Estimating percentile speeds from maximum operating speed frontier

• Autor(a) principal: António Lobo
• Data de Publicação: 2014
• Outros Autores: Carlos Rodrigues, António Couto
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://repositorio-aberto.up.pt/handle/10216/85063
• topic: Engenharia de infra-estruturas, Engenharia civil; Infrastructures engineering, Civil engineering

Most operating speed studies have focused on modeling a specific percentile speed, most notably the 85th, as a function of the road geometries. This method has resulted in some drawbacks, such as the loss of information due to speed data aggregation, the inability to capture speed dispersion, and few references about the effects of the driving culture and vehicle characteristics on the practiced speeds. Therefore, an operating speed frontier model to improve speed prediction capabilities, is presented. The deterministic component of the model represents the maximum operating spot speed as a function of the local geometric features, whereas the disturbance term includes the nongeometric effects, such as driving behavior, type of vehicle, and road environment. Data are collected in 88 curves and tangents of Portuguese two-lane highways located outside urban areas; approximately 18,000 free-flow vehicles were observed. Following an innovative approach to operating speed modeling, the model is estimated with a stochastic frontier regression between the speeds of all free-flow vehicles and the geometric features at the measurement sites. In addition to the maximum operating speed, the new model is capable of estimating any percentile speed through the cumulative function of the one-sided disturbance while avoiding speed data aggregation. Moreover, the road geometric features required to implement the model are easy to obtain either by consulting the design project or by performing on-site measurements; this ability contributes to the model's applicability in different regions.