032302430
1032302437
DOI:10.1201/9781003304357-12