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EWGT 2020, 23rd EURO Working Group on Transportation Meeting, Paphos, CHYPRE, 16-/09/2020 - 18/09/2020; For the case of public transport, we consider the problem of demand estimation. Given an origin-destination matrix representing the public transport demand, the distribution of flow among different lines can be obtained assuming that it corresponds to a certain equilibrium characterized by an optimization problem. The knowledge of that matrix is expensive and sometimes unaffordable in practice. In this work, we explore its estimation through the numerical solution of a bilevel ptimization problem.; For the case of public transport, we consider the problem of demand estimation. Given an origin-destination matrix representing the public transport demand, the distribution of flow among different lines can be obtained assuming that it corresponds to a certain equilibrium characterized by an optimization problem. The knowledge of that matrix is expensive and sometimes unaffordable in practice. In this work, we explore its estimation through the numerical solution of a bilevel ptimization problem.

For the case of public transport, we consider the problem of demand estimation. Given an origin-destination matrix representing the public transport demand, the distribution of flow among different lines can be obtained assuming that it corresponds to a certain equilibrium characterized by an optimization problem. In particular we will focus on the assignment model proposed by Cepeda et al. (2006). However the knowledge of origin-destination matrix is expensive and sometimes unaffordable in practice. Traditionally, it is estimated using statistical or econometrical considerations. In this work, we explore the estimation through the numerical solution of a bilevel optimization problem. One disadvantage of this formulation is the difficulty of obtaining descent directions, hence, for the resolution of the optimization problem we use a derivative-free method. This method was applied for small networks getting good results.

We present an algorithm for optimal guidance of users in road networks. It is a "stochastic-on-time-arrival (SOTA)"-like algorithm which calculates optimal guidance strategies with reliable paths, for road network origin-destination pairs. Our contribution consists here in extending an existing SOTA algorithm, in order to include robustness of the guidance strategy, towards path failures. The idea of SOTA algorithms is to calculate the maximum probability of reaching a destination node, starting from any node of a road network, and given a time budget. This calculus gives the optimal path for every origin-destination pair of nodes in the network, with an associated optimal adaptive guidance strategy. We propose here an extension of this approach in order to take into account the existence and the performance of alternative detours of the selected paths, in the calculus of the guidance strategy. We take into account the fact that one or many links of the selected optimal path may fail during the travel. We then consider that users may be sensitive to path changing. That is to say that they may prefer paths with efficient alternative detours, with respect to paths without, or with less efficient detours, even with a loss in the average travel time, and/or in its reliability. In order to take into account such behaviors, we propose a model that includes the existence as well as the performance of detours for selected paths, in the calculus of the travel time reliability. This new way of calculating travel time reliability guarantees a kind of robustness of the guidance strategies., 9 pages, 3figures, conference

IEEE 19th International Conference on Intelligent Transportation Systems, Rio de Janeiro, BRESIL, 01-/11/2016 - 04/11/2016; A considerable number of studies have been undertaken on using smart card data to analyse urban mobility. Most of these studies aim to identify recurrent passenger habits, reveal mobility patterns, reconstruct and predict passenger flows, etc. Forecasting mobility demand is a central problem for public transport authorities and operators alike. It is the first step to efficient allocation and optimisation of available resources. This paper explores an innovative approach to forecasting dynamic Origin-Destination (OD) matrices in a subway network using long Short-term Memory (LSTM) recurrent neural networks. A comparison with traditional approaches, such as calendar methodology or Vector Autoregression is conducted on a real smart card dataset issued from the public transport network of Rennes Metropole, France. The obtained results show that reliable short-term prediction (over a 15 minutes time horizon) of OD pairs can be achieved with the proposed approach. We also experiment with the effect of taking into account additional data about OD matrices of nearby transport systems (buses in this case) on the prediction accuracy.

TRISTAN IX - 9th Triennial Symposium on Transportation Analysis, Oranjestad, Aruba, PAYS-BAS, 13-/06/2016 - 17/06/2016; The proposed approach follows and develops the research direction initiated almost 50 years ago, during which Origin/Destination (O/D) matrices were in a very simple way evaluated by means of Gravity Models (see Casey (1955)). In the classical framework, the population density of locations and the distance between two locations are often considered as relevant factors to estimate the number of trips from one zone to another one. Our novel model is similar in the subject since it considers compromises and covariates to get insight of the mobility. But it overcomes the barriers of Gravity Modelling which is mainly static and push it in a dynamic context. Following the traces of Mixed-Membership modeling (see Airoldi et al. (2014)), which can perform analyses of O/D matrices (as in Come et al. (2014)), we propose to merge a gravity model approach with Latent Dirichlet Allocation (LDA) in order to deal with non stationary O/D matrices. Our proposal called G-LDA which stands for Gravity LDA, allows us to uncover the hidden patterns underlying urban mobility by taking into account temporal effects on usages and the effects of contextual socio-economical variables. In the following, we describe the novel extension of the LDA model and give some results on a dataset collected on the Paris Bike Shared System (BSS).

