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The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. Most travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Currently, systems that collect floating car data are designed to transmit the data in a limited form and relatively infrequently due to the cost of data transmission. That means the reported locations of vehicles are far apart in time and space. In order to extract traffic information from the data, it first needs to be matched to the underlying digital road network. Matching such sparse data to the network, especially in dense urban, area is challenging. This thesis introduces a map-matching and path inference algorithm for sparse GPS probes in urban networks. The method is utilized in a case study in Stockholm and showed robustness and high accuracy compared to a number of other methods in the literature. The method is used to process floating car data from 1500 taxis in Stockholm City. The taxi data had been ignored because of its low frequency and minimal information. The proposed method showed that the data can be processed and transformed into information that is suitable for traffic studies. The thesis implemented the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control. / <p>QC 20121107</p>

[pt] Esta dissertação estima os efeitos agregados e distributivos da recente expansão da infraestrutura de transporte no Rio de Janeiro (Brasil), desencadeada pela Copa do Mundo de 2014 e pelos Jogos Olímpicos de 2016. Em preparação para os eventos esportivos, a cidade investiu mais de 4,5 bilhões de dólares em seu sistema de transporte público, que incluiu a extensão de uma linha de metrô, a construção de um VLT e dois corredores de BRT que se estendem por aproximadamente 108 quilômetros. O Capítulo 1 fornece uma descrição da nova infraestrutura de transporte e seus potenciais efeitos nos tempos de deslocamento. Os tempos de deslocamento (na ausência dos investimentos) são calculados usando metodologia de regressão random forest e dados de tempos de deslocamento de 2011 e 2018. As estimativas sugerem que a nova infraestrutura reduziu significativamente os tempos de viagem. Os capítulos restantes exploram duas metodologias diferentes para estimar os impactos dos investimentos em transporte. O Capítulo 2 utiliza as datas de anúncio e inauguração das novas estações de BRT e metrô na cidade do Rio de Janeiro para investigar os efeitos da expansão da infraestrutura de transportes no crescimento e reorganização da atividade econômica. Os endereços das empresas foram georeferenciados para construir um painel com informações sobre número de empresas e empregos por célula de 100 metros quadrados de 2006 a 2016. Aplicando uma metodologia de diferenças em diferenças, eu estimo os efeitos heterogêneos da expansão do transporte de acordo com as características dos trabalhadores e da indústria. Todos os efeitos são obtidos para oito diferentes anéis de distância de 250m a 2km. O Capítulo 3 tem como objetivo medir os efeitos da infraestrutura de transporte sobre os salários, a produtividade e o bem-estar da cidade, investigando impactos heterogêneos para trabalhadores com alto e baixo nível de qualificação. Para responder a essas perguntas, eu construo um extensa base de dados para a Região Metropolitana do Rio de Janeiro, que combina informações sobre residência e emprego para cada grupo de trabalhadores dentro de cada área de ponderação do Censo 2010. Para medir os efeitos de equilíbrio geral, eu desenvolvo um modelo de estrutura interna de cidade que possui trabalhadores heterogêneos e diferentes externalidades de produção para cada grupo de trabalhador. Eu estimo os parâmetros estruturais usando o método de momentos. Por fim, realizo exercícios contrafactuais para avaliar os impactos da recente expansão da infraestrutura de transporte no Rio de Janeiro usando os tempos de viagem de 2018 coletados do API do Google Maps e os tempos de viagem na ausência dos investimentos (computados no primeiro capítulo). Resultados mostram que os investimentos de transporte levaram a menor concentração residencial e maior concentração de empregos. Melhores serviços de transporte permitem que os cidadãos trabalhem em locais de alta produtividade e morem em locais de alta amenidade, o que aumenta o bem-estar de todos os trabalhadores. Entretanto, os benefícios não são divididos igualmente. Os trabalhadores altamente qualificados se beneficiam duplamente, uma vez que têm maiores benefícios de economias de aglomeração e, consequentemente, são capazes de pagar por custos mais altos de moradia. Ademais, as áreas no entorno das novas estações tiveram um aumento na atividade econômica. A maior parte do impacto é caracterizada por pequenas empresas, dos setores de comércio e serviços. Além disso, a maior parte da força de trabalho empregada por essas empresas é pouco qualificada. / [en] This dissertation assesses the aggregate and distributional effects of the recent transport infrastructure expansion in Rio de Janeiro (Brazil) triggered by 2014 Football World Cup and 2016 Olympic Games. In preparation for the sports events, the city invested more than 4.5 billion dollars in its public transport system, which included the extension of a subway line, the construction of a light-rail system and two BRT corridors that stretch approximately 108 kilometers. Chapter 1 provides a description of new transport infrastructure and estimates its potential effects on commuting times. I compute travel times in the absence of the investments using random forest regression methodology and data from 2011 and 2018 travel times. Estimates suggest that the new infrastructure significantly reduced travel times. The remaining chapters explore two different methodologies to account for the impacts of the transport investments. Chapter 2 explores the timing of announcement and inauguration of new BRT and subway stations in Rio de Janeiro City to investigate the effects of the expansion of transport infrastructure on growth and reorganization of economic activity. Firm s addresses were geocoded to construct a panel data set that contains information on number of firms and jobs per 100 meter s grid cell from 2006 to 2016. Applying a difference-in-differences methodology on this novel data set, I estimate the heterogeneous effects of the transport expansion according to workers characteristics and industry. All effects are obtained for eight different distance rings ranging from 250m to 2km. Chapter 3 aims to measure the effects of transportation infrastructure on the city s wages, productivity and welfare, investigating heterogeneous impacts for high and low skilled workers. To answer these questions, I construct an extensive database for the Rio de Janeiro Metropolitan Area that combines information on residence and employment for each skill group inside each city block. In order to measure general equilibrium effects, I develop a model of internal city structure that features heterogeneous workers and production externalities across worker s skill levels. I estimate structural parameters using generalized method of moments. Finally, I perform contrafactual exercises to assess the impacts of the recent transport infrastructure expansion in Rio de Janeiro using 2018 travel times collected from Google Maps API and travel times computed in the first chapter. Results show that connecting new areas to the central business district results in lower residential concentration and higher employment concentration. The improvement of transportation services allows citizens to work in high productivity locations and live in high amenity locations, which leads to higher overall welfare. Nevertheless, benefits are not evenly split. High-skilled workers benefit twice since they have higher benefits from agglomeration and, consequently, they are able to pay for higher residential prices from lower commuting costs. Moreover, areas in the vicinity of the new transport stations saw an increase in economic activity. The bulk of the impact is characterized by small firms, from the commerce and service sectors. Additionally, most of the workforce employed by these firms are low-skilled.

