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Lin, Hongzhi. "Activity-based travel demand modeling system in suburban area /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ms-b30082341f.pdf.
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Thesis (Ph.D.)--City University of Hong Kong, 2009."Submitted to Department of Management Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 112-124)
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Butler, Melody Nicole. "An assessment tool for the appropriateness of activity-based travel demand models." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45948.
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As transportation policies are changing to encourage alternative modes of transportation to reduce congestion problems and air quality impacts, more planning organizations are considering or implementing activity-based travel demand models to forecast future travel patterns. The proclivity towards operating activity-based models is the capability to model disaggregate travel data to better understand the model results that are generated with respect to the latest transportation policy implementations. This thesis first examines the differences between the two major modeling techniques used in the United States and then describes the assessment tool that was developed to recommend whether a region should convert to the advanced modeling procedures. This tool consists of parameters that were decided upon based on their known linkages to the advantages of activity-based models.
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Ali, Mir Shabbar. "An accessibility-activity based approach for modelling rural travel demand in developing countries." Thesis, University of Birmingham, 2001. http://etheses.bham.ac.uk//id/eprint/900/.
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For most rural populations in developing countries, travel to access basic needs is considered a burden, in terms of wastage of their daily time and efforts. Lack of adequate access to health, education and market centres is found to be responsible for problems like high mortality rate and low literacy rate and high sense of isolation. Recent research has recommended that time constraints should be incorporated in attempting to model rural travel behaviour. The research reported in this thesis integrates household accessibility analysis within an activity-based framework to model travel demand. The conceptual development recognised the derived nature of travel. The household access needs are transformed into individual activities through household role allocation. The spatial and temporal constraints on the activities along with monetary, cultural and social constraints on individuals determine accessibility of the activities to the individuals. Probabilistic behavioural models have been developed to model individual activity choice and the resulting travel. Household data collected from representative rural areas of Pakistan were used to analyse rural activity-travel behaviour. Household activities analysed were Work, Education, Market, Health and Leisure. The results indicated the varying nature of these activities and that of individuals responsible for carrying out the activities. It was found that Household Heads are responsible for carrying out most out-of-home activities required to fulfil household needs. Models developed were applied to various situations. The models in general were found to validate the concepts developed in the research. Prediction results for activities Work and Education were in agreement to the observed data. Results for activities Market, Health and Leisure showed that a time horizon must be considered to recognise the nondaily nature of these activities. Models addressing time horizon decision showed better agreement between predicted and observed travel demand.
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Nehra, Ram S. "Modeling time space prism constraints in a developing country context." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000299.
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Gim, Tae-Hyoung. "Utility-based approaches to understanding the effects of urban compactness on travel behavior: a case of Seoul, Korea." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50331.
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Automobile use is associated with significant problems such as air pollution and obesity. Decisions to use the automobile or its alternatives, including walk, bicycle, and public transit, are believed to be associated with urban form. However, in contrast to the hypothesis that compact urban form significantly reduces automobile travel, previous studies reported only a modest effect on travel behavior. These studies, largely built on microeconomic utility theory, are not sufficient for assessing the effect of compactness, for several reasons: (1) The studies postulate that travel invokes only disutility, but travel may also provide intrinsic utility or benefits insomuch as people travel for its own sake; (2) the studies have traditionally focused on how urban compactness reduces the distance between trip origin and destination and accordingly reduces trip time, but urban compactness also increases congestion and reduces trip speed, and thus increases trip time; and (3) the studies have mostly examined automobile commuting, but people travel for various purposes, using different travel modes, and the impact of urban compactness on the utility of non-automobile non-commuting travel has not been duly examined.
On this ground, to better explain the effects that urban compactness has on travel behavior, this dissertation refines the concept of travel utility using two additions to the microeconomic utility theory: activity-based utility theory of derived travel demand and approaches to positive utility of travel. Accordingly, it designs a conceptual model that specifies travel utility as an intermediary between urban compactness and travel behavior and examines the behavior associated with and utility derived from travel mode choices for alternative purposes of travel. Twenty individual models are derived from the conceptual model and tested within the context of Seoul, Korea, using a confirmatory approach of structural equation modeling and data from geographic information systems and a structured sample survey, which is initially designed and validated by semi-structured interviews and subsequent statistical tests. By comparing the individual models, this research concludes that the urban compactness effect on travel behavior, represented by trip frequencies and supplemented by mode shares, is better explained when travel utility is considered and if travel purposes are separately examined. Major empirical findings are that urban compactness affects travel behavior mainly by increasing the benefits of travel in comparison to its modest effect on the cost reduction and people’s behavioral response to urban compactness is to shift modes of commuting travel, decrease travel for shopping, and increase travel for leisure. These purpose-specific findings have implications for transportation planners and public health planners by assisting them in linking plans and policies concerning urban compactness to travel purposes.
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Bowman, John L. (John Lawrence). "Activity based travel demand model system with daily activity schedules." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11557.
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Chen, Siyu S. M. Massachusetts Institute of Technology Department of Civil and Environmental Engineering. "Calibrating activity-based travel demand model system via microsimulation." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123233.
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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 79-83).
This thesis addresses the problem of calibrating activity-based travel demand model systems. After estimation, it is common practice to use aggregate measurements to calibrate the estimated model system's parameters. However, calibration of activity-based model systems has received much less attention. Existing calibration approaches are myopic heuristics in the sense that they do not consider inter-dependency among choice-models and do not have a systematic way to adjust model parameters. Also, other simulation-based approaches do not perform well in large-scale applications. In this thesis, we focus on utility-maximizing nested logit activity-based model systems and calibrating count based aggregate statistics like OD flows, mode shares, activity shares and so on. We formulate the calibration problem as a simulation-based optimization problem and propose a stochastic gradient-based solution procedure to solve it. The solution procedure relies on microsimulation to calculate expected aggregate statistics of interest to the calibration problem. Additionally, we derive approximate analytical expressions for the gradient of the objective function -that are evaluated through microsimulation on mini-batches of the population. The proposed solution procedure is sensitive to the fundamental structure of the activity-based model system and is non-myopic in considering the dependencies across its model components. Finally, we show -through a real-world application- that the proposed solution procedure outperforms other state-of-the-art purely simulation-based optimization approaches in terms of computational efficiency, stability, and convergence. We also compare various gradient-based solution algorithms to determine the best algorithm to update the parameters. This work has the potential to facilitate wider and easier application of activity-based model systems.
by Siyu Chen.
S.M. in Transportation
S.M.inTransportation Massachusetts Institute of Technology, Department of Civil and Environmental Engineering
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Pattabhiraman, Varun R. (Varun Ramakrishna). "A needs-based approach to activity generation for travel demand analysis/." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/74470.
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Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2012.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 105-110).
This thesis develops a needs-based framework for behavioral enhancement of conventional activity-based travel demand models. Operational activity-based models specify activity generation models based on empirical considerations which are weakly founded in a behavioral theory. This thesis aims to enhance the specification of the activity generation models by developing the conceptual and analytical relationship between individuals' activity choices and need-satisfaction. The theory of needs hypothesizes that individuals conduct activities to satisfy their needs. Each activity that an individual conducts may satisfy one or several of their needs. Conversely, each need may be satisfied by one or several activities. This thesis models an individual's choice of activity dimensions including frequency, sequence, location, mode, time-of-travel, etc. as one that maximizes his/her need-satisfaction. A conceptual model of the relationship between needs and activities is developed based on inventory theory. Every need is associated with a psychological inventory that reflects the level of satisfaction with respect to the need. When an activity that satisfies a need is conducted, the need is satisfied and the corresponding psychological inventory is replenished by a quantity called the activity production. Over time, this inventory gets consumed and the need builds up. The choice of activity dimensions is modeled as a psychological inventory maximizing (i.e. utility-maximizing) problem, subject to time and cost budget constraints. The framework also accounts for satiation in need-satisfaction. An analytical model is formulated, solved and empirically estimated for a single need and the activity that satisfies the need under steady-state conditions. The problem is solved in two stages, for discrete (location) and continuous (duration and frequency) decision variables. The properties of the general solution are studied, and then explored for a translog form of the activity production function. An empirical estimation method that can be applied to single day travel diary data is proposed and validated using Monte-Carlo experiments. The model is empirically estimated using standard travel diary data from the Denver metropolitan area. Estimation results indicate the potential of the needs-based approach to enrich the specification of activity generation models in conventional activity-based model systems. A conceptual framework to extend the single need model is discussed. Extensions to models of multiple needs that capture interactions between different needs are also discussed. The flexible framework can also be extended to model social interactions including intrahousehold activity allocation and joint activity participation by households and social circles. An extension to a dynamic needs-based activity generation model is also discussed, which may be integrated with transportation simulators to predict individuals' activity choices in response to real-time information.
by Varun R. Pattabhiraman.
