Variable analysis for freeway work zone capacity prediction

Abstract

In order to keep the freeway network in a good state, regular road works such as maintenance and extension are necessary. These road works create physical changes on freeway and result capacity reduction. If the capacity can be predicted, a systematic planning of traffic management can be executed for maintaining certain capacity. However the current work zone capacity prediction models are not sufficiently accurate. The conventional capacity prediction models take limited number of work zone variables into account. Only a few studies mentioned the quantitative impacts of some variables on the capacity while no study is dedicated on. This research evaluates and quantifies the impact of various variables on the capacity. First a comprehensive summary of variables is given. Then all variables are evaluated and selected. A modified neuro-fuzzy model is used to analyze the relation between the capacity and the considered variables for a Dutch case study. The following twelve variables show great influence: percentage of heavy vehicles, lane width, lateral distance, number of lanes, distance to ramp, month factor, sight deprivation, temporary speed limit, work zone length, work zone transition length, work zone layout, work phase. The output of this research can support for a comparably accurate freeway work zone capacity prediction. Furthermore, traffic management measures can be developed based on the findings for each individual variable, e.g. dynamic speed limit measure can be implemented by considering an optimal combination of temporary speed limit and work zone transition length.