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For the location-routing problem of medical waste treatment centers, this paper proposed a multi-objective optimization model considering the public risk. Firstly, from the perspective of the government, the public and the processing center contractor, this paper established the mathematical model which simultaneously considered the operation cost, public risk and transportation cost.Secondly, according to the characteristics of the constructed model, this paper designed an improved multi-objective salp swarm algorithm to solve the problem. Finally, taking the planning projects of the medical waste treatment center in Chengdu, Sichuan province as an example, this paper verified the feasibility of the model and the effectiveness of the algorithm through the method of comparative analysis. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an important and challenging task in building smart city. Accurate forecasting requires consideration of spatio-temporal characteristics consisting of multiple influences such as holiday, similar node, and weather. In order to accurately capture the spatio-temporal characteristics of road network intersections, this paper proposed a prediction model based on graph convolutional neural network, temporal algorithm Prophet and Pearson correlation coefficient to achieve accurate prediction of intersection traffic considering spatial structure, similar nodes, holidays and other influencing factors. Firstly, it introduced pearson correlation coefficient to reduce the influence of similar nodes to improve the temporal algorithm for capturing temporal features. Secondly, it used the graph convolution neural network to capture spatial features; Finally, it determined the fusion weights of the graph convolutional network and the temporal algorithm by linear regression to obtain the results of spatio-temporal fusion prediction. This paper finally extracted the intersection traffic data based on the analysis of Chengdu taxi trajectory data and conducted traffic prediction experiments. The results show that the accuracy of the model proposed in this paper is better than that of most existing baseline methods, compared with the T-GCN, ASTGCN, and AGCRN models, the MAE is reduced by 1.623, 0.724, 0.161, respectively, and the accuracy is improved by 0. 144, 0. 068, and 0. 021, respectively, which verifies the effectiveness of the model in traffic intersection flow prediction. [ABSTRACT FROM AUTHOR]

To ensure the safety of the urban transit network so as to improve the operation efficiency of the entire traffic network, this paper took the transit network of Chengdu city for instance, it analyzed the invulnerability under random and deliberate attacks based on revealing the network topology. The simulation results show that the node number removed from the network affects the invulnerability little in both random attacks and deliberate attacks on the basis of node degree. At the same time, it finds that the less node number removed from the network, the worse invulnerability the network shows in deliberate attacks based on not only node betweeness but node efficiency. Furthermore, the impacts of deliberate attacks based on node betweeness on the network invulnerability are more remarkable than those based on node degree and efficiency. [ABSTRACT FROM AUTHOR]

To study the impact on house value of distance to stations of west extension line of metro line 2 in Chengdu within 800 meters radius area, this paper surveyed residential real estate data along the urban rail transit stations within 800 meters radius area, selected appropriate characteristics variables, and constructed semi-logarithmic equations and logarithmic equations based on hedonic price model, and then built overall equation and distance interval equation by a single distance variable and distance interval variables, and verified the impact on house value of distance to stations of west extension line of metro line 2 in Chengdu. Result shows that, within 800 meters to the metro stations, house values experience an overall downward trend with the increase of distance to the metro stations, the significance of the impact is high. But based on the different distance intervals, house values have seen a trend of "low in the two ends, high in the middle, declining in the overall". And the degree of impact on house value of distance variables, compare to other major variables (distance to the CBD, developer brand, etc.), is relatively low. [ABSTRACT FROM AUTHOR]