S.M.in Transportation
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Kim, Kihong. "Recent Advances in Activity-Based Travel Demand Models for Greater Flexibility." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4225.
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Most existing activity-based travel demand models are implemented in a tour-based microsimulation framework. Due to the significant computational and data storage benefits, the demand microsimulation allows a greater amount of flexibility in terms of demographic market segmentation, temporal scale, and spatial resolution, and thus the models can represent a wider range of travel behavior aspects associated with various policies and scenarios. This dissertation proposes three innovative methodologies, one for each of the three key dimensions, to fulfill the greater level of details toward a more mature state of activity-based travel demand models.
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Zhou, Liren. "Modeling the impacts of an employer based travel demand management program on commute travel behavior." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002309.
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Yin, Weihao. "An Agent-based Travel Demand Model System for Hurricane Evacuation Simulation." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/52344.
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This dissertation investigates the evacuees' behavior under hurricane evacuation conditions and develops an agent-based travel demand model system for hurricane evacuation simulation using these behavioral findings. The dissertation econometrically models several important evacuation decisions including evacuate-stay, accommodation type choice, evacuation destination choice, evacuation mode choice, departure time choice, and vehicle usage choice. In addition, it explicitly considers the pre-evacuation preparation activities using activity-based approach. The models are then integrated into a two-module agent-based travel demand model system.
The dissertation first develops the evacuate-stay choice model using the random-coefficient binary logit specification. It uses heterogeneous mean of the random parameter across households to capture shadow evacuation. It is found that the likelihood of evacuation for households that do not receive any evacuation notice decreases as their distance to coast increase on average. The distance sensitivity factor, or DSF, is introduced to construct the different scenarios of geographical extent of shadow evacuation.
The dissertation then conducts statistical analysis of the vehicle usage choice. It identifies the contributing factors to households' choice of the number of vehicles used for evacuation and develop predictive models of this choice that explicitly consider the constraint imposed by the number of vehicles owned by the household. This constraint is not accommodated by ordered response models. Data comes from a post-storm survey for Hurricane Ivan. The two models developed are variants of the regular Poisson regression model: the Poisson model with exposure and right-censored Poisson regression. The right-censored Poisson model is preferred due to its inherent capabilities, better fit to the data, and superior predictive power. The multivariable model and individual variable analyses are used to investigate seven hypotheses. Households traveling longer distances or evacuating later are more likely to use fewer vehicles. Households with prior hurricane experience, greater numbers of household members between 18 and 80, and pet owners are more likely to use a greater number of vehicles. Income and distance from the coast are insignificant in the multivariable models, although their individual effects have statistically significant linear relationship. However, the Poisson based models are non-linear. The method for using the right-censored Poisson model for producing the desired share of vehicle usage is also provided for the purpose of generating individual predictions for simulation.
The dissertation then presents a descriptive analysis of and econometric models for households' pre-evacuation activities based on behavioral intention data collected for Miami Beach, Florida. The descriptive analysis shows that shopping - particularly food, gasoline, medicine, and cash withdrawal - accounts for the majority of preparation activities, highlighting the importance of maintaining a supply of these items. More than 90% of the tours are conducted by driving, emphasizing the need to incorporate pre-evacuation activity travel into simulation studies. Households perform their preparation activities early in a temporally concentrated manner and generally make the tours during daylight. Households with college graduates, larger households, and households who drive their own vehicles are more likely to engage in activities that require travel. The number of household members older than 64 has a negative impact upon engaging in out-of-home activities. An action day choice model for the first tour suggests that households are more likely to buy medicine early but are more likely to pick up friends/relatives late. Households evacuating late are more likely to conduct their activities late. Households with multiple tours tend to make their first tour early. About 10% of households chain their single activity chains with their ultimate evacuation trips. The outcomes of this paper can be used in demand generation for traffic simulations.
The dissertation finally uses the behavioral findings and develops an agent-based travel demand model system for hurricane evacuation simulation, which is capable of generating the comprehensive household activity-travel plans. The system implements econometric and statistical models that represent travel and decision-making behavior throughout the evacuation process. The system considers six typical evacuation decisions: evacuate-stay, accommodation type choice, evacuation destination choice, mode choice, vehicle usage choice and departure time choice. It explicitly captures the shadow evacuation population. In addition, the model system captures the pre-evacuation preparation activities using an activity-based approach.
A demonstration study that predicts activity-travel patterns using model parameters estimated for the Miami-Dade area is discussed. The simulation results clearly indicate the model system produced the distribution of choice patterns that is consistent with sample observations and existing literature. The model system also identifies the proportion of the shadow evacuation population and their geographical extent. About 23% of the population outside the designated evacuation zone would evacuate. The shadow evacuation demand is mainly located within 3.1 miles (5 km) of the coastline. The output demand of the model system works with agent-based traffic simulation tools and conventional trip-based simulation tools.
The agent-based travel demand model system is capable of generating activity plans that works with agent-based traffic simulation tools and conventional trip-based simulation tools. It will facilitate the hurricane evacuation management.Ph. D.
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Subbiah, Rajesh. "An activity-based energy demand modeling framework for buildings: A bottom-up approach." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23084.
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Energy consumption by buildings, due to various factors such as temperature regulation, lighting, poses a threat to our environment and energy resources. In the United States, statistics reveal that commercial and residential buildings combined contribute about 40 percent of the overall energy consumption, and this figure is expected to increase. In order to manage the growing demand for energy, there is a need for energy system optimization, which would require a realistic, high-resolution energy-demand model. In this work, we investigate and model the energy consumption of buildings by taking into account physical, structural, economic, and social factors that influence energy use. We propose a novel activity based modeling framework that generates an energy demand profile on a regular basis for a given nominal day. We use this information to generate a building-level energy demand profile at highly dis-aggregated level. We then investigate the different possible uses of generated demand profiles in different What-if scenarios like urban-area planning, demand-side management, demand sensitive pricing, etc. We also provide a novel way to resolve correlational and consistency problems in the generation of individual-level and building-level "shared" activities which occur due to individuals\' interactions.Master of Science
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Roberts, Craig Arnold. "Modeling the relationships between microscopic and macroscopic travel activity on freeways : bridging the gap between current travel demand models and emerging mobile emission models." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/32873.
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梁凱倫 and Hoi-lun Helen Leung. "The application of activity-based transport demand modeling in Hong Kong: a feasibility study." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41549193.
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Leung, Hoi-lun Helen. "The application of activity-based transport demand modeling in Hong Kong a feasibility study /." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41549193.
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Justen, Andreas. "A time-space constrained approach for modeling travel and activity patterns." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2011. http://dx.doi.org/10.18452/16378.
