Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data

A comprehensive understanding of city structures and urban dynamics can greatly improve the efficiency and quality of urban planning and management,while the traditional approaches of which,such as manual surveys,usually incur substantial labor and time.In this paper,we propose a data-driven framework to sense urban structures and dynamics from large-scale vehicle mobility data.First,we divide the city into fine-grained grids,and cluster the grids with similar mobility features into structured urban areas with a proposed distance-constrained clustering algorithm(DCCA).Second,we detect irregular mobility traffic patterns in each area leveraging an ARIMA-based anomaly detection algorithm(ADAM),and correlate them to the urban social and emergency events.Finally,we build a visualization system to demonstrate the urban structures and crowd dynamics.We evaluate our framework using real-world datasets collected from Xiamen city,China,and the results show that the proposed framework can sense urban structures and crowd comprehensively and effectively.

V2V Online Data Offloading Method Based on Vehicle Mobility

As people are accustomed to getting information in the vehicles,mobile data offloading through Vehicular Ad Hoc Networks(VANETs)becomes prevalent nowadays.However,the impacts caused by the vehicle mobility(such as the relative speed and direction between vehicles)have great effects on mobile data offloading.In this paper,a V2V online data offloading method is proposed based on vehicle mobility.In this mechanism,the network service process was divided into continuous and equal-sized time slots.Data were transmitted in a multicast manner for the sake of fairness.The data offloading problem was formalized to maximize the overall satisfaction of the vehicle users.In each time slot,a genetic algorithm was used to solve the maximizing problem to obtain a mobile data offloading strategy.And then,the performance of the algorithm was enhanced by improving the algorithm.The experiment results show that vehicle mobility has a great effect on mobile data offloading,and the mobile data offloading method proposed in the paper is effective.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Vertical Distribution Characteristics of PM2.5 Observed by a Mobile Vehicle Lidar in Tianjin, China in 2016

We present mobile vehicle lidar observations in Tianjin, China during the spring, summer, and winter of 2016. Mobile observations were carried out along the city border road of Tianjin to obtain the vertical distribution characteristics of PM2.5. Hygroscopic growth was not considered since relative humidity was less than 60% during the observation experiments. PM2.5 profile was obtained with the linear regression equation between the particle extinction coefficient and PM2.5 mass concentration. In spring, the vertical distribution of PM2.5 exhibited a hierarchical structure. In addition to a layer of particles that gathered near the ground, a portion of particles floated at 0.6–2.5-km height. In summer and winter, the fine particles basically gathered below 1 km near the ground. In spring and summer, the concentration of fine particles in the south was higher than that in the north because of the influence of south wind. In winter, the distribution of fine particles was opposite to that measured during spring and summer. High concentrations of PM2.5 were observed in the rural areas of North Tianjin with a maximum of 350 μg m^–3 on 13 December2016. It is shown that industrial and ship emissions in spring and summer and coal combustion in winter were the major sources of fine particles that polluted Tianjin. The results provide insights into the mechanisms of haze formation and the effects of meteorological conditions during haze–fog pollution episodes in the Tianjin area.

Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping

In a complex urban scene,observation from a single sensor unavoidably leads to voids in observations,failing to describe urban objects in a comprehensive manner.In this paper,we propose a spatio-temporal-spectral-angular observation model to integrate observations from UAV and mobile mapping vehicle platform,realizing a joint,coordinated observation operation from both air and ground.We develop a multi-source remote sensing data acquisition system to effectively acquire multi-angle data of complex urban scenes.Multi-source data fusion solves the missing data problem caused by occlusion and achieves accurate,rapid,and complete collection of holographic spatial and temporal information in complex urban scenes.We carried out an experiment on Baisha Town,Chongqing,China and obtained multi-sensor,multi-angle data from UAV and mobile mapping vehicle.We first extracted the point cloud from UAV and then integrated the UAV and mobile mapping vehicle point cloud.The inte-grated results combined both the characteristics of UAV and mobile mapping vehicle point cloud,confirming the practicability of the proposed joint data acquisition platform and the effectiveness of spatio-temporal-spectral-angular observation model.Compared with the observation from UAV or mobile mapping vehicle alone,the integrated system provides an effective data acquisition solution toward comprehensive urban monitoring.