Impact of Weather Conditions on Macroscopic Traffic Stream Variables in an Intelligent Transportation System
"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. The deep learning models require a large amount of data for accurate prediction.The ITS infrastructure provides dense and complete traffic data. The installation and maintenance of ITS infrastructures are costly; therefore, the majority of road segments are dependent on cost-effective alternate sources of traffic data. The alternate source of traffic data provides sparse, incomplete, and erroneous information. To overcome the data sparsity issue, we proposed a mechanism to generate fine-grained synthetic traffic data using the SUMO traffic simulator. We studied the impact of rainfall on the traffic stream variables on the arterial, subarterial, and collector roads. An empirical model is designed and calibrated for a variety of traffic and weather conditions. The Krauss car-following model in SUMO is upgraded to use the proposed empirical model for computing the vehicle speed. The simulation model is validated by comparing the synthetic data with the ground truth data under various traffic and weather conditions. We find that the empirical model accurately captures the effect of rainfall on the traffic stream variables, and the synthetic data shows a very good match with the ground truth data. We adopted multiple deep learning models because of their underlying characteristics to extract the spatiotemporal features from the traffic and weather data. Convolutional Neural Network (CNN) model has the characteristics to extract neighboring pixels correlation. The sequence learning models, Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) learn dependencies in the data based on the past and the current information. We designed the hybrid deep learning models, CNN-LSTM and LSTM-LSTM. The former model extracts the spatiotemporal features and the latter model uses these features as memory. The latter model predicts the traffic stream variables depending upon the memory and the temporal input. The hybrid models are effective in learning the long-term dependency between the traffic and weather data. We performed various experiments to validate the deep learning models, we use the synthetic traffic data generated by SUMO using the empirical model for different road types (arterial, sub-arterial, and collector) and different road networks (single, small, and large). The results show that the deep learning model trained with the traffic and rainfall data gives better prediction accuracy than the model trained without rainfall data. The performance of the LSTM-LSTM model is better than the other models in all the scenarios. Considering the large road network, where roads are prone to waterlogging, under long-term dependency LSTM-LSTM outperforms the other deep learning models including RNN, CNN, LSTM, CNN-LSTM, and existing models. For the worst-case scenario, the traffic prediction error of LSTM-LSTM is between 3-15% for 15 to 60-minute future time instances, which is in line with the accuracy needed for ITS applications."
- PhD Theses