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Gegenstand der Arbeit ist die Entwicklung eines tour-basierten Verkehrsnachfragemodells zur Abbildung von Aktivitätenketten unter Berücksichtigung von raum-zeitlichen constraints. Den theoretischen Hintergrund bildet ein hierarchisch organisierter Entscheidungsprozess, um die theoretisch möglichen Entscheidungskombinationen zu reduzieren und damit eine wahrscheinlichkeitsbasierte Berechnung zu ermöglichen. Als Beispieltour dient die Aktivitätenkette ‚Wohnen-Arbeit-Sekundäraktivität-Wohnen’, auf deren Basis auch die statistischen Analysen der Mobilitätsbefragung Santiagos durchgeführt werden. Unter Verwendung eines GIS werden so genannte ‚Suchräume’ (Aktionsräume in denen Sekundäraktivitäten durchgeführt werden) ermittelt. Ein Ergebnis der Datenanalyse sind Grenzwerte der maximalen täglichen Reisezeit für eine Reihe von Modus-Kombinationen. Die Zeitfenster von Startzeiten und Aktivitätendauer werden in Abhängigkeit sozioökonomischer Gruppen ermittelt. Die Bestimmung der Suchräume erfolgt in Abhängigkeit von Arbeitsdauer sowie Distanz zwischen Wohn- und Arbeitsort. Beide Kriterien erwiesen sich in der Analyse als statistisch signifikant. Der Vergleich zwischen Modell und Empirie (Santiagos Mobilitätsbefragung) deutet darauf hin, dass die Suchräume geeignet sind und die Mehrheit der beobachteten Zielwahlentscheidungen beinhalten. Zur Berechnung der Wahrscheinlichkeitspfade (unter Verwendung der Programmsyntax von SPSS) wird ein im Umfang auf sieben Ziele reduziertes Alternativenset pro Wohn- und Arbeitsstandort bestimmt. Dabei werden Erreichbarkeit und Attraktivität der Ziele innerhalb des Suchraumes berücksichtigt. Die erzielten Ergebnisse stützen das Argument, dass die raum-zeitlichen constraints (tägliche Reisezeit, Suchräume) eine effektive Reduktion der kombinatorischen Vielfalt zulassen. Die Erfahrungen aus der Berechnung der Beispieltour eignen sich zum Übertrag auf weitere Tour-Typen, um eine Modellierung der städtischen Gesamtverkehrsnachfrage zu ermöglichen.In this thesis we develop a tour-based approach for modeling activity and travel pattern considering time-space constraints. A hierarchical structure of choice-making builds theoretical background for the model and is based on a set of axiomatic rules. Our central argument is that the time-space constraints can be used for reducing the number of choices and, respectively, control the combinatorics associated with the probabilistic approach. The empirical analysis of our use case, a tour of type ‘Home-Work-SecondaryActivity-Home’, is based on Santiago’s travel survey. In addition, we apply GIS to estimate the so-called search spaces (potential areas where secondary activities are realized) and justify their sizes with the empirical findings. From the data analysis we identify thresholds for the tour-based maximum daily travel times considering a set of mode combinations. We define regimes of starting times and duration of activities depending on socio-economic user groups. The estimation of search spaces is realized considering the time spent at work as well as the distance between the home and work locations. Both criteria were found to be statistically significant. The comparison of modeled results with survey observations allowed concluding that the search spaces are realistic since they capture most of the observed trip destinations. For the estimation of spatial path flows of activities and trips (using SPSS programming language), we define a final choice set of no more than seven alternatives per primary location considering zone-based accessibility and land-use attractiveness. The obtained results support the argument that time-space constraints (daily travel time, search spaces) allow an effective control of combinatorial complexity. Basing on the experience obtained in process of modeling the exemplary tour, the approach can be applied to further tour types offering the possibility to estimate the entire transport demand of Santiago city.
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Concas, Sisinnio. "The Interaction Between Urban Form and Transit Travel." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3564.
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This study presents an analytical model of the interaction between urban form and the demand for transit travel, in which residential location, transit demand, and the spatial dispersion of non-work activities are endogenously determined. In this model, travel demand is considered a derived demand brought about by the necessity to engage in out-ofhome activities whose geographical extent is affected by urban form. In a departure from the urban monocentric model, residential location is defined as a job-residence pair in an urban area in which jobs, residences, and non-work activities are dispersed. Transit demand is then determined by residential location, work trips, non-work trip chains, and goods consumption.
Theoretically derived hypotheses are empirically tested using a dataset that integrates travel and land-use data. There is evidence of a significant influence of land-use patterns on transit patronage. In turn, transit demand affects consumption and non-work travel. Although much reliance has been placed on population density as a determinant of transit demand, it is found here that population density does not have a large impact on transit demand and, moreover, that the effect decreases when residential location is endogenous. To increase transit use, urban planners have advocated a mix of residential and commercial uses in proximity to transit stations. In this study, it is found that the importance of transit-station proximity is weakened by idiosyncratic preferences for residential location. In addition, when population density and residential location are jointly endogenous, the elasticity of transit demand with respect to walking distance to a transit station decreases by about 33 percent over the case in which these variables are treated an exogenous.
The research reported here is the first empirical work that explicitly relates residential location to trip chaining in a context in which individuals jointly decide residential location and the trip chain. If is found that households living farther from work use less transit and that trip-chaining behavior explains this finding. Households living far from work engage in complex trip chains and have, on average, a more dispersed activity space, which requires reliance on more flexible modes of transportation. Therefore, reducing the spatial allocation of non-work activities and improving transit accessibility at and around subcenters would increase transit demand. Similar effects can be obtained by increasing the presence of retail locations in proximity to transit-oriented households. Although focused on transit demand, the framework can be easily generalized to study other forms of travel.
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Shen, Ni. "Modeling of Airline and Passenger Dynamics in the National Airspace System (NAS)." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/77267.
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This dissertation is a collection of several models to understand airline and passenger dynamics in the National Airspace System (NAS).
Agent-based modeling is one of the most widely used modeling simulation-analysis approaches to understanding the dynamic behavior of complex systems. The usefulness of agent-based modeling has been demonstrated by simulating the complex interactions between airlines, travelers, and airports of a small-scale transportation system. Three airlines, one low cost and two network airlines are simulated to examine how each airline behaves over time to maximize their profit margins for a given passenger demand and operation cost structure. Passenger mode choice and itinerary choice sub modules are embedded in the framework to characterize traveler agent's response to the evolved airline schedule. An airport delay model was implemented to estimate the average delay at each airport. The estimated delay fed into the mode choice and itinerary choice models to update the travel time related variables.
International passenger demand is a very important component of the air transportation system in the United States. The proportion of international enplanements relative to total enplanements increased from 8% in 1990 to 11% in 2008. Nine linear regression models are developed to forecast the enplanements from the United States to each of nine international regions. The international enplanements from the CONUS to each world region are modeled as a function of GDP and GDP per capita of both the United States and the specific region. A dummy variable is also used to account for the effects of September 11, 2001. The total number of international enplanements is forecast to increase from 74.7 million in 2008 to 184.4 million in 2028. The average annual growth rate is expected to be 4.7%.
The European Union – United States Open Skies Agreement, which became effective March 30, 2008. Mathematical models are developed to forecast the effect of EU-US Open Skies Agreement on commercial airline passenger traffic over the North Atlantic Ocean. Nine econometric models were developed to forecast passenger traffic between the United States and nine selected European countries between 2008 through 2020. 68 new nonstop flights between the United States airports and the European airports are predicted by the model in 2020 using the airport pair passenger demand forecast. London, Heathrow is demonstrated as an example for rerouting the excess air travel passengers from one airport to other airports when the airport operational capacity is exceeded.
The proportion of international enplanements relative to total enplanements within CONUS increased from 8% in 1990 to 11% in 2008. 51% of the sampled international and U.S. territories passengers served by U.S. carriers had at least one domestic coupon in 2007. The number of DOI passengers through airport-pairs in each of the historical years (1990-2007) is estimated based on the adjusted 100% international itineraries including pure international itineraries plus the non-CONUS itineraries. The total number of DOI enplanements is estimated to grow from 37.3 million in 1990 to 79.4 million in 2007. 193 CONUS airports are estimated to have at least 10,000 DOI enplanements in 2007. The number of DOI enplanements is forecast to grow from 79.4 million in 2007 to 206.2 million in 2030 with average growth rate of 4.2% per year.
