Measuring Traffic
Peter J. Bickel, Chao Chen, Jaimyoung Kwon, John Rice, Erik van Zwet,, Pravin Varaiya
TL;DR
This paper discusses the challenges and solutions in processing large-scale, variable-quality traffic data from sensors, focusing on sensor malfunction detection, data imputation, velocity estimation, and travel time forecasting.
Contribution
It introduces statistical methods for handling sensor data issues and improving traffic performance measurement in a statewide system.
Findings
Effective sensor malfunction detection techniques
Improved data imputation methods for missing or bad data
Enhanced travel time forecasting accuracy
Abstract
A traffic performance measurement system, PeMS, currently functions as a statewide repository for traffic data gathered by thousands of automatic sensors. It has integrated data collection, processing and communications infrastructure with data storage and analytical tools. In this paper, we discuss statistical issues that have emerged as we attempt to process a data stream of 2 GB per day of wildly varying quality. In particular, we focus on detecting sensor malfunction, imputation of missing or bad data, estimation of velocity and forecasting of travel times on freeway networks.
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