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.

Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

Demand Prediction of Ride-Hailing Pick-Up Location Using Ensemble Learning Methods

Ride-hailing and carpooling platforms have become a popular way to move around in urban cities. Based on the principle of matching riders with drivers, with Uber, Lyft and Didi having the largest market share. The challenge remains being able to optimally match rider demand with driver supply, reducing congestion and emissions associated with Vehicle clustering, deadheading, ultimately leading to surge pricing where providers raise the price of the trip in order to attract drivers into such zones. This sudden spike in rates is seen by many riders as disincentive on the service provided. In this paper, data mining techniques are applied to ultimately develop an ensemble learning model based on historical data from City of Chicago Transport provider’s dataset. The objective is to develop a dynamic model capable of predicting rider drop-off location using pick-up location data then subsequently using drop-off location data to predict pick-up points for effective driver deployment under multiple scenarios of privacy and information. Results show neural network algorithms perform best in generalizing pick-up and drop-off points when given only starting point information. Ensemble learning methods, Adaboost and Random forest algorithm are able to predict both drop-off and pick-up points with a MAE of one (1) community area knowing rider pick-up point and Census Tract information only and in reverse predict potential pick-up points using the Drop-off point as the new starting point.

User Decision-based Analysis of Urban Electric Vehicle Loads

The grid load attributable to electric vehicles (EVs)is affected by the choice behaviors of EV users. To analyze theeffects of factors such as travel demand and electricity priceson user behavior, a logit discrete choice model is introducedto simulate the users decisions to charge/travel. Based on aquasi-steady-state traffic network, a model for cluster electricvehicles considering the user’s behavior is designed to obtain theprobability distribution of the user’s behavior and the chargeand discharge curves of cluster EVs under various scenarios. Thevalidity of the proposed model is verified using an IEEE 9-nodetraffic network case and an urban traffic network case. Furthermore,the impact of the electricity price, traffic conditions, andother factors on the load curves of urban EVs is analyzed.

Three-stage algorithms for the large-scale dynamic vehicle routing problem with industry 4.0 approach

Companies are eager to have a smart supply chain especially when they have adynamic system. Industry 4.0 is a concept which concentrates on mobility andreal-time integration. Thus, it can be considered as a necessary component thathas to be implemented for a dynamic vehicle routing problem. The aim of thisresearch is to solve large-scale DVRP (LSDVRP) in which the delivery vehiclesmust serve customer demands from a common depot to minimize transit costswhile not exceeding the capacity constraint of each vehicle. In LSDVRP, it isdifficult to get an exact solution and the computational time complexity growsexponentially. To find near-optimal answers for this problem, a hierarchicalapproach consisting of three stages: “clustering, route-construction, routeimprovement”is proposed. The major contribution of this paper is dealing withLSDVRP to propose the three-stage algorithm with better results. The resultsconfirmed that the proposed methodology is applicable.

A 3 Billion Yuan Lithium Battery New Energy Vehicle Industrial Cluster Project Landed in Daye

On December 24,a new industrial partner entered the friend circle of new energy vehicle industrial cluster in Daye;a new material project with a total investment of 3 billion yuan held ground-breaking ceremony,signaling its formal landing in Daye.This Project is invested and constructed by Hubei Zhongxing New Advanced Material Co.,Ltd,the Project involves total investment of