Modelling the Operating Speed in Segments of Two-Lane Highways from Probe Vehicle Data: A Stochastic Frontier Approach

Abstract

Most of the existing operating speed statistical models are applicable to individual design elements, particularly horizontal curves and tangents. A segment approach to operating speed has rarely been followed, with a few exceptions mainly related to the performance assessment of urban and freeway corridors, or design consistency studies using speed profiles built from successive design elements. This study introduces a new model to predict operating speeds in segments of two-lane highways. The maximum operating speed is given by a stochastic frontier function of the average daily traffic and road geometrics; the asymmetric disturbance accounts for the diversity in drivers' behaviour and vehicle characteristics, allowing estimating any percentile speed. The model was calibrated using probe vehicle data from noncongested roads. The accuracy of the average daily traffic in representing the actual driving conditions was further validated using simultaneous speed-traffic measurements. The new model aims to assist practitioners in the evaluation of design consistency from a macroscopic perspective since the early stages of road planning and design, as well as to support the definition of speed limits at new or existing infrastructures.

Operating Vehicles’ Speed Prediction Models

“…According to Eboli et al (2015) [65] the main control element in the traffic system is the driver who specifically determines the direction and speed of the vehicle at every moment during driving. Similarly, Lobo et al (2018) [69] state that speed is one of the most important factors in traffic and that the driver's perception of operating speeds on different road routes, and thus travel time, strongly influence the driver's choice of route as well as the traffic situation consequently. Those are some of the reasons why certain authors studied the drivers' behaviour [38], [54] or used driving simulators as measuring devices for predicting the operating speed.…”

Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

“…In our approach, we utilize measured vehicle speed and calculated travel times (based on derived speed information) as input parameters for modelling. Speed is one of the most important factors because drivers' perception of the operating speeds of different routes and, consequently, of travel times and costs strongly influences their route choice and therefore traffic state (Lobo et al, 2018). Since the output (traffic congestion information) does not need to be categorically exact nor precise, we apply fuzzy logic theory to approximately model traffic congestion.…”