Innovative Method for Estimating Large Truck Volume Using Aggregate Volume and Occupancy Data Incorporating Empirical Knowledge Into Linear Programming

Abstract

Monitoring large trucks (LT) plays an essential role in the operation and planning of transportation and economics. Although many “advanced” traffic detectors (e.g., infrared, microwave, and magnetic sensors) can identify LT, those technologies are not as widely deployed as loop detectors, and their performance is disputed. While many previous studies have investigated estimation of truck traffic using gross volume and occupancy information from loop detectors, most of them are based on mean effective vehicle length (MEVL), and thereby vulnerable to dynamic traffic conditions and dependence on exogenous data sources. This paper aims to develop an original method of estimating LT volume using detector output (i.e., volume and occupancy). A linear optimization problem is formulated by minimizing the difference between observed occupancy and theoretical “best guess.” LT and short vehicle (SV) lengths and their counts are devised as decision variables to compensate for variations in speed and length. To assess the proposed method, volume and occupancy data output in 30 s granularity from three different cases are prepared: microscopic simulation, Tennessee Radar Detector System (TN-RDS), and California Freeway Performance Measures System (PeMS). For the simulation, 12 different scenarios were built up and the results indicate that the mean absolute error of LT estimates increases with traffic volume and truck penetration. In two real-world cases, the LT estimates are close to ground truth from video and weigh-in-motion (WIM) station records. Furthermore, this simple and online LT volume estimate approach can be extended to other stations and to monitor a wide range of LT activities.