Relevant bibliographies by topics / Transportation demand modeling

Contents

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

Academic literature on the topic 'Transportation demand modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Transportation demand modeling"

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Zhuk, Mykola, and Halyna Pivtorak. "About passenger travels demand modeling in urban transportation systems." Transport technologies 2020, no. 1 (2020): 45–53. http://dx.doi.org/10.23939/tt2020.01.045.

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Santos, Tiago A., and C. Guedes Soares. "Modeling transportation demand in short sea shipping." Maritime Economics & Logistics 19, no. 4 (2017): 695–722. http://dx.doi.org/10.1057/mel.2016.9.

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van Vuren, Tom. "Modeling of transport demand – analyzing, calculating, and forecasting transport demand." Transport Reviews 40, no. 1 (2019): 115–17. http://dx.doi.org/10.1080/01441647.2019.1635226.

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Birolini, Sebastian, Mattia Cattaneo, Paolo Malighetti, and Chiara Morlotti. "Integrated origin-based demand modeling for air transportation." Transportation Research Part E: Logistics and Transportation Review 142 (October 2020): 102050. http://dx.doi.org/10.1016/j.tre.2020.102050.

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Ghaemi, Seyed Ali, and Mansour Hadji Hosseinlou. "Modeling the Urban Freight-Transportation System Using the System Dynamics Approach." Systems 11, no. 8 (2023): 409. http://dx.doi.org/10.3390/systems11080409.

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The dynamic and complex interactions between the urban freight-transportation system and population, economy, traffic flow, fuel consumption, and environmental pollution, make policymaking in this system one of the fundamental challenges of urban management. In this regard, a systemic approach in urban freight-transportation system modelling should be considered to solve the problems of the system. One of the main problems of this system is the mismatch between the freight-transportation capacity and the total freight-transportation demand. Considering the lack of sufficient studies in the fie
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Zhang, Dapeng, Sara Luchian, Joshua Raycroft, and Darryle Ulama. "Induced Travel Demand Modeling for High-Speed Intercity Transportation." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 3 (2019): 189–98. http://dx.doi.org/10.1177/0361198119837189.

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Research shows that induced demand—ridership generated by improved level-of-service—can vary broadly, from near zero to 80% of the total ridership across high-speed rail projects. Such a wide range can yield very different economic figures which consequently have impacts on investment decisions. Because of data collection challenges and limited modeling techniques, however, this important aspect of intercity passenger transportation is not sufficiently understood. For example, many feasibility studies estimate induced demand by simply applying a fixed multiplier on top of demand diverted from
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Sun, Yan, Xia Liang, Xinya Li, and Chen Zhang. "A Fuzzy Programming Method for Modeling Demand Uncertainty in the Capacitated Road–Rail Multimodal Routing Problem with Time Windows." Symmetry 11, no. 1 (2019): 91. http://dx.doi.org/10.3390/sym11010091.

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Demand uncertainty is an important issue that influences the strategic, tactical, and operational-level decision making in the transportation/logistics/supply chain planning. In this study, we explore the effect of demand uncertainty on the operational-level freight routing problem in the capacitated multimodal transportation network that consists of schedule-based rail transportation and time-flexible road transportation. Considering the imprecise characteristic of the demand, we adopt fuzzy set theory to model its uncertainty and use trapezoidal fuzzy numbers to represent the fuzzy demands.
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Miller, Eric J. "Modeling the Demand for New Transportation Services and Technologies." Transportation Research Record: Journal of the Transportation Research Board 2658, no. 1 (2017): 1–7. http://dx.doi.org/10.3141/2658-01.

