Relevant bibliographies by topics / Mode choice modeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mode choice modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Mode choice modeling"

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Moschovou, T. P., and G. A. Giannopoulos. "Modeling Freight Mode Choice in Greece." Procedia - Social and Behavioral Sciences 48 (2012): 597–611. http://dx.doi.org/10.1016/j.sbspro.2012.06.1038.

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Abou-Zeid, Maya, and Darren M. Scott. "Current issues in mode choice modeling." Transportation 38, no. 4 (April 24, 2011): 581–85. http://dx.doi.org/10.1007/s11116-011-9340-2.

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Tsamboulas, D., A. P. Evmorfopoulos, and P. Moraiti. "Modeling airport employees commuting mode choice." Journal of Air Transport Management 18, no. 1 (January 2012): 74–77. http://dx.doi.org/10.1016/j.jairtraman.2011.10.005.

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Zhang, Jian, Chun Jing Ma, Xiang Feng Ji, and Gang Zhong. "Modeling and Sensitivity Analysis for Railway Passengers’ Public Transit Mode Choice." Applied Mechanics and Materials 641-642 (September 2014): 678–84. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.678.

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Comprehensive terminal integrates different modes of transport and sharing rates of different traffic modes influence its planning greatly. To determine the sharing rates, it’s necessary to study mode choice probability of passengers. Based on the theory of disaggregate choice, with the survey of mode choices of passengers arriving by rail in Nanjing south railway station, this paper establishes multinomial logit (MNL) model and nest logit (NL) model, considering three types of modes of transport including the subway, regular bus, and taxi. Models are calibrated with SPSS and validated through statistical indicators. Therefore, important factors influencing choice are obtained. The accuracy test results of two kinds of models show that NL model has higher prediction accuracy, and can overcome the shortcomings of traditional logit model’s independent and irrelevant alternatives (IIA). Finally, this paper develops the sensitivity analysis using the NL upper model as an example, and gives the sensitivity physical meaning, namely the development level of taxi.
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Cheng, Long, Xuewu Chen, Ming Wei, Jingxian Wu, and Xianyao Hou. "Modeling Mode Choice Behavior Incorporating Household and Individual Sociodemographics and Travel Attributes Based on Rough Sets Theory." Computational Intelligence and Neuroscience 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/560919.

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Most traditional mode choice models are based on the principle of random utility maximization derived from econometric theory. Alternatively, mode choice modeling can be regarded as a pattern recognition problem reflected from the explanatory variables of determining the choices between alternatives. The paper applies the knowledge discovery technique of rough sets theory to model travel mode choices incorporating household and individual sociodemographics and travel information, and to identify the significance of each attribute. The study uses the detailed travel diary survey data of Changxing county which contains information on both household and individual travel behaviors for model estimation and evaluation. The knowledge is presented in the form of easily understood IF-THEN statements or rules which reveal how each attribute influences mode choice behavior. These rules are then used to predict travel mode choices from information held about previously unseen individuals and the classification performance is assessed. The rough sets model shows high robustness and good predictive ability. The most significant condition attributes identified to determine travel mode choices are gender, distance, household annual income, and occupation. Comparative evaluation with the MNL model also proves that the rough sets model gives superior prediction accuracy and coverage on travel mode choice modeling.
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Wang, Yanli, Bing Wu, Zhi Dong, and Xin Ye. "A Joint Modeling Analysis of Passengers’ Intercity Travel Destination and Mode Choices in Yangtze River Delta Megaregion of China." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5293210.

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Joint destination-mode travel choice models are developed for intercity long-distance travel among sixteen cities in Yangtze River Delta Megaregion of China. The model is developed for all the trips in the sample and also by two different trip purposes, work-related business and personal business trips, to accommodate different time values and attraction factors. A nested logit modeling framework is applied to model trip destination and mode choices in two different levels, where the lower level is a mode choice model and the upper level is a destination choice model. The utility values from various travel modes in the lower level are summarized into a composite utility, which is then specified into the destination choice model as an intercity impedance factor. The model is then applied to predict the change in passenger number from Shanghai to Yangzhou between scenarios with and without high-speed rail service to demonstrate the applicability. It is helpful for understanding and modeling megaregional travel destination and mode choice behaviors in the context of developing country.
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Moeckel, Rolf, Rhett Fussell, and Rick Donnelly. "Mode choice modeling for long-distance travel." Transportation Letters 7, no. 1 (August 2014): 35–46. http://dx.doi.org/10.1179/1942787514y.0000000031.

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Aljarad, Saad N., and William R. Black. "Modeling Saudi Arabia-Bahrain corridor mode choice." Journal of Transport Geography 3, no. 4 (December 1995): 257–68. http://dx.doi.org/10.1016/0966-6923(95)00025-9.

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Outwater, Maren L., Steve Castleberry, Yoram Shiftan, Moshe Ben-Akiva, Yu Shuang Zhou, and Arun Kuppam. "Attitudinal Market Segmentation Approach to Mode Choice and Ridership Forecasting: Structural Equation Modeling." Transportation Research Record: Journal of the Transportation Research Board 1854, no. 1 (January 2003): 32–42. http://dx.doi.org/10.3141/1854-04.

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The San Francisco Bay Area Water Transit Authority is evaluating expanded ferry service, as required by the California Legislature. As part of this process, Cambridge Systematics developed forecasts using a combination of market research strategies and the addition of nontraditional variables into the mode choice modeling process. The focus of this work was on expanding the mode choice model to recognize travelers' attitudes and different market segments. Structural equation modeling was used to simultaneously identify the attitudes of travel behaviors and the causal relationships between traveler's socioeconomic profile and traveler attitudes. Six attitudinal factors were extracted, and three of these were used to partition the ferry-riding market into eight segments. These market segments were used to estimate stated preference mode choice models for 14 alternative modes, which separated the travelers' reactions to time savings by market segment and which recognized that mode choices are different for market segments that are sensitive to travel stress or the desire to help the environment. The new mode choice models were applied within the framework of the Metropolitan Transportation Commission's regional travel model and calibrated to match modal shares, modes of access to each ferry terminal, ridership by route and time period, and person trips by mode at screening line crossings. Additional validation tests of significant changes in ferry service in recent years were used to confirm the reasonableness of the stated preference model. The model has been applied for three future year alternatives and to test the sensitivities of pricing, service changes, and alternative transit modes.
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Roh, Hyuk-Jae, Satish Sharma, Prasanta K. Sahu, and Babak Mehran. "Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling." Modelling and Simulation in Engineering 2018 (2018): 1–38. http://dx.doi.org/10.1155/2018/8169036.

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Until recently, a majority of modeling tasks of transportation planning, especially in discrete choice modeling, is conducted with the help of commercial software and only concerned about the result of parameter estimates to get a policy-sensitive interpretation. This common practice prevents researchers from gaining a systematic knowledge involved in estimation mechanism. In this research, to shed a light on these limited modeling practices, a standard discrete choice model’s parameter is estimated using Quasi-Newton method, DFP, and BFGS. Two extended algorithms, called DFP-GSM and BFGS-GSM, are proposed for the first time to overcome the weakness of the Quasi-Newton method. The golden section method (GSM) incorporates a nonlinear programming technique to choose an optimal step size automatically. Partial derivatives of log-likelihood function are derived and coded using Visual Basic Application (VBA). Through extensive numerical evaluation, estimation capability of each proposed estimation algorithms is compared in terms of performance measures. The proposed algorithms show a stable estimation performance and the reasons were studied and discussed. Furthermore, useful insights educated in custom-built modeling are present.
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Dissertations / Theses on the topic "Mode choice modeling"

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Edara, Praveen Kumar. "Mode Choice Modeling Using Artificial Neural Networks." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/9845.

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Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data become available. The primary goal of this thesis is to develop mode choice models using artificial neural networks and compare the results with traditional mode choice models like the multinomial logit model and linear regression method. The data used for this modeling is extracted from the American Travel Survey data. Data mining procedures like clustering are used to process the extracted data. The results of three models are compared based on residuals and error criteria. It is found that neural network approach produces the best results for the chosen set of explanatory variables. The possible reasons for such results are identified and explained to the extent possible. The three major objectives of this thesis are to: present an approach to handle the data from a survey database, address the mode choice problem using artificial neural networks, and compare the results of this approach with the results of traditional models vis-à-vis logit model and linear regression approach. The results of this research work should encourage more transportation researchers and professionals to consider artificial intelligence tools for solving transportation planning problems.
Master of Science
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Ashiabor, Senanu Y. "Modeling Intercity Mode Choice and Airport Choice in the United States." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26221.

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The aim of this study was to develop a framework to model travel choice behavior in order to estimate intercity travel demand at nation-level in the United States. Nested and mixed logit models were developed to study national-level intercity transportation in the United States. A separate General Aviation airport choice model to estimates General Aviation person-trips and number of aircraft operations though more than 3000 airports was also developed. The combination of the General Aviation model and the logit models gives the capability to estimate a full spectrum of intercity travel demand in the United States. The logit models were calibrated using a nationwide revealed preference survey (1995 American Travel Survey). Separate models were developed for business and non-business trip purposes. An airport choice model is integrated into the mode choice model to estimate both the market share between any origin-destination pair and other modes of transportation, and the market share split between airports associated with the origin-destination pairs. The explanatory variables used in the utility functions of the models are travel time, travel cost, and travelerâ s household income. The logit models are used to estimate the market share of automobile and commercial air transportation between 3091 counties and 443 commercial service airports in the United States. The model was also used to estimate market share for on-demand air taxi services. Given an input county-to-county trip demand table, the models were used to estimate county-to-county travel demand by automobile and commercial airline between all counties and commercial service airports in the United States. The model has been integrated into a computer software framework called the Transportation Systems Analysis Model (TSAM) that estimates nationwide intercity travel demand in the United States.
Ph. D.
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Bouscasse, Hélène. "Essays on travel mode choice modeling : a discrete choice approach of the interactions between economic and behavioral theories." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2106/document.

