Analyzing intra-metropolitan variation in highway traffic performance in Los Angeles using archived real-time data

Abstract

We conduct a case study of highway system performance in Los Angeles County. We use the Los Angeles Archived Data Management System, a comprehensive archive of regional real-time multi-modal transportation system data, to analyze effects of systematic, functional, random, and land use factors on performance variation over different time periods of the day. To understand functional class effects, we use cluster analysis on geometric and demand parameters to identify functionally similar groups of highway segments. We compare performance between groups and across segments within groups. We perform regression analysis to test the influence of various factors on performance. We find that after controlling for time of day, accidents, and adjacent population density, group or peer effects have significant influence. This suggests that peer group level, as opposed to regional, performance measurement and monitoring is useful. Our research has significant implications for transportation system monitoring and planning.