Journées de méthodologie statistiques, PARIS, FRANCE, 31-/03/2015 - 02/04/2015; Nous présentons dans ce papier plusieurs méthodes de transfert de support de données de déplacement. Le support spatial est l'objet théorique sur lequel est localisée géographiquement une donnée spatialisée. Les analyses pouvant être effectuées dépendent de sa finesse. Pour se dispenser de faire de nouvelles enquêtes lorsque le maillage est inadapté ou insuffisant nous transférons des données de déplacement sous la forme de matrice origine/destination d'un support vers un autre, l'objectif étant de préserver la qualité de l'information. Pour ce faire nous utilisons des données auxiliaires permettant de plus ou moins bien contrôler la répartition socio-spatiale des origines/destinations. Nous traitons ici du fichier détail des mobilités professionnelles du recensement qui apporte une connaissance sur les déplacements domicile-travail entre communes. Nous proposons plusieurs méthodes pour transférer ces données sur le carroyage de population de 1 km.

ICNAAM - International Conference of Numerical Analysis and Applied Mathematics, Rhodes, GRECE, 22-/09/2014 - 28/09/2014; We present in this article a model for the guidance of users in road traffic networks. It is well known since decades that a path with a reliable travel time can be obtained by maximizing, over the different possible paths, the probability of realizing a travel time less than a given time budget. We propose here an adaptation of this approach, in order to introduce robustness in the selection of the optimal path. A robust path here is a path that is not likely to change during the travel, and that admits acceptable alternative detours in case of failure.

International audience; Aggregate modal choice model calibration. - Estimation of aggregated mode choice models has to face the problem of reliability of data used for calibration. Samples in travel surveys that are used for these estimations are generally too small for the needs of aggregate models. Therefore the number of observed trips per origin-destination is very small for the majority of them. Solution which is generally chosen needs on the one hand to reduce the number of zones by zones aggregation and on the other hand to keep for calibration only those origins-destinations for which sample size is above a minimum threshold. But we demonstrated that calibration results are highly sensitive to the choice of zones aggregation and to the choice of the threshold. In response to this problem this paper proposes an estimation method which allows very small-scale zoning to be retained and to keep the available information. The method is based on an iterative process whose convergence appears quite rapid and more importantly gives stable calibration results regarding zoning and threshold choices. To overcome one of the main problems of this method, which do not allow the calculation of confidence interval, we have used bootstrap method. This empirical analysis of mode choice between car and public transport on Lyon data allows us to study the benefits of our method in comparison with more conventional methods.; L'estimation des modèles agrégés de choix modal doit faire face au problème de la fiabilité des données utilisées pour le calage. Les enquêtes ménages déplacements utilisées pour ce calage conduisent le plus souvent à des effectifs de déplacements observés très faibles pour la très grande majorité des origines-destinations des matrices par mode. La solution généralement retenue conduit d'une part à réduire le nombre de zones en agrégeant les zones entre elles et d'autre part à ne conserver que les origines-destinations dont on estime que l'effectif enquêté est suffisant. Nous montrons toutefois que l'estimation d'un tel modèle est très sensible au choix du découpage et au choix du seuil de " fiabilité " en nombre de déplacements observés. Pour faire face à ce problème, nous proposons une méthode d'estimation qui permet de conserver un découpage très fin et qui permet d'utiliser toute l'information disponible. Cette méthode repose sur un processus itératif dont la convergence apparaît assez rapide et qui surtout conduit à des résultats stables en fonction du choix du découpage et du choix du seuil de " fiabilité " retenu pour le processus d'agrégation. La principale limite de cette méthode est de ne pas permettre le calcul direct d'intervalles de confiance. Nous avons donc utilisé la technique du bootstrap pour pallier cette limite. Nous pouvons ainsi tester la qualité de cette méthode sur le contexte du choix modal entre la voiture particulière et les transports collectifs sur l'agglomération lyonnaise.