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This paper briefly discusses the current state of the art of urban travel demand modeling and research needs in the field. Special emphasis is given to both the challenges and the opportunities posed by modern information technology and the data, transportation services, and travel behaviors that this technology is generating. Travel demand modeling has made very significant strides over the past 20 years, especially in the development of operational activity- and tour-based regional travel demand forecasting systems. These model systems represent first-generation agent-based microsimulation m
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Zhuravleva, Natalya, Ilya Guliy, and Vyacheslav Shavshukov. "Simulation modeling of changes in demand for rail transportation." IOP Conference Series: Earth and Environmental Science 403 (December 19, 2019): 012230. http://dx.doi.org/10.1088/1755-1315/403/1/012230.

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Rengaraju, V. R., and V. Thamizh Arasan. "Modeling for Air Travel Demand." Journal of Transportation Engineering 118, no. 3 (1992): 371–80. http://dx.doi.org/10.1061/(asce)0733-947x(1992)118:3(371).

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Dissertations / Theses on the topic "Transportation demand modeling"

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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.<br>"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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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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Singleton, Patrick Allen. "A Theory of Travel Decision-Making with Applications for Modeling Active Travel Demand." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/1494.

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The continuing evolution of urban travel patterns and changing policy goals and priorities requires that transportation researchers and practitioners improve their abilities to plan and forecast the demand for travel. Walking and bicycling - the primary forms of active travel - are generating increasing interest for their potential to reduce automobile use, save governmental and consumer costs, and improve personal and social health outcomes. Yet, current transportation planning tools, namely regional travel demand forecasting models, poorly represent these active travel modes, if at all. More
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Unal, Leyla. "Modeling Of Freight Transportation On Turkish Highways." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610783/index.pdf.

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Transportation planners are often faced with the problem of estimating passenger and freight flows between regions. In the literature there are many models for passenger flows. However, models about freight flows are more limited. Modeling freight flow is also more complex than modeling passenger flow and there are many agents related with freight flows. In addition, data availability is a critical factor. In this research, freight flows between provinces in T&uuml<br>rkiye are forecasted by demand analysis. Transportation is one of the important activities of human beings and plays an import
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Yankov, Y. Daniel. "Discrete event system modeling of demand responsive transportation systems operating in real time." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002466.

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Yankov, Daniel Y. "Discrete Event System Modeling Of Demand Responsive Transportation Systems Operating In Real Time." Scholar Commons, 2008. https://scholarcommons.usf.edu/etd/575.

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Demand responsive transportation is a variable route service of passengers or freight from specific origin(s) to destination(s) in response to the request of users. Operational planning of DRT system encompasses the methods to provide efficient service to the passengers and to the system operators. These methods cover the assignments of vehicles to transportation requests and vehicle routings under various constraints such as environmental conditions, traffic and service limitations. Advances in the information and communication technologies, such as the Internet, mobile communication devices,
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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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Farzana, Fatema Hoque. "Estimation and Prediction of Mobility and Reliability Measures Using Different Modeling Techniques." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3880.

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The goal of this study is to investigate the predictive ability of less data intensive but widely accepted methods to estimate mobility and reliability measures. Mobility is a relatively mature concept in the traffic engineering field. Therefore, many mobility measure estimation methods are already available and widely accepted among practitioners and researchers. However, each method has their inherent weakness, particularly when they are applied and compared with real-world data. For instances, Bureau of Public Roads (BPR) Curves are very popular in static route choice assignment, as part of
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Kressner, Josephine D. "Leveraging targeted marketing data in travel demand modeling: validation and applications." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51870.

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To date, the collection of comprehensive household travel data has been a challenge for most metropolitan planning organizations (MPOs) and state departments of transportation (DOTs) due mainly to high costs. Urban population growth, the expansion of metropolitan regions, and the general unwillingness of the public to complete surveys conflict with limited public funds. The purpose of this research is to leverage targeted marketing data, sometimes referred to as consumer data or just simply marketing data, for travel demand modeling applications. This research reveals a first step in exploring
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Lu, Chenxi. "Improving Analytical Travel Time Estimation for Transportation Planning Models." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/237.

