Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer ReviewedPostprint (published version

Assessing spatiotemporal correlations from data for short-term traffic prediction using multi-task learning

Traffic flow prediction is a fundamental problem for efficient transportation control and management. However, most current data-driven traffic prediction work found in the literature have focused on predicting traffic from an individual task perspective, and have not fully leveraged the implicit knowledge present in a road-network through space and time correlations. Such correlations are now far easier to isolate due to the recent profusion of traffic data sources and more specifically their wide geographic spread. In this paper, we take a multi-task learning (MTL) approach whose fundamental aim is to improve the generalization performance by leveraging the domain-specific information contained in related tasks that are jointly learned. In addition, another common factor found in the literature is that a historical dataset is used for the calibration and the assessment of the proposed approach, without dealing in any explicit or implicit way with the frequent challenges found in real-time prediction. In contrast, we adopt a different approach which faces this problem from a point of view of streams of data, and thus the learning procedure is undertaken online, giving greater importance to the most recent data, making data-driven decisions online, and undoing decisions which are no longer optimal. In the experiments presented we achieve a more compact and consistent knowledge in the form of rules automatically extracted from data, while maintaining or even improving, in some cases, the performance over single-task learning (STL).Peer ReviewedPostprint (published version

Machine learning for early detection of traffic congestion using public transport traffic data

The purpose of this project is to provide better knowledge of how the bus travel times is affected by congestion and other problems in the urban traffic environment. The main source of data for this study is second-level measurements coming from all buses in the Linköping region showing the location of each vehicle.The main goal of this thesis is to propose, implement, test and optimize a machine learning algorithm based on data collected from regional buses from Sweden so that it is able to perform predictions on the future state of the urban traffic.El objetivo principal de este proyecto es proponer, implementar, probar y optimizar un algoritmo de aprendizaje automático basado en datos recopilados de autobuses regionales de Suecia para que poder realizar predicciones sobre el estado futuro del tráfico urbano.L'objectiu principal d'aquest projecte és proposar, implementar, provar i optimitzar un algoritme de machine learning basat en dades recollides a partir d'autobusos regionals de Suècia de manera per poder realitzar prediccions sobre l'estat futur del trànsit urbà

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference