3,780 research outputs found
An efficient algorithm for modelling and dynamic prediction of network traffic
Network node degradation is an important problem in internet of things given the ubiquitous high number of personal computers, tablets, phones and other equipments present nowadays. In order to verify the network traffic degradation as one or multiple nodes in a network fail, this paper proposes one algorithm based on product form results (PRF) for fractionally auto regressive integrated moving average (FARIMA) model, namely PFRF. In this algorithm, the prediction method is established by FARIMA model, through equations for queuing state and average queue length in steady state derived from queuing theory. Experimental simulations were conducted to investigate the relationships between average queue length and service rate. Results demonstrated that, not only it has good adaptability, but has also achieved promising magnitude of 9.87 as standard deviation which shows its high prediction accuracy, given the low-magnitude difference between original value and the algorithm
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Location data can be extremely useful to study commuting patterns and
disruptions, as well as to predict real-time traffic volumes. At the same time,
however, the fine-grained collection of user locations raises serious privacy
concerns, as this can reveal sensitive information about the users, such as,
life style, political and religious inclinations, or even identities. In this
paper, we study the feasibility of crowd-sourced mobility analytics over
aggregate location information: users periodically report their location, using
a privacy-preserving aggregation protocol, so that the server can only recover
aggregates -- i.e., how many, but not which, users are in a region at a given
time. We experiment with real-world mobility datasets obtained from the
Transport For London authority and the San Francisco Cabs network, and present
a novel methodology based on time series modeling that is geared to forecast
traffic volumes in regions of interest and to detect mobility anomalies in
them. In the presence of anomalies, we also make enhanced traffic volume
predictions by feeding our model with additional information from correlated
regions. Finally, we present and evaluate a mobile app prototype, called
Mobility Data Donors (MDD), in terms of computation, communication, and energy
overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201
Empirical Formulation of Highway Traffic Flow Prediction Objective Function Based on Network Topology
Accurate Highway road predictions are necessary for timely decision making by the transport authorities. In this paper, we propose a traffic flow objective function for a highway road prediction model. The bi-directional flow function of individual roads is reported considering the net inflows and outflows by a topological breakdown of the highway network. Further, we optimise and compare the proposed objective function for constraints involved using stacked long short-term memory (LSTM) based recurrent neural network machine learning model considering different loss functions and training optimisation strategies. Finally, we report the best fitting machine learning model parameters for the proposed flow objective function for better prediction accuracy.Peer reviewe
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