Systematic Literature Review on the Four Step Travel Demand Model (FSM) and its Feasibility for Bi nuclei Cities Context

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Adnya Manjarekar, Anand Shivapur, Vilas Karjinni

10.14445/23488352/IJCE-V12I7P103

How to Cite?

Adnya Manjarekar, Anand Shivapur, Vilas Karjinni, "Systematic Literature Review on the Four Step Travel Demand Model (FSM) and its Feasibility for Bi nuclei Cities Context," SSRG International Journal of Civil Engineering, vol. 12,  no. 7, pp. 23-50, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I7P103

Abstract:

The Four Step Travel Demand Model (FSM) has been extensively used in urban transportation planning to forecast travel demand and mobility pa tterns. However, its application to Bi nuclei cities remains underexplored in systematic literature reviews. FSM, originally designed for monocentric urban structures, faces limitations in capturing interdependencies, congestion spillovers, and distinct tr avel behaviours between two dominant centres. Despite its widespread use, no studies have systematically reviewed FSM s feasibility for bi nuclei cities, leaving a significant gap in understanding how FSM can be adapted for such urban settings. Therefore, this study conducts a Systematic literature review to evaluate FSMs and additionally check their feasibility in bi nuclei cities, examining their methodologies, challenges, and required adaptations. The study systematically reviews 54 research articles, id entifying how FSM has evolved from traditional transportation models to modern AI enhanced forecasting techniques. The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) approach was applied, and academic databases such as Dimensio ns were used for article selection. Key research contributions from 1954 to 2024 were analyzed through methodology, focusing on FSM s trip generation, trip distribution, mode choice, and traffic assignment methodologies. The study identified Regression Ana lysis, Gravity Models, Logit Based Mode Choice, and Dynamic Traffic Assignment (DTA) as the most suitable FSM methods for bi nuclei cities while highlighting challenges such as inter nuclei travel complexities, congestion spillovers, and heterogeneous mode preferences. Software tools like CUBE, VISUM, VISSIM, AIMSUN, Trans CAD, MAT Sim, and Python based models were evaluated for FSM s computational feasibility in bi nuclei settings. The findings enhance urban mobility planning and FSM adaptability to comple x structures.

Keywords:

Four Step Travel Demand Model (FSM), Bi Nuclei cities , Urban transportation planning , Systematic Literature Review (SLR), Traffic assignment , Multi modal transport systems

References:

