873 research outputs found
Machine Learning Approaches for Traffic Flow Forecasting
Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions.
The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data.
In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used.
The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning.
The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction
Critical incident stages identification and reasonable prediction of traffic
incident duration are essential in traffic incident management. In this paper,
we propose a traffic incident duration prediction model that simultaneously
predicts the impact of the traffic incidents and identifies the critical groups
of temporal features via a multi-task learning framework. First, we formulate a
sparsity optimization problem that extracts low-level temporal features based
on traffic speed readings and then generalizes higher level features as phases
of traffic incidents. Second, we propose novel constraints on feature
similarity exploiting prior knowledge about the spatial connectivity of the
road network to predict the incident duration. The proposed problem is
challenging to solve due to the orthogonality constraints, non-convexity
objective, and non-smoothness penalties. We develop an algorithm based on the
alternating direction method of multipliers (ADMM) framework to solve the
proposed formulation. Extensive experiments and comparisons to other models on
real-world traffic data and traffic incident records justify the efficacy of
our model
Modelling of Floods in Urban Areas
This Special Issue publishes the latest advances and developments concerning the modelling of flooding in urban areas and contributes to our scientific understanding of the flooding processes and the appropriate evaluation of flood impacts. This issue contains contributions of novel methodologies including flood forecasting methods, data acquisition techniques, experimental research in urban drainage systems and/or sustainable drainage systems, and new numerical and simulation approaches in nine papers with contributions from over forty authors
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Consumer's privacy is a main concern in Smart Grids (SGs) due to the
sensitivity of energy data, particularly when used to train machine learning
models for different services. These data-driven models often require huge
amounts of data to achieve acceptable performance leading in most cases to
risks of privacy leakage. By pushing the training to the edge, Federated
Learning (FL) offers a good compromise between privacy preservation and the
predictive performance of these models. The current paper presents an overview
of FL applications in SGs while discussing their advantages and drawbacks,
mainly in load forecasting, electric vehicles, fault diagnoses, load
disaggregation and renewable energies. In addition, an analysis of main design
trends and possible taxonomies is provided considering data partitioning, the
communication topology, and security mechanisms. Towards the end, an overview
of main challenges facing this technology and potential future directions is
presented
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