M. Rahman, I.K. Shafie
Pages: 247-268
Abstract
Traffic calming devices (TCD) like speed bumps are effective measures in reducing speed on residential roads but in a limited section of the road. Previous studies found that if the entrance speed of the traffic calming road (TCR) is higher than the posted speed limit (30 kmph), it will affect speed reduction by TCD. Little research has been undertaken on predicting speed at the entrance of a TCR. Thus, the purpose of this study is to predict the variables of interest in the context of the study area by applying a methodological approach described in this study; a combination of multiple linear regression, and artificial neural networking (ANN) models. Seventeen variables derived from four categories including road design, roadside environment, traffic, and speed were considered in the proposed model. Two approaching criterion was defined in this study: a minor (speed limit 30 km/h) and a major approach (speed limit 40/50 km/h). Over 1000 vehicle speeds were collected from almost 20 different urban roads in Japan. Entrance characteristics, lane width, barrier types, posted speed limit, etc. have found as significant predictors in the regression output. Furthermore, the ANN technique was employed where the input variables were the significant predictors of regression models, and variable importance was also calculated using sensitivity analysis. Although the results of sensitivity and regression are different, sensitivity plays a role in illuminating the relationship between road factors and speed. By comparing the prediction performance between the two models this study reveals that developing an ANN model followed by regression output could be a consistent method for analyzing the entry speed of TCR for varying approaches. Additionally, the findings can be used to effectively predict the entry speed of TCR without installing a gateway design and help to achieve a safer speed through modifications to the road features.
Keywords: entry speed; traffic calmed road; road features; regression analysis; Artificial Neural Network