In recent years, there has been an increasing use of secondary airports both in Europe and the U.S. Regional airports have long been considered as a possible source of relief to reduce airport congestion at the hub airport and to efficiently accommodate future air travel demand. The conditions under which the secondary airports develop in a metropolitan area are examined. Fifteen multi-airport systems including 19 Operational Evolution Plan airports and 25 active secondary airports are identified in the National Airspace System. Diverse trends of traffic distribution among airports in the same metropolitan area are observed. We observed that the number of markets served at the secondary airports is less than that at the primary airport in the same metropolitan area. Most of the secondary airports are currently dominated by the low-cost carriers. The share of seats supplied by the low-cost carriers at the secondary airports has increased during the period 1990-2008. Full service carriers concentrate their service mainly on the primary airport in all the multi-airport systems analyzed. The average seat capacity per aircraft at the secondary airports is higher than that of primary airports in most of the multi-airport systems. The secondary airports mainly serve the domestic O&D passengers.Ph. D.
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Ren, Fang. "Geovisualizing and modeling physical and Internet activities in space-time toward an integrated analysis of activity patterns in the information age /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1196200534.
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Lim, Choon Giap. "An integrative assessment of the commercial air transportation system via adaptive agents." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26541.
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Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2009.Committee Chair: Dimitri Mavris; Committee Member: Daniel Schrage; Committee Member: Hojong Baik; Committee Member: Jung-Ho Lewe; Committee Member: Kurt Neitzke. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Goutham, Mithun. "Machine learning based user activity prediction for smart homes." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.
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Ichikawa, Sandra Matiko. "Aplicação de minerador de dados na obtenção de relações entre padrões de encadeamento de viagens codificados e características sócio-econômicas." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/18/18137/tde-15032016-133420/.
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O principal objetivo deste trabalho é analisar a aplicabilidade de um minerador de dados para obter relações entre padrões de viagens encadeadas e características sócio-econômicas de viajantes urbanos. Para representar as viagens encadeadas, as viagens correspondentes a cada indivíduo do banco de dados foram codificadas em termos de seqüência de letras que indicam uma ordem cronológica em que atividades são desenvolvidas. O minerador de dados utilizado neste trabalho é árvore de decisão e classificação, uma ferramenta de análise disponível no software S-Plus. A análise foi baseada na pesquisa origem-destino realizada pelo Metrô-SP na região metropolitana de São Paulo, por meio de entrevistas domiciliares, em 1987. Um dos importantes resultados é que indivíduos que têm atributos sócio-econômicos e de viagens similares não se comportam de maneira similar; pelo contrário, eles fazem diferentes padrões de viagens encadeadas, as quais podem ser descritas em termos de probabilidade ou freqüência associada a cada padrão. Portanto, o minerador de dados deve possuir a habilidade para representar essa distribuição. A consistência do resultado foi analisada comparando-os com alguns resultados encontrados na literatura referente a análise de viagem baseada em atividades. A principal conclusão é que árvore de decisão e classificação aplicada a dados individuais, contendo encadeamento de viagem codificado e atributos socioeconômicos e de viagem, permite extrair conhecimento e informações ocultas que ajudam a compreender o comportamento de viagem de viajantes urbanos.The main aim of this work is to analyze the applicability of a data miner for obtaining relationships between trip-chaining patterns and urban trip-makers socioeconomic characteristics. In order to represent the trip-chains, trips corresponding to each individual in the data set were coded in terms of letters indicating a chronological order in which activities are performed. Data miner applied in this work is decision and classification tree, an analysis tool available in S-Plus software package. The analysis was based on the origin-destination home-interview survey carried out by Metrô-SP in São Paulo metropolitan area. One of the important findings is that individuals having similar socieconomic and trip attributes do not behave in a similar way; on the contrary, they make different trip-chaining patterns, which may be described in term of probability or frequency associated to each pattern. Therefore, the data miner should have ability to represent that distribution. The consistency of results was analyzed by comparing them with some results found in literature related to activity-based travel analysis. The main conclusion is that decision and classification tree applied to individual data, containing coded trip-chaining and socioeconomic and trip attributes, allows extracting hidden knowledge and information that help to understand the travel behaviour of urban trip-makers.
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Taco, Pastor Willy Gonzales. "Redes neurais artificiais aplicadas na modelagem individual de padrões de viagens encadeadas a pé." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/18/18137/tde-18092015-163322/.
Full textAbstract:
O objetivo deste trabalho foi desenvolver um modelo para reconhecer e reproduzir padrões de viagens encadeadas a pé. O processo de modelagem foi conduzido através da aplicação das técnicas das Redes Neurais Artificiais (RNAs), utilizando-se de uma rede estática MLP e de rede dinâmica Elman. A análise do desempenho do modelo foi baseada nos dados de uma pesquisa de Origem-Destino realizada, em 1987, pelo METRÔ-SP na Região Metropolitana de São Paulo. Na modelagem foi fixado o modo de viagem a pé, e, na abordagem seqüencial, padrões de viagens individuais foram representados em termos de dois componentes: duração da viagem e tipo de atividades. A análise foi realizada partindo da classificação geral e específica para cada segmento do encadeamento de viagens, o que permitiu a comparação dos resultados entre padrões de viagens observados e os reproduzidos pelas redes. Na classificação geral, cinco dos padrões previstos com maior freqüência pelas RNAs representaram em média 58,9% dos indivíduos no conjunto de dados usado para testar o desempenho do modelo. Para o vetor de duas e quatro viagens, as redes neurais reproduziram 50% das durações de viagem e 90% das atividades, tais como Trabalho e Escola. Embora esses resultados não pareçam muito robustos, não significa que eles estejam errados. As porcentagens acima representam a probabilidade de uma pessoa realizar viagens com aquelas durações ou tipo de atividades.The main objective of this work was to develop a model for recognizing and reproduzing trip-chaining patterns by walk. The process of modeling was conducted applying the techniques of Artificial Neural Networks (ANNs), by using one of the static networks MLP and the Elman dynamic network. The analysis of the performance of the model was based on the origin-destination home-interview survey carried out by METRÔ-SP in São Paulo Metropolitan Area in 1987. The mode of trip by walk was fixed in the model, and, in the sequential approach, individual travel patterns were represented in terms of two components: trip duration and activity type. The analysis was accomplished starting from the general and specific classifications for each segment of the chained trips, which allowed the comparison of the results between the observed travel patterns and reproduced ones through ANNs. In general classification, 5 of the patterns most frequently predicted by the ANNs represented 58.9% of the individuals in the dataset used for testing the model performance. For the vectors of two and four trips, the neural networks reproduced 50% of trip durations and 90% of the activities, such as work and school. Although those results seem not so robust, it does not mean that they are wrong. The percentages above represent the probability of a person making trips with those durations or type of activities.
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Aguiar, Marcelo Figueiredo Massulo. "Redução no tamanho da amostra de pesquisas de entrevistas domiciliares para planejamento de transportes: uma verificação preliminar." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18137/tde-28032014-193530/.