[1] Amr M. Sakr et al., “An Integrated Approach of Travel Demand Modeling and Decision-Making Tools for the Assessment of Transportation Projects,” Journal of Transportation Engineering, Part A: Systems, vol. 149, no. 10, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Susan Powell, “Transportation Planning Models,” Journal of the Operational Research Society, vol. 38, no. 12, pp. 1218-1219, 1987.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Joe Castiglione, Mark A. Bradley, and John Gliebe, Activity-Based Travel Demand Models: A Primer, Transportation Research Board, pp. 1-159, 2015.
[Google Scholar] [Publisher Link]
[4] Michael G. McNally, The Four Step Model, Handbook of Transport Modelling, Emerald Group Publishing Limited vol. 1, pp. 35-53, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[5] David A. Hensher, and Kenneth John Button, Handbook of Transport Modelling Article Information, Emerald Group Publishing Limited, pp. 1-9, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[6] John Bates, History of Demand Modelling, Emerald Group Publishing Limited, pp. 11-34, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Robert Buchanan Mitchell, and Chester Rapkin, Urban Traffic: A Function of Land Use, Columbia University Press, pp. 1-226, 1954.
[Google Scholar] [Publisher Link]
[8] Marvin L. Manheim, Fundamentals of Transportation Systems Analysis, MIT Press, pp. 1-658, 1979.
[Google Scholar] [Publisher Link]
[9] Michael Greyson McNally, On the Formation of Household Travel/Activity Patterns: A Simulation Approach, University of California, Irvine, pp. 1-388, 1986.
[Google Scholar] [Publisher Link]
[10] Michael Florian, Marc Gaudry, and Christian Lardinois, “A Two-Dimensional Framework for the Understanding of Transportation Planning Models,” Transportation Research Part B: Methodological, vol. 22, no. 6, pp. 411-419, 1988.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Edward Weiner, “Urban Transportation Planning in the United States: An Historical Overview,” Report no. DOT-T-97-24, US Department of Transportation, Washington, DC, pp. 1-311, 1997.
[Google Scholar] [Publisher Link]
[12] William A. Martin, and Nancy A. McGuckin, “Travel Estimation Techniques for Urban Planning,” National Cooperative Highway Research Program, Transportion Research Board, Washington D.C, vol. 365, pp. 1-180, 1998.
[Google Scholar] [Publisher Link]
[13] Juan de Dios Ortúzar, and Luis G. Willumsen, Modelling Transport, Wiley, pp. 1-499, 2001.
[Google Scholar] [Publisher Link]
[14] David Moher et al., “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement,” PLoS Medicine, vol. 6, no. 7, pp. 1-8, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Neal R. Haddaway et al., “PRISMA2020: An R Package and Shiny App for Producing PRISMA 2020-Compliant Flow Diagrams, with Interactivity for Optimised Digital Transparency and Open Synthesis,” Campbell Systematic Reviews, vol. 18, no. 2, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Robin Whittemore, and Kathleen Knafl, “The Integrative Review: Updated Methodology,” Jounal of Advanced Nursing, vol. 52, no. 5, pp. 546-553, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Margarete Sandelowski, “Qualitative Analysis: What it is and How to Begin,” Research in Nursing & Health, vol. 18, no. 4, pp. 371-375, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Michael Quinn Patton, Qualitative Research and Evaluation Methods, 3rd ed., SAGE Publication, pp. 1-598, 2002.
[Google Scholar] [Publisher Link]
[19] Zsolt Berki, and Janos Monigl, “Trip Generation and Distribution Modelling in Budapest,” Transportation Research Procedia, vol. 27, pp. 172-179, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Houshmand Masoumi, “Home-Based Urban Commute and Non-Commute Trip Generation in Less-Studied Contexts: Evidence from Cairo, Istanbul, and Tehran,” Case Studies on Transport Policy, vol. 10, no. 1, pp. 130-144, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Akash Anand, and Varghese George, “Modeling Trip-Generation and Distribution Using Census, Partially Correct Household Data, and GIS,” Civil Engineering Journal, vol. 8, no. 9, pp. 1936-1957, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] K. Isaac Takyi, “Trip Generation Analysis in a Developing Country Context,” Transportation Research Record, no. 1285, pp. 9-21, 1990.
[Google Scholar] [Publisher Link]
[23] Fan Yang et al., “A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers,” Sustainability, vol. 12, no. 18, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mykhailo Krystopchuk et al., “Exploring the Patterns of Resident Resettlement in Rural and Suburban Areas and Their Influence on the Passenger Trip Generation,” Periodica Polytechnica Transportation Engineering, vol. 50, no. 2, pp. 191-204, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Boon Hoe Goh, Choon Wah Yuen, and Chiu Chuen Onn, “Improvement of Trip Generation Rates for Mixed-Use Development in Klang Valley, Malaysia,” Scientific Reports, vol. 13, pp. 1-9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Abdul Basith Siddiqui et al., Modelling Transit and Automobile TRIP Generation Propensities of Post Secondary Students in the Greater Toronto and Hamilton Area: A Cross Sectional Study, Canadian Journal of Civil Engineering vol. 5 0, no. 9, pp. 1 33, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Maxime Lenormand, Aleix Bassolas, and José J. Ramasco, “Systematic Comparison