KeRanOrdinary's Stars
nachiket92/conv-social-pooling
Code for model proposed in: Nachiket Deo and Mohan M. Trivedi,"Convolutional Social Pooling for Vehicle Trajectory Prediction." CVPRW, 2018
adbrebs/taxi
Winning entry to the Kaggle taxi competition
chironyf/hotel-management-system
宾馆管理系统。可以订房、续费、退房、订单管理、员工管理以及业务数据统计可视化展示等
czgdp1807/MECOptimalOffloading
Optimization of Offloading Scheme Algorithm for Large Number of Tasks in Mobile-Edge Computing
jordan8409212/RL-for-binary-computation-offloading-in-wireless-powered-MEC-networks
It's a implementation about the paper Liang Huang, Suzhi Bi, and Ying-jun Angela Zhang, "Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks", on https://ieeexplore.ieee.org/document/8771176
vinPopulaire/Risk_aware_MEC_offloading
Code for Data Offloading in UAV-assisted Multi-access Edge Computing Systems: A Resource-based Pricing and User Risk-awareness Approach paper https://www.mdpi.com/1424-8220/20/8/2434/pdf
trungmanhhuynh/Scene-LSTM
Data and Code for "Scene-LSTM: A model for human trajectory prediction" (ISVC 2019)
alexcaselli/Federated-Learning-for-Human-Mobility-Models
Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility
sunnywalden/xlsx_over_web
Django开发的excel表格展示系统，将本地xlsx文件导入到数据库，显示到JS页面 online excel manage with django
caipeide/VTGNet
VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments
JerryIshihara/lyft-motion-prediction-for-autonomous-vehicle
Deep learning models for self-driving vehicles to predict other car/cyclist/pedestrian (called "agent")'s motion.
RayneSun/VTPLSTM
Inspired by the performence of Social LSTM. I created a model that could predict vehicle's trajectory in 5s. The model uses LSTM as center. I trained it at NGSIM. Still fix it...
hafizas101/Deep-learning-based-human-action-recognition-using-human-skeletons
yeonduing/DRL-based-MEC
Mobile Edge Computing Server based on Deep Reinforcement Learning (One-on-one)
AshAswin/vehicle-trajectory-prediction-with-Gaussian-Process-regression
chrisHuxi/Trajectory_Predictor
A python implementation of multi-model estimation algorithm for trajectory tracking and prediction, research project from BMW ABSOLUT self-driving bus project.
Pichairen/CarNumberAndSpeed
A simple method of vehicle counting and speed prediction
hafizas101/Master-s-thesis
Trajectory estimation of vehicles in crowded and crossroad scenarios
bnuliujing/EchoStateNetworks
k-kotera/EchoStateNetwork-Tensorflow
Minimal and Simple Tensorflow implementation of EchoStateNetwork
PoojaPatel35/Human-Activity-Recognition-Mobily-Path-Prediction
Individual Mobility is the study that depicts how individuals move inside a region or system. As of late a few researches have been accomplished for this reason and there has been a flood in enormous informational accessible in individual developments. Most of these information’s are gathered from cellphone or potentially GPS with variable accuracy relying upon the distance from the tower. Enormous scope information, for example, cell phone follows are significant hotspot for urban modeling. The individual travel designs breakdown into a solitary likelihood distribution however despite the assorted variety of their travel history people follow basic reproducible examples. This similitude in movement example can help us in an extremely different zones of utilizations, for example, city arranging, traffic building, spread of disease and versatile infections. The motive of this project is to show that by utilizing a measure of direct estimation that human directions do follow a few high reproducible scaling designs. Activity recognition expects to perceive the activities and objectives of at least one operator from a progression of perceptions on the specialists' activities and the natural conditions. Human movement acknowledgment, which is one of the developing fields of research, plans to figure out which action is finished by people. Some true applications, for example, health monitoring, abnormal behavior detection, and sport. In this way, it is a troublesome issue given the enormous number of perceptions delivered each second, the fleeting idea of the perceptions, and the absence of an unmistakable method to relate accelerometer information to known developments. Keen PDAs presently fuse numerous different and ground-breaking sensors, for example, GPS sensors, vision sensors, sound sensors, light sensors, temperature sensors, course sensors and speeding up sensors. This project is about utilizations telephone-based accelerometers to perform activity recognition, which includes identifying the physical movement a user is performing
Behtash-BehinAein/Destination_Prediction
Predict user destination based on past trips
Leme34/StudentCMS
学生、教师、公告、选课、成绩cms管理系统，excel数据表的导入导出
priyakarode31/Bike-Rental-Prediction
Bike sharing is an innovative approach to urban mobility, combining the convenience and flexibility of a bicycle with the accessibility of public transportation. A bike sharing system is a service in which users can rent/use bicycles available for shared use on a short term basis for a price. Such system usually aim to reduce congestion, noise and air pollution by providing affordable access to bikes for short distance trips as opposed to motorised vehicles. The number of users on any given day can vary greatly for such systems depending upon various factors. Our aim is to use and optimise Machine Learning Models that effectively predict the number of ride-sharing bikes that will be used in any given day, using available information about that particular day.
fmkazemi/Prediction-and-Visualization-on-Citi-Bike-ridership-data-for-the-Mayor-of-New-York-City
Predict how long a trip will take given a starting point and destination, and a better understanding of Citi Bike ridership for the Mayor of New York City through visualizing data.
aleksejhoffaerber/deep1
Prediction of user behavior based on smartphone sensor data.
alodieboissonnet/TaxiDestinationPrediction
Neural network to predict taxi destination in Python
duonghung86/Vehicle-trajectory-tracking
Haibin86/Project-to-predict-traffic-accident-severity-by-machine-learning
To reduce traffic accidents is an important public safety challenge around the world. Accidents can be prevented by revealing hidden patterns in the data and giving this invaluable data and model as a warning to the government, traffic police and respective drivers of vehicles. Different factors involved in traffic collisions have a substantial effect on each other, and explained the severity of traffic accidents. Thus, one traffic accident severity prediction should be useful and instructive for accidents reduction.
Stacatophile/Bike-Sharing-Prediction-Model
A Linear Regression model to predict the demand for bikes based on factors like temperature, weather situation, humidity, if it is a holiday etc.