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This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The deve
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Books on the topic "Transportation demand modeling"

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Mahmassani, Hani S. Synthesis of traveler choice research: Improving modeling accuracy for better transportation decisionmaking. U.S. Department of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center, 2013.

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Tex.) Innovations in Travel Demand Modeling Conference (2006 Austin. Innovations in travel demand modeling: Summary of a conference. Transportation Research Board, 2008.

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Tex.) Innovations in Travel Demand Modeling Conference (2006 Austin. Innovations in travel demand modeling: Papers : summary of a conference [held] May 21-23, 2006, Austin Texas. Transportation Research Board, 2008.

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Wallace, Brett Patrick. Evaluation of travel demand management strategies in the trip generation phase of a network-based modeling approach. Washington State Dept. of Transportation, 1998.

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Lundgren, Jan T. Optimization approaches to travel demand modelling. Dept. of Mathematics, Linköping University, 1989.

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Johnstone, Nick. Modelling passenger demand, energy consumption and pollution emissions in thetransport sector. Department of Applied Economics, University of Cambridge, 1995.

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Modeling of Transport Demand: Analyzing, Calculating, and Forecasting Transport Demand. Elsevier, 2018.

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Profillidis, V. A., and G. N. Botzoris. Modeling of Transport Demand: Analyzing, Calculating, and Forecasting Transport Demand. Elsevier, 2018.

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Antoniou, Constantinos, Dimitrios Efthymiou, and Emmanouil Chaniotakis. Demand for Emerging Transportation Systems: Modeling Adoption, Satisfaction, and Mobility Patterns. Elsevier, 2019.

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Antoniou, Constantinos, Dimitrios Efthymiou, and Emmanouil (Manos) Chaniotakis. Demand for Emerging Transportation Systems: Modeling Adoption, Satisfaction, and Mobility Patterns. Elsevier, 2019.

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Book chapters on the topic "Transportation demand modeling"

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Dash, Subhakanta, and S. P. Mohanty. "A transportation model with rough cost, demand, and supply." In Modeling and Applications in Operations Research. CRC Press, 2023. http://dx.doi.org/10.1201/9781003462422-11.

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Chebbi, Olfa, and Jouhaina Chaouachi. "Modeling On-demand Transit Transportation System Using an Agent-Based Approach." In Computer Information Systems and Industrial Management. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24369-6_26.

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Yadav, Susheel, Anil K. Agrawal, and Manu K. Vora. "Modeling a Supply Chain with Price-Dependent Stochastic Demand and Discrete Transportation Lead Time." In Asset Analytics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2774-3_4.

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Ben-Akiva, Moshe E., and John L. Bowman. "Activity Based Travel Demand Model Systems." In Equilibrium and Advanced Transportation Modelling. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5757-9_2.

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Karoń, Grzegorz. "Travel Demand and Transportation Supply Modelling for Agglomeration without Transportation Model." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41647-7_35.

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"Transit Demand Modeling." In Transportation Engineering Basics. American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/9780784404645.ch14.

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"Transit Demand Modeling." In Transportation Engineering Basics. American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/9780784404645.ch16.

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"Travel Demand and Network Modeling." In Transportation Planning Handbook. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119174660.ch6.

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Zhao, Zhan, Haris N. Koutsopoulos, and Jinhua Zhao. "Uncovering spatiotemporal structures from transit smart card data for individual mobility modeling." In Demand for Emerging Transportation Systems. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-815018-4.00007-3.

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Waraich, Rashid A., Gil Georges, Matthias D. Galus, and Kay W. Axhausen. "Adding Electric Vehicle Modeling Capability to an Agent-Based Transport Simulation." In Transportation Systems and Engineering. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8473-7.ch075.