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Cette thèse a pour objectif d’incorporer des éléments de théories de psychologie et d’économie comportementale dans des modèles de choix discret afin d’améliorer la compréhension du choix modal réalisé à l’échelle régionale. Les estimations se basent sur une enquête de type choice experiment présentée en première partie. Une deuxième partie s’intéresse à l’incorporation de variables latentes pour expliquer le choix modal. Après une revue de littérature sur les modèles de choix hybrides, c’est-à-dire des modèles combinant modèle d’équations structurelles et modèle de choix discret, un tel modèle est estimé pour montrer comment l’hétérogénéité d’outputs économiques (ici, la valeur du temps) peut être expliquée à l’aide de variables latentes (ici, le confort perçu dans les transports en commun) et de variables observables (ici, la garantie d’une place assise). La simulation de scénarios montre cependant que le gain économique (diminution de la valeur du temps) est plus élevé lorsque les politiques agissent sur des dimensions palpables que sur des dimensions latentes. S’appuyant sur un modèle de médiation, l’estimation d’un modèle d’équations structurelles montre par ailleurs que l’effet de la conscience environnementale sur les habitudes de choix modal est partiellement médié par l’utilité indirecte retirée de l’usage des transports en commun. Une troisième partie s’intéresse à deux formalisations de l’utilité issues de l’économie comportementale : 1) l’utilité dépendante au rang en situation de risque et 2) l’utilité dépendante à la référence. Dans un premier temps, un modèle d’utilité dépendante au rang est inséré dans des modèles de choix discret et, en particulier, un modèle à classes latentes, afin d’analyser l’hétérogénéité intra- et inter-individuelle lorsque le temps de déplacement n’est pas fiable. La probabilité de survenue d’un retard est sur-évaluée pour les déplacements en train et sous-évaluée pour les déplacements en voiture, en particulier pour les automobilistes, les usagers du train prenant d’avantage en compte l’espérance du temps de déplacement. Dans les modèles prenant en compte l’aversion au risque, les fonctions d’utilité sont convexes, ce qui implique une décroissance,de la valeur du temps. Dans un deuxième temps, une nouvelle famille de modèles de choix discret généralisant le modèle logit multinomial, les modèles de référence, est estimée. Sur mes données, ces modèles permettent une meilleure sélection des variables explicatives que le logit multinomial et l’estimation d’outputs économiques plus robustes, notamment en cas de forte hétérogénéité inobservée. La traduction économique des modèles de référence montre que les meilleurs modèles empiriques sont également les plus compatibles avec le modèle de dépendance à la référence de Tversky et Kahneman
The objective of this thesis is to incorporate aspects of psychology and behavioral economics theories in discrete choice models to promote a better understanding of mode choice at regional level. Part II examines the inclusion of latent variables to explain mode choice. A literature review of integrated choice and latent variable models – that is, models combining a structural equation model and a discrete choice model – is followed by the estimation of an integrated choice and latent variable model to show how the heterogeneity of economic outputs (here, value of time) can be explained with latent variables (here, perceived comfort in public transport) and observable variables (here, the guarantee of a seat). The simulation of scenarios shows, however, that the economic gain (decrease in value of time) is higher when policies address tangible factors than when they address latent factors. On the basis of a mediation model, the estimation of a structural equation model furthermore implies that the influence of environmental concern on mode choice habits is partially mediated by the indirect utility derived frompublic transport use. Part III examines two utility formulations taken from behavioral economics: 1) rankdependent utility to model risky choices, and 2) reference-dependent utility. Firstly, a rank-dependent utility model is included in discrete choice models and, in particular, a latent-class model, in order to analyze intra- and inter-individual heterogeneity when the travel time is subject to variability. The results show that the probability of a delay is over-estimated for train travel and under-estimated for car travel, especially for car users, as train users are more likely to take into account the expected travel time. In the models that account for risk aversion, the utility functions are convex, which implies a decrease in value of time. Secondly, a new family of discrete choice models generalizing the multinomial logit model, the reference models, is estimated. On my data, these models allow for a better selection of explanatory variables than the multinomial logit model and a more robust estimation of economic outputs, particularly in cases of high unobserved heterogeneity. The economic formulation of reference models shows thatthe best empirical models are also more compatible with Tversky et Kahneman’s reference-dependent model
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Chung, Soon. "Mode choice modeling with neural network : Boston area case study." FIU Digital Commons, 1997. http://digitalcommons.fiu.edu/etd/2358.

