Spatial Analysis of Travel Behavior and Response to Traveler Information

Transportation planners have long recognized that it is urgent to integrate emerging spatial analysis with travel behavior studies. A clearer understanding of the spatial interactions among travelers and the complex environment they face has the potential to reap benefits of the ongoing technologies of travel behavior, spatial analysis and Advanced Traveler Information Systems (ATIS). Considering that spatial patterns have been overlooked in the literature of travel behavior and ATIS, the main objective of this research is to use robust methods of spatial analysis to enhance the understanding of how the associations between traveler decisions, built environment and socio-demographic characteristics are organized spatially. This dissertation takes a significant step towards filling this gap by using innovative spatial data description methods, e.g. geo-imputation, dynamic buffer analysis, spatial statistics to model the travel behavior of both the general population and university students. This study starts by developing a unique database from extensive behavioral data combined with a variety of spatial measurements, taking advantage ofincreased GIS capabilities. Five different activity-based databases from different regions are used, combined with their related socio-demographic and land use data. Among them are two general population travel surveys from North Carolina, which were conducted in Charlotte and at the Greater Triangle in 2003 and 2006, respectively. The Virginia Add-on for the general population was conducted in 2008, while two waves of the Virginia University Student Travel Survey (USTS) were conducted in 2009 and 2010. The general population and the university students are compared with each other in terms of how they traveled and responded to ATIS. Issues addressed in this dissertation include two aspects. The first one is how to describe data in space more accurately. When there is a need to know the exact locations of residences (geo-coordinate), but such information is unknown, geo-imputation is used as a fundamental method of assigning synthetic locations randomly to these residences based on available zonal information. After locating the residences by using geo-imputation, dynamic buffer analysis is used to capture locally built environment characteristics around residences, which place emphasis on capturing accessibility. The second issue is modeling travel behavior in space. Particular emphasis is placed on modeling associations between trip making, trip decision changes and their associated explanatory variables. The general population is compared with the university students who represent an energetic and technology-savvy subgroup of the population. Different spatial scales are used for these two groups: the regional level is used for the general population; the university campus is used as a special trip generator for the university students. At the regional level, a unique model structure, i.e. Geographically Weighted Regression (GWR), is used to allow associations to change across space, referred to as spatial heterogeneity. Significant spatial heterogeneity is found in the associations between trip-making and built environment, as well as in the model oftravelers\u27 information acquisition behavior and their travel decision adjustments. The spatial heterogeneity in the trip-making models suggests that there is higher spatial variability in favor of the statement that better land use design can help reduce auto trips. It is important to note that these potentially useful insights would have remained uncovered if using a non-spatial model that does not take spatial heterogeneity into account. At the special trip generator level, when local models don\u27t work well, the university campus is studied as a case which represents a combination of livable environments and a group of people who have different life cycles compared with the general population. Particular spatial analysis is applied to capture the association between trip-making and students\u27 residential proximity to campus. The models confirm there are rings of mobility around the campus. Different from the traditional travel demand model for the general population, this varied level of mobility of students based on their residential proximity of campus is important and must be considered in the students\u27 travel demand model

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Roadway System Assessment Using Bluetooth-Based Automatic Vehicle Identification Travel Time Data

This monograph is an exposition of several practice-ready methodologies for automatic vehicle identification (AVI) data collection systems. This includes considerations in the physical setup of the collection system as well as the interpretation of the data. An extended discussion is provided, with examples, demonstrating data techniques for converting the raw data into more concise metrics and views. Examples of statistical before-after tests are also provided. A series of case studies were presented that focus on various real-world applications, including the impact of winter weather on freeway operations, the economic benefit of traffic signal retiming, and the estimation of origin-destination matrices from travel time data. The technology used in this report is Bluetooth MAC address matching, but the concepts are extendible to other AVI data sources

