Abstract
The objective of this study is to quantify the correlation between nearly 100 external factors and over 70 performance measures of the Florida multimodal transportation system, based on the empirical data covering a 10 year period (2009–2018). We first use time-lagged cross-correlation to quantify the correlation between each pair of external factors and performance measures. We then identify the highly correlated external factors with all performance measures or a subset of them. We find that Percentage of Population in Poverty (Florida), Number of Housing Units (Florida), House Price Index (National), Consumer Price Index–Rent Price Index (Florida), and Percentage of Population in Poverty (National) are the external factors highly correlated with the whole system, while the external factors highly correlated with different modes vary. This paper thus contributes to the transportation performance measurement literature by proposing a practical procedure based on time series analysis to help transportation agencies identify important external factors for tracking and monitoring.
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