Gotowa bibliografia na temat „Traffic matrix”

To obtain overlay network traffic and delay information between two hosts is important for network management, monitoring, design, planning and assessment. Traffic matrix and delay matrix represent the traffic and delay information between two hosts, so introduce the concept of the overlay network traffic matrix and delay matrix. Compressive sensing theory restores traffic matrix and delay matrix but is not suitable for overlay network. This paper improves compressive sensing algorithm to make it more applicable to overlay network traffic matrix and delay matrix restoration. After calculating the traffic matrix and delay matrix this paper quantifies overlay network congestion, which reflect the current network security situation. The experimental results show the restoration effect of traffic matrix and delay matrix is well and the congestion degree reflects the actual network state.

This paper proposes an efficient origin-estimation bandwidth (OD band) model, which provides dedicated progression bands for arterial traffic based on the real-time dynamic matrix of their estimated OD pairs. The innovations of the OD band model are as follows: First, the dynamics of through and turning-in/out traffics are analyzed based on the matrix of their estimated OD pairs, and used to generate the traffic movement sequence at continuous intersections; Second, the end-time of green interval for lag-lag phase sequence at continuous intersections is determined according to the relevant constraints, the relationship between the start/end-time of green interval and the minimum/maximum green intervals; Third, the bandwidths of the two directions of the artery ware produced, after being weighted by their traffic demands. The intuitiveness, convenience, and feasibility of the OD band model were fully demonstrated through a case study. Overall, the OD band model helps to produce bi-directional progression bands for traffic with many turning movements on the artery, and enables the through and turning-in/out traffics to proceed through continuous intersections, when the signals at those intersections are green.

Abstract: In the present era, the population of people living in cities and the number of vehicles on the road is growing by the day. The necessity to govern lanes, thruways, and streets has become a significant concern as the urban population and, as a result, the number of vehicles has grown. Today's traffic framework places minimal emphasis on real-time traffic conditions, resulting in inefficient traffic management systems. Therefore, to overcome such limitations or drawbacks of the present system, the current research proposes a smart and efficient traffic management system that can analyze real-time traffic and take appropriate action. This is achieved by the application of an image processing technique, that would capture the real-time pictures of the paths to compare with the reference image of the path. The evaluation matrix is created to decide the amount of time each light must be on. In addition, an evaluation matrix is created. The purpose of the evaluation matrix is to determine how long each light must be turned on. The MATLAB 7.8 was used to perform the study.

The traffic matrix is essential for many network management tasks because it provides the amount of traffic that flows between every origin-destination (OD) pair in a network. We focus on two traffic matrix problems: compression and estimation. Traffic matrices contain substantial amounts of data because they are collected at short timescales over long periods. We investigate techniques to compress traffic matrices using wavelet transforms, which allows us to isolate common underlying patterns present within traffic matrix datasets. In addition, it is expensive for network operators to measure traffic matrices directly. We demonstrate a new traffic matrix estimation method that is based on using our compressing transforms and methods for sparse estimation. Our estimation method also relies on inexpensive link-level measurements. We assess the performance of our wavelet-based traffic matrix model by testing it on two traffic matrix datasets.<br>La matrice de trafic est essentielle pour de nombreuses tâches de gestion de réseau parce qu'elle fourni le volume de trafic qui circule entre chaque paire d'origine et destination (OD) dans un réseau. Nous concentrons sur deux problèmes avec la matrice de trafic: la compression et l'estimation. Les matrices de trafic contiennent de grandes quantités de données parce qu'elles sont recueillies à des délais courts pendant de longues périodes. Nous examinons des techniques pour compresser les matrices de trafic en utilisant les transformées en ondelettes, qui nous permet d'isoler les tendances communes dans les matrices de trafic. En plus, il est coûteux pour les opérateurs de réseau de measurer les matrices de traficdirectement. Nous développons une nouvelle méthode pour estimer la matrice de trafic qui est basée sur les transformées en ondelettes et les méthodes d'estimation rares. Notre méthode d'estimation s'appuie également sur le volume de trafic traversant chaque lien dans un réseau, ce qui est peu coûteux de recueillir. Nous évaluons la performance de notre modèle pour la matrice de trafic en le testant sur deux ensembles de données.