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O trabalho tem por principal objetivo verificar, preliminarmente, a possibilidade de reduzir a quantidade de indivíduos na amostra de Pesquisa de Entrevistas Domiciliares, sem prejudicar a qualidade e representatividade da mesma. Analisar a influência das características espaciais e de uso de solo da área urbana constitui o objetivo intermediário. Para ambos os objetivos, a principal ferramenta utilizada foi o minerador de dados denominado Árvore de Decisão e Classificação contido no software S-Plus 6.1, que encontra as relações entre as características socioeconômicas dos indivíduos, as características espaciais e de uso de solo da área urbana e os padrões de viagens encadeadas. Os padrões de viagens foram codificados em termos de sequência cronológica de: motivos, modos, durações de viagem e períodos do dia em que as viagens ocorrem. As análises foram baseadas nos dados da Pesquisa de Entrevistas Domiciliares realizada pela Agência de Cooperação Internacional do Japão e Governo do Estado do Pará em 2000 na Região Metropolitana de Belém. Para se atingir o objetivo intermediário o método consistiu em analisar, através da Árvore de Decisão e Classificação, a influência da variável categórica Macrozona, que representa as características espaciais e de uso de solo da área urbana, nos padrões de viagens encadeadas realizados pelos indivíduos. Para o objetivo principal, o método consistiu em escolher, aleatoriamente, sub-amostras contendo 25% de pessoas da amostra final e verificar, através do Processamento de Árvores de Decisão e Classificação e do teste estatístico Kolmogorov - Smirnov, se os modelos obtidos a partir das amostras reduzidas conseguem ilustrar bem a freqüência de ocorrência dos padrões de viagens das pessoas da amostra final. Concluiu-se que as características espaciais e de uso de solo influenciam os padrões de encadeamento de viagens, e portanto foram incluídas como variáveis preditoras também nos modelos obtidos a partir das sub-amostras. A conclusão principal foi a não rejeição da hipótese de que é possível reduzir o tamanho da amostra de pesquisas domiciliares para fins de estudo do encadeamento de viagens. Entretanto ainda são necessárias muitas outras verificações antes de aceitar esta conclusão.The main aim of this work is to verify, the possibility of reducing the sample size in home-interview surveys, without being detrimental to the quality and representation. The sub aim of this work is to analyze the influence of spatial characteristics and land use of an urban area. For both aims, the main analyses tool used was Data Miner called the Decision and Classification Tree which is in the software S-Plus 6.1. The Data Miner finds relations between trip chaining patterns and individual socioeconomic characteristics, spatial characteristics and land use patterns. The trip chaining patterns were coded in terms of chronological sequence of trip purpose, travel mode, travel time and the period of day in which travel occurs. The analyses were based on home-interview surveys carried out in the Belém Metropolitan Area in 2000, by Japan International Cooperation Agency and Pará State Government. In order to achieve the sub aim of this work, the method consisted of analyzing, using the Decision and Classification Tree, the influence of the categorical variable \"Macrozona\", which represents spatial characteristics and urban land use patterns, in trip chaining patterns carried by the individuals. Concerning the main aim, the method consisted of choosing sub-samples randomly containing 25% of the final sample of individuals and verifying (using Decision and Classification Tree and Kolmogorov-Smirnov statistical test) whether the models obtained from the reduced samples can describe the frequency of the occurrence of the individuals trip chaining patterns in the final sample well. The first conclusion is that spatial characteristics and land use of the urban area have influenced the trip chaining patterns, and therefore they were also included as independent variables in the models obtained from the sub-samples. The main conclusion was the non-rejection of the hypothesis that it is possible to reduce the sample size in home-interview surveys used for trip-chaining research. Nevertheless, several other verifications are necessary before accepting this conclusion.
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Cyganski, Rita. "Was zieht uns an? Empirische Grundlagen für eine verbesserte Abbildung der Einkaufszielwahl in Verkehrsnachfragemodellen." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/22101.
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Eine große Herausforderung für Verkehrsmodelle stellt die korrekte Abbildung der Entscheidungsmuster bei der Zielwahl dar. Diese bestimmt die räumlichen Strukturen der Nachfrage und steht in engem Zusammenhang mit zentralen Ergebnissen der Nachfragemodellierung.
Rund ein Drittel der Alltagswege in Deutschland sind Einkaufs- und Erledigungwege. Zahlreiche Arbeiten zeigen die Bedeutung von habitualisierten Verhaltensmustern bei der Wahl eines Einkaufsortes. Die Motive der Geschäftswahl gelten als sehr vielfältig. Besondere Bedeutung wird zudem den Primäraktivitätenorten zugeschrieben. Gleichwohl erfolgt die Abbildung der Zielwahl in der Nachfragemodellierung zumeist sehr vereinfachend. Gewöhnlich wird von einem Versorgungseinkauf mit der Geschäftsgröße und der Anreisezeit ausgegangen.
Diese Arbeit zeigt anhand empirischer Auswertungen Möglichkeiten einer verhaltensorientierten Abbildung der Einkaufszielwahl in mikroskopischen Personenverkehrsmodellen auf. Im Fokus stehen die Variabilität der Geschäftswahl, die ausschlaggebenden Motive sowie die räumlichen Bezugspunkte der Suche. Am Beispiel des Erwerbs von Nahrungs- und Genussmitteln, von Textilien sowie von Unterhaltungselektronik werden Unterschiede zwischen Einkaufswaren verschiedener Fristigkeit, aber auch zwischen verschiedenen Personengruppen herausgearbeitet.
Simulationsrechnungen mit dem Nachfragemodell TAPAS zeigen, dass eine Differenzierung der Einkaufsart sowie die Nutzung eines motivgestützen Erreichbarkeitsmaßes die Modellierungsergebnisse stark verbessern. Die Arbeit stellt erweiterte Indikatoren für eine Berücksichtigung der räumlichen Bezugspunkte bei der Beurteilung der Modellierungsergebnisse bereit. Auch stehen mit den Analysen der Aktivitätenräume, der Umwegfaktoren, der Lage der Einkaufsorte sowie der kumulierten Reiseweiten Informationen zur Verfügung, die generell für die Definition adäquater Suchräume und Bezugspunkte für die Modellierung städtischer Untersuchungsgebiete genutzt werden können.A major challenge in travel demand modelling is the correct representation of decision patterns underlying the choice of destinations. This choice determines the spatial structures of demand and is closely related to central modelling results. Around one third of everyday trips in Germany are for shopping and errands. Numerous studies show the importance of habitualised behavioral patterns when choosing a shopping location. The motives for choosing a shop are considered to be very diverse. Particular importance is attributed to primary activity locations. Nevertheless, the representation of the target choice in demand modelling is usually very simplified. Usually, a supply purchase is implicitly assumed, with the size of the shop and travel time from the previous location being the most important choice criteria. Using empirical analyses, this dissertation shows possibilities for a behavior-oriented depiction of shopping location choice in microscopic passenger transport models. These are discussed in terms of their usability for modeling. The analyses focus on the variability of destinations, the decisive motives and the spatial reference points of the location search. Using the example of the purchase of food and beverages, textiles and consumer electronics, differences between shopping goods of different periodicity and also different groups of people are presented. Simulation calculations with the demand model TAPAS show that a differentiation of the type of purchase and the use of a motive-based accessibility measure greatly improves the modelling results. The dissertation provides extended indicators for a consideration of spatial reference points in the evaluation of the modelling results. Furthermore, the analyses of activity areas, diversion factors, the location of shopping locations and cumulative travel distances provide information that can be generally used to define adequate search areas and reference points for the modelling of urban study areas.Einkaufsverhalten
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Copperman, Rachel Batya Anna 1982. "A comprehensive assessment of children's activity-travel patterns with implications for activity-based travel demand modeling." 2008. http://hdl.handle.net/2152/17843.
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Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number of trips made by a household. In addition, children’s travel and activity participation have direct implication for adults’ activity-travel patterns. A better understanding of children’s activity-travel patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting of activity-based travel demand modeling systems. In contrast to the need to examine and model children’s activity-travel patterns, existing activity-based research and modeling systems have almost exclusively focused their attention on the activity-travel patterns of adults. Therefore, the goal of this research effort is to contribute to the area of activity-based travel demand analysis by comprehensively examining children’s activity-travel patterns, and by developing a framework for incorporating children within activity-based travel demand modeling systems. This dissertation provides a comprehensive review of previous research on children’s activity engagement and travel by focusing on the dimensions characterizing children’s activity-travel patterns and the factors affecting these dimensions. Further, an exploratory analysis examines the weekday and weekend activity participation characteristics of school-going children. The study focuses on the overall time-use of children in different types of activities, as well as on several dimensions characterizing the context of participation in activities. In addition, the dissertation discusses the treatment of children within current activity-based travel demand modeling systems and conceptualizes an alternative framework for simulating the daily activity-travel patterns of children. An empirical analysis is undertaken of the post-school out-of-home activity-location engagement patterns of children aged 5 to 17 years. Specifically, this research effort utilizes a multinomial logit model to analyze children’s post-school location patterns, and employs a multiple discrete-continuous extreme value (MDCEV) model to study the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during the after-school period. Finally, the paper identifies the need and opportunities for further research in the field of children’s travel behavior analysis.text
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Xu, Yitu. "Using Volunteer Tracking Information for Activity-Based Travel Demand Modeling and Finding Dynamic Interaction-Based Joint-Activity Opportunities." 2011. http://trace.tennessee.edu/utk_gradthes/927.