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Demand forecasting is an essential element in the analysis of transportation systems. It is concerned with the behavior of consumers of transportation services and facilities. We choose geographic, demographic, and socioeconomic characteristics of consumers that may affect the travel demand of each selected. We use an artificial neural network to predict travel demand with characteristics selected from three different database sources: Census Summary Tape files, TIGER/Line files, and Federal Transit Administration's National Transit GIS database. A neural network is an information processing system that is intensely parallel and neural networks are capable of learning how to classify and associate input/output patterns. This capability makes neural network a suitable approach for mode choice modeling for this study. A neural network has two phases: the training and the testing. In the training phase, we find weights between inputs and outputs, and in the testing phase, neural network calculates outputs representing travel demand with weights from the training phase.
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Ye, Xin. "An exploration of the relationship between mode choice and complexity of trip chaining patterns." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000417.

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Zuehlke, Kai M. "Impossibility of Transit in Atlanta: GPS-Enabled Revealed-Drive Preferences and Modeled Transit Alternatives for Commute Atlanta Participants." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19788.

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Alharbi, Abdulmajeed A. "Investigating Survey Response Rates and Analytic Choice of Survey Results fromUniversity Faculty in Saudi Arabia." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1585051418774214.

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Rice, William Ronald. "How We Got to School A Study of Travel Choices of Christchurch Primary School Pupils." Thesis, University of Canterbury. Civil Engineering, 2008. http://hdl.handle.net/10092/1242.

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There has been a noticeable swing towards school pupils being driven to and from school, and away from active modes like walking and cycling, in recent decades. This has had a number of side effects. Less reliance on active modes of transport has been a contributing factor in the reducing levels of physical activity for school children. Traffic volumes associated with school trips have also increased. This increased has tended to contribute to an increase in traffic congestion, adverse environmental effects and reductions in levels of sustainability. School trip traffic contributes specifically to congestion at school gates. Schools have been identified as having significant effects on the transportation system adjacent to them. Schools which seek Resource Consents for new or changed activities are often being required to take measures to mitigate their adverse effects The purpose of this study is to explore the factors contributing to primary school pupils' travel choices. This will help to identify travel choice patterns which may, in turn, be useful in developing policies and planning initiatives which contribute to achieving an efficient and sustainable transport system. A range of literature relevant to school and general commuting travel demand was reviewed. A case study involving the pupils of twenty two Christchurch primary schools was carried out. Pupils and their parents were surveyed to establish mode choices and the factors influencing those choices. The study found that between 55% and 60% of pupils surveyed travel to and from school by car. 30% to 35% walk or scooter, and 5% to 7% cycle. This compares with 34% travelling by car in the late 1980s. In addition, a greater proportion of those pupils who walk, scooter or cycle to school are accompanied by an adult than in the past. The results of the study also suggested that School Travel Plans, when combined with the energy and commitment to implement them can have a significant effect on school travel choices. As part of the case study, parents were asked to rank the importance of a number of factors which could influence choices regarding their children's school travel. The responses from parents identified safety concerns, regarding both road and personal safety, as the major factor behind decisions regarding their children's travel choices. Time constraints coupled with the complexity of travel requirements of many families were identified as significant factors. Multinomial Logit Models for both mode choice and pupils travel independence were then produced for both the journey to and from school. These models were based on the results of the case study. The models produced indicate that, at a school level, there is a correlation between increasing school roll and an increasing proportion of pupils travelling by car. A slight negative correlation between school decile and car usage was also indicated. This is contrary to the normally accepted understanding that in most transport situations there is a positive correlation between increasing affluence and car usage. Superior model results were obtained at a disaggregated individual level, using nine variables relating to the school, the neighbourhood, and the home, than the results obtained using the school based variables of. However, it is not considered that the effort required to obtain information on the additional variables is justified when estimating mode choices of pupils at an individual school. It is therefore recommended that a model using Decile, Average Age, and School Roll variables be used to estimate mode choices at an individual school. At a family level, there was a strong positive correlation between distance from school, age of the pupils, and the number of major roads between school and home, and car usage. It became apparent that the decisions made regarding children's school travel are very complex. Families juggle a number of factors, many of which are in conflict with one another. For example a desire to care for the environment may be in conflict with the demand to get the children to school, and get to work on time. This complex interrelationship between factors has resulted in some instances where normally accepted "Rules of Thumb", such as the understanding that increased car usage is generally associated with increasing wealth, do not appear to be applicable to school travel. The complexity of interrelationships has further meant that it has not been possible to quantify the impact of any one factor on its own.
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El, amine Samar. "Agent-based modeling of the social and economic factors affecting the choice of transportation mode : application to the Beirut city." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCA035/document.