An Origin-Destination (OD) traffic matrix provides a major input to the design, planning and management of a telecommunications network. Since the Internet is being proposed as the principal delivery mechanism for telecommunications traffic at the present time, and this network is not owned or managed by a single entity, there are significant challenges for network planners and managers needing to determine equipment and topology configurations for the various sections of the Internet that are currently the responsibility of ISPs and traditional telcos. Planning of these sub-networks typically requires a traffic matrix of demands that is then used to infer the flows on the administrator's network. Unfortunately, computation of the traffic matrix from measurements of individual flows is extremely difficult due to the fact that the problem formulation generally leads to the need to solve an under-determined system of equations. Thus, there has been a major effort f rom among researchers to obtain the traffic matrix using various inference techniques. The major contribution of this thesis is the development of inference techniques for traffic matrix estimation problem according to three different approaches, viz: (1) deterministic, (2) statistical, and (3) dynamic approaches. Firstly, for the deterministic approach, the traffic matrix estimation problem is formulated as a nonlinear optimization problem based on the generalized Kruithof approach which uses the Kullback distance to measure the probabilistic distance between two traffic matrices. In addition, an algorithm using the Affine scaling method is developed to solve the constrained optimization problem. Secondly, for the statistical approach, a series of traffic matrices are obtained by applying a standard deterministic approach. The components of these matrices represent estimates of the volumes of flows being exchanged between all pairs of nodes at the respective measurement points and they form a stochastic counting process. Then, a Markovian Arrival Process of order two (MAP-2) is applied to model the counting processes formed from this series of estimated traffic matrices. Thirdly, for the dynamic approach, the dual problem of the multi-commodity flow problem is formulated to obtain a set of link weights. The new weight set enables flows to be rerouted along new paths, which create new constraints to overcome the under-determined nature of traffic matrix estimation. Since a weight change disturbs a network, the impact of weight changes on the network is investigated by using simulation based on the well-known ns2 simulator package. Finally, we introduce two network applications that make use of the deterministic and the statistical approaches to obtain a traffic matrix respectively and also describe a scenario for the use of the dynamic approach.

This thesis addresses the traffic matrix estimation problem. Knowledge about the origin-destination (OD) traffic matrix (TM) allows us to solve problems in network engineering; the OD traffic matrix gives us a valuable information about who is communicating with whom in a network, at any given time. Traffic analysis, based on detailed packet or flow level measurements, usually not available throughout a large network due to hardware limitations or/and security reasons. An alternative approach is to use mathematical and statistical techniques for the estimation using the incomplete data accessible via SMNP. In my PhD thesis I describe the traffic data collection and, perform a critical evaluation of existing and novel methods for TM estimation on the real and synthetic data, and test the statistical and non-statistical methods. I discuss the weaknesses and strengths of the various methods, and highlight differences in the results for the simulated traffic and the real one obtained from European and American subnetworks. This research also addresses the variability of Internet traffic in backbone networks is essential for the creation of the better traffic model (vital for the estimation techniques), to better plan and manage existing networks, as well as to design next generation networks. I show the difference between the real traffic in high-bandwidth networks with the classic and most commonly used traffic models, propose a high-bandwidth TM simulation technique which can help with the evaluation of the traffic matrix algorithms in the case when the real TM necessary for the algorithms verification is difficult to obtain.<br>La matrice de trafic Origine Destination représente le volume de trafic entre chaque source et chaque destination sur un réseau. Elle est utile pour le dimensionnement du réseau, la définition du routage, etc. . . Elle ne peut pas être obtenue directement, mais doit au contraire être estimée, par des méthodes le plus souvent statistiques, partir du volume de trafic agrégé sur chacun des liens du réseau. Les méthodes existantes font des hypothèses réductrices sur les modèles de trafic; le trafic est le plus souvent considéré comme stationnaire et poissonnien. Le sujet de cette thèse est de généraliser les techniques existantes pour des modèles de trafic plus réalistes, et de valider les algorithmes proposés sur du trafic réel dans le cas d'un réseaux local et d' un réseau de cœur, utilisant un simulateur des réseaux large bande, développé dans le cadre du projet

During 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.<br>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.<br>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..<br>Estadística i investigació operativa

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.<br>Includes bibliographical references (leaves 104-106). Also available in electronic version. Access restricted to campus users.

Road traffic congestion is an increasing societal problem. Road agencies and users seeks accurate and reliable travel speed information. This thesis developed a network-scale traffic state prediction based on Convolutional Neural Network (CNN). The method can predict the speed over the network accurately by preserving road connectivity and incorporating historical datasets. When dealing with an extensive network, the thesis also developed a clustering method to reduce the complexity of the prediction. By accurately predict the traffic state over a network, traffic operators can manage the network more effectively and travellers can make informed decision on their journeys.