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Technology used for real-time locating is being used to identify and track the movements of individuals in real time. With the increased use of mobile technology by individuals, we are now able to explore more potential interactions between people and their living environment using real-time tracking and communication technologies.
One of the potentials that has hardly been taken advantage of is to use cell phone tracking information for activity-based transportation study. Using GPS-embedded smart phones, it is convenient to continuously record our trajectories in a day with little information loss. As smart phones get cheaper and hence attract more users, the potential information source for self-tracking data is pervasive. This study provides a cell phone plus web method that collects volunteer cell phone tracking data and uses an algorithm to identify the allocation of activities and traveling in space and time. It also provides a step that incorporates user-participated prompted recall attribute identification (travel modes and activity types) which supplements the data preparation for activity-based travel demand modeling.
Besides volunteered geospatial information collection, cell phone users’ real-time locations are often collected by service providers such as Apple, AT&T and many other third-party companies. This location data has been used in turn to boost new location-based services. However, few applications have been seen to address dynamic human interactions and spatio-temporal constraints of activities. This study sets up a framework for a new kind of location-based service that finds joint-activity opportunities for multiple individuals, and demonstrates its feasibility using a spatio-temporal GIS approach.
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"Demographic Evolution Modeling System for Activity-Based Travel Behavior Analysis and Demand Forecasting." Doctoral diss., 2014. http://hdl.handle.net/2286/R.I.25138.
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abstract: The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population with an array of socio-demographic and socio-economic attributes has drawn remarkable attention due to privacy and cost constraints in collecting and disclosing full scale data. Although, there has been enormous progress in producing synthetic population, there has been less progress in the development of population evolution modeling arena to forecast future year population. The objective of this dissertation is to develop a well-structured full-fledged demographic evolution modeling system, capturing migration dynamics and evolution of person level attributes, introducing the concept of new household formations and apprehending the dynamics of household level long-term choices over time. A comprehensive study has been conducted on demography, sociology, anthropology, economics and transportation engineering area to better understand the dynamics of evolutionary activities over time and their impacts in travel behavior. This dissertation describes the methodology and the conceptual framework, and the development of model components. Demographic, socio-economic, and land use data from American Community Survey, National Household Travel Survey, Census PUMS, United States Time Series Economic Dynamic data and United States Center for Disease Control and Prevention have been used in this research. The entire modeling system has been implemented and coded using programming language to develop the population evolution module named `PopEvol' into a computer simulation environment. The module then has been demonstrated for a portion of Maricopa County area in Arizona to predict the milestone year population to check the accuracy of forecasting. The module has also been used to evolve the base year population for next 15 years and the evolutionary trend has been investigated.Dissertation/Thesis
Ph.D. Civil and Environmental Engineering 2014
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Eluru, Naveen. "Developing advanced econometric frameworks for modeling multidimensional choices : an application to integrated land-use activity based model framework." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-12-1549.
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The overall goal of the dissertation is to contribute to the growing literature on the activity-based framework by focusing on the modeling of choices that are influenced by land-use and travel environment attributes. An accurate characterization of activity-travel patterns requires explicit consideration of the land-use and travel environment (referred to as travel environment from here on). There are two important categories of travel environment influences: direct (or causal) and indirect (or self-selection) effects. The direct effect of travel environment refers to how travel environment attributes causally influence travel choices. This direct effect may be captured by including travel environment variables as exogenous variables in travel models. Of course, determining if a travel environment variable has a direct effect on an activity/travel choice of interest is anything but straightforward. This is because of a potential indirect effect of the influence of the travel environment, which is not related to a causal effect. That is, the very travel environment attributes experienced by a decision maker (individual or household) is a function of a suite of a priori travel related choices made by the decision maker.
The specific emphasis of the current dissertation is on moving away from considering travel environment choices as purely exogenous determinants of activity-travel models, and instead explicitly modeling travel environment decisions jointly along with activity-travel decisions in an integrated framework. Towards this end, the current dissertation formulates econometric models to analyze multidimensional choices. The multidimensional choice situations examined (and the corresponding model developed) in the research effort include: (1) reason for residential relocation and associated duration of stay (joint multinomial logit model and a grouped logit model), (2) household residential location and daily vehicle miles travelled (Copula based joint binary logit and log-linear regression model), (3) household residential location, vehicle type and usage choices (copula based Generalized Extreme Value and log-linear regression model) and (4) activity type, travel mode, time period of day, activity duration and activity location (joint multiple discrete continuous extreme value (MDCEV) model and multinomial logit model (MNL) with sampling of alternatives). The models developed in the current dissertation are estimated using actual field data from Zurich and San Francisco. A variety of policy exercises are conducted to illustrate the advantages of the econometric models developed. The results from these exercises clearly underline the importance of incorporating the direct and indirect effects of travel environment on these choice scenarios.text
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"Modeling the Role and Influence of Children in Household Activity-Based Travel Model Systems." Master's thesis, 2010. http://hdl.handle.net/2286/R.I.8757.
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abstract: Rapid developments are occurring in the arena of activity-based microsimulation models. Advances in computational power, econometric methodologies and data collection have all contributed to the development of microsimulation tools for planning applications. There has also been interest in modeling child daily activity-travel patterns and their influence on those of adults in the household using activity-based microsimulation tools. It is conceivable that most of the children are largely dependent on adults for their activity engagement and travel needs and hence would have considerable influence on the activity-travel schedules of adult members in the household. In this context, a detailed comparison of various activity-travel characteristics of adults in households with and without children is made using the National Household Travel Survey (NHTS) data. The analysis is used to quantify and decipher the nature of the impact of activities of children on the daily activity-travel patterns of adults. It is found that adults in households with children make a significantly higher proportion of high occupancy vehicle (HOV) trips and lower proportion of single occupancy vehicle (SOV) trips when compared to those in households without children. They also engage in more serve passenger activities and fewer personal business, shopping and social activities. A framework for modeling activities and travel of dependent children is proposed. The framework consists of six sub-models to simulate the choice of going to school/pre-school on a travel day, the dependency status of the child, the activity type, the destination, the activity duration, and the joint activity engagement with an accompanying adult. Econometric formulations such as binary probit and multinomial logit are used to obtain behaviorally intuitive models that predict children's activity skeletons. The model framework is tested using a 5% sample of a synthetic population of children for Maricopa County, Arizona and the resulting patterns are validated against those found in NHTS data. Microsimulation of these dependencies of children can be used to constrain the adult daily activity schedules. The deployment of this framework prior to the simulation of adult non-mandatory activities is expected to significantly enhance the representation of the interactions between children and adults in activity-based microsimulation models.Dissertation/Thesis
M.S. Civil and Environmental Engineering 2010
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Sener, Ipek N. "Accommodating flexible spatial and social dependency structures in discrete choice models of activity-based travel demand modeling." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-08-1609.