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En milieu urbain et périurbain, les transports et la mobilité sont étroitement liés aux activités socio-économiques. Ils sont influencés par la psychologie personnelle de l'individu, les normes sociales, la résistance au changement, l'attitude personnelle, les habitudes, les peurs et les croyances. A Beyrouth, la possession de véhicules a augmenté rapidement en raison de la pénurie de transports en commun amenant l’opportunité de développer des services de mobilité durable et d’accessibilité. Cela nécessite que les modes de transport soient abordables, efficaces et respectueux de l'environnement, ainsi que des technologies qui minimisent la consommation d'énergie en mettant l'accent sur trois points principaux: réduire les besoins de déplacement, adopter des modes de transport plus respectueux de l'environnement et améliorer l'efficacité de la technologie des véhicules. Cette thèse porte sur la modélisation orientée-agent du comportement de déplacement. Nous avons analysé les données obtenues à partir d’une enquête menée à Beyrouth. Une relation entre l’emploi, le statut social et le choix du mode de transport a été mise en évidence. Un modèle de simulation orientée agent, incluant les architectures et les algorithmes des agents, du trafic routier utilisant une approche économique institutionnelle a été proposé. Les questions scientifiques concernent les effets de facteurs économiques, sociaux, professionnels et liés aux ménages sur le choix d'un mode de transport. Ce travail fait partie d'un projet de la société S&A (Belgique et Liban) visant à fournir des outils permettant la transition des moyens de transport vers une pratique plus respectueuse
In urban and peri-urban environments, transport and mobility are strongly linked to socio-economic activities. They are influenced by individual's personal psychology, social norms, resistance to change, personal attitude, habits, fears and beliefs. Beirut is a mono-centric city with transport supply poverty. Vehicle ownership has increased rapidly in response to the shortage of public transportation. Currently, there is an opportunity to develop mobility management services and establish sustainable mobility and accessibility in Beirut. This requires that transport modes be affordable, efficient and environmentally friendly, as well as technologies that minimize the energy consumption by focusing on three main points: reducing travel needs, adopting more environmentally friendly modes of transportation, and improving the efficiency of vehicle technology. This thesis focuses on the modeling of displacement behavior, based on agent-oriented modeling. We analyzed the data obtained from a survey we conducted for Beirut. We established a relation between employment, social status and mode of transportation selection. The subject of the thesis is an agent-oriented simulation model of road traffic using an institutional economic approach. We propose an architecture, algorithms and methods to implement the agents model in order to model and reproduce their modes of transport behavior. Associated scientific questions are the effects of economic, social, career and household factors on choosing a mode of transport. This work is part of a project of the company S&A (Belgium and Lebanon) to provide tools to enable the transition of means of transport to a more respectful practice
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Hole, Arne Risa. "Modelling commuters' mode choice in Scotland." Thesis, University of St Andrews, 2005. http://hdl.handle.net/10023/14115.

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This thesis contributes to the literature on the choice of transport mode for commuting trips, with special focus on the difference between urban and rural commuting in Scotland. The thesis begins by giving an overview of discrete choice theory and some empirical models consistent with this theory, before reviewing the literature on empirical applications of mode choice models for commuting trips. In the following, multinomial, nested and mixed logit models using data from a survey of commuters in the University of St Andrews are developed. The models are used to estimate aggregate mode-choice elasticities that can assist the development of efficient car reduction policies in St Andrews and other small towns in rural areas. The direct elasticities of the car mode are found to be comparable to estimates reported in studies of urban commuting, while the demand for public transport is found to be considerably more elastic. The value of in-vehicle travel time is found to be lower than in most studies of urban commuting, reflecting that the roads in the St Andrews area are relatively uncongested. Subsequently, current car drivers' willingness to use a Park and Ride service prior to the implementation of such a service are examined. The results show that the modal shift away from parking on-site will be small unless the new service is accompanied by measures aimed at making parking on-site less attractive such as introducing parking charges. Finally, the effect of the 'compact city' on modal split and congestion are examined. As well as making urban transport more sustainable as a result of an increase in the use of public transport, making cities more compact is found to contribute to lower levels of congestion in urban areas through a reduction in complex trip chains.
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Books on the topic "Mode choice modeling"

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Barmby, Tim. Modelling the effects of absenteeism control: An application of a hybrid binary choice model. Hull: University of Hull, Department of Economics and Commerce, Labour Economics Unit, 1988.

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Zuev, Sergey, Ruslan Maleev, and Aleksandr Chernov. Energy efficiency of electrical equipment systems of autonomous objects. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1740252.

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When considering the main trends in the development of modern autonomous objects (aircraft, combat vehicles, motor vehicles, floating vehicles, agricultural machines, etc.) in recent decades, two key areas can be identified. The first direction is associated with the improvement of traditional designs of autonomous objects (AO) with an internal combustion engine (ICE) or a gas turbine engine (GTD). The second direction is connected with the creation of new types of joint-stock companies, namely electric joint-stock companies( EAO), joint-stock companies with combined power plants (AOKEU). The energy efficiency is largely determined by the power of the generator set and the battery, which is given to the electrical network in various driving modes. Most of the existing methods for calculating power supply systems use the average values of disturbing factors (generator speed, current of electric energy consumers, voltage in the on-board network) when choosing the characteristics of the generator set and the battery. At the same time, it is obvious that when operating a motor vehicle, these parameters change depending on the driving mode. Modern methods of selecting the main parameters and characteristics of the power supply system do not provide for modeling its interaction with the power unit start-up system of a motor vehicle in operation due to the lack of a systematic approach. The choice of a generator set and a battery, as well as the concept of the synthesis of the power supply system is a problem studied in the monograph. For all those interested in electrical engineering and electronics.
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Bekbasarov, Isabay. Study of the process of driving piles and dies on models. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1074097.

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The monograph presents the results of experimental and theoretical studies conducted using models of driven piles and tape dies. The influence of the cross-section size, length, shape of the trunk and the lower end of the piles on their submergability, energy intensity of driving and load-bearing capacity was evaluated. The design and technological features of new types of piles are considered. A method for determining the load-bearing capacity of a pile model based on the results of dynamic tests has been developed. Similarity conditions and formulas are presented that provide modeling of the pile driving process in the laboratory. The influence of the shape of the tape dies on their submersibility, energy consumption of the driving and the bearing capacity of the foundations arranged in the vyshtampovannyh pits was evaluated. The method of determining the load-bearing capacity of a belt Foundation model based on the results of pit vyshtampovyvaniya is described. Recommendations on the choice of optimal parameters of piles and foundations, arranged in vystupovani pits. Recommended for researchers, specialists of design and construction organizations, doctoral students, postgraduates, undergraduates and students of construction and water management specialties.
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Metropolitan Planning Organization (Mass.). Central Transportation Planning Staff. and Travel Model Improvement Program (U.S.), eds. Transfer penalties in urban mode choice modeling. Washington, DC: Travel Model Improvement Program, U.S. Dept. of Transportation, U.S. Environmental Protection Agency ; Distributed in cooperation with Technology Sharing Program, Research and Special Programs Administration, US Dept. of Transportation, 1997.

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Ratcliff, Roger, and Philip Smith. Modeling Simple Decisions and Applications Using a Diffusion Model. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.3.

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The diffusion model is one of the major sequential-sampling models for two-choice decision-making and choice response time in psychology. The model conceives of decision-making as a process in which noisy evidence is accumulated until one of two response criteria is reached and the associated response is made. The criteria represent the amount of evidence needed to make each decision and reflect the decision maker’s response biases and speed-accuracy trade-off settings. In this chapter we examine the application of the diffusion model in a variety of different settings. We discuss the optimality of the model and review its applications to a number of cognitive tasks, including perception, memory, and language tasks. We also consider its applications to normal and special populations, to the cognitive foundations of individual differences, to value-based decisions, and its role in understanding the neural basis of decision-making.
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Zeitlin, Vladimir. Primitive Equations Model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198804338.003.0002.

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The chapter gives the foundations of modelling of large-scale atmospheric and oceanic motions and presents the ‘primitive equations’ (PE) model. After a concise reminder on general fluid mechanics, the main hypotheses leading to the PE model are explained, together with the tangent-plane (so-called f and beta plane) approximations, and ‘traditional’ approximation to the hydrodynamical equations on the rotating sphere. PE are derived in parallel for the ocean and for the atmosphere. It is then shown that, with a judicious choice of the vertical coordinate, the ‘pseudo-height’, in the atmosphere, these two sets of equations are practically equivalent. The main properties of PE are derived and the key concepts of wave–vortex dichotomy, and of slow and fast motions, are explained. The essential notion of potential vorticity is introduced and its conservation by fluid masses is demonstrated. Inertia–gravity waves are explained and their properties presented. Limitations of the hydrostatic hypothesis are demonstrated.
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Modeling application for disability insurance as a retirement decision: A hazard model approach using choice-based sampling. Syracuse, N.Y: Metropolitan Studies Program, The Maxwell School of Citizenship and Public Affairs, Syracuse University, 1992.

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Brazier, John, Julie Ratcliffe, Joshua A. Salomon, and Aki Tsuchiya. Modelling health state valuation data. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198725923.003.0005.

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This chapter examines the technical issues in modelling health state valuation data. Most measures of health define too many states to directly value all of them (e.g. SF-6D defines 18,000 health states). The solution has been to value a subset and by using modelling to predict the values of all states. This chapter reviews two approaches to modelling: one using multiattribute utility theory to determine health values given an assumed functional form; and the other is using statistical modelling of SF-6D preference data that are skewed, bimodal, and clustered by respondents. This chapter examines the selection of health states for valuation, data preparation, model specification, and techniques for modelling the data starting with ordinary least squares (OLS) and moving on to more complex techniques including Bayesian non-parametric and semi-parametric approaches, and a hybrid approach that combines cardinal preference data with the results of paired data from a discrete choice experiment.
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Sanderson, Benjamin Mark. Uncertainty Quantification in Multi-Model Ensembles. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.707.

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Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.
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O'Donoghue, Cathal. Practical Microsimulation Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198852872.001.0001.