Urban Air Mobility Market Study

The Booz Allen Team explored market size and potential barriers to Urban Air Mobility (UAM) by focusing on three potential markets – Airport Shuttle, Air Taxi, and Air Ambulance. We found that the Airport Shuttle and Air Taxi markets are viable, with a significant total available market value in the U.S. of $500 billion, for a fully unconstrained scenario. In this unconstrained best-case scenario, passengers would have the ability to access and fly a UAM at any time, from any location to any destination, without being hindered by constraints such as weather, infrastructure, or traffic volume. Significant legal and regulatory, weather, certification, public perception, and infrastructure constraints exist, which reduce the market potential for these applications to only about 0.5$2.5 billion, in the near term. However, we determined that these constraints can be addressed through ongoing intra-governmental partnerships, government and industry collaboration, strong industry commitment, and existing legal and regulatory enablers. We found that the Air Ambulance market is not a viable market if served by electric vertical takeoff and landing (eVTOL) vehicles due to technology constraints but may potentially be viable if a hybrid VTOL aircraft are utilized

Enabling experiences - The role of tour operators and tour leaders in creating and managing package tourism experiences

siirretty Doriast

National Performance Management Research Dataset (NPMRDS) - Speed Validation for Traffic Performance Measures (FHWA-OK-17-02)

This report presents research detailing the use of the first version of the National Performance Management Research Data Set (NPMRDS v.1) comprised of highway vehicle travel times used for computing performance measurements in the state of Oklahoma. Data extraction, preprocessing, and statistical analysis were performed on the dataset and acomprehensive study of dataset characteristics, influencing variables, outliersand anomalies was carried out. In addition, a study on filtering and removing speed data outliers across multiple road segments is developed, and a comparative analysis of raw baseline speed data and cleansed data is performed. A method for improved congestion detection is investigated and developed. Identification and a computational comparison analysis of travel time reliability performance metrics for both raw and cleansed datasets is shown. An outlier removal framework is formulated, and a cleansed and complete version of NPMRDS v.1 is generated. Finally, a validation analysis on the cleansed dataset is presented. In the end, research affirmsthat understanding domain specific characteristics is vital for filtering data outliers and anomalies of this dataset,which in turn is key for calculating accurate performance measurements. Thus, careful consideration for outlierremoval must be taken into account when computing travel time reliability metrics using the NPMRDS.October 2015-October 2017N

Application of Hierarchical Linear Modeling in a Tourism Context: Understanding Couples\u27 Intention to Purchase Local Food in South Carolina

Despite the collective nature of tourism activity, the research focusing on group behavior in tourism literature is very rare. People usually travel with groups, mostly with their families who have influence on their travel decisions. Food is one of the unique aspects of a destination which has become a marketing tool for tourism planners and an important travel decision. An understanding of the preference of local food among tourists will create positive impact on the region and exploring the underlying factors of this preference will be beneficial for future marketing plans. The purpose of this study was to understand the factors influencing local food purchase intention of tourist couples visiting coastal areas of South Carolina through the use of modified Theory of Reasoned action and provide a better understanding of their decision making process by using Hierarchical Linear Modelling (HLM) as data analyzing technique. Data was collected in Charleston and Beaufort, South Carolina from 190 tourist couples in October 2014. The variables influencing intention to purchase wildcaught and aquacultured oysters were tested. Results show that even if women have negative attitude towards oysters, their intention to purchase local seafood is not different than men. Positive importance of eating oysters in destination have stronger influence on intention to purchase seafood at individual and couple level. Study results also indicate as couples get older they influence each other in a positive way. This study provided theoretical implications by applying a modified Theory of Reasoned action in tourism decision making process, methodological implications by bringing a new way to understand this process by testing the relationships at individual and couple level. In addition to theoretical and methodological implications this study offered practical implications by providing insight into tourist’s intention to purchase aquacultured and wildcaught oysters during their vacation

