A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. In addition to traffic counts, the present contribution takes advantage of probe trajectories, whose capture is made possible by new measurement technologies. It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the information about the flow distribution on links and containing inherently the ODM assignment. Further, an original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimisation problem, where the functional to be minimized consists of five convex functions, each modelling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having close origins and destinations, positivity of traffic flows. A primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations

Estimation/updating of origin-destination flows: recent trends and opportunities from trajectory data

Understanding the spatial and temporal dynamics of mobility demand is essential for many applications over the entire transport domain, from planning and policy assessment to operation, control, and management. Typically, mobility demand is represented by origin-destination (o-d) flows, each representing the number of trips from one traffic zone to another, for a certain trip purpose and mode of transport, in a given time interval (Cascetta, 2009, Ortuzar and Willumsen, 2011). O-d flows have been generally unobservable for decades, thus the problem of o-d matrix estimation is still one of the most challenging in transportation studies. In recent times, unprecedented tracing and tracking capabilities have become available. The pervasive penetration of sensing devices (smartphones, black boxes, smart cards, ...) adopting a variety of tracing technologies/methods (GPS, Bluetooth, ...) could make in many cases o-d flows now observable. The increasing availability of trajectory data sources has provided new opportunities to enhance observability of human mobility and travel patterns between origins and destinations, recently explored by researchers and practitioners, bringing innovation and new research directions on origin-destination (o-d) matrix estimation. The purpose of this thesis is to develop a deep understanding of the opportunities and the limitations of trajectory data to assess its potential for ameliorating the o-d flows estimation/updating problem and for conducting o-d related analysis. The proposed work involves both real trajectory data analysis and laboratory experiments based on synthetic data to investigate the implications of the trajectory data sample distinctive features (e.g. sample representativeness and bias) on demand flows accuracy. Final considerations and results might provide useful guidelines for researchers and practitioners dealing with various types of trajectory data sample and conducting o-d related applications

Dynamic OD matrix estimation exploiting ICT traffic measurements

Pla de Doctorats Industrials de la Generalitat de CatalunyaDuring the last decades, urban mobility has become the main concern for city councils and transportation operators. The main problem is the traffic congestion that easily appears in urban networks, producing negative economic impacts for the associated cost and, what is becoming more relevant from the sustainability point of view. In this context, the transportation operators and planners make use of traffic simulation models that assist their strategic decisions aiming at improving the mentioned problems. The dynamic OD matrices estimation problem is a crucial step in transportation modeling and simulation because they contain the total number of vehicles that are circulating throughout the city, including their origins, destinations, and their departing time and describe the associated mobility patterns in terms of trip distributions. As this information is not directly observable in reality, this problem has been widely studied and many different methodologies have been proposed in order to obtain the suitable OD matrices that reflect the urban mobility of the studied area. The common approach is to use the counting stations data sets to estimate, using a minimization problem, the OD matrices that produce them. This is called the bi-level optimization approach. However, the main problem of this approach is that it is mathematically underdetermined, because many different OD matrices can produce the same traffic counts on certain links of the urban network, but presenting totally different trip distributions that could not correspond to the socio-demographic structure originating them. In this thesis, we address the different studies measuring the structural similarity between the estimated OD matrix and the reliable OD matrices, which are the ground truth OD matrix in synthetic experiments or the historical OD matrix in the real ones. The appearance of new sources of traffic data from the growth of the information and communication technologies (ICT) appeals to the researchers to use it for reducing such underdetermination, adding it to the OD estimation problem. GPS devices are increasingly used by vehicles and a huge volume of data is generated every day that, implicitly, contains information of the traffic state under real conditions. These data can be analyzed and processed in order to clean, filter and extract this information and can be then introduced into the OD estimation problem. Most of the theoretical research since the ICT technologies are available assume implicitly or explicitly that GPS tracking data can be done through a controlled collection process. However, in the practical world, GPS data are supplied by companies that use different data collection policies and constraints imposed by privacy policies, which invalidate some of these theoretical hypotheses. One of the main research aspects of this thesis is to investigate how these commercial data can be used for the OD estimation problem. However, the introduction of such information in the bi-level optimization problem is not direct and many alternatives arise. This thesis proposes a data-driven estimation of the dynamic assignment matrix to introduce the GPS data information to an analytical model, reducing the underdetermination of the problem. Moreover, such estimation replaces the dynamic traffic assignment reducing also the computational effort of the OD estimation problem. As this thesis results from the collaboration between the simulation software company PTV Group and the Universitat Politècnica de Catalunya, all the experiments of this thesis have been carried out in PTV Visum and using the already existing products. Moreover, the results have been analyzed both from the computational performance and from the quality aspect.Durant les últimes dècades, les externalitats que es deriven de la mobilitat urbana han estat una de les principals preocupacions dels ajuntaments, gestors metropolitans i operadors de transport. El principal problema és la congestió, que fàcilment apareix en infraestructures urbanes i que impacta negativament en la nostra economia i, el que és més greu, en la sostenibilitat del planeta en que vivim. La contaminació i el soroll provocats per la congestió no només afecten nocivament a la qualitat de l’aire, sinó que també afecten la salut ciutadana i mediambiental. En aquest context, els operadors i planificadors de trànsit utilitzen models de planificació i simulació de trànsit que els aporten coneixement per dur a terme decisions estratègiques i operatives que mitiguin els problemes associats a la mobilitat urbana. El problema d’estimació de les matrius origen-destinació (OD) és un tema crucial en la modelització i simulació del trànsit. Aquestes contenen el nombre total de vehicles que circulen per la ciutat, incloent informació sobre els l’origen, destinació i temps de sortida de cadascun en un horitzó temporal. D’aquesta manera, la distribució de viatges definida en les matrius OD descriu el patró de mobilitat de la xarxa. No obstant això, aquesta informació no és directament observable en un cas pràctic real i, per aquest motiu, es tracta d’un problema profundament estudiat. S’han desenvolupat diferents metodologies que procuren obtenir matrius OD apropiades, és a dir, que reprodueixin correctament la mobilitat de la zona estudiada. L’enfoc més comú consisteix en usar dades recollides per sensors de trànsit que compten vehicles en certs punts de la xarxa per estimar les matrius OD mitjançant la resolució d’un problema de minimització. De tota manera, aquest problema complex és altament indeterminat i diferents matrius OD, que representen realitats sociodemogràfiques i patrons de mobilitat diferents, poden reproduir els mateixos comptatges de vehicles en les vies de la xarxa dotades de sensors. Per tant, moltes línies de recerca han usat diferents tipus de dades de transport addicionals, com ara velocitats mitjanes i densitats de flux, per reduir els graus de llibertat del problema. L’estructura d’una matriu OD descriu el nombre de viatges i la forma com es distribueixen espaialment en la xarxa urbana, des del seu origen a la seva destinació, traçant així el patró de mobilitat global de la xarxa d’estudi. Com que dues matrius OD poden generar els mateixos comptatges, és absolutament necessari fer un estudi exhaustiu de la similaritat de les seves estructures. En aquesta tesi, enfoquem les diferents propostes mesurant sempre el grau de similaritat estructural entre la matriu OD estimada i una matriu OD de referència, sent aquesta la matriu OD històrica en casos reals o la matriu fonamental en el cas dels experiments sintètics. L’aparició de noves fonts de dades de trànsit degut al creixement de les tecnologies de la informació i comunicació (TIC) obre noves línies de recerca adreçades a reduir la indeterminació del problema d’estimació de les matrius OD. L’ús d’aparells GPS en vehicles va en augment, fet que contribueix a la generació diària de grans volums de dades. Aquestes contenen, de manera implícita, informació de l’estat del trànsit en condicions reals. Mitjançant un procés de neteja, filtratge i extracció es pot derivar informació del trànsit per a després introduir-la al problema de l’estimació de matrius OD. El conjunt de dades GPS de tipus comercials no permet conèixer el procediment de recol·lecció de dades i, sovint, està subjectes a polítiques de protecció i privacitat que no permeten assumir certes hipòtesis de qualitat i control en relació als orígens i destinacions. En aquesta tesi, investiguem el valor que poden afegir aquests conjunts de dades comercials per a l’estimació de matrius OD. La introducció d’aquestes dades al problema d’optimització binivell no és directa i existeixen diverses alternatives. Els enfocs analítics no permeten introduir directament aquestes dades perquè la relació entres les dades GPS i els fluxos OD no és elemental. Per altra banda, la versatilitat dels mètodes de simulació-optimització permeten usar-los directament, però l’inconvenient és l’esforç computacional associat. Aquesta tesi proposa un model de la matriu dinàmica d’assignacions basat en dades (data-driven) per aprofitar la informació implícita de les dades GPS i reduir, així, la indeterminació del problema. A més, aquesta tècnica substitueix la necessitat de recórrer a un model de simulació y redueix l’esforç computacional del problema. Aquesta tesi és fruit de la col·laboració entre l’empresa de software de simulació PTV Group i la Universitat Politècnica de Catalunya. Tots els experiments d’aquesta tesi han estat implementats en PTV Visum i usant els productes existents. A més, els resultats de la tesi han estat sempre analitzats des d’una doble perspectiva: computacional i de la qualitat. Aquesta última té com a objectiu analitzar la matriu OD pel que fa a la seva similaritat estructural amb la matriu de referència.Durante las últimas décadas, las externalidades que se derivan de la movilidad urbana han sido una de las principales preocupaciones de los ayuntamientos, gestores metropolitanos, y operadores de tráfico. El principal problema es la congestión, que fácilmente aparece en infraestructuras urbanas y que impacta de forma negativa en nuestra economía y, lo que es más grave, en la sostenibilidad del planeta que habitamos. La contaminación y el ruido provocados por la congestión no solo afectan nocivamente a la calidad del aire, sino que también perjudican la salud ciudadana y medioambiental. En este contexto, los operadores y planificadores de transporte usan modelos de planificación y simulación de tráfico que les aportan conocimiento para tomar decisiones estratégicas y operativas que mitiguen los problemas asociados a la movilidad urbana. El problema de la estimación de las matrices origen-destino (OD) es un tema crucial en la modelización y simulación de tráfico. Éstas contienen el número total de vehículos que circulan por la ciudad, incluyendo información sobre el origen, destino y tiempo de salida de cada uno de los vehículos en un horizonte temporal. De esta manera, la distribución de viajes definida en las matrices OD describe el patrón de movilidad de la red. Aun así, esta información no es directamente observable en un caso práctico real y, por este motivo, se trata de un problema extensamente estudiado. Se han desarrollado diferentes metodologías con el fin de obtener las matrices OD más apropiadas, es decir, aquellas que reproducen adecuadamente la movilidad de la zona estudiada. El enfoque más común consiste en usar datos recogidos por sensores de tráfico que cuentan vehículos en ciertos puntos de la red para estimar las matrices OD mediante la resolución de un problema de minimización. Aun así, este complejo problema es altamente indeterminado y diferentes matrices OD, que representan realidades sociodemográficas y patrones de movilidad distintos, pueden reproducir los mismos conteos de vehículos en las vías de la red dotadas de sensores. Por consiguiente, muchas líneas de investigación han utilizado de forma adicional diferentes tipos de datos de tráfico, como velocidades medias y densidades de flujo, para reducir los grados de libertad del problema. La estructura de una matriz OD describe el número de viajes y la forma como se distribuyen espacialmente en la red urbana, desde su origen hasta su destino, trazando, así, el patrón de movilidad global de la red de estudio. Como dos matrices OD pueden reproducir los mismos conteos, es absolutamente necesario hacer un análisis exhaustivo de la similitud de sus estructuras. En esta tesis, abordamos las diferentes propuestas midiendo siempre el grado de similitud estructural entre la matriz OD estimada y una matriz OD de referencia, siendo ésta la matriz OD histórica en casos reales o la matriz fundamental en el caso de los experimentos sintéticos. La aparición de nuevas fuentes de datos de tráfico debido al crecimiento de las tecnologías de la información y comunicación (TIC) abre nuevas líneas de investigación dirigidas a reducir la indeterminación del problema de estimación de las matrices OD. El uso de aparatos GPS en vehículos va en aumento, hecho que contribuye a la generación diaria de grandes volúmenes de datos. Éstos contienen, de manera implícita, información del estado del tráfico en condiciones reales. Mediante un proceso de limpieza, filtrado, y extracción se puede derivar información del tráfico para luego introducirla en el problema de estimación de matrices OD. El conjunto de datos GPS de tipo comercial no permite conocer el procedimiento de recolecta de datos y, a menudo, está sujeto a políticas de protección y privacidad que no permiten asumir ciertas hipótesis de calidad y control en relación a los orígenes y destinos. En esta tesis, investigamos el valor que pueden añadir estos conjuntos de datos comerciales para la estimación de matrices OD. La introducción de estos datos en el problema de optimización binivel no es directa y existen diferentes alternativas. Los enfoques analíticos no permiten incorporar directamente estos datos puesto que la relación entre los datos GPS y los flujos OD no es elemental. Por otro lado, la versatilidad de los métodos de simulación-optimización permiten usarlos directamente, pero el inconveniente es el esfuerzo computacional asociado. Esta tesis propone un modelo de la matriz dinámica de asignaciones basado en datos (data-driven) para aprovechar la información implícita de los datos GPS y reducir, así, la indeterminación del problema de estimación. Además, esta técnica reemplaza la necesidad de recurrir a un modelo de simulación y reduce el esfuerzo computacional del problema. Esta tesis es fruto de la colaboración entre la empresa de software de simulación PTV Group y la Universitat Politècnica de Catalunya. Todos los experimentos de la tesis han sido implementados en PTV Visum y usando los productos existentes. Además, los resultados de la tesis han sido siempre analizados desde una doble perspectiva: computacional y de calidad. Esta última tiene como objetivo analizar la matriz OD estimada respeto la similitud estructural con la matriz de referencia..Postprint (published version