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The purpose of this book is to bring together for the first time a description, with examples, of the main methods used in microsimulation modelling, used in the field of income-distribution analysis. The book provides a practical complement to the Handbook of Microsimulation Modelling, published in 2014. It is structured to develop and use the different types of models used in the field, with a focus on household-targeted policy. The book aims to fill a gap in the literature in providing a greater degree of codified knowledge through a practical guide to developing and using microsimulation models. At present, the training of researchers and analysts that use and develop microsimulation modelling is done on a relatively ad-hoc basis through occasional training programmes and lecture series, built around lecture notes. This book would enable a more formalized and organized approach. Each chapter addresses a separate modelling approach in a similar, consistent way, describing in practical terms the key methodological skills for each approach: · It provides some policy context to each modelling approach so as to understand the modelling choices made and structures developed. · As a very data-intensive modelling approach, each chapter describes key data analysis and data-preparation methods. · As a modelling approach that is used extensively for deciding policy, often involving huge budgets, validation is key. Each chapter describes an approach to validating the model. · Depending upon the policy context, the analysis is assessed in different ways. Each chapter contains a section devoted to measurement issues and tabulating output from the models. · Last, each chapter contains an example simulation of a policy analysis using the chapter’s methodological approach.
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Book chapters on the topic "Mode choice modeling"

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Ran, Bin, and David Boyce. "Combined Mode/Departure Time/Route Choice Models." In Modeling Dynamic Transportation Networks, 275–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80230-0_12.

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Cascetta, E., and A. Papola. "A Dynamic Mode Transit Service Choice Model to Design Ex-Urban Transport Service Timetables." In Schedule-Based Dynamic Transit Modeling: theory and applications, 25–41. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-6467-3_2.

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Leurent, Fabien M. "Multicriteria Assignment Modeling: Making Explicit the Determinants of Mode or Path Choice." In Equilibrium and Advanced Transportation Modelling, 153–74. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5757-9_8.

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Langer, Dorothea, and Angelika C. Bullinger. "User Requirement? Travel Mode Choice Routines Across Different Trip Types." In HCI International 2020 – Late Breaking Papers: Digital Human Modeling and Ergonomics, Mobility and Intelligent Environments, 111–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59987-4_9.

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Mariel, Petr, David Hoyos, Jürgen Meyerhoff, Mikolaj Czajkowski, Thijs Dekker, Klaus Glenk, Jette Bredahl Jacobsen, et al. "Econometric Modelling: Extensions." In Environmental Valuation with Discrete Choice Experiments, 83–101. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62669-3_6.

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AbstractThis chapter is devoted to advanced issues of econometric modelling. The topics covered are, among others, models in willingness to pay space, the meaning of scale heterogeneity in discrete choice models and the application of various information processing rules such as random regret minimisation or attribute non-attendance. Other topics are anchoring and learning effects when respondents move through a sequence of choice tasks as well as different information processing strategies such as lexicographic preferences or choices based on elimination-by-aspects.
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Chorus, Caspar G. "A Random Regret Minimization-based Discrete Choice Model." In Random Regret-based Discrete Choice Modeling, 5–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29151-7_2.

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Ran, Bin, and David Boyce. "A Solution Algorithm for an Ideal Route Choice Model." In Modeling Dynamic Transportation Networks, 117–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80230-0_6.

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Mariel, Petr, David Hoyos, Jürgen Meyerhoff, Mikolaj Czajkowski, Thijs Dekker, Klaus Glenk, Jette Bredahl Jacobsen, et al. "Econometric Modelling: Basics." In Environmental Valuation with Discrete Choice Experiments, 61–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62669-3_5.

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AbstractThis chapter addresses basic topics related to choice data analysis. It starts by describing the coding of attribute levels and choosing the functional form of the attributes in the utility function. Next, it focuses on econometric models with special attention devoted to the random parameter mixed logit model. In this context, the chapter compares different coefficient distributions to be used, addresses specifics of the cost attribute coefficient and it pays attention to potential correlations between random coefficients. Finally, topics related to the estimation procedure such as assuring its convergence or random draws are discussed.
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Balzarini, A., L. Honzak, G. Pirovano, G. M. Riva, and R. Zabkar. "WRF-Chem Model Sensitivity Analysis to Chemical Mechanism Choice." In Air Pollution Modeling and its Application XXIII, 557–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04379-1_92.

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Shukla, Nagesh, Jun Ma, Rohan Wickramasuriya, Nam Huynh, and Pascal Perez. "Modelling Mode Choice of Individual in Linked Trips with Artificial Neural Networks and Fuzzy Representation." In Artificial Neural Network Modelling, 405–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_19.

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Conference papers on the topic "Mode choice modeling"

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Qian, Bingyi, Bin Shuai, and Lixia Huang. "Modeling Traffic Mode Choice Behavior Based on WC-OWA Operator." In Third International Conference on Transportation Engineering (ICTE). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41184(419)52.

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Zhao, Dan, Chunfu Shao, Juan Li, Chunjiao Dong, and Yinhong Liu. "Travel Mode Choice Modeling Based on Improved Probabilistic Neural Network." In Seventh International Conference on Traffic and Transportation Studies (ICTTS) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41123(383)65.

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Luo, Jian, Shu-sheng Wang, and Xu-hong Li. "Modeling Travel Mode Choice Behavior and Individual Time Preference Heterogeneity." In First International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/40932(246)226.

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Azari, Kian Ahmadi, Sulistyo Arintono, and Hussain Hamid. "Modeling tourist's mode choice behavior under parking and cordon charge policies." In 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization (ICMSAO). IEEE, 2011. http://dx.doi.org/10.1109/icmsao.2011.5775579.

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Yagi, S., and A. Mohammadian. "Modeling Tour Mode and Destination Choice in the Jakarta Metropolitan Area." In Ninth International Conference on Applications of Advanced Technology in Transportation (AATT). Reston, VA: American Society of Civil Engineers, 2006. http://dx.doi.org/10.1061/40799(213)105.

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Sayed, Tarek, Arash Tavakolie, and Abdolmehdi Razavi. "Comparison of Two Neuro-Fuzzy Approaches Applied to Mode Choice Modeling." In Seventh International Conference on Applications of Advanced Technologies in Transportation (AATT). Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40632(245)109.

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Li, Yigang, Enjian Yao, Yang Yang, and Hekai Zhuang. "Modeling the Tourism Travel Mode and Route Choice Behaviour based on Nested Logit Model." In 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE). IEEE, 2020. http://dx.doi.org/10.1109/icite50838.2020.9231425.

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Wang, Shengyou, Chunjiao Dong, Chunfu Shao, Chaoying Yin, Yan Zheng, and Yujie Yuan. "Analyzing and Modeling for Mode Choice Behavior of Commuters in Metropolitan Areas." In 20th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482933.373.

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Liu, Xiaobo, Jianmei Zhu, Minghua Huang, and Guo Lu. "Current Practice of Modeling the Impact of Roadway Tolls on Mode Choice." In 11th International Conference of Chinese Transportation Professionals (ICCTP). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41186(421)39.

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Shin, Jaekwan, and Scott Ferguson. "Modeling Noncompensatory Choices With a Compensatory Model for a Product Design Search." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47632.

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Market-based product design has typically used compensatory models that assume a simple additive part-worth rule. However, marketing literature has demonstrated that consumers use various heuristics called noncompensatory choices to simplify their choice decisions. This study aims to explore the suitability of compensatory modeling of these noncompensatory choices for the product design search. This is motivated by the limitations of the existing Bayesian-based noncompensatory mode, such as the screening rule assumptions, probabilistic representation of noncompensatory choices, and discontinuous choice probability functions in the Bayesian-based noncompensatory model. Results from using compensatory models show that noncompensatory choices can lead to distinct segments with extreme part-worths. In addition, the product design search problem suggests that the compensatory model would be preferred due to small design errors and inexpensive computational burden.
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Reports on the topic "Mode choice modeling"

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Zhao, Fang. FSUTMS Mode Choice Modeling: Factors Affecting Transit Use and Access. Tampa, FL: University of South Florida, July 2002. http://dx.doi.org/10.5038/cutr-nctr-rr-2000-19.

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Javanmardi, Mahmoud. Mode Choice Modeling Using Personalized Travel Time and Cost Data. Tampa, FL: University of South Florida, May 2015. http://dx.doi.org/10.5038/cutr-nctr-rr-2015-07.

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Wang, Chih-Hao, and Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling? Mineta Transportation Institute, June 2021. http://dx.doi.org/10.31979/mti.2021.2011.

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The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above.
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Hamill, Daniel D., Jeremy J. Giovando, Chandler S. Engel, Travis A. Dahl, and Michael D. Bartles. Application of a Radiation-Derived Temperature Index Model to the Willow Creek Watershed in Idaho, USA. U.S. Army Engineer Research and Development Center, August 2021. http://dx.doi.org/10.21079/11681/41360.

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The ability to simulate snow accumulation and melting processes is fundamental to developing real-time hydrological models in watersheds with a snowmelt-dominated flow regime. A primary source of uncertainty with this model development approach is the subjectivity related to which historical periods to use and how to combine parameters from multiple calibration events. The Hydrologic Engineering Center, Hydrological Modeling System, has recently implemented a hybrid temperature index (TI) snow module that has not been extensively tested. This study evaluates a radiatative temperature index (RTI) model’s performance relative to the traditional air TI model. The TI model for Willow Creek performed reasonably well in both the calibration and validation years. The results of the RTI calibration and validation simulations resulted in additional questions related to how best to parameterize this snow model. An RTI parameter sensitivity analysis indicates that the choice of calibration years will have a substantial impact on the parameters and thus the streamflow results. Based on the analysis completed in this study, further refinement and verification of the RTI model calculations are required before an objective comparison with the TI model can be completed.
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