Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning

Parameter tuning is recognized today as a crucial ingredient when tackling an optimization problem. Several meta-optimization methods have been proposed to find the best parameter set for a given optimization algorithm and (set of) problem instances. When the objective of the optimization is some scalar quality of the solution given by the target algorithm, this quality is also used as the basis for the quality of parameter sets. But in the case of multi-objective optimization by aggregation, the set of solutions is given by several single-objective runs with different weights on the objectives, and it turns out that the hypervolume of the final population of each single-objective run might be a better indicator of the global performance of the aggregation method than the best fitness in its population. This paper discusses this issue on a case study in multi-objective temporal planning using the evolutionary planner DaE-YAHSP and the meta-optimizer ParamILS. The results clearly show how ParamILS makes a difference between both approaches, and demonstrate that indeed, in this context, using the hypervolume indicator as ParamILS target is the best choice. Other issues pertaining to parameter tuning in the proposed context are also discussed.Comment: arXiv admin note: substantial text overlap with arXiv:1305.116

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

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

Résolution des problèmes dynamiques de tournées de véhicules : de métaheuristiques à base de solution unique aux métaheuristiques parallèles à base de population

Beaucoup de problèmes dans le monde réel ont une nature dynamique et peuvent être modélisés comme des problèmes dynamiques d'optimisation combinatoire. Cependant, les travaux de recherches sur l'optimisation dynamique se concentrent essentiellement sur les problèmes d'optimisation continue et ils ciblent rarement les problèmes combinatoires. Une des applications dans le domaine des problèmes dynamiques combinatoires ayant reçu un intérêt croissant au cours de ces dernières décennies est le système de transport en ligne où dynamique. Un problème typique de ce domaine est le Problème Dynamique de Tournées de Véhicules (PDTV). Dans ce dernier, le dynamisme peut être attribué à plusieurs facteurs (conditions météorologiques, nouvelle commande client, annulation d'une commande précédente, véhicule tombant en panne, etc.). Dans un tel problème, les informations ne sont pas complètement connues a priori, mais plutôt révélées au décideur progressivement avec le temps. Par conséquent, les solutions des différentes instances doivent être trouvées au fur et à mesure du temps, simultanément avec les informations entrantes. Ces problèmes font appel à une méthodologie capable de suivre les solutions optimales au cours du temps. Dans cette thèse, le problème dynamique de tournées de véhicules est étudié et le développement de méthodologies générales appelées métaheuristiques pour sa résolution est traité. Leur capacité à s'adapter à l'évolution de l'environnement et leur robustesse sont discutées. Les résultats des expérimentations montrent grâce à des mesures de performance dynamique que nos méthodes sont efficaces sur ce problème et ont donc un grand potentiel pour d'autres problèmes combinatoires dynamiques.Many problems in the real world have dynamic nature and can be modeled as dynamic combinatorial optimization problems. However, research on dynamic optimization focuses on continuous optimization problems, and rarely targets combinatorial problems. One of the applications in dynamic combinatorial problems that has received a growing interest during the last decades is the on-line or dynamic transportation systems. A typical problem of this domain is the Dynamic Vehicle Routing Problems (DVRPs). In this latter, the dynamism can be attributed to several factors (weather condition, new customer order, cancellation of old demand, vehicle broken down, etc.). In such application, information on the problem is not completely known a priori, but instead is revealed to the decision maker progressively with time. Consequently, solutions for different instances have to be found as time proceeds, concurrently with managing the incoming information. Such problems call for a methodology to track their optimal solutions through time. In this thesis, dynamic vehicle routing problem is addressed and developing general methodologies called metaheuristics to tackle this problem is investigated. Their ability to adapt to the changing environment and their robustness are discussed. Results of experiments demonstrate thanks to dynamic performance measures that our methods are effective on this problem and hence have a great potential for other dynamic combinatorial problems

Passenger flow forecasting on transportation network: sensitivity analysis of the spatiotemporal features

International audiencePredicting the crowding level in train stations or the passenger load in trains can be useful to enrich the information available to passengers and improve train regulation processes or service quality levels. The main issue to handle when forecasting passenger flows is the structural variability of the related time series induced by the irregularity of train schedule and the influence of several contextual factors, such as calendar information and the characteristics of the served station. Forecasts depend on different contextual variables that generally have a spatial component, a temporal component, or both. We study the sensitivity of the spatiotemporal features of machine learning forecast models. Our main goal is to understand how the spatiotemporal features affect the performance of the models. First, we propose to study the impact of spatial and temporal inputs such as the served station, the train route or direction, and the type of day on the forecasting results to set up the best way to build a set of machine learning models to predict the passenger load of trains. Second, we address the effect of the temporal aggregation level on model performances for the forecasting task. The proposed models are based on ensemble machine learning approaches and have been deployed on a line of the Paris greater area railway network. A fine-grained evaluation is conducted as a support of the model's sensitivity analysis

A Multi-Criteria Multi-Modal Predictive Trip Planner: Application on Paris Metropolitan Network

International audiencePublic transport route planning is of growing interest in smart cities and especially in metropolitan areas where congestions and traffic jams are frequently recorded. The availability of multiple data sources, such as passenger load in trains or ticketing logs, provides an interesting opportunity to develop decision support tools to help passengers better plan their trips around the city and to enhance their travel experience. We present, in this paper, a multi-criteria journey planner that incorporates train load predictions as criteria. To this end, on the one hand, we enrich the proposed routes with predictive indicators of passenger flow such as the load on board the trains. These indicators are computed for each section of the itinerary using machine learning algorithms. On the other hand, we design a journey planner that incorporates the predicted load in its search criteria

Scaling Time-Dependent Origin-Destination Matrix Using Growth Factor Model

International audienceDemand estimation in public transport is critical for transport stakeholders. Thanks to the emerging technologies in recent years, many sources of mobility data are available to model passengers flow in public transport network. One of the most added-value mobility data is smart card Origin-Destination (OD) data. These data could inform us on when, where and how flows transit within the network. The OD matrix used in this work is obtained from smart card data collected by Automated Fare Collection (AFC) system in the Greater Paris Area which is called Navigo Pass. Despite its immense value, this matrix doesn't cover the entire passenger flow. This is due to fraud, other types of tickets (e.g. the standard paper ticket) and uncertainties in destination estimates. In this paper we propose a twostep approach for correcting and scaling smart card OD matrix based on adapted Growth Factor model considering the complexity caused by temporal variation of the OD matrix. In the first step we map all the OD pairs in the OD matrix over our area of study to infer their departure and arrival stations and time. In the second step we exploit passengers' counting data and use growth factor model to scale the OD matrix to obtain a new corrected matrix which can present the real flow in the transit network. We apply our proposed methodology to scale an OD matrix constructed only from smart card validation data which presents between 40% to 65% of the overall flow. For this purpose, passengers' counting data are exploited

Predictive Multimodal Trip Planner: A New Generation of Urban Routing Services

International audienceRoute planning in public transport receive an increasing interest in smart cities and particularly in metropolitan cities where crowded and jammed traffic is daily recorded in transportation network. The availability of digital footprints such as ticketing logs, or load on board the trains provide a relevant opportunity to develop innovative decision-making tools for urban routing of passengers in order to assist them to better planning their journeys. This planning must consider the forthcoming evolution of the traffic in order to adapt its response to the next state of the network and thus avoid unpleasant situations for the passengers. In this paper, we present a system for individual trip planning that incorporates short and long terms forecasting of different indicators related to the station attendance, occupancy of the trains, and delays in train schedule