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Battery-electric and plug-in hybrid-electric vehicles are envisioned by many as a way to reduce CO2 traffic emissions, support the integration of renewable electricity generation, and increase energy security. Electric vehicle modeling is an active field of research, especially with regards to assessing the impact of electric vehicles on the electricity network. However, as highlighted in this chapter, there is a lack of capability for detailed electricity demand and supply modeling. One reason for this, as pointed out in this chapter, is that such modeling requires an interdisciplinary approa
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Conference papers on the topic "Transportation demand modeling"

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Anton, Laurenţiu Lucian, Mladen Čičić, and Ragnar Kristjánsson. "Traffic-Based Demand Modeling for EV Fast-Charging Stations: A Case Study in Iceland." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920108.

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Sridhar, Narasi, Francois Ayello, Theresa Stewart, and Ali Mosleh. "Probabilistic Assessment of Hydrogen Transportation System Components." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00121.

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Abstract Many countries are planning to transport hydrogen through existing natural gas pipeline infrastructure. The integrity of pipelines for hydrogen service has been examined extensively, but other components in the system, such as valves, compressors, pressure regulators, and many other discrete devices, have not been evaluated to any great extent. This paper presents a uniform approach to modeling the performance of these components under hydrogen blend service using a Bayesian network method. The baseline failure rate of system components under natural gas transport can be derived from
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Kazi, Monzure-Khoda, Akhilesh Gandhi, and M. M. Faruque Hasan. "Process and Network Design for Sustainable Hydrogen Economy." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.125411.

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This study presents a comprehensive approach to optimizing hydrogen supply chain network (HSCN), focusing initially on Texas, with potential scalability to national and global regions. Utilizing mixed-integer nonlinear programming (MINLP), the research decomposes into two distinct modeling stages: broad supply chain modeling and detailed hub-specific analysis. The first stage identifies optimal hydrogen hub locations, considering county-level hydrogen demand, renewable energy availability, and grid capacity. It determines the number and placement of hubs, county participation within these hubs
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Pollock, Sam, Merkebe Getachew Demissie, and Lina Kattan. "Modelling Link-Level Shared Micromobility Demand: Regression and Neural Network Approaches." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919661.

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Jones, Nathaniel, Jacob Putnam, and Costin Untaroiu. "Study of Advanced Occupant Models to Quantify Injury Risk for eVTOL Vehicles." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1201.

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Urban transportation is currently evolving from traditional ground-based vehicles (cars, taxis, and buses) to include air-based electric vertical take-off and landing (eVTOL) vehicles which can be utilized for on-demand transportation, cargo transport, and emergency services. These new eVTOL vehicles are designed to be small, lightweight, and able to operate autonomously without user intervention. Safety is a big part of eventual eVTOL adoption, however gaps in the consideration of safety features exist. Anthropomorphic test devices (ATDs) are used in aerospace crashworthiness standards to qua
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Wang, Shengyou, Zhihua Xiong, Shuling Wang, Ying Hu, Xiong Yang, and Peiwen Chen. "Analyzing and Modeling Post-Earthquake Emergency Traffic Demand." In 13th Asia Pacific Transportation Development Conference. American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482902.020.

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Maiorino, Mario, Stephanie Stockar, Marco Sorrentino, and Giorgio Rizzoni. "Modeling and Simulation of Residential Power Demand Including Transportation." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9852.

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Personal transportation has a significant impact on the residential electric energy usage due to the interaction of alternative fueled vehicles with the electric grid. This phenomenon is projected to grow significantly, as several studies confirm that the market penetration of alternative fueled vehicles will steadily increase in the future. This paper presents a control-oriented model that predicts the daily residential power demand considering multiple energy carriers and different types of alternative fueled vehicles for personal transportation. The model has been used to perform an energy
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Hafezi, Mohammad Hesam, Hugh Millward, and Lei Liu. "Activity-Based Travel Demand Modeling: Progress and Possibilities." In International Conference on Transportation and Development 2018. American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784481561.014.

