DLOREAN: Dynamic Location-aware Reconstruction of multiway Networks

This paper presents a method for learning time-varying higher-order interactions based on node observations, with application to short-term traffic forecasting based on traffic flow sensor measurements. We incorporate domain knowledge into the design of a new damped periodic kernel which lever- ages traffic flow patterns towards better structure learning. We introduce location-based regularization for learning models with desirable geographical properties (short-range or long-range interactions). We show using experiments on synthetic and real data, that our approach performs better than static methods for reconstruction of multiway interactions, as well as time-varying methods which recover only pair-wise interactions. Further, we show on real traffic data that our model is useful for short-term traffic forecasting, improving over state-of-the-art

Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.Comment: 34 pages, 12 figures, 5 table

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting. Document type: Articl

Tunnel Traffic Forecasting Using Deep Learning

Tunnel traffic congestion can increase the risk of traffic accidents, tunnel fires, and environmental effect. Despite numerous studies on traffic forecasting using deep learning, research on tunnel traffic remains limited.Utilizing traffic flow data from the Norwegian Public Road Administration, this thesis analyzes the applicability of recurrent neural networks for tunnel traffic prediction. The data is retrieved from different sources and traffic sensors near or inside the tunnels are selected through a geo-spatial analysis. The recurrent neural network is designed to be trained on either a single tunnel or several tunnels. Furthermore, based on their geographical location and population density, the tunnels are classified as urban or sub-urban. Based on the results of the experiments and the sample of tunnels used,the recurrent neural network outperformed the baseline for urban tunnels in terms of root-mean-squared-error. However, the performance advantage was not significant for sub-urban tunnels. The addition of features such as temporal features and category features provided no significant results.These findings are discussed in the final sections of the thesis

Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting

Graph neural networks (GNNs) have been widely applied in multi-variate time-series forecasting (MTSF) tasks because of their capability in capturing the correlations among different time-series. These graph-based learning approaches improve the forecasting performance by discovering and understanding the underlying graph structures, which represent the data correlation. When the explicit prior graph structures are not available, most existing works cannot guarantee the sparsity of the generated graphs that make the overall model computational expensive and less interpretable. In this work, we propose a decoupled training method, which includes a graph generating module and a GNNs forecasting module. First, we use Graphical Lasso (or GraphLASSO) to directly exploit the sparsity pattern from data to build graph structures in both static and time-varying cases. Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model. The experimental results on three real-world datasets show that our novel approach has competitive performance against existing state-of-the-art forecasting algorithms while providing sparse, meaningful and explainable graph structures and reducing training time by approximately 40%. Our PyTorch implementation is publicly available at https://github.com/HySonLab/GraphLASS

Time delay estimation of traffic congestion propagation due to accidents based on statistical causality

The accurate estimation of time delays is crucial in traffic congestion analysis, as this information can be used to address fundamental questions regarding the origin and propagation of traffic congestion. However, the exact measurement of time delays during congestion remains a challenge owing to the complex propagation process between roads and high uncertainty regarding future behavior. To overcome this challenge, we propose a novel time delay estimation method for the propagation of traffic congestion due to accidents using lag-specific transfer entropy (TE). The proposed method adopts Markov bootstrap techniques to quantify uncertainty in the time delay estimator. To the best of our knowledge, our proposed method is the first to estimate time delays based on causal relationships between adjacent roads. We validated the method's efficacy using simulated data, as well as real user trajectory data obtained from a major GPS navigation system in South Korea.Comment: http://www.aimspress.com/article/doi/10.3934/era.202303

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility