Data-driven optimization of bus schedules under uncertainties

Plusieurs sous-problèmes d’optimisation se posent lors de la planification des transports publics. Le problème d’itinéraires de véhicule (PIV) est l’un d’entre eux et consiste à minimiser les coûts opérationnels tout en assignant exactement un autobus par trajet planifié de sorte que le nombre d’autobus entreposé par dépôt ne dépasse pas la capacité maximale disponible. Bien que les transports publics soient sujets à plusieurs sources d’incertitude (à la fois endogènes et exogènes) pouvant engendrer des variations des temps de trajet et de la consommation d’énergie, le PIV et ses variantes sont la plupart du temps résolus de façon déterministe pour des raisons de résolubilité. Toutefois, cette hypothèse peut compromettre le respect de l’horaire établi lorsque les temps des trajets considérés sont fixes (c.-à-d. déterministes) et peut produire des solutions impliquant des politiques de gestion des batteries inadéquates lorsque la consommation d’énergie est aussi considérée comme fixe. Dans cette thèse, nous proposons une méthodologie pour mesurer la fiabilité (ou le respect de l’horaire établi) d’un service de transport public ainsi que des modèles mathématiques stochastiques et orientés données et des algorithmes de branch-and-price pour deux variantes de ce problème, à savoir le problème d’itinéraires de véhicule avec dépôts multiples (PIVDM) et le problème d’itinéraires de véhicule électrique (PIV-E). Afin d’évaluer la fiabilité, c.-à-d. la tolérance aux délais, de certains itinéraires de véhicule, nous prédisons d’abord la distribution des temps de trajet des autobus. Pour ce faire, nous comparons plusieurs modèles probabilistes selon leur capacité à prédire correctement la fonction de densité des temps de trajet des autobus sur le long terme. Ensuite, nous estimons à l'aide d'une simulation de Monte-Carlo la fiabilité des horaires d’autobus en générant des temps de trajet aléatoires à chaque itération. Nous intégrons alors le modèle probabiliste le plus approprié, celui qui est capable de prédire avec précision à la fois la véritable fonction de densité conditionnelle des temps de trajet et les retards secondaires espérés, dans nos modèles d'optimisation basés sur les données. Deuxièmement, nous introduisons un modèle pour PIVDM fiable avec des temps de trajet stochastiques. Ce problème d’optimisation bi-objectif vise à minimiser les coûts opérationnels et les pénalités associées aux retards. Un algorithme heuristique basé sur la génération de colonnes avec des sous-problèmes stochastiques est proposé pour résoudre ce problème. Cet algorithme calcule de manière dynamique les retards secondaires espérés à mesure que de nouvelles colonnes sont générées. Troisièmement, nous proposons un nouveau programme stochastique à deux étapes avec recours pour le PIVDM électrique avec des temps de trajet et des consommations d’énergie stochastiques. La politique de recours est conçue pour rétablir la faisabilité énergétique lorsque les itinéraires de véhicule produits a priori se révèlent non réalisables. Toutefois, cette flexibilité vient au prix de potentiels retards induits. Une adaptation d’un algorithme de branch-and-price est développé pour évaluer la pertinence de cette approche pour deux types d'autobus électriques à batterie disponibles sur le marché. Enfin, nous présentons un premier modèle stochastique pour le PIV-E avec dégradation de la batterie. Le modèle sous contrainte en probabilité proposé tient compte de l’incertitude de la consommation d’énergie, permettant ainsi un contrôle efficace de la dégradation de la batterie grâce au contrôle effectif de l’état de charge (EdC) moyen et l’écart de EdC. Ce modèle, combiné à l’algorithme de branch-and-price, sert d’outil pour balancer les coûts opérationnels et la dégradation de la batterie.The vehicle scheduling problem (VSP) is one of the sub-problems of public transport planning. It aims to minimize operational costs while assigning exactly one bus per timetabled trip and respecting the capacity of each depot. Even thought public transport planning is subject to various endogenous and exogenous causes of uncertainty, notably affecting travel time and energy consumption, the VSP and its variants are usually solved deterministically to address tractability issues. However, considering deterministic travel time in the VSP can compromise schedule adherence, whereas considering deterministic energy consumption in the electric VSP (E-VSP) may result in solutions with inadequate battery management. In this thesis, we propose a methodology for measuring the reliability (or schedule adherence) of public transport, along with stochastic and data-driven mathematical models and branch-and-price algorithms for two variations of this problem, namely the multi-depot vehicle scheduling problem (MDVSP) and the E-VSP. To assess the reliability of vehicle schedules in terms of their tolerance to delays, we first predict the distribution of bus travel times. We compare numerous probabilistic models for the long-term prediction of bus travel time density. Using a Monte Carlo simulation, we then estimate the reliability of bus schedules by generating random travel times at each iteration. Subsequently, we integrate the most suitable probabilistic model, capable of accurately predicting both the true conditional density function of the travel time and the expected secondary delays, into the data-driven optimization models. Second, we introduce a model for the reliable MDVSP with stochastic travel time minimizing both the operational costs and penalties associated with delays. To effectively tackle this problem, we propose a heuristic column generation-based algorithm, which incorporates stochastic pricing problems. This algorithm dynamically computes the expected secondary delays as new columns are generated. Third, we propose a new two-stage stochastic program with recourse for the electric MDVSP with stochastic travel time and energy consumption. The recourse policy aims to restore energy feasibility when a priori vehicle schedules are unfeasible, which may lead to delays. An adapted algorithm based on column generation is developed to assess the relevance of this approach for two types of commercially available battery electric buses. Finally, we present the first stochastic model for the E-VSP with battery degradation. The proposed chance-constraint model incorporates energy consumption uncertainty, allowing for effective control of battery degradation by regulating the average state-of-charge (SOC) and SoC deviation in each discharging and charging cycle. This model, in combination with a tailored branch-and-price algorithm, serves as a tool to strike a balance between operational costs and battery degradation

On the predictability limit of convection models of the Earth's mantle

International audienceReconstructing convective flow in the Earth's mantle is a crucial issue for a diversity of disciplines, from seismology to sedimentology. The common and fundamental limitation of these reconstructions based on geodynamic modelling is the unknown initial conditions. Because of the chaotic nature of convection in the Earth's mantle, errors in initial conditions grow exponentially with time and limit forecasting and hindcasting abilities. In this work we estimate for the first time the limit of predictability of Earth's mantle convection. Following the twin experiment method, we compute the Lyapunov time (i.e. e-folding time) for state-of-the art 3D spherical convection models, varying rheology and Rayleigh number. Our most Earth-like and optimistic solution gives a Lyapunov time of 136±13 My. Rough estimates of the uncertainties in best guessed initial conditions are around 5%, leading to a limit of predictability for mantle convection of 95 My. Our results suggest that error growth could produce unrealistic convective structures over timescales shorter than that of Pangea dispersal

Cross-validation of ELISA and a portable surface plasmon resonance instrument for IgG antibody serology with SARS-CoV-2 positive individuals.

We report on the development of surface plasmon resonance (SPR) sensors and matching ELISAs for the detection of nucleocapsid and spike antibodies specific to the novel coronavirus 2019 (SARS-CoV-2) in human serum, plasma and dried blood spots (DBS)

Formal synthesis of (+-)-Guanacastepene A: a tandem ring-closing metathesis approach

DOI:10.1021/ol049452x S1523-7060(04)09452-0International audienc

Synthesis of polyoxygenated bicyclic systems containing medium sized rings from carbohydrates via tandem metathesis of dienynes

DOI:10.1021/ol016329m S1523-7060(01)06329-5International audienc

Predicting the probability distribution of bus travel time to measure the reliability of public transport services

An important aspect of the quality of a public transport service is its reliability, which is defined as the invariability of the service attributes. In order to measure reliability during the service planning phase, a key piece of information is the long-term prediction of the density of the travel time, which conveys the uncertainty of travel times. This work empirically compares probabilistic models for the prediction of the conditional probability density function (PDF) of the travel time and proposes a simulation framework taking as input the latter distributions to approximate the expected secondary delays, a measure of the reliability of public transport services. Two types of probabilistic models, namely similarity-based density estimation models and a smoothed logistic regression for probabilistic classification model, are compared on a dataset of more than 41,000 trips and 50 bus routes of the city of Montréal. A similarity-based density estimation model using a -nearest neighbors method and a log-logistic distribution predicted the best estimate of the true conditional PDF of the travel time and generated the most accurate approximations of the expected secondary delays on this dataset. This model reduced the mean squared error of the expected secondary delay by approximately 9% compared to the benchmark model, namely a random forest. This result highlights the added value of modeling the conditional PDF of the travel time with probabilistic models

Personality in young horses and ponies evaluated during breeding shows: phenotypic link with jumping competition results

International audienceAnimal personality, the result of temperament being modulated by life events, is an important factor to be considered when breeding and using domestic horses. In the breeding of sport horses, personality appears as a secondary trait in selection objectives after competition performance. Moreover, the per-sonality trait of fearfulness may be viewed as a risk factor for riders. This study aimed to estimate the variability of personality characteristics measured during breeding shows and their phenotypic correla-tion with performance in jumping competitions. Data for personality characteristics were recorded during 67 breeding shows in France on 876 jumping horses, 424 jumping ponies and 45 leisure ponies aged 2 or 3 years. Their behavior was assessed during 1) customary rounds (CR) of breeding shows (conforma-tion, free jumping and height measurement at withers) and 2) specific tests (ST) conducted in-hand that measured fearfulness (novel object, novel surface and suddenness tests) and tactile sensitivity. Not all the animals were evaluated on all the behavior tests. Jumping performances from 4 to 7 years old were recorded for 724 of the horses and for 313 of the ponies in official competitions specific for horses or ponies. Environmental effects were estimated using general linear model taking into account breeding show, age and sex. The breeding show effect was significant on 23 out of 28 characteristics. Age and sex influenced approximately one third of the characteristics: younger animals were more fearful; males moved and whinnied more; geldings appeared slightly more fearful during ST. Jumping performances were mostly independent of personality characteristics. In horses, performances were phenotypically pos-itively linked with 3 characteristics during jumping CR (whinnies ( P = 0.05), main gait when entering ( P = 0.02), evasive behaviors ( P = 0.03)) and with posture during conformation evaluation CR ( P = 0.04). In ponies, jumping performances were phenotypically positively linked only with whinnies: during CR of height measurement ( P = 0.02) and during all ST ( P = 0.01). As no main fear variables were significantly related to jumping performances in the two studied populations, it seems that less fearful horses and ponies may perform well in show jumping

Analyse des résultats des tests de tempérament simplifiés sur des jeunes chevaux et poneys de selle français : relation avec la performance et première évaluation de l'héritabilité

National audienc