Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network Adverse weather conditions have a significant impact on the safety, mobility, and efficiency of highway networks. Weather contributed to 23 percent of all nonreoccurring delay and approximately 544 million vehicle hours of delay each year (2014). Nearly 2.3 billion dollars each year are spent by transportation agencies for winter maintenance that contribute to close to 20 percent of most DOTxe2x80x99s yearly budgets (2014). These safety and mobility factors make it important to develop new and more effective methods to address road conditions during adverse weather conditions. Given weather and traffic sensors installed along side of the highway networks, how can we automatically detect weather and traffic change events and prevent from the traffic delay or harsh weather accidents? To this end, we propose a novel framework to address this problem. This paper develops techniques for efficiently detecting rapid weather change events and analyzing their impacts on the traffic flow characteristics of a highway network. It is composed of three components, including 1) detection of rapid weather change events in a highway network using the streaming weather information from a sensor network of weather stations; 2) detection of rapid traffic change events on the traffic flow characteristics (e.g., travel time) of the highway network; and 3) analysis of correlations between the detected weather and traffic change events in space and time. The proposed approach was applied to a weather dataset provided by New York State Mesonet and a traffic flow dataset the National Performance Management Research Data Set (NPMRDS) provided by NYSDOT. The empirical results provide potential evidence about the significant impacts of rapid weather change events on traffic flow characteristics of the Interstate 90 (I90) Highway in the state of New York. We show the quantitative performance evaluation of our change event detection algorithm and three baseline methods on manually labeled the weather dataset and our method outperforms baselines in terms of precision, recall and Fscore. We present the analysis of Top K detected change events as case studies and also provide the spatiotemporal correlation statistics of top k weather and traffic change events. The limitations of the proposed approach and the empirical study are also discussed.

Weather Classification on Roads for Drivers Assistance using Deep Transferred Features Extreme weather conditions such as heavy rain, fog and scorching heat of sun increase the risk factor of road accidents and traffic congestion. This impels valuable lives and property into tremendous dangers and causes dilatory. Therefore, automatic weather detection on roads for the assistance of drivers becomes very crucial. Weather detection and forecasting is usually figured out by using temperature, humidity and wind sensors. In this paper, we introduce a methodology which exploits visual data to detect the weather condition. We propose a weather detection system based on stateoftheart deep learning techniques by transferring the learned weights of pretrained inception v3 model to our problem. Proposed system can be utilized to generate alerts for upcoming vehicleu0027s drivers to change their driving behavior according to the weather condition. The system can also be helpful for Intelligent Transportation System (ITS) authorities so that they can provide road safety to the citizens with efficient way and improve the current ITS. The system is trained using selfgenerated dataset. Proposed system achieved an accuracy of 98% on unseen dataset, which is very high as compare to prior models.