Overview

Accurate prediction of the macroscopic traffic stream variables such as speed and flow is essential for the traffic operation and management in an Intelligent Transportation System (ITS). Adverse weather conditions like fog, rainfall, and snowfall affect the driver's visibility, vehicle's mobility, and road capacity. Accurate traffic forecasting during inclement weather conditions is a non-linear and complex problem as it involves various hidden features such as time of the day, road characteristics, drainage quality, etc. With recent computational technologies and huge data availability, such a problem is solved using data-driven approaches. Traditional data-driven approaches used shallow architecture which ignores the hidden influencing factor and is proved to have limitations in a high dimensional traffic state. Deep learning models are proven to be more accurate for predicting traffic stream variables than shallow models because they extract the hidden features using the layerwise architecture. The impact of weather conditions on traffic is dependent on various hidden features. The rainfall effect on traffic is not directly proportional to the distance between the weather stations and the road segment because of terrain feature constraints. The prolonged rainfall weakens the drainage system, affects soil absorption capability, which causes waterlogging. Therefore, to capture the spatial and prolonged impact of weather conditions, we proposed the soft spatial and temporal threshold mechanism. Another concern with weather data is the traffic data has a high spatial and temporal resolution compared to it. Therefore, missing weather data is difficult to ignore, the spatial interpolation techniques such as Theissen polygon, inverse distance weighted method, and linear regression methods are used to fill out the missing weather data. i

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9781805458395

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