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Chu, Zhaoming, Lin Cheng, and Hui Chen. "A Review of Activity-Based Travel Demand Modeling." In The Twelfth COTA International Conference of Transportation Professionals. American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412442.006.

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Li, Dehui, Jiangtao Shan, Jiangyan Wang, and Peng Yue. "Transportation Demand Modeling Method Research—A Case Study of Chengdu." In International Conference On Civil Engineering And Urban Planning 2012. American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412435.144.

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Reports on the topic "Transportation demand modeling"

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Xu, Xun, Puneet Kamboj, Saif Almutairi, Thomas Gertin, Anvita Arora, and Mohamed Hejazi. Decarbonizing Road Freight Transportation in Saudi Arabia. King Abdullah Petroleum Studies and Research Center, 2025. https://doi.org/10.30573/ks--2025-dp10.

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Saudi Arabia needs to decarbonize road freight transportation to fulfill its emission reduction commitments. In this study, we utilize the GCAM-KSA modeling framework to explore potential mitigation pathways for the Kingdom’s road freight sector. We evaluate the emission implications of several announced and potential decarbonization strategies, including the modal shift to electric freight rail, fuel efficiency improvements for heavy-duty vehicles (HDV), and the adoption of zero-emission vehicles. The results suggest that achieving an economy-wide net-zero target will require policy initiativ
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Kodupuganti, Swapneel R., Sonu Mathew, and Srinivas S. Pulugurtha. Modeling Operational Performance of Urban Roads with Heterogeneous Traffic Conditions. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1802.

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The rapid growth in population and related demand for travel during the past few decades has had a catalytic effect on traffic congestion, air quality, and safety in many urban areas. Transportation managers and planners have planned for new facilities to cater to the needs of users of alternative modes of transportation (e.g., public transportation, walking, and bicycling) over the next decade. However, there are no widely accepted methods, nor there is enough evidence to justify whether such plans are instrumental in improving mobility of the transportation system. Therefore, this project re
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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, 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
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Pulugurtha, Srinivas S., Sarvani Duvvuri, and Sonu Mathew. Risk Factors Associated with Crash Injury Severity Involving Trucks. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2117.

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Nearly 499,000 motor vehicle crashes involving trucks were reported across the United States in 2018, out of which 22% resulted in fatalities and injuries. Given the growing economy and demand for trucking in the future, it is crucial to identify the risk factors to understand where, when, and why the likelihood of getting involved in a severe or moderate injury crash with a truck is higher. This research, therefore, focuses on capturing and exploring risk factors associated with surrounding land use and demographic characteristics in addition to crash, driver, and on-network characteristics b
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Hertel, Thomas, David Hummels, Maros Ivanic, and Roman Keeney. How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? GTAP Working Paper, 2003. http://dx.doi.org/10.21642/gtap.wp26.

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With the proliferation of Free Trade Agreements (FTAs) over the past decade, demand for quantitative analysis of their likely impacts has surged. The main quantitative tool for performing such analysis is Computable General Equilibrium (CGE) modeling. Yet these models have been widely criticized for performing poorly (Kehoe, 2002) and having weak econometric foundations (McKitrick, 1998; Jorgenson, 1984). FTA results have been shown to be particularly sensitive to the trade elasticities, with small trade elasticities generating large terms of trade effects and relatively modest efficiency gain
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Arches National Park Visitor Use, Access, and Experience Study - Final Report, November 2020. National Park Service, 2024. http://dx.doi.org/10.36967/2306864.

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This report describes the results of the Arches National Park Visitor Use, Access, and Experience Study conducted from August 31 through October 31, 2019. This study was commissioned by the National Park Service (NPS) to help inform transportation planning and visitor use management at Arches National Park (ARCH). The specific purposes of the study are to: 1) collect descriptive information about vehicle traffic volumes, parking demand, trail use levels, and visitor densities at popular attraction sites; 2) estimate statistical relationships among transportation conditions, visitor use levels,
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