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On-ramp control is an effective way to mitigate traffic congestion in freeways. In this study, a traffic control approach is developed based on the OD data of a regional freeway to alleviate the traffic overload in freeway bottlenecks. We first locate the major driver sources of the freeway bottlenecks and identify the on-ramps for implementing the traffic control schemes. Next, the differential evolution algorithm is employed to calculate the optimal control time at each traffic control on-ramp. The results indicate that the major driver sources of the freeway bottlenecks are limited. Traffic congestion in the freeway bottlenecks can be effectively mitigated by adaptively controlling the waiting time of vehicles at the on-ramps of their major driver sources.

Predicting individual mobility of subway passengers in large crowding events is crucial for subway safety management and crowd control. However, most previous models focused on individual mobility prediction under ordinary conditions. Here, we develop a passenger mobility prediction model, which is also applicable to large crowding events. The developed model includes the trip-making prediction part and the trip attribute prediction part. For trip-making prediction, we develop a regularized logistic regression model that employs the proposed individual and cumulative mobility features, the number of potential trips, and the trip generation index. For trip attribute prediction, we develop an n -gram model incorporating a new feature, the trip attraction index, for each cluster of subway passengers. The incorporation of the three new features and the clustering of passengers considerably improves the accuracy of passenger mobility prediction, especially in large crowding events.

The perception of human behavior that is different from normal has always been a common concern in many fields. The widespread use of smartphones makes it possible to obtain a large amount of user activities at fine spatial scales. In this context, this article first systematically summarizes the research on human behavior patterns in the era of mobile phone big data. Although the current research has made certain research progress, there are certain limitations in data source selection and activity semantic analysis. Then it focuses on the relevant research on the perception of abnormal events by mobile phone positioning big data, including the research progress in abnormal event early warning and emergency response, and believes that there is more room for development in related research. The analysis of these studies provides reference value for handling similar emergencies in the future, especially for post-disaster emergency response and resource allocation. It provides strong theoretical support.