A privacy-preserving vehicle trajectory clustering framework

As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles.However,uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage.Therefore,one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy.We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model(IKV)based on the variational autoencoder(VAE)and an improved K-means algorithm.In the framework,the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server;the server uses the hidden variables for clustering analysis and delivers the analysis results to the client.The IKV’workflow is as follows:first,we train the VAE with historical vehicle trajectory data(when VAE’s decoder can approximate the original data,the encoder is deployed to the edge computing device);second,the edge device transmits the hidden variables to the server;finally,clustering is performed using improved K-means,which prevents the leakage of the vehicle trajectory.IKV is compared to numerous clustering methods on three datasets.In the nine performance comparison experiments,IKV achieves optimal or sub-optimal performance in six of the experiments.Furthermore,in the nine sensitivity analysis experiments,IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations.These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments,such as carpooling tasks,but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.

Investigating distance halo effect of fixed automated speed camera based on taxi GPS trajectory data

Background:The deterrence effect of automated speed camera(ASC)is still inconclusive.Moreover,it is pointed out that ASC may have varying deterrence effects on different types of road users(e.g.,taxis).Objective:This study intends to investigate the distance halo effect of fixed ASC(hereafter called ASC)on taxis.Method:More than 1.34 million taxis’GPS trajectory data were collected.A novel indicator,the delta speed(defined as the difference between the traveling speed and the speed limit),was proposed to continuously describe the variations in traveling speeds.The upstream and downstream critical delta speeds during each time period on weekdays and weekends were obtained by using K-means clustering method,respectively.Based on the critical delta speeds,the ranges of upstream and downstream distance halo effects of ASC during different time periods on weekdays and weekends were determined separately and compared.Results:The downstream critical delta speed is smaller than the upstream one.The upstream and downstream distance halo effects of ASC on taxis are within a range of 8-2180 m and an area of 10-580 m to the ASC location,respectively.There are no obvious difference in the ranges of upstream and downstream distance halo effects of ASC on taxis between different time periods or between weekdays and weekends.Conclusion:The present study confirms that the upstream and downstream distance halo effects of ASC on taxis have different ranges and the stabilities of time-of-day and day-of-week.Practical application:The findings of this study can provide a basic reference for reasonably deploying ASCs within a region.

航空器进场盛行轨迹挖掘及特征分析

航空器在进场过程中,受空域拥挤程度、临时航线使用等因素的影响,可能会产生绕飞、等待、延误、变更航线等行为,使航空器不能严格按飞行计划程序执行.提出航空器进场盛行轨迹挖掘方法,通过对轨迹数据的经纬度、高度及监视时间的四维数据进行处理,提取特征点,采用K-means方法聚类得到航空器运行的中心轨迹.采集上海虹桥机场(ZSSS)—北京首都国际机场(ZBAA)的ADS-B数据,将轨迹数据按照白天(8:00~20:00)和夜间(20:00~次日8:00)时间段进行划分,筛选得到符合城市对机场、机型、跑道条件的进场轨迹数据,对比分析不同时间段聚类中心航迹的差异.研究结果表明,航空器实际运行过程中会受多种因素的影响,产生变更航线的行为,对实际聚类中心产生较大影响,白天相较于夜间时段,聚类簇中心航迹分布更为离散.

Energy-efficient trajectory planning for amulti-UAV-assisted mobile edge computing system

We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things devices.Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs.This problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and SPs.To tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three phases.In the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time.In the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same UAV.In the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each cluster.Compared with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales.