Para tomar decisiones sobre alternativas para reducir los efectos negativos de la congestión, se debe planificar el transporte. La planificación requiere, según el enfoque clásico, el conjunto secuencial de cuatro (04) modelos de transporte para tomar decisiones pertinentes. El cuarto y último modelo corresponde a la asignación de tráfico, el cual requiere de la función BPR. La función BPR (Bureau of Public Roads) estima el tiempo de viaje (Tf) respecto al flujo de transporte (V), a la capacidad (C), al tiempo de viaje en flujo libre (to) y a los parámetros (α y β), los cuales deben reflejan el desempeño de la infraestructura vial. En Lima Metropolitana, como en otras ciudades de Latinoamérica, suele emplearse valores estandarizados de α y β en la función BPR, esto significa que dichos valores no están ajustado a las condiciones actuales de la ciudad. A partir de ello, se genera incertidumbre en la estimación de los datos de salida de la función (tiempo de viaje) y en efecto, incertidumbre en la eficiencia de las decisiones para disminuir la congestión. El objetivo de la presente investigación es calibrar la función BPR, es decir, estimar los valores de α y β. Y, en consecuencia, asegurar que esta represente, de manera confiable, el comportamiento vial en Lima Metropolitana, específicamente en la vía colectora: Avenida San Luis. La metodología se desarrolla en tres (03) etapas: Levantamiento de información, determinación de los parámetros y análisis de los resultados. Como resultado de la calibración se obtiene valores de α=2.22 y β=1.19, y se demuestra que la función calibrada representa de forma adecuada los tiempos de viaje observados en la vía en estudio. En cambio, la utilización de valores estandarizados no resulta ser la mejor para dicha representación, y por el contrario, presenta errores significativos. / In order to take decisions about alternatives to reduce the congestion’s negative effects, transport must be planified. Planification requires, according to the classic approach, the four-model transport sequential set to take the pertinent decisions. The fourth and last model corresponds to the traffic allocation, that requires the BPR function. The BPR function (Bureau of Public Roads) estimates the travel time (Tf) regarding the transport’s flow (V), the capacity (C), the travel time in free flow (to) and the parameters (α y β), that reflect the performance of the road infraestructure. In the city of Lima, as in others Latinamerican cities, it is common to use standarised values of α and β in BPR function. That means that those values are not adjusted to the city’s current conditions. On that basis, uncertainty arises on the function of the data output (travel time) and also on the efficiency of the decisions taken to diminish the traffic congestion. The main approach of this investigation is to calibrate the BPR function, that means, to estimate the functions of α y β, and, because of it, ensure that this function represents the traffic behavoir in the city of Lima in a reliable way, specifically in the collector road: San Luis Avenue. The methodology is developed in three stages: gathering of information, determination of the parameters and analysis of the results. As a result of the calibration, the values obtained are α=2.22 and β=1.19, and it is demonstraded that the calibrated functions represent in a reliable way the travel times observed in the studied avenue. However, the use of standarised values is not the best one for such representation, and, on the contrary, it shows significative errors. / Tesis

Dans un contexte de mobilité durable, le vélo en libre-service (VLS) représente un élément important dans les politiques de mobilité urbaine pour favoriser les modes doux dans le centre urbain. Son développement rapide dans le monde nécessite des recherches pour mieux comprendre le succès de ce mode de déplacement. Notre contribution à travers cette recherche est d’analyser les éléments socio-économiques importants ainsi que les caractéristiques du système VLS qui influent son usage pour construire des modèles de prédiction de la demande en VLS. Partant de l’hypothèse que les éléments socio-économiques autour des stations de VLS constituent des facteurs qualitativement et quantitativement explicatifs pour l’usage du VLS, notre démarche de recherche repose d’abord sur une analyse statistique, temporelle et spatiale des éléments explicatifs du système VLS pour ensuite les quantifier. Ces éléments sont utilisés pour modéliser la génération de la demande journalière en VLS par la méthode de régression linéaire multiple. Les modèles estimant les flux entrants et sortants journaliers en VLS ont été développés. Ils sont basés sur l’environnement socio-économique autour des stations et des variables d’offre de VLS. Ces modèles sont utiles pour comprendre le fonctionnement du système VLS, l’améliorer et estimer la demande des nouvelles stations dans une perspective d’élargissement d’un système VLS existant. Les modèles développés peuvent être également utilisés pour la localisation et le dimensionnement des stations d’un nouveau système VLS. La démarche de détermination, de quantification des variables explicatives et de modélisation forment un cadre de travail pour modéliser la demande d’autres modes de déplacement partagés. / In a context of sustainable transport, the bike sharing is an important factor in the policies to promote soft modes of transport in the urban center. Its rapid development in the world requires a need to deepen the usage of this mode of travel. Our contribution through this research is to analyze the important built-environment factors and the characteristics of bike sharing system that influence the use of bike sharing in order to build predictive models of demand for bike sharing. Assuming that socio-economic elements around the bike sharing stations are qualitatively and quantitatively explanatory for the use of bike sharing, our approach are based primarily on statistical analysis of temporal and spatial elements explaining bike sharing usage in order to determine and quantify the important built-environment variables. These variables are then used to model the generation of the daily demand of bike sharing using multiple linear regression method. The models estimating inflows and outflows of bike sharing using socioeconomic variables determined in a buffer area of each station are built. These models are useful for estimating the demand for new stations in an enlargement perspective of an existing bike sharing system or location and sizing of a new bike sharing system. The similar process of determination, quantification of the explanatory variables and modelling can be used to form a framework to predict the demand of other vehicle sharing systems.

In both developed and emerging-economies, major cities continue to experience increasing traffic congestion. To address this issue, complex Traffic Management Systems (TMS) are employed in recent years to help manage traffic. These systems fuse traffic-surveillance-related information from a variety of sensors deployed across traffic networks. A TMS requires real-time information to make effective control decisions and to deliver trustworthy information to users, such as travel time, congestion level, etc. There are three fundamental inputs required by TMS, namely, traffic volume, vehicular speed, and traffic density. Using conventional traffic loop detectors one can directly measure flow and velocity. However, traffic density is more difficult to measure. The situation becomes more difficult for multi-lane motorways due to drivers lane-change behaviour. This research investigates statistical modelling and analysis of traffic flow. It contributes to the literature of transportation and traffic management and research in several aspects. First, it takes into account lane-changes in traffic modelling through incorporating a Markov chain model to describe the drivers lane-change behaviour. Secondly, the lane change probabilities between two adjacent lanes are not assumed to be fixed but rather they depend on the current traffic condition. A discrete choice model is used to capture drivers lane choice behaviour. The drivers choice probabilities are modelled by several traffic-condition related attributes such as vehicle time headway, traffic density and speed. This results in a highly nonlinear state equation for traffic density. To address the issue of high nonlinearity of the state space model, the EKF and UKF is used to estimate the traffic density recursively. In addition, a new transformation approach has been proposed to transform the observation equation from a nonlinear form to a linear one so that the potential approximation in the EKF & UKF can be avoided. Numerical studies have been conducted to investigate the performance of the developed method. The proposed method outperformed the existing methods for traffic density estimation in simulation studies. Furthermore, it is shown that the computational cost for updating the estimate of traffic densities for a multi-lane motorway is kept at a minimum so that online applications are feasible in practice. Consequently the traffic densities can be monitored and the relevant information can be fed into the traffic management system of interest.