Aggregate modal choice model calibration. - Estimation of aggregated mode choice models has to face the problem of reliability of data used for calibration. Samples in travel surveys that are used for these estimations are generally too small for the needs of aggregate models. Therefore the number of observed trips per origin-destination is very small for the majority of them. Solution which is generally chosen needs on the one hand to reduce the number of zones by zones aggregation and on the other hand to keep for calibration only those origins-destinations for which sample size is above a minimum threshold. But we demonstrated that calibration results are highly sensitive to the choice of zones aggregation and to the choice of the threshold. In response to this problem this paper proposes an estimation method which allows very small-scale zoning to be retained and to keep the available information. The method is based on an iterative process whose convergence appears quite rapid and more importantly gives stable calibration results regarding zoning and threshold choices. To overcome one of the main problems of this method, which do not allow the calculation of confidence interval, we have used bootstrap method. This empirical analysis of mode choice between car and public transport on Lyon data allows us to study the benefits of our method in comparison with more conventional methods., L'estimation des modèles agrégés de choix modal doit faire face au problème de la fiabilité des données utilisées pour le calage. Les enquêtes ménages déplacements utilisées pour ce calage conduisent le plus souvent à des effectifs de déplacements observés très faibles pour la très grande majorité des origines-destinations des matrices par mode. La solution généralement retenue conduit d'une part à réduire le nombre de zones en agrégeant les zones entre elles et d'autre part à ne conserver que les origines-destinations dont on estime que l'effectif enquêté est suffisant. Nous montrons toutefois que l'estimation d'un tel modèle est très sensible au choix du découpage et au choix du seuil de " fiabilité " en nombre de déplacements observés. Pour faire face à ce problème, nous proposons une méthode d'estimation qui permet de conserver un découpage très fin et qui permet d'utiliser toute l'information disponible. Cette méthode repose sur un processus itératif dont la convergence apparaît assez rapide et qui surtout conduit à des résultats stables en fonction du choix du découpage et du choix du seuil de " fiabilité " retenu pour le processus d'agrégation. La principale limite de cette méthode est de ne pas permettre le calcul direct d'intervalles de confiance. Nous avons donc utilisé la technique du bootstrap pour pallier cette limite. Nous pouvons ainsi tester la qualité de cette méthode sur le contexte du choix modal entre la voiture particulière et les transports collectifs sur l'agglomération lyonnaise.

ECTQG 2015 - 19th European colloquium of Theoretical and Quantitative Geography, BARI, ITALIE, 03-/09/2015 - 07/09/2015; We observed that mapping flow is to vary the size of a straight directed link between origin (i) and destination (j) places, proportionally to a flow matrix value (Fij). According to Bertin's (1967), this practice reflects the exact application of the visual variable 'size' on the flow feature. Every other aesthetics and semiotic principles - as colored or curvilinear features - can be taken into account without fundamental consequences. This is very popular since earlier flow maps. It even seems to be the unique way to map flow, but is it relevant? Is it obvious that flow mapping raises one dimension of the features while the human eye may perceive several? Assuming that first hypothesis is exact means in fact that the human eye will perceive only one feature's dimension: its width (Fij). Is it really the case? We raise a reasonable doubt about the relevance of this assumption and will examine it in this paper. Our aim is to demonstrate the legitimacy of the conventional approach of flow mapping which leads to the first hypothesis. To do this, we will place us in the theoretical framework defined by Grasland (2009) leading to a joint (analysis of positions and flow. However, the present proposed perspective differs as the analysis is focused on the perceived information on a flow map. Therefore, we'll assume also Bertin's (1967) and Tobler's (1987) cartographic principles for mapping statistical flow data. We will then examine the corresponding cartographic process from two cognitive perspectives, regarding both semiotic and semantic aspects of the feature's flow map. We will explore a set of four assumptions (fig. 1). The fig. 1 describes through assumptions n°1 to n°4 what human eye perceived on a flow map and what it means. Firstly: the eye perceives only the feature's width (ass. n°1). The second assumption suggests the possibility of the perception of the feature's visual area, i.e regarding the amount of color (ass. n°2). It will lead us in a third assumption to the perceived geometric feature's area, that is to say the arithmetic product of the feature's width and length (ass. n°3). This theoretical semantic switching is not trivial; it corresponds to the perception of a new dimension: the cartographic distance, derived from the formal 'graphic distance' (Müller, 1979; Tobler, 1997; L'hostis, 2003), that is equal to the feature's length (Lij) perceived on the map. This effective joint perception of (Lij) and (Fij) leads us to introduce geographic space in flow mapping process, considering Tobler's (1969) first law of geography. This seems in other words to integrate the fact that flow values (Fij) are projected over a geographical area, consisting of (i,j) places that are more or less distant (Dij) or close in the reality... The places' remoteness influences the significance of the perceived flow value as well as in a flow map (but not in a plan as a spatialized graph which has no geographic properties). Assuming the fourth hypothesis (ass. n°4) is exact involves us carrying out a map of movement (Mij) rather than a map of flow (Fij). We demonstrate on selected bilateral international trade flows from Chelem database that if the cartographic construction of (Fij) and (Mij) is the same and unique (ass. n°1 and n°4 are equals), they differ only in their interpretation (Fij) C (Mij) for one main raison: at a world scale, flow mapping can only be an approximation of the geographical movement (fig. 2), as was done earlier by Minard (1862).