LSTM encoder-predictor for short-term train load forecasting

ECML/PKDD - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Würtzburg, ALLEMAGNE, 16-/09/2019 - 20/09/2019The increase in the amount of data collected in the transport domain can greatly benefit mobility studies and help to create high value-added mobility services for passengers as well as regulation tools for operators. The research detailed in this paper is related to the development of an advanced machine learning approach with the aim of forecasting the passenger load of trains in public transport. Predicting the crowding level on public transport can indeed be useful for enriching the information available to passengers to enable them to better plan their daily trips. Moreover, operators will increasingly need to assess and predict network passenger load to improve train regulation processes and service quality levels. The main issues to address in this forecasting task are the variability in the train load series induced by the train schedule and the influence of several contextual factors, such as calendar information. We propose a neural network LSTM encoder-predictor combined with a contextual representation learning to address this problem. Experiments are conducted on a real dataset provided by the French railway company SNCF and collected over a period of one and a half years. The prediction performance provided by the proposed model are compared to those given by historical models and by traditional machine learning models. The obtained results have demonstrated the potential of the proposed LSTM encoder-predictor to address both one-step-ahead and multi-step forecasting and to outperform other models by maintaining robustness in the quality of the forecasts throughout the time horizon

Representation Learning of public transport data. Application to event detection

5th International Workshop and Symposium TransitData 2019, Paris, France, 08-/07/2019 - 10/07/2019On the basis of data collected by counting sensors deployed on trains, this paper deals with a forecasting of passenger load in public transport taking into account train operation. Providing passengers with train load forecasting, in addition to the expected arrival time of the next train, can indeed be useful for a better planning of their journeys, which can prevent over-crowding situations in the trains [6] [7]. The proposed approach is built on both a hierarchy of recurrent neural networks [8] and representation learning [9] with the aim to explore the ability of such mobility data processing to simultaneously perform a forecasting task and highlight the impact of events on the public transport operation and demand. An event refers here to an unexpected passenger transport activity or to a modification in transport operation compared to those corresponding to normal conditions. Two kind of historical data are used, namely train load data and automatic vehicle location (AVL) data. This latter source contains all information related to the train operation (delay, time of arrival/departure of vehicles ...). The proposed methodology is applied on a railway transit network line operated by the French railway company SNCF in the suburban of Paris. The historical dataset used in the experiments covers the period from 2015 to 2016

A Unified Approach to Shot Change Detection and Camera Motion Characterization

This paper describes an original approach which jointly addresses two fundamental issues of video partitioning which represent the early important stage of any content-based video indexing system. These two issues are the detection of shot changes, and the labeling of the shot configuration related to the camera movement in terms of static shot, panning, traveling, zooming,... They are both derived from the computation, at each time instant, of the dominant motion in the image represented by a 2D affine model, and from the variation of the size of its associated support. The successive steps of the method rely on statistical techniques ensuring robustness and efficiency. In particular, it can cope with scenes containing moving objects. Results on a real documentary video are reported and validate the proposed approach

Modèle LSTM encodeur-prédicteur pour la prévision court-terme de l'affluence dans les transports collectifs

CAP 2019, Conférence sur l'Apprentissage Automatique, Toulouse, France, 03-/07/2019 - 05/07/2019Les possibilités offertes en termes de collecte et de stockage de données permettent de renouveler les approches de modélisation dans le domaine du transport. L'exploitation croisée de différentes sources de données a pour vocation la création de services à forte valeur ajoutée pour l'usager. Les travaux détaillées dans cet article portent sur le développement de modèles de prévision a base de méthodes d'apprentissage notamment profond, pour la prévision court-terme de la charge (nombre de passagers) des trains. Cette prévision de l'affluence dans les trains peut servir à enrichir l'information voyageur à destination des usagers des transports collectifs qui peuvent ainsi mieux planifier leur déplacement. Elle peut également servir aux opérateurs de transport pour une régulation "à la demande" de l'offre de transport. La principale difficulté dans la prévision est liée à la variabilité intrinsèque des séries temporelles des charges à prédire, induite par l'influence de plusieurs paramètres dont ceux liés à l'exploitation (horaire, retard, type de mission...) et au contexte (information calendaire, grand évènement, météo,...). Nous proposons un modèle LSTM encodeur-prédicteur pour résoudre cette tâche de prévision. Plusieurs expérimentations sont menées sur des données réelles du réseau Transilien de la SNCF sur une durée d'un an et demi. Les résultats de prévision sont détaillées en vue de comparer les performances d'un tel modèle à plusieurs horizons temporels avec celles d'autres modèles plus classiques utilisées en prévision

A unified approach to shot change detection and camera motion characterization

International audienc

A Unified Approach to Shot Change Detection and Camera Motion Characterization

This paper describes an original approach which jointly addresses two fundamental issues of video partitioning which represent the early important stage of any content-based video indexing system. These two issues are the detection of shot changes, and the labeling of the shot configuration related to the camera movement in terms of static shot, panning, traveling, zooming,... They are both derived from the computation, at each time instant, of the dominant motion in the image represented by a 2D affine model, and from the variation of the size of its associated support. The successive steps of the method rely on statistical techniques ensuring robustness and efficiency. In particular, it can cope with scenes containing moving objects. Results on a real documentary video are reported and validate the proposed approach

Forecasting passenger load in a transit network using data driven models

WCRR 2019, 12th World Congress on Railway Research, TOKYO, JAPON, 28-/10/2019 - 01/11/2019Passenger load forecasting can be valuable in transportation planning, operation management and for enriching the information available to passengers, particularly in high-density megacities. This paper investigates the long and short term forecasting of passenger loads in a transit network by using multiple sources of data (on-board headcount data and train timetables). With each passenger load being treated as a time series, one of the main challenges of this study is related to the dependence of the temporal dynamics of the time series to be predicted on the railway timetable. Machine learning models are proposed to predict the passenger load on each train passing each station. We will compare different models, including a random forest, and a gradient boosting tree. Different types of features (calendar, hour, last passenger load, train delay, and train route) will be considered to measure their contributions to the prediction task. The experiments are conducted on a real historical dataset covering the period from 2015 to 2016. The dataset was collected on a railway transit network line operated by SNCF in suburban Paris

LSTM encoder-predictor for short-term train load forecasting

ECML/PKDD - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Würtzburg, ALLEMAGNE, 16-/09/2019 - 20/09/2019The increase in the amount of data collected in the transport domain can greatly benefit mobility studies and help to create high value-added mobility services for passengers as well as regulation tools for operators. The research detailed in this paper is related to the development of an advanced machine learning approach with the aim of forecasting the passenger load of trains in public transport. Predicting the crowding level on public transport can indeed be useful for enriching the information available to passengers to enable them to better plan their daily trips. Moreover, operators will increasingly need to assess and predict network passenger load to improve train regulation processes and service quality levels. The main issues to address in this forecasting task are the variability in the train load series induced by the train schedule and the influence of several contextual factors, such as calendar information. We propose a neural network LSTM encoder-predictor combined with a contextual representation learning to address this problem. Experiments are conducted on a real dataset provided by the French railway company SNCF and collected over a period of one and a half years. The prediction performance provided by the proposed model are compared to those given by historical models and by traditional machine learning models. The obtained results have demonstrated the potential of the proposed LSTM encoder-predictor to address both one-step-ahead and multi-step forecasting and to outperform other models by maintaining robustness in the quality of the forecasts throughout the time horizon