Ex-ante online risk assessment for building emergency evacuation is essential to protect human life and property. Current risk assessment methods are limited by the tradeoff between accuracy and efficiency. In this paper, we propose an online method that overcomes this tradeoff based on multimedia data (e.g. videos data from surveillance cameras) and deep learning. The method consists of two parts. The first estimates the evacuee position as input for training the assessment model to then perform risk assessment in real scenarios. The second considers a social force model based on the evacuation simulation for the output of training model. We verify the proposed method in simulation and real scenarios. Model sensitivity analyses and large-scale tests demonstrate the usability and superiority of the proposed method. By the method, the computation time of risk assessment could be decreased from 10 minutes (by traditional simulation method) to 2.18 s.

Ride-hailing, as a popular shared-transportation method, has been operated in many areas all over the world. Researchers conducted various researches based on global cases. They argued on whether car-hailing is an effective travel mode for emission reduction and drew different conclusions. The detailed emission performance of the ride-hailing system depends on the cases. Therefore, there is an urgent demand to reduce the overall picking up distance during the dispatch. In this study, we try to satisfy this demand by proposing an optimization method combined with a prediction model to minimize the global void cruising distance when solving the dispatch problem. We use Didi ride-hailing data on one day for simulation and found that our method can reduce the picking up distance by 7.51 compared with the baseline greedy algorithm. The proposed algorithm additionally makes the average waiting time of passengers more than 4 minutes shorter. The statistical results also show that the performance of our method is stable. Almost the metric in all cases can be kept in a low interval. What is more, we did a day-to-day comparison. We found that, despite the different spatial-temporal distribution of orders and drivers on different day conditions, there are little differences in the performance of the method. We also provide temporal analysis on the changing pattern of void cruising distance and quantity of orders on weekdays and weekends. Our findings show that our method can averagely reduce more void cruising distance when ride-hailing is active compared with the traditional greedy algorithm. The result also shows that the method can stably reduce void cruising distance by about 4000 to 5000m per order across one day. We believe that our findings can improve deeper insight into the mechanism of the ride-hailing system and contribute to further studies.

A pipeline network is an important transportation mode of natural gas, and different external factors will affect the development of natural gas scheduling plans to different degrees. However, the specific correlation between each external environmental factor and pipeline network scheduling decision is not clear at this stage. This paper developed a hybrid method with Pearson’s correlation coefficient and Spearman’s correlation coefficient to study the correlations between climate temperature, total gas supply, economic conditions, other energy consumption and natural gas pipeline scheduling plans. The results showed that the correlation between natural gas pipeline output and climate temperature is good, presenting a significance level of 5% and below; in contrast, the correlations with economic conditions and other factors are less significant but still reach a significance level of 10%. Meanwhile, taking energy consumption as the object of study, it was found that the correlation between natural gas consumption and electric energy, crude oil and crude coal is good, showing a significance level of 5% and below. Among them, there is a significant positive correlation between natural gas consumption and electric energy consumption, and between natural gas consumption and crude oil consumption, which reveals the synergistic effects within the energy system.