This paper describes efforts to optimize the parameters for a platoon identification and accommodation algorithm that minimizes vehicle emissions. The algorithm was developed and implemented in the AnyLogic framework, and was validated by comparing it to the results of prior research. A four-module flowchart was developed to analyze the traffic data and to identify platoons. The platoon end time was obtained from the simulation and used to calculate the offset of the downstream intersection. The simulation calculates vehicle emissions with the aid of the VT-Micro microscopic emission model. Optimization experiments were run to determine the relationship between platoon parameters and minimum- and maximum-emission scenarios. Optimal platoon identification parameters were found from these experiments, and the simulation was run with these parameters. The total time of all vehicles in the simulation was also found for minimum and maximum emissions scenarios. Time-space diagrams obtained from the simulations demonstrate that optimized parameters allow all cars to travel through the downstream intersection without waiting, and therefore cause a decrease in emissions by as much as 15.5%. This paper also discusses the outcome of efforts to leverage high resolution data obtained from WV-705 corridor in Morgantown, WV. The proposed model was developed for that purpose and implemented in the AnyLogic framework to simulate this particular road network with four coordinated signal-controlled intersections. The simulation was also used to calculate vehicle CO, HC, NOx emissions with the aid of the VT-Micro microscopic emission model. Offset variation was run to determine the optimal offsets for this particular road network with traffic volume, signal phase diagram and vehicle characteristics. A classifier was developed by discriminant analysis based on significant attributes of HRD. Equation of this classifier was developed to distinguish between set of timing plans that produce maximum emission from set of timing plans that produce maximum emission. Also, current work investigates the potential use of the GPS-based and similar priority systems by giving preemption through signalized intersections. Two flowcharts are developed to consider presence of emergency vehicle (EV) in the system so called EV life cycle and EV preemption (EVP). Three scenarios are implemented, namely base case scenario when no EV is involved, EV scenario when EV gets EVP only, and EV scenario when EV gets preemption by signals and right-of-way by other vehicles. Research makes an attempt to compare emission results of these scenarios to find out whether EV effects vehicle emission in the road network and what is the level of this influence if any. / Master of Science

Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. / Ph. D.

Thesis (MScEng)-- Stellenbosch University, 2013. / ENGLISH ABSTRACT: Speed-flow-density relationships are the most useful tools in the highway design and planning process. They are useful in predicting the roadway capacity, in determining the adequate level-of-service of traffic flow and in determining travel time for a given roadway. Two-lane two-way rural highways constitute the vast majority of the rural road network in South Africa. Nowadays in the Western Cape and other provinces of South Africa, the speed-flow-density relationships normally used for rural transportation studies are derived from the Highway Capacity Manual, which reflects the traffic conditions in the North American situation. Since the North American traffic conditions may be different from the South African conditions, a need to investigate speed-flow-density relationships on these highways in South Africa arises in order to justify any investment made on these roads. In this context, a video technique was used to collect traffic flow data during morning peak hours on two rural two-lane two-way highways in the Western Cape Province in order to investigate these relationships. Through the use of Adobe premiere C.S 6 software, travel time of individual vehicles and distance headways were measured and used in computation of average speed and average density. Several researchers have developed models to describe the relationships between traffic characteristics on uninterrupted flow facilities. In this study, some of these models were tested using collected data in order to investigate which model fits the data satisfactorily. Statistical methods were used to evaluate the ability of each model to predict the flow characteristics over the whole range of data. Average speed and density data were used through regression analysis to perform curve fitting and testing of these developed models. In the next stage, the model which provided a best representation of the data on each section was selected and through the application of the steady-state equation (2.1), flow-density and speed-flow relationships were established on these sections. The available data were also used to investigate the impact the observation time has on the speed-flow curve and the resulting capacity value. Finally, the developed speed-flow curves were used to determine the capacities of the study sections. These capacity values were used to determine if the shoulder usage contributes in increasing the capacity of two-lane two-way highways by comparing them to the capacity provided by HCM. / AFRIKAANSE OPSOMMING: Spoed-vloei-digtheid verhoudings is baie handig in die beplanning en ontwerp van paaie. Dit kan ook gebruik word in die voorspelling van kapasiteit, diensvlak en reistyd. Twee-laan twee-rigting paaie maak die grootste deel van die Suid-Afrikaanse padnetwerk uit en vir die beplanning daarvan word van Amerikaanse spoed-vloei-digtheid verhoudings gebruik gemaak aangesien daar nog nie voorheen ‘n studie hiervan in SA gemaak is nie. Video-opnames is gebruik om verkeersvloeidata op twee paaie in die omgewing van Stellenbosch te versamel. Die reistyd en digtheid van individuele voertuie is tydens spitstye waargeneem. Die data is gebruik om te bepaal watter modelle die beste is om die spoed-vloei-digtheid verhoudings vir hierdie paaie te modelleer. Die beste modelle is dan gebruik om die kapasiteit van die paaie te bepaal en dit te vergelyk met die Amerikaanse waardes.

Cette thèse porte sur le problème de l'estimation des temps de parcours, de véhicules, par section de route dans un contexte urbain, en utilisant les données GPS à faible densité d’échantillon. L'un des défis de cette thèse est d'utiliser ce genre de données. Dans le cadre de ce travail de recherche, j'ai développé une carte numérique avec son nouveau système d'information géographique (SIG), qui traite la problématique du map-matching, où nous avons apporté des améliorations, ainsi que le problème du plus court chemin.La thèse s'inscrit dans le cadre du projet PUMAS (Plate-forme Urbaine de Mobilité Avancée et Soutenable), ce qui est un avantage pour nos recherches en ce qui concerne le processus de collecte de données réelles sur le terrain ainsi que pour faire nos tests. Le projet PUMAS est un projet préindustriel qui a pour objectif d'informer sur la situation du trafic mais également de développer et de mettre en œuvre une plate-forme de mobilité durable afin de l'évaluer dans la région, notamment à Rouen, France. Le résultat offre un cadre pour tout contrôleur de la situation, gestionnaire ou chercheur pour accéder à de vastes réserves de données sur l'estimation du flux du trafic, sur les prévisions et sur l'état du trafic. / This dissertation is concerned with the problem of estimating travel time per links in urban context using sparsely sampled GPS data. One of the challenges in this thesis is use the sparsely sampled data. A part of this research work, i developed a digital map with its new geographic information system (GIS), dealing with map-matching problem, where we come out with an enhancement tecnique, and also the shortest path problem.The thesis research work was conduct within the project PUMAS, which is an avantage for our research regarding the collection process of our data from the real world field and also in making our tests. The project PUMAS (Plate-forme Urbaine de Mobilité Avancée et Soutenable / Urban Platform for Sustainable and Advanced Mobility) is a preindustrial project that has the objective to inform about the traffic situation and also to develop an implement a platform for sustainable mobility in order to evaluate it in the region, specifically Rouen, France. The result is a framework for any traffic controller or manager and also estimation researcher to access vast stores of data about the traffic estimation, forecasting and status.

This thesis project presents a traffic micro simulation study that investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves simulations for a mixed traffic environment of conventional and automated vehicles both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is also accessed for its potential to improve traffic efficiency in a full market penetration of connected automated vehicles with enhanced capabilities such as having shorter time head ways.

A confiabilidade do tempo de viagem é atualmente considerada como um fator de elevada importância nos estudos de demanda por transportes, com base no reconhecimento que sistemas congestionados são uma realidade inevitável nos grandes centros urbanos, gerando incertezas nas estimativas do tempo de viagem e tornando sua representação através de uma variável de tempo médio excessivamente simplista. O acesso terrestre aos aeroportos em São Paulo constitui um contexto interessante para o estudo da confiabilidade, considerando os altos custos atribuídos à eventual perda do voo e o ambiente de alta variabilidade de tempos de viagem na região. O estudo da confiabilidade do tempo de viagem tem sido em geral desenvolvido com um enfoque exclusivamente quantitativo, usando modelos matemáticos que se baseiam em teorias de maximização da utilidade estimados a partir de dados de preferência declarada. Em geral, são ignorados: (i) os efeitos de fatores latentes no comportamento, (ii) o fato de que o comportamento nem sempre reflete as intenções, (iii) a complexidade dos fatores envolvidos nas escolhas e (iv) os fatores que descrevem o contexto em que ocorre a decisão. O trabalho utiliza métodos mistos para a coleta e análise dos dados, procurando obter um conjunto abrangente de informações sobre o comportamento. Tanto a coleta de dados como os modelos estimados baseiam-se nos fundamentos da Teoria do Comportamento Planejado, que afirma que o comportamento revelado pode ser estimado a partir de uma intenção que, por sua vez, pode ser estimada a partir de atitudes, normas subjetivas e controle percebido. O controle percebido representa a percepção individual quanto à facilidade em desempenhar um comportamento. Neste estudo, a confiabilidade do tempo de viagem é incluída como um fator de controle percebido, assim como outros indicadores de controle não comumente considerados. A análise é conduzida usando-se uma técnica de Modelos de Equações Estruturais denominada Mínimos Quadrados Parciais. O uso desta técnica permitiu uma descrição abrangente dos mecanismos envolvidos no processo de escolha de acesso terrestre ao aeroporto e confirmou a importância dos fatores latentes na escolha, particularmente os relacionados ao controle percebido e real. Foi possível também verificar que conjuntos distintos de fatores influenciam a formação da intenção (e portanto a preferência declarada) e o comportamento propriamente dito (e portanto o comportamento revelado). / Travel time reliability is now considered a major factor in explaining travel demand since its underlying cause congestion seems to be an unavoidable reality in large urban centers. This brings uncertainty to travel time estimates, rendering its representation through travel time averages excessively simplistic. Ground access to airports serving the city of São Paulo makes an interesting context to study reliability, given the considerable annoyance and cost associated with the possibility of missing a flight and the high variability of travel times prevailing in the area. Studies of the reliability of travel time have generally been based on a purely quantitative approach, using utility-based mathematical models, mostly estimated with stated preference data. They usually ignore: (i) the effects of latent factors on behavior, (ii) the fact that behavior does not always reflect intentions, (iii) the complexity of factors involved in choice processes and (iv) the factors describing the choice context. This study uses mixed methods for data collection and analysis, aiming to gather a comprehensive set of information about behavior. Both data collection and modeling are based on the Theory of Planned Behavior, which states that behavior can be predicted from intention; intention, by its turn, can be predicted from attitudes, subjective norms and perceived behavioral control. The latter refers to peoples perception of the ease or difficulty of performing the behavior of interest. In this study, we include travel time reliability as a perceived behavioral control factor, in addition to other indicators of control that are not commonly considered. Analysis is conducted using Partial Least Squares, a technique from the family of Structural Equations Models. The use of this technique allowed for a more complete description of the mechanisms involved in the choice process of ground access to airports and confirmed the importance of latent factors on choice, particularly those related to perceived and actual control. The results also indicate that different sets of factors affect the formation of intention (and thus the stated choice) and the behavior itself (and thus actual behavior).