Modelling the Free Flow Speed and 85th Percentile Speed Using Artificial Neural Network (ANN) and Genetic Algorithm

Abstract

One of the main and crucial purposes of highway system is to provide a safe, comfortable, and efficient mode of transportation. Vehicular speed is considered as one of the high-priority issue to transportation engineers, planners and researchers, where it is a significant measure of effectiveness of traffic performance particularly under mixed traffic condition, which affects the traffic movement and level of service at different transportation facilities. In order to propose a new analytical method for vehicular speed, speed prediction models based on Artificial Neural Network (ANN) models and Genetic Algorithm (GA) have been developed for major urban arterial roads in Jordan. Prediction models have been developed with several input parameters and included within the ANN and GA. Traffic and geometric variables such as left lateral clearance, right lateral clearance, speed limit, Present Condition Index (PCI), shoulder width, lane width, access point density, International Roughness Index (IRI) and Present Serviceability Rating (PSR) have been included in both models. Meanwhile, the outputs of prediction models have been 85th percentile speed and Mean Free Flow Speed (MFFS) km/hr. The computational results show that the speeds predicted by the developed models are highly correlated with the measured ones. ANN modeling has a better performance than GA in estimation of 85th percentile speed. This study provides useful information to predict the FFS and 85th percentile speed on roads depending on variables, which are available on any constructed or to be constructed roads.