With the recent widespread deployment of intelligent transportation systems (ITS) in North America there is an abundance of data on traffic systems and thus an opportunity to use these data in the calibration of microscopic traffic simulation models. Even though ITS data have been utilized to some extent in the calibration of microscopic traffic simulation models, efforts have focused on improving the quality of the calibration based on aggregate form of ITS data rather than disaggregate data. In addition, researchers have focused on identifying the parameters associated with car-following and lane-changing behavior models and their impacts on overall calibration performance. Therefore, the estimation of the Origin-Destination (OD) matrix has been considered as a preliminary step rather than as a stage that can be included in the calibration process. This research develops a methodology to calibrate the OD matrix jointly with model behavior parameters using a bi-level calibration framework. The upper level seeks to identify the best model parameters using a genetic algorithm (GA). In this level, a statistically based calibration objective function is introduced to account for disaggregate form of ITS data in the calibration of microscopic traffic simulation models and, thus, accurately replicate dynamics of observed traffic conditions. Specifically, the Kolmogorov-Smirnov test is used to measure the "consistency" between the observed and simulated travel time distributions. The calibration of the OD matrix is performed in the lower level, where observed and simulated travel times are incorporated into the OD estimator for the calibration of the OD matrix. The interdependent relationship between travel time information and the OD matrix is formulated using a Extended Kalman filter (EKF) algorithm, which is selected to quantify the nonlinear dependence of the simulation results (travel time) on the OD matrix. The two test sites are from an urban arterial and a freeway in Houston, Texas. The VISSIM model was used to evaluate the proposed methodologies. It was found that that the accuracy of the calibration can be improved by using disaggregated data and by considering both driver behavior parameters and demand.

With ever increasing traffic demand, cities are facing more serious problems from traffic congestion. It is extremely important to have an accurate estimation of travel demand for strategic planning and control. Lack of such knowledge before implementing major transport infrastructure projects could result in huge economic losses. Thus, this research develops new methods using big traffic data and are thoroughly tested on the Brisbane network. These methods can be readily integrated into the existing state-of-the-art and practice for a better estimation of travel demand.

Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.

Transit signal priority (TSP) systems have become an integral part of regional intelligent transportation systems integration. They modify signal operations to facilitate faster transit through intersections, improve transit service reliability, reduce delays, and enhance overall transit operational efficiency. Agencies are planning and implementing next-generation TSP systems. However, multifaceted challenges complicate TSP implementation, particularly in urban regions with high traffic density and multiple stakeholders. Exploring these challenges can help develop more efficient deployment strategies and improve system performance. This study’s researchers, therefore, examined TSP implementation challenges in the metropolitan Chicago area and interviewed stakeholders and experts from Illinois and other states to identify successes and obstacles in TSP deployment. They applied a thematic analysis and a non-negative matrix factorization method to identify key barriers. To prioritize these challenges, they developed a survey using the fuzzy analytic hierarchy process framework and had experts directly involved in TSP operations in the Chicago region participate in this survey. Study findings indicated that organizational factors—such as interagency coordination issues and labor shortages—are significant challenges. Regulatory and policy constraints, including the absence of standardized regional guidelines and concerns over equipment compatibility, further hinder TSP deployment. Financial constraints, particularly the reliance on fragmented funding sources, create uncertainty in project execution. Technological limitations, such as integration difficulties with legacy traffic control systems and the high costs of infrastructure upgrades, also impede implementation.

In Illinois, hot-mix asphalt overlaid concrete pavements typically exhibit reflective cracking of joints and cracks from the pavement below, resulting in shortened life and maintenance issues. Over the years, various patching, fabric, and crack and seat techniques were attempted with few positive results. This led to more aggressive techniques to eliminate the slab action of the concrete pavement where the pavement would be broken or rubblized into pieces typically less than 12 inches. Since the first rubblizing project in 1990, policy, procedures, and specifications have evolved to the point that rubblization is the mainstream option in dealing with problematic concrete pavements. This report summarizes the performance of several interstate rubblizing projects in Illinois by analyzing available data in Illinois Department of Transportation’s pavement management system. Condition rating survey data allowed the serviceability of these projects to be evaluated by surface mix types and asphalt performance grades. Traffic in the form of 18,000 lb equivalent single axle loads was determined for the projects to evaluate fatigue and rutting as well as compare section performance to the design procedure. The research team reviewed plans, design procedures, and specifications to determine best practices and identify where improvements might be made. Data showed that the use of stone matrix asphalt surface mixtures and mixes using PGXX-28 asphalt binders provides improved performance. Overall, rubblizing has shown good to excellent performance. To provide additional life with improved performance, recommendations include adopting softer asphalt grades, increasing the use of stone matrix asphalt, and improving procedures for protecting culverts.

