Narrow Results

Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.

Certain capacity degradation levels increase travel times on road networks, while traffic demand remains met. Resilience of a road network is higher, if it can take-in higher levels of degradation without leaving any part of the demand unmet. It is important for planners to quantify this, and it can be obtained as the output of an optimization problem. The resultant measure of resilience is demand-specific. To generalize the resilience measure, its sensitivity to change in demand should be studied. We observe that irrespective of the difference in network size or network topology, resilience decreases with increase in demand. We perform computational experiments on different network topologies to investigate the relationship between network resilience and traffic demand. Based on this, we introduce the area under the demand-resilience curve as a generalized index of resilience (GIR). We compare the GIR with traditional network indicators and find that it is in certain ways, better.