of Trip Distribution Laws and Models,” Journal of Transport Geography, vol. 51, pp. 158-169, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Yiolanda Englezou, Stelios Timotheou, and Christos G. Panayiotou, “Dynamic Origin-Destination Matrix Estimation for Networks Operating Under Free-flow Conditions Using Macroscopic Flow Dynamics,” IFAC-PapersOnLine, vol. 58, no. 10, pp. 213-218, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Dongya Li, Wei Wang, and De Zhao, “Designing a Novel Two-Stage Fusion Framework to Predict Short-Term Origin–Destination Flow,” Journal of Transportation Engineering, Part A: Systems, vol. 149, no. 5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Shahriar Afandizadeh Zargari, Amirmasoud Memarnejad, and Hamid Mirzahossein, “Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning,” Sensors, vol. 21, no. 21, pp. 1-18, 2021. [CrossRef] [Google Scholar] [Publisher Link]
[31] Xavier Ros-Rocaa, Lídia Monterob, and Jaume Barceló, “Investigating the Quality of Spiess-Like and SPSA Approaches for Dynamic OD Matrix Estimation,” Transportmetrica A: Transport Science, vol. 17, no. 3, pp. 235-257, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[32] A.S. Chairunnisa et al., “Modeling Origin Destination Matrix in the Inter-Island Cluster of North Liukang Using Gravity Model,” IOP Conference Series: Materials Science and Engineering: The 3rd EPI International Conference on Science and Engineering 2019 (EICSE2019), Sulawesi, Indonesia, vol. 875, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[33] M I Ramli et al., “An Estimation of Origin-Destination Matrices for a Public Transport Network in Makassar Using Macrosimulation Visum,” IOP Conference Series: Materials Science and Engineering: The 3rd EPI International Conference on Science and Engineering 2019 (EICSE2019), Sulawesi, Indonesia, vol. 875, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Anselmo Ramalho Pitombeira-Neto, Carlos Felipe Grangeiro Loureiro, and Luis Eduardo Carvalho, “A Dynamic Hierarchical Bayesian Model for the Estimation of Day-to-Day Origin-Destination,” Networks and Spatial Economics, vol. 20, pp. 499-527, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Wenming Rao et al, “Investigating Impact of the Heterogeneity of Trajectory Data Distribution on Origin-Destination Estimation: A Spatial Statistics Approach,” IET Intelligent Transport System, vol. 14, no. 10, pp. 1218-1227, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Ennio Cascetta, Francesca Pagliara, and Andrea Papola, “Alternative Approaches to Trip Distribution Modelling: A Retrospective Review and Suggestions for Combining Different Approaches,” Papers in Regional Science, vol. 86, no. 4, pp. 597-620, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Anu Plavara Alex, Manju Vasudevan Saraswathy, and Kuncheria Palampoikayil Isaac, “Latent Variable Enriched Mode Choice Model for Work Activity in Multi Modal Condition Prevalent in India,” International Journal for Traffic and Transport Engineering, vol. 6, no. 4, pp. 378-389, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Ling Ding, and Ning Zhang, “A Travel Mode Choice Model Using Individual Grouping Based on Cluster Analysis,” Procedia Engineering, vol. 137, pp. 786-795, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Xu Yi, Kang Chen, and Zhongzhen Yang, “The Trend of the Structural Change of the City Taxi Market in China: A Modal Split Analysis in the Context of Different Market Access Rules,” Transportation Planning and Technology, vol. 47, no. 4, pp. 598-621, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[40] I.M. Kariyana et al., “Urban Transportation Mode Choice Model in Bali,” Civil Engineering and Architecture, vol. 12, no. 6, pp. 4031-4044, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Thomas Kjær Rasmussen et al., “Local Detouredness: A New Phenomenon for Modelling Route Choice and Traffic Assignment,” Transportation Research Part B: Methodological, vol. 190, pp. 1-31, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Nitidetch Koohathongsumrit, Wasana Chankham, and Warapoj Meethom, “Multimodal Transport Route Selection: An Integrated Fuzzy Hierarchy Risk Assessment and Multiple Criteria Decision-Making Approach,” Transportation Research Interdisciplinary Perspectives, vol. 28, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[43] David Leffler et al., “An Adaptive Route Choice Model for Integrated Fixed and Flexible Transit Systems,” Transportmetrica B: Transport Dynamics, vol. 12, no. 1, pp. 1-28, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Shin-Hyung Cho, Shin-Hyoung Park, and Sangho Choo, “Exploring the Travel Behavioral Differences for the Elderly Mobility on Public Transit,” Transportation Letters, vol. 17, no. 1, pp. 61-71, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Fan Ding et al., “A Hybrid Method for Intercity Transport Mode Identification Based on Mobility Features and Sequential Relations Mined from Cellular Signaling Data,” Computer-Aided Civil and Infrastructure Engineering, vol. 39, no. 21, pp. 3206-3224, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Jiankun Le, and Jing Teng, “Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai,” Urban Rail Transit, vol. 9, pp. 127-146, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Dewan Ashraful Parvez, “Joint Econometric Model Framework for Transportation Network Company Users’ Trip Fare and Destination Choