Bluetooth Data collection technique is recently proven as a reliable data collection technique that provides the opportunity to modify traditional methodologies to improve system performance. Actual volume in the network is a result of the timing plans which are designed and modified based on the volume which is generated using existing timing plans in the system. This interdependency between timing plan and volume on the network is a dynamic process and should be captured to obtain actual traffic states in the network. The current practice is to calculate synthetic origin destination information based on detector volume that doesn't necessarily represent the volume scenario accurately. The data from Bluetooth technology can be utilized to calculate dynamic volume on the network which can be further used as input for signal timing design. Application of dynamic volume improves the system performance by providing the actual volume in system to design optimal timing plans. This thesis proposes a framework that can be used to integrate data obtained from the Bluetooth technology with the traditional methods to design timing plans. The proposed methodology utilizes the origin destination information obtained from Bluetooth data, detector data, characteristics of intersections such as number of lanes, saturation flow rate and existing timing plans as a basis for the calculation of the dynamic volume for the various movements at each intersection. The study shows that using the Bluetooth based OD matrix to calculate accurate dynamic volumes results in better system performance compared to the traditional way of using the static detector volume alone. / Master of Science

Spatial interaction models represent a class of models that are used for modeling origin destination flow data. The interest in such models is motivated by the need to understand and explain the flows of tangible entities such as persons or commodities or intangible ones such as capital, information or knowledge between regions. The focus here is on the log-normal version of the model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination-flows with formal approaches that allow for two different types of spatial dependence in flow magnitudes. (authors' abstract) / Series: Working Papers in Regional Science

Accurate estimation of Origin and Destination (O-D) of passengers has been an essential objective for public transit agencies because knowledge of passengers’ flow enables them to forecast ridership, and plan for bus schedules, and bus routes. However, obtaining O-D information using traditional ways, such as conducting surveys, cannot fulfill today’s requirements of intelligent transportation and route planning in smart cities. Estimating bus passengers’ O-D using Wi-Fi and Bluetooth signals detected from their mobile devices is the primary objective of this project. For this purpose, we collected anonymized passengers’ data using SMATS TrafficBoxTM sensor provided by “SMATS Traffic Solutions” company. We then performed pre-processing steps including data cleaning, feature extraction, and data normalization, then, built various models using data mining techniques. The main challenge in this project was to distinguish between passengers’ and non-passengers’ signals since the sensor captures all signals in its surrounding environment including substantial noise from devices outside of the bus. To address this challenge, we applied Hierarchical and K-Means clustering algorithms to separate passengers from non-passengers’ signals automatically. By assigning GPS data to passengers’ signals, we could find commuters’ O-D. Moreover, we developed a second method based on an online analysis of sequential data, where specific thresholds were set to recognize passengers’ signals in real time. This method could create the O-D matrix online. Finally, in the validation phase, we compared the ground truth data with both estimated O-D matrices in both approaches and calculated their accuracy. Based on the final results, our proposed approaches can detect more than 20% of passengers (compared to 5% detection rate of traditional survey-based methods), and estimate the origin and destination of passengers with an accuracy of about 93%. With such promising results, these approaches are suitable alternatives for traditional and time-consuming ways of obtaining O-D data. This enables public transit companies to enhance their service offering by efficiently planning and scheduling the bus routes, improving ride comfort, and lowering operating costs of urban transportation.

Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, and external influence. Experiments on the NYC Citi Bike system show the effectiveness and efficiency of our model when compared with the state-of-the-art methods.

Public transportation offers a crucial solution to the travel demand in light of national and global economic, energy, and environmental challenges. If implemented effectively, public transit offers an affordable, convenient, and sustainable transportation mode. Implementation of new technologies for information-harvesting may lead to more effective transit operations. This study examines the potential of automated data collection technologies to analyzing and understand the origin-destination flow patterns, which is essential for transit route planning and stop location placement. This thesis investigates the collection and analysis of data of passengers onboard San Luis Obispo Transit buses in February and March 2017 using Bluetooth (BT) and automatic passenger counter (APC) data. Five BlueMAC detectors were placed on SLO Transit buses to collect Bluetooth data. APC data was obtained from San Luis Obispo Transit. The datasets were used to establish a data processing method to exclude invalid detections, to identify and process origin and destination trips of passengers, and to make conclusions regarding passenger behavior. The filtering methods were applied to the Bluetooth data to extract counts of unique passenger information and to compare the filtered data to the ground-truth APC data. The datasets were also used to study the San Luis Obispo Downtown Farmer’s Market and its impact on transit ridership demand. The investigation revealed that after carefully employing the filters on BT data there were no consistent patterns in differences between unique passenger counts obtained from APC data and the BT data. As a result, one should be careful in employing BT data for transit OD estimation. Not every passenger enables Bluetooth or owns a Bluetooth device, so relying on the possession of Bluetooth-enabled devices may not lead to a random sample, resulting in misleading travel patterns. Based on the APC data, it was revealed that transit ridership is 40% higher during the days during which Higuera Street in Downtown San Luis Obispo is used for Farmer’s Market – a classic example of tactical urbanism. Increase in transit ridership is one of the aspects of tactical urbanism that may be further emphasized. With rapidly-evolving data collection technologies, transit data collection methods could expand beyond the traditional onboard survey. The lessons learned from this study could be expanded to provide a robust and detailed data source for transit operations and planning.

Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize the origin region of interaction, variables that characterize the destination region of interaction, and variables that measure the separation between origin and destination regions. A violation of standard minimal assumptions for least squares estimation may be associated with two problems: spatial autocorrelation within the residuals, and spatial autocorrelation within explanatory variables. This paper compares a spatial econometric solution with the spatial statistical Moran eigenvector spatial filtering solution to accounting for spatial autocorrelation within model residuals. An example using patent citation data that capture knowledge flows across 257 European regions serves to illustrate the application of the two approaches.

Neste trabalho são estimados os efeitos das transferências de renda representadas pelo Programa Bolsa Família (PBF), nos fluxos migratórios entre os municípios brasileiros de 2008 a 2010. Parte-se de um Modelo de Escolha Discreta, e são obtidas e estimadas as equações agregadas de fluxo origem-destino com dependência espacial. Para desenvolver a análise, os indivíduos são divididos entre seis perfis de renda familiar per capita, e pela natureza do PBF, o foco da análise se dá principalmente nos quatro primeiros perfis. Então, são sugeridos alguns mecanismos pelos quais o PBF e sua gestão podem atuar sobre a decisão de migrar ou permanecer no local. Os mecanismos idealizados são diferentes de acordo com o status do indivíduo, de ser beneficiário do programa ou não, de acordo com seu perfil de renda, e se a característica do programa diz respeito ao seu município de residência, ou a outro município para o qual possa migrar. Como robustez, estima-se um modelo Logit, da probabilidade de o indivíduo ter realizado qualquer migração de acordo com algumas características individuais, dentre as quais a de ser beneficiário do programa, e de acordo com características de seu município de origem, estando as de gestão do PBF contidas neste segundo grupo. Os resultados em geral replicam os principais achados da literatura de migração, e apontam para relevância e não neutralidade dos recursos e da gestão municipal do Programa Bolsa Família na decisão locacional dos indivíduos / In this master thesis we estimated the effect of cash transfers in the migration flows among Brazilian municipalities from 2008 to 2010. We consider the cash transfer program named Bolsa Família (PBF), and aggregated origin-destination flows with spatial dependence equations in a Discrete Choice Model. To develop our analysis, the individuals are separated in six per capita family income profiles. In face of the PBF aim, we focus on the first four profiles. Then, some mechanisms that can influence the decision to migrate or to stay in the same municipality are suggested. Each mechanism differs acording to the individual status, of being a beneficiary of the program or not, acording to his income profile, and also acording to the analysed feature of the program being about his own municipality, or of another one, for where he can migrate. As robustness, we estimated a Logit model of the individual probability of having migrated, given his caracteristics, among which being beneficiary, and given the characteristics of his origin municipality, among which the PBF management features. The results seem to replicate the main finds of migration literature, and point to the relevance of being concerned with the management of the program and the release of resources, because of its influences on individual location decision

Populations and land development have the potential to shift as economies change at a rate that is faster than currently employed for updating a transportation plan for a region. This thesis uses the Foursquare location-based social networking check-in data to analyze the origin-destination travel demand for Austin, Texas. A doubly-constrained gravity model has been employed to create an origin-destination model. This model was analyzed in comparison to a singly-constrained gravity model as well as the Capital Area Metropolitan Planning Organization's 2010 Urban Transportation Study's origin-destination matrices through trip length distributions, the zonal origin-destination flow patterns, and the zonal trip generation and attraction heat maps in an effort to validate the methodology. / text

Spatial interaction models of the gravity type are used in conjunction with sample data on flows between origin and destination locations to analyse international and interregional trade, commodity, migration and commuting patterns. The focus is on the classical log-normal model version and spatial econometric extensions that have recently appeared in the literature. These new models replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for spatial dependence in flow magnitudes. The paper also discusses problems that arise in applied practice when estimating (log-normal) spatial interaction models. (authors' abstract)

Neste trabalho são estimados os efeitos das transferências de renda representadas pelo Programa Bolsa Família (PBF), nos fluxos migratórios entre os municípios brasileiros de 2008 a 2010. Parte-se de um Modelo de Escolha Discreta, e são obtidas e estimadas as equações agregadas de fluxo origem-destino com dependência espacial. Para desenvolver a análise, os indivíduos são divididos entre seis perfis de renda familiar per capita, e pela natureza do PBF, o foco da análise se dá principalmente nos quatro primeiros perfis. Então, são sugeridos alguns mecanismos pelos quais o PBF e sua gestão podem atuar sobre a decisão de migrar ou permanecer no local. Os mecanismos idealizados são diferentes de acordo com o status do indivíduo, de ser beneficiário do programa ou não, de acordo com seu perfil de renda, e se a característica do programa diz respeito ao seu município de residência, ou a outro município para o qual possa migrar. Como robustez, estima-se um modelo Logit, da probabilidade de o indivíduo ter realizado qualquer migração de acordo com algumas características individuais, dentre as quais a de ser beneficiário do programa, e de acordo com características de seu município de origem, estando as de gestão do PBF contidas neste segundo grupo. Os resultados em geral replicam os principais achados da literatura de migração, e apontam para relevância e não neutralidade dos recursos e da gestão municipal do Programa Bolsa Família na decisão locacional dos indivíduos / In this master thesis we estimated the effect of cash transfers in the migration flows among Brazilian municipalities from 2008 to 2010. We consider the cash transfer program named Bolsa Família (PBF), and aggregated origin-destination flows with spatial dependence equations in a Discrete Choice Model. To develop our analysis, the individuals are separated in six per capita family income profiles. In face of the PBF aim, we focus on the first four profiles. Then, some mechanisms that can influence the decision to migrate or to stay in the same municipality are suggested. Each mechanism differs acording to the individual status, of being a beneficiary of the program or not, acording to his income profile, and also acording to the analysed feature of the program being about his own municipality, or of another one, for where he can migrate. As robustness, we estimated a Logit model of the individual probability of having migrated, given his caracteristics, among which being beneficiary, and given the characteristics of his origin municipality, among which the PBF management features. The results seem to replicate the main finds of migration literature, and point to the relevance of being concerned with the management of the program and the release of resources, because of its influences on individual location decision