As the crisis of climate change looms, transportation policy in California continues to emphasize sustainability in land-use and transportation decisions. This report evaluates the implementation of two sustainability-focused policies (Senate Bill 375 and 743) by local governments and the current-state-of-the-practices by metropolitan planning organizations (MPOs) in assisting them in California. The research focuses on local governments’ access and use of vehicle miles traveled (VMT) and transportation data for achieving VMT and greenhouse gas (GHG) reduction by land-use planning. The research also examines the role of MPOs in providing needed transportation data, training, and technical support to its local members to help them quantify VMT impacts of land-use plans and projects. Through surveys and interviews of 539 city and county governments and 18 MPOs in California, the research team analyzes current practices. Findings include that local governments utilize automobile data more frequently than alternative modes, and do not heavily rely on MPOs for obtaining transportation data. Local governments recognized the need for more transportation data and were particularly interested in publicly available sources. The MPOs reported that VMT/annual average daily traffic data and origin-destination matrix were most frequently requested. More sophisticated data such as travel-time matrix and mode-share scheme were considered useful by the MPOs, while requests were very low. Based on the results of the conducted surveys, interviews, and our analyses, we developed a transportation data guide for local governments that could help them access data for achieving VMT/GHG emission reduction via land-use planning. The guide has two parts, one for general plan update and any land-use plans along with SB 375 and the other for VMT analysis for project-level California Environmental Quality Act reviews. The guide includes several different approaches that can be chosen and tried by local governments depending on their resource level and geographic, social, and physical characteristics. These findings can facilitate the access and use of transportation data to aid in sustainable land-use planning for the betterment of local communities and the planet.

Steel girder ends are susceptible to corrosion damage due to deicing salts, water, and other contaminants leaking from failed expansion joints. When corrosion becomes significant, it leads to a reduction in the sectional properties of steel girders and consequently reduces bearing and shear resistance. Conventional repair methods, although effective, require substantial time and resources to complete, causing public inconvenience from necessary traffic closures. Therefore, there is a need for practical, rapid, and robust repair methods suitable for implementation by local Department of Transportation (DOT) maintenance personnel. In this study, five innovative repair methods were systematically evaluated through a selection process called the House of Quality Matrix. After completing the comprehensive evaluation and additional numerical simulations, the "sandwich panel" repair method was selected for further investigation. The "sandwich panel" repair method encased the corroded region with a filler material reinforced by threaded rods. Two thin steel plates installed on both girder sides served as stay-in-place formwork. This expedited the installation process and eliminated the labor-intensive steps of jacking, welding, and formwork disassembly, thus making the repair more cost-effective and less time-consuming. The structural performance of the repair method was evaluated experimentally by conducting seven large-scale tests. Various test parameters were considered in the tests, including (1) threaded rod layout, (2) filler material, and (3) support condition. The experimental results indicated that the method was effective in restoring their original design strength. A parametric study complemented the experimental evaluation, using the finite element models benchmarked with experimental results. Design guidelines and recommendations were developed based on the experimental and numerical results.

Despite growing interest in low-speed automated shuttles, pilot deployments have only just begun in a few places in the U.S., and there is a lack of studies that estimate the impacts of a widespread deployment of automated shuttles designed to supplement existing transit networks. This project estimated the potential impacts of automated shuttles based on a deployment scenario generated for a sample geographic area: Santa Clara County, California. The project identified sample deployment markets within Santa Clara County using a GIS screening exercise; tested the mode share changes of an automated shuttle deployment scenario using BEAM, an open-source beta software developed at the Lawrence Berkeley National Laboratory to run traffic simulations with MATSim; elaborated the model outputs within the R environment; and then estimated the related impacts. The main findings have been that the BEAM software, despite still being in its beta version, was able to model a scenario with the automated shuttle service: this report illustrates the potential of the software and the lessons learned. Regarding transportation aspects, the model estimated automated shuttle use throughout the county, with a higher rate of use in the downtown San José area. The shuttles would be preferred mainly by people who had been using gasoline-powered ride hail vehicles for A-to-B trips or going to the bus stop, as well as walking trips and a few car trips directed to public transport stops. As a result, the shuttles contributed to a small decrease in emissions of air pollutants, provided a competitive solution for short trips, and increased the overall use of the public transport system. The shuttles also presented a solution for short night trips—mainly between midnight and 2 am—when there are not many options for moving between points A and B. The conclusion is that the automated shuttle service is a good solution in certain contexts and can increase public transit ridership overall.