Monte Carlo Simulation of Household Travel Survey Data for Sydney, Australia: Bayesian Updating Using Different Local Sample Sizes

2004 ◽  
Vol 1870 (1) ◽  
pp. 102-108 ◽  
Author(s):  
Graham Pointer ◽  
Peter R. Stopher ◽  
Philip Bullock
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Survey Data ◽  
Bayesian Updating ◽  
Sample Sizes ◽  
Travel Survey ◽  
Household Travel Survey ◽  
Local Sample ◽  
Household Travel ◽  
Sydney Australia

scholarly journals Evaluating the Effects of Household Characteristics on Household Daily Traffic Emissions Based on Household Travel Survey Data

2019 ◽  
Vol 11 (6) ◽  
pp. 1684 ◽  
Author(s):  
Chengcheng Xu ◽  
Shuyue Wu
Keyword(s):  
Survey Data ◽  
Household Income ◽  
Traffic Emissions ◽  
Contributing Factors ◽  
Travel Survey ◽  
Household Characteristics ◽  
Positive Effects ◽  
Household Travel Survey ◽  
Household Travel ◽  
Pm2.5 Emissions

This study aimed to investigate the effects of household characteristics on household traffic emissions. The household travel survey data conducted in the Jiangning District of Nanjing City, China were used. The vehicle emissions of household members’ trips were calculated using average emission factors by average speed and vehicle category. Descriptive statistics analysis showed that the average daily traffic emissions of CO, NOx and PM2.5 per household are 8.66 g, 0.55 g and 0.04 g respectively. The household traffic emissions of these three pollutants were found to have imbalanced distributions across households. The top 20% highest-emission households accounted for nearly two thirds of the total emissions. Based on the one-way ANOVA tests, the means of CO, NOx and PM2.5 emissions were found to be significantly different over households with different member numbers, automobile numbers, annual income and access to the subway. Finally, the household daily traffic emissions were linked with household characteristics based on multiple linear regressions. The contributing factors are slightly different among the three different emissions. The number of private vehicles, number of motorcycles, and household income significantly affect all three emissions. More specifically, the number of private vehicles has positive effects on CO and PM2.5 emissions, but negative effect on NOx emissions. The number of motorcycles and the household income have positive effects on all three emissions.

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