This study presents a spatial approach for the macrolevel traffic crashes analysis based on point‐of‐interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran’s I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

A pedestrian counting method based on Haar-like detection and template-matching algorithm is presented. The aim of the method is to count pedestrians that are in a metro station automatically using video surveillance camera. The most challenging problem is to count pedestrians accurately in the case of not changing the position of the surveillance camera, because the view that surveillance camera uses in a metro station is always short-shot and nondirect downward view. In this view, traditional methods find it difficult to count pedestrians accurately. Hence, we propose this novel method. In addition, in order to improve counting accuracy more, we present a method to set the parameter value with a threshold-curve instead of a fixed threshold. The results of experiments show the high accuracy of our method.

In this paper, we introduce a trial-and-error model to study information diffusion in a social network. Specifically, in every discrete period, all individuals in the network concurrently try a new technology or product with certain respective probabilities. If it turns out that an individual observes a better utility, he will then adopt the trial; otherwise, the individual continues to choose his prior selection. We first demonstrate that the trial and error behavior of individuals characterizes certain global community structures of a social network, from which we are able to detect macro-communities through the observation of micro-behavior of individuals. We run simulations on classic benchmark testing graphs, and quite surprisingly, the results show that the trial and error dynamics even outperforms the Louvain method (a popular modularity maximization approach) if individuals have dense connections within communities. This gives a solid justification of the model. We then study the influence maximization problem in the trial-and-error dynamics. We give a heuristic algorithm based on community detection and provide experiments on both testing and large scale collaboration networks. Simulation results show that our algorithm significantly outperforms several well-studied heuristics including degree centrality and distance centrality in almost all of the scenarios. Our results reveal the relation between the budget that an advertiser invests and marketing strategies, and indicate that the mixing parameter, a benchmark evaluating network community structures, plays a critical role for information diffusion.

Previous studies revealed that the susceptibility, contacting preference, and recovery probability markedly alter the epidemic outbreak size and threshold. The recovery probability of an infected node is closely related to its obtained resources. How to allocate limited resources to infected neighbors is extremely important for containing the epidemic spreading on complex networks. In this paper, we proposed an epidemic spreading model on complex networks, in which we assume that the node has heterogeneous susceptibility and contacting preference, and susceptible nodes are willing to share their resources to neighbors. Through a developed heterogeneous mean-field theory and a large number of numerical simulations, we find that the recovered nodes provide resources uniformly to their infected neighbor nodes, and the epidemic spreading can be suppressed optimally on homogeneous and heterogeneous networks. Besides, altering the susceptibility and contacting preference does not qualitatively change the results. The susceptibility of the node decreases, which makes the outbreak threshold of epidemic spreading increase, and the outbreak size decreases. Our theory agrees well with the numerical simulations.

The weaving of main road and on-ramp traffic in urban expressway is known to lead to congestion under heavy traffic conditions. This is mainly due to the complete lane changing within a limited distance. The interference between vehicles on main road and ramp causes the traffic chaos and ultimately leads to congestion. This study proposed a novel control strategy based on cooperative vehicle infrastructure system (CVIS) to eliminate this interference and improve efficiency and safety. The strategy was divided into three phases. The first was destination oriented lane control which was to reduce conflicts in the weaving area. The second was main carriageway platoon control which was to reduce the interference caused by merging in main road. And, the third was weaving lane platoon control which was to reduce the interference caused by separating from main road. Simulation results showed that both traffic and energy indexes had a significant improvement in the ideal condition, and a test with real smart vehicles in Beijing Proving Ground has been planned.