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Spatial and social dependence shape human activity-travel pattern decisions and their antecedent choices. Although the transportation literature has long recognized the importance of considering spatial and social dependencies in modeling individuals’ choice behavior, there has been less research on techniques to accommodate these dependencies in discrete choice models, mainly because of the modeling complexities introduced by such interdependencies. The main goal of this dissertation, therefore, is to propose new modeling approaches for accommodating flexible spatial and social dependency structures in discrete choice models within the broader context of activity-based travel demand modeling. The primary objectives of this dissertation research are three-fold. The first objective is to develop a discrete choice modeling methodology that explicitly incorporates spatial dependency (or correlation) across location choice alternatives (whether the choice alternatives are contiguous or non-contiguous). This is achieved by incorporating flexible spatial correlations and patterns using a closed-form Generalized Extreme Value (GEV) structure. The second objective is to propose new approaches to accommodate spatial dependency (or correlation) across observational units for different aspatial discrete choice models, including binary choice and ordered-response choice models. This is achieved by adopting different copula-based methodologies, which offer flexible dependency structures to test for different forms of dependencies. Further, simple and practical approaches are proposed, obviating the need for any kind of simulation machinery and methods for estimation. Finally, the third objective is to formulate an enhanced methodology to capture the social dependency (or correlation) across observational units. In particular, a clustered copula-based approach is formulated to recognize the potential dependence due to cluster effects (such as family-related effects) in an ordered-response context. The proposed approaches are empirically applied in the context of both spatial and aspatial choice situations, including residential location and activity participation choices. In particular, the results show that ignoring spatial and social dependencies, when present, can lead to inconsistent and inefficient parameter estimates that, in turn, can result in misinformed policy actions and recommendations. The approaches proposed in this research are simple, flexible and easy-to-implement, applicable to data sets of any size, do not require any simulation machinery, and do not impose any restrictive assumptions on the dependency structure.text
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"A Tour Level Stop Scheduling Framework and A Vehicle Type Choice Model System for Activity Based Travel Forecasting." Doctoral diss., 2014. http://hdl.handle.net/2286/R.I.27462.
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abstract: This dissertation research contributes to the advancement of activity-based travel forecasting models along two lines of inquiry. First, the dissertation aims to introduce a continuous-time representation of activity participation in tour-based model systems in practice. Activity-based travel demand forecasting model systems in practice today are largely tour-based model systems that simulate individual daily activity-travel patterns through the prediction of day-level and tour-level activity agendas. These tour level activity-based models adopt a discrete time representation of activities and sequence the activities within tours using rule-based heuristics. An alternate stream of activity-based model systems mostly confined to the research arena are activity scheduling systems that adopt an evolutionary continuous-time approach to model activity participation subject to time-space prism constraints. In this research, a tour characterization framework capable of simulating and sequencing activities in tours along the continuous time dimension is developed and implemented using readily available travel survey data. The proposed framework includes components for modeling the multitude of secondary activities (stops) undertaken as part of the tour, the time allocated to various activities in a tour, and the sequence in which the activities are pursued.
Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice.Dissertation/Thesis
Doctoral Dissertation Civil and Environmental Engineering 2014
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Fourie, P. J. (Pieter Jacobus). "An initial implementation of a multi-agent transport simulator for South Africa." Diss., 2009. http://hdl.handle.net/2263/25793.
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Transport demand planning in South Africa is a neglected field of study, using obsolete methods to model an extremely complex, dynamic system composed of an eclectic mix of First and Third World transport technologies, infrastructure and economic participants. We identify agent-based simulation as a viable modelling paradigm capable of capturing the effects emerging from the complex interactions within the South African transport system, and proceed to implement the Multi-Agent Transport Simulation Toolkit (MATSim) for South Africa's economically important Gauteng province. This report describes the procedure followed to transform household travel survey, census and Geographic Information System (GIS) data into an activity-based transport demand description, executed on network graphs derived from GIS shape files. We investigate the influence of network resolution on solution quality and simulation time, by preparing a full network representation and a small version, containing no street-level links. Then we compare the accuracy of our data-derived transport demand with a lower bound solution. Finally the simulation is tested for repeatability and convergence. Comparisons of simulated versus actual traffic counts on important road network links during the morning and afternoon rush hour peaks show a minimum mean relative error of less than 40%. Using the same metric, the small network differs from the full representation by a maximum of 2% during the morning peak hour, but the full network requires three times as much memory to execute, and takes 5.2 times longer to perform a single iteration. Our census- and travel survey-derived demand performs significantly better than uniformly distributed random pairings of home- and work locations, which we took to be analogous to a lower bound solution. The smallest difference in corresponding mean relative error between the two cases comes to more than 50%. We introduce a new counts ratio error metric that removes the bias present in traditional counts comparison error metrics. The new metric shows that the spread (standard deviation) of counts comparison values for the random demand is twice to three times as large as that of our reference case. The simulation proves highly repeatable for different seed values of the pseudo-random number generator. An extended simulation run reveals that full systematic relaxation requires 400 iterations. Departure time histograms show how agents 'learn' to gradually load the network while still complying with activity constraints. The initial implementation has already sparked further research. Current priorities are improving activity assignment, incorporating commercial traffic and public transport, and the development and implementation of the minibus taxi para-transit mode. CopyrightDissertation (MEng)--University of Pretoria, 2009.
Industrial and Systems Engineering
unrestricted
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Zimmermann, Maëlle. "Route choice and traffic equilibrium modeling in multi-modal and activity-based networks." Thèse, 2019. http://hdl.handle.net/1866/22664.
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Athuru, Sudhakar Reddy. "Travel demand modeling activity analysis for person allocation and internet use /." Diss., 2004. http://etd.library.vanderbilt.edu/ETD-db/available/etd-07282004-153154/.
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CHEN, CHANG YI, and 陳昌益. "An Activity-Based Study on the Elderly Travel Demand in Urban Area." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/79038393272675956624.
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碩士淡江大學
運輸管理學系
89
For the aged society it is important planning to provide user-friendly transportation environment so that aged people can live their independent life freely. According to the conclusion of observation, the key problem of recent transportation environment for the elderly is that we don’t realize the daily activity-travel pattern of aged people. This Study applies the activity-based approach to explore the unique transportation demand of the elderly, developing the discretionary sequential activity-travel demand model, including activity generation model and activity duration model. We first select samples from the elderly who live in Taipei city, above 65 years old, can make decisions by his own mind. The analysis shows that the daily activity-travel patterns of the elderly are differ from those of common people. In this study, we can realize the activity pattern of many type activities, and know the important elements of the activity generation and duration. We also can use the model to predict the activity pattern of many kinds of activities. The interactive relationship between activities that we also have mentioned and studied, and construct a simple structure.
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Pinjari, Abdul Rawoof. "Modeling residential self-selection in activity-travel behavior models : integrated models of multidimensional choice processes." 2008. http://hdl.handle.net/2152/17899.
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The focus of transportation planning, until the past three decades or so, was to provide adequate transportation infrastructure supply to meet the mobility needs of the population. Over the past three decades, however, in view of increasing suburban sprawl and auto dependence, the focus of transportation planning has expanded to include the objective of sustainable development. Contemporary efforts toward sustainability include, for example, integrated land-use and transportation planning, travel demand management, congestion pricing, and transit and non-motorized travel oriented development. Consequently, in an effort to understand individuals’ behavioral responses to (and to assess the effectiveness of) these policies, the travel demand modeling field evolved along three distinct directions: (a) Activity-based travel demand modeling, (b) Built environment and travel behavior modeling, and (c) Integrated land-use -- transportation modeling. The three fields of research, however, have progressed in a rather disjoint fashion. The overarching goal of this dissertation is to contribute toward the research needs that are at the intersection of the three fields of research identified above, and to bring the three research areas together into a unified research stream. This is achieved by the simultaneous consideration of the following three aspects, each of which is of high importance in each direction of research identified above: (1) The activity-based and tour-based approaches to travel behavior analysis, (2) Residential self-selection effects, and (3) Integrated modeling of long-term land-use related choices and medium- and short-term travel-related choices. To this end, a series of integrated models of multidimensional choice processes are formulated to jointly analyze long-term residential location decisions and medium- and short-term activity-travel decisions (such as auto ownership, bicycle ownership, commute mode choice, and daily time-use). The models are estimated and applied using data from the 2000 San Francisco Bay Area Travel Survey to understand and disentangle the multitude of relationships between long-, medium-, and short-term choices. This dissertation also formulates a multiple discrete-continuous nested extreme value model that can accommodate inter-alternative correlations and flexible substitution patterns across mutually exclusive subsets (or nests) of alternatives in multiple discrete-continuous choice models.text
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Yang, Shih-Hsien, and 楊士賢. "The Establishment of Household Activity Participation and Membership Assignment Models – A Case Study of Taipei Metropolitan Activity-Based Travel Demand Model." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/77119795114095930822.
