Accident; analysis and prevention · 2018
Effects of modal shares on crash frequencies at aggregate level.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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Mohammadi Mehdi, Shafabakhsh Gholamali, Naderan Ali
Аннотация
INTRODUCTION: Long-range transportation plans often involve proposals for improvements/ changes in different modes of travel. This means that modal share of trips generated at each traffic analysis zone (TAZ) by mode of travel needs to be predicted/ forecasted for safety evaluation purposes. The objective of this research study is to develop a series of aggregate crash prediction models (ACPMs) that relate with the modal split step of the conventional four-step demand models.
METHOD: The models are developed utilizing network and vehicular, socio-economical, trip production/attraction and trip frequencies by mode at TAZ-level as explanatory variables in a generalized linear regression with the assumption of a negative binomial error structure. Crash frequencies are split into total crashes (TC) and severe crashes (SC).
RESULTS: The models prove promising in estimating crash frequencies upon changes in modal shares, which is essential in safety assessment of alternate transportation demand management (TDM) scenarios. Trips made in car, bus, and bus Service mode became significant in the estimated TC and trips made in car, taxi, school service, bus service and moped mode became significant in the estimated SC ACPMs.
CONCLUSIONS: The ACPMs may be used from two different points of view. First and most appropriate use is to consider these as tools to forecast future crash frequencies and develop long-term plans to counteract. In the second point of view, ACPMs act as the primary planning tool to identify how any increase in a specific mode-ridership will contribute to crash frequencies. This is of great interest in developing plans that involve increased use of a specific mode.
PRACTICAL APPLICATION: As modal shares are forecasted in certain years into the future by the modal split step of demand modeling, crash frequencies could also be forecasted and safety implications of mobility improvement scenarios (e.g. increased number of trips by bus, car, etc.) would be evaluated.
METHOD: The models are developed utilizing network and vehicular, socio-economical, trip production/attraction and trip frequencies by mode at TAZ-level as explanatory variables in a generalized linear regression with the assumption of a negative binomial error structure. Crash frequencies are split into total crashes (TC) and severe crashes (SC).
RESULTS: The models prove promising in estimating crash frequencies upon changes in modal shares, which is essential in safety assessment of alternate transportation demand management (TDM) scenarios. Trips made in car, bus, and bus Service mode became significant in the estimated TC and trips made in car, taxi, school service, bus service and moped mode became significant in the estimated SC ACPMs.
CONCLUSIONS: The ACPMs may be used from two different points of view. First and most appropriate use is to consider these as tools to forecast future crash frequencies and develop long-term plans to counteract. In the second point of view, ACPMs act as the primary planning tool to identify how any increase in a specific mode-ridership will contribute to crash frequencies. This is of great interest in developing plans that involve increased use of a specific mode.
PRACTICAL APPLICATION: As modal shares are forecasted in certain years into the future by the modal split step of demand modeling, crash frequencies could also be forecasted and safety implications of mobility improvement scenarios (e.g. increased number of trips by bus, car, etc.) would be evaluated.