This paper discusses the development of fuzzy logic model for estimating the 85
percentile speed of urban roads. Spot speed survey was conducted on four randomly selected urban road segments for a typical weekday and a weekend. The considered road segment attribute data are length of the road segment, number of access points/intersecting links, number of pedestrian crossings, number of lanes, hourly traffic volume, hourly pedestrian volume and current posted speed limits of the selected roads. Such attribute data were collected and used as input variables in the model. Two models for weekday and weekend were developed based on the field survey data. Both models were calibrated using the neuro-fuzzy technique for optimizing the fuzzy logic model (FLM) parameters. Analyses of estimated results show that the FLM can estimate the 85
percentile speed to a reasonable level.