Adapting a dynamic OD matrix estimation approach for private traffic based on bluetooth data to passenger OD matrices

The primary data input used in principal traffic models comes from Origin-Destination (OD) trip matrices, which describe the patterns of commuters across the network. In this way, OD matrices become a critical requirement in Advanced Transport Control and Management and/or Information Systems that are supported by Dynamic Traffic Assignment models (DTA models). Dynamic Transit Assignment models are a research topic, but once a dynamic transit assignment be available to practitioners, the problem of estimating the time-dependent number of trips between transportation zones shall be a critical aspect for real applications. However, OD matrices are not directly observable, neither for private nor public transport, and the current practice consists on adjusting an initial or seed matrix from link/segment counts which are provided by counting stations or data gathering in the field (detection layout). The emerging Information and Communication Technologies, especially those based on the detection of the electronic signature of on-board devices provide a rich source of data that can be used in space-state models for dynamic matrix estimation. We present a linear Kalman filter approach that makes use of counts of passengers and travel times provided by Bluetooth devices to simplify an underlying space-state model. The formulation for dynamic passenger OD matrix estimation proposed was originally developed for auto trip matrices, but in this paper, we explore the possibility of adapting the approach to the estimation of OD matrices in public transport networks.Peer ReviewedPostprint (author’s final draft

Exploring the direct and indirect use of ICT measurements in DODME (Dynamic OD Matrix Estimation)

The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin–destination (OD) matrix that describes the time dependencies of travel patterns; this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alter-natives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin–destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process.Peer ReviewedPostprint (published version

A use of information and communication technologies in the framework of advanced management of transportation systems: dynamic OD matrix estimation

Origin-Destination (OD) trip matrices are the primary data input used in principal traffic and transit models, which describe the patterns of trips/passengers across the area of study. In this way, OD matrices become a critical requirement in Advanced Transport Management and/or Information Systems that are supported by Dynamic Assignment models. In the future, once combined dynamic traffic and transit assignment tools will be available to practitioners, the problem of estimating the time-dependent number of trips/passengers between transportation zones would be a critical aspect for real applications. However, because OD matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link/segment counts which are provided by an existing layout of traffic counting stations or data gathering in the field (detection layout) for non-dynamic models. The typical approaches to time-dependent OD estimation have been based either on Kalman-Filtering or on bi-level mathematical programming approaches that can be considered in most cases as ad hoc heuristics. The advent of the new Information and Communication Technologies (ICT) makes available new types of real-time traffic and passenger data with higher quality and accuracy, allowing new modeling hypotheses which lead to more computationally efficient algorithms. This paper presents a Kalman Filtering approach that explicitly exploits data available from Bluetooth sensors to simplify an underlying space-state model, and describes the validation of the proposal through a set of simulation experiments, either on networks or corridors. Those involve car data provided by the detection of the electronic signature of on-board devices. Finally, an extension of the framework to the estimation of passenger matrices is addressed when data from passenger’s electronic signature devices are available.Peer ReviewedPostprint (author’s final draft

Link dependent origin-destination matrix estimation : nonsmooth convex optimisation with Bluetooth-inferred trajectories

This thesis tackles the traditional transport engineering problem of urban traffic demand estimation by using Bluetooth data and advanced signal processing algorithms. It proposes a method to recover vehicles trajectories from Bluetooth detectors and combining vehicle trajectories with traditional traffic datasets, traffic is estimated at a city level using signal processing algorithms. Involving new technologies in traffic demand estimation gave an opportunity to rethink traditional approaches and to come up with new method to jointly estimate origin-destinations flows and route flows. The whole methodology has been applied and evaluated with real Brisbane traffic data

A Kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices

Time-dependent origin–destination (OD) matrices are essential input for dynamic traffic models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions, and they are traditionally used in the design and evaluation of advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new information and communication technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This article presents ad hoc, Kalman filtering procedures that explicitly exploit Bluetooth sensor traffic data, and it reports the numerical results from computational experiments performed at a network test site.Peer ReviewedPostprint (author’s final draft

Advanced traffic data for dynamic OD demand estimation: the state of the art and benchmark study

In this paper, the use of advanced traffic data is discussed to contribute to the ongoing debate about their applications in dynamic OD estimation. This is done by discussing the advantages and disadvantages of traffic data with support of the findings of a benchmark study. The benchmark framework is designed to assess the performance of the dynamic OD estimation methods using different traffic data. Results show that despite the use of traffic condition data to identify traffic regime, the use of unreliable prior OD demand has a strong influence on estimation ability. The greatest estimation occurs when the prior OD demand information is aligned with the real traffic state or omitted and using information from AVI measurements to establish accurate and meaningful values of OD demand. A common feature observed by methods in this paper indicates that advanced traffic data require more research attention and new techniques to turn them into usable information.Peer ReviewedPostprint (author's final draft