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博士中原大學
土木工程研究所
99
Taipei Rapid Transit Systems Demand Model (TRTS model) has been established for about 30 years and developed for the 4th generation. However, TRTS model is still based on the conventional method - trip-based model. Trip is derived demand from activity participation; therefore, trip-based model is theoretically incomplete. On the contrary, the activity-based travel demand model has been developed and applied in some U.S. metropolitan areas in planning practice with great and reasonable modeling effect. The Department of Rapid Transit Systems (DORTS) of Taipei City Government upgraded the latest TRTS-IV in 2009, which collected 9,000 household travel surveys in Taipei Metropolitan area. The characteristics of local travel behavior could be characterized from the large and stable survey data, which is helpful to establish related models as well. We conceptualized the probable framework of activity-based travel demand model with firstly focusing on the household activity participation and membership assignment models. By applying hierarchical linear model for the former and logit model for the latter, the hierarchical framework of exogenous variables was specified; therefore, variables needed to be separated with different levels to reflect context effect. Furthermore, the sequence of membership assignment was identified based on local travel behavior following hierarchical model. By the context effect and the local travel behavior, the parameters of models were verified and the estimation effect was shown better than conventional models.
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"The built environment, activity space, and time allocation: An activity-based framework for modeling the land use and travel connection." THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL, 2007. http://pqdtopen.proquest.com/#viewpdf?dispub=3272808.
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Paleti, Ravi Venkata Durga Rajesh. "On integrating models of household vehicle ownership, composition, and evolution with activity based travel models." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-12-6687.
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Activity-based travel demand model systems are increasingly being deployed to microsimulate daily activity-travel patterns of individuals. However, a critical dimension that is often missed in these models is that of vehicle type choice. The current dissertation addresses this issue head-on and contributes to the field of transportation planning in three major ways. First, this research develops a comprehensive vehicle micro-simulation framework that incorporates state-of-the-art household vehicle type choice, usage, and evolution models. The novelty of the framework developed is that it accommodates all the dimensions characterizing vehicle fleet/usage decisions, as well as accommodates all dimensions of vehicle transactions (i.e., fleet evolution) over time. The models estimated are multiple discrete-continuous models (vehicle type being the discrete component and vehicle mileage being the continuous component) and spatial discrete choice models that explicitly accommodate for multiple vehicle ownership and spatial interactions among households. More importantly, the vehicle fleet simulator developed in this study can be easily integrated within an activity-based microsimulation framework.
Second, the vehicle fleet evolution and composition models developed in this dissertation are used to predict the vehicle fleet characteristics, annual mileage, and the associated fuel consumption and green-house gas (GHG) emissions for future years as a function of the built environment, demographics, fuel and related technology, and policy scenarios. This exercise contributes in substantial ways to the identification of promising strategies to increase the penetration of alternative-fuel vehicles and fuel-efficient vehicles, reduce energy consumption, and reduce greenhouse gas emissions.
Lastly, this research captures several complex interactions between vehicle ownership, location, and activity-travel decisions of individuals by estimating 1) a joint tour-based model of tour complexity, passenger accompaniment, vehicle type choice, and tour length, and 2) an integrated model of residential location, work location, vehicle ownership, and commute tour characteristics. The methodology used for estimating these models allows the specification and estimation of multi-dimensional choice model systems covering a wide spectrum of dependent variable types (including multinomial, ordinal, count, and continuous) and may be viewed as a major advance with the potential to lead to redefine the way activity-based travel model systems are structured and implemented.text
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Ferdous, Nazneen. "A new estimation approach for modeling activity-travel behavior : applications of the composite marginal likelihood approach in modeling multidimensional choices." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-08-4224.
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The research in the field of travel demand modeling is driven by the need to understand individuals’ behavior in the context of travel-related decisions as accurately as possible. In this regard, the activity-based approach to modeling travel demand has received substantial attention in the past decade, both in the research arena as well as in practice. At the same time, recent efforts have been focused on more fully realizing the potential of activity-based models by explicitly recognizing the multi-dimensional nature of activity-travel decisions. However, as more behavioral elements/dimensions are added, the dimensionality of the model systems tends to explode, making the estimation of such models all but infeasible using traditional inference methods. As a result, analysts and practitioners often trade-off between recognizing attributes that will make a model behaviorally more representative (from a theoretical viewpoint) and being able to estimate/implement a model (from a practical viewpoint).
An alternative approach to deal with the estimation complications arising from multi-dimensional choice situations is the technique of composite marginal likelihood (CML). This is an estimation technique that is gaining substantial attention in the statistics field, though there has been relatively little coverage of this method in transportation and other fields. The CML approach is a conceptually and pedagogically simpler simulation-free procedure (relative to traditional approaches that employ simulation techniques), and has the advantage of reproducibility of the results. Under the usual regularity assumptions, the CML estimator is consistent, unbiased, and asymptotically normally distributed.
The discussion above indicates that the CML approach has the potential to contribute in the area of travel demand modeling in a significant way. For example, the approach can be used to develop conceptually and behaviorally more appealing models to examine individuals’ travel decisions in a joint framework. The overarching goal of the current research work is to demonstrate the applicability of the CML approach in the area of activity-travel demand modeling and to highlight the enhanced features of the choice models estimated using the CML approach. The goal of the dissertation is achieved in three steps as follows: (1) by evaluating the performance of the CML approach in multivariate situations, (2) by developing multidimensional choice models using the CML approach, and (3) by demonstrating applications of the multidimensional choice models developed in the current dissertation.text
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"Integrated Model of the Urban Continuum with Dynamic Time-dependent Activity-Travel Microsimulation: Framework, Prototype, and Implementation." Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.14529.
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abstract: The development of microsimulation approaches to urban systems modeling has occurred largely in three parallel streams of research, namely, land use, travel demand and traffic assignment. However, there are important dependencies and inter-relationships between the model systems which need to be accounted to accurately and comprehensively model the urban system. Location choices affect household activity-travel behavior, household activity-travel behavior affects network level of service (performance), and network level of service, in turn, affects land use and activity-travel behavior. The development of conceptual designs and operational frameworks that represent such complex inter-relationships in a consistent fashion across behavioral units, geographical entities, and temporal scales has proven to be a formidable challenge. In this research, an integrated microsimulation modeling framework called SimTRAVEL (Simulator of Transport, Routes, Activities, Vehicles, Emissions, and Land) that integrates the component model systems in a behaviorally consistent fashion, is presented. The model system is designed such that the activity-travel behavior model and the dynamic traffic assignment model are able to communicate with one another along continuous time with a view to simulate emergent activity-travel patterns in response to dynamically changing network conditions. The dissertation describes the operational framework, presents the modeling methodologies, and offers an extensive discussion on the advantages that such a framework may provide for analyzing the impacts of severe network disruptions on activity-travel choices. A prototype of the model system is developed and implemented for a portion of the Greater Phoenix metropolitan area in Arizona to demonstrate the capabilities of the model system.Dissertation/Thesis
Ph.D. Civil and Environmental Engineering 2012
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Wang, Joshua. "A Prism- and Gap-based Approach to Shopping Destination Choice." Thesis, 2011. http://hdl.handle.net/1807/31625.
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This thesis presents a prism- and gap-based approach for modelling shopping destination choice in the Travel/Activity Scheduler for Household Agents (TASHA). The gap-location choice model improves upon TASHA’s existing destination choice model in 3 key ways: 1) Shifting from a zone-based to a disaggregate location choice model, 2) Categorizing shopping trips into meaningful types, and 3) Accounting for scheduling constraints in choice set generation and location choice. The model replicates gap and location choices reasonably well at an aggregate level and shows that a simple yet robust model can be developed with minimal changes to TASHA’s existing location choice model. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic activity scheduling environment.