Freeway Traffic Speed Estimation Using Single Loop Outputs

Traffic speed is one of the most important indicators for traffic control and management. Unfortunately, speed can not be measured directly from single inductance loops, the most commonly used detectors. To calculate space-mean speed, a constant g is often adopted to convert lane occupancy to traffic density. However, as will be illustrated by our data in this study, such a formula consistently underestimates speed whenever a significant number of trucks and/or other longer vehicles are present. This is due to the fact that the g value is actually not a constant, but rather a function of vehicle length. To calculate the g value suitably, we need to know the long vehicle (LV) percentage or mean vehicle length in real-time. However, such information is not directly available from single loop outputs. In this paper, we show how the occupancy variance obtained from single loop data can be used to estimate long vehicle percentage, and how a log linear regression model for mean vehicle length estimation based only on single loop outputs can be developed. The estimated mean vehicle length is used to calculate the corresponding g value in real-time in order to estimate speed more accurately. Our speed estimations with corrected g are very close to the speeds observed by the speed trap in the current study.