BIG DATA ANALYTICS IN TRANSPORTATION NETWORKS USING THE NPMRDS

Urban traffic congestion is common and the cause for loss of productivity (due to trip delays) and higher risk to passenger safety (due to increased time in the automobile), not to mention an increase in fuel consumption, pollution, and vehicle wear. The fiduciary effect is a tremendous burden for citizens and states alike. One way to alleviate these ill effects is increasing state roadway and highway capacity. Doing so, however, is cost prohibitive. A better option is improving performance measurements in an effort to manage current roadway assets, improve traffic flow, and reduce road congestion. Variables like segment travel time, speed, delay, and origin-to-destination trip time are measures frequently used to monitor traffic and improve traffic flow on the state roadways. In 2014, ODOT was given access to the FHWA’s National Performance Management Research Data Set (NPMRDS), which includes average travel times divided into contiguous segments with travel time measured every 5 minutes. Travel times are subsequently segregated into passenger vehicle travel time and freight travel time. Both types of time are calculated using GPS location transmitted by way of participating drivers traveling along interstate highways. This thesis presents research detailing the use of NPMRDS dataset consisting of highway vehicle travel times, for computing performance measurements in the state of Oklahoma. Data extraction, preprocessing, and statistical analysis were performed on the dataset. A comprehensive study of the dataset characteristics, including influencing variables that affect data measurements are presented. A process for identifying anomalies is developed, and recommendations for improving accuracy and alleviating data anomalies are reported. Furthermore, a process for filtering and removing speed data outliers across multiple road segments is developed, and comparative analysis of raw baseline speed data and cleansed data is performed. Identification and computational comparison of travel time reliability performance measurements is done. A method for improved congestion detection is investigated and developed. Finally, traffic analytics using machine learning is performed to identify and to classify congested segments and a novel approach for identifying non-recurrent congestion sources using Bayesian inference of speed data is also developed and introduced

Imputação da ocupação de vias em redes de tráfego urbano por meio de máquina de vetores de suporte por mínimos quadrados

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Seguindo o exemplo de muitas cidades pelo mundo, Santos, no estado de São Paulo, implementou um "Sistema Inteligente de Trafego" (SIT) para controle da sua malha urbana. O SIT instalado se utiliza de detectores do tipo "Laço indutivo" para colher os dados de trafego e, com base nesses dados, realizar o controle semafórico. Embora baratos e de fácil instalação, os detectores "Laço indutivos" são bastante suscetíveis a defeitos e medições incorretas, que podem inclusive perdurar por vários dias. A perda de dados compromete ou até mesmo impede o controle por parte do SIT. Para contornar esse problema são utilizadas técnicas de imputação de dados. Atualmente a técnica de imputação, utilizada pelo SIT de Santos, é baseada na ponderação dos dados a montante do sensor defeituoso. Este trabalho busca encontrar um novo algorítimo de imputação, mais sofisticado, visando a melhora no desempenho do SIT. Na literatura, o algoritmo de aprendizagem: "Maquina de Vetores de Suporte por Mínimos Quadrados" (MVS-MQ) surge como o candidato mais promissor para a imputação de dados de trafego. Por isso, ele é o algoritmo selecionado para o estudo de viabilidade realizado neste trabalho. Os resultados obtidos mostram que a técnica MVS-MQ é uma opção mais interessante para imputação de dados de trafego do que a técnica atual.Following the example of many cities around the world, Santos - SP, implemented a Intelligent Transportation System (ITS) to control its urban traffic. The ITS installed have loop detectors to gather data and, based on this data, control the traffic lights. Although inexpensive and easy to install, the loop detectors are highly prone to missing and erroneous data, that can even last for several days. The missing data considerably degrades or even unables the control. To address this problem data imputation techniques are used. The imputation technique currently used in Santos is calculated with the weighted data from the upstream sensors. This paper seeks a new imputation algorithm, more sophisticated, aiming a better performance of the SIT. In the literature, the learning algorithm Least Squares - Support Vector Machines (LS-SVM) is presented as the most promising candidat for traffic data imputation. Therefore it was the selected algorithm for the research done in this paper. The results shows that LS-SVM is a more suitable option for traffic data imputation than the current one