PANDA: predicting road risks after natural disasters leveraging heterogeneous urban data

Natural disasters cause extensive damages to urban cities, demanding authorities to take urgent and effective measures to restore normalcy on transportation. During a disaster, restoring road transportation in a timely manner for rescue, supply and also prevent the risk of road accidents due to obstacles is of vital importance. Traditional post-disaster road obstacle work relies on a manual investigation which is time-consuming and labor-intensive. Predicting road risks can provide decision support for emergency management departments, reducing the damage caused by disasters. In this paper, we propose a three-phase framework for predicting road risks post-disaster leveraging heterogeneous urban data. Firstly, We use a clustering algorithm to extract and classify urban road networks based on the floating car data. Then we extract the spatiotemporal features of the urban roads. Through social network data, we collect historical risk-prone data using the crowdsensing method. To address the challenges of the small amount of labeled data, we train our model based on self-training. We verified the validity of this model by using a real dataset in Xiamen island which proves that our model accurately predicts road risk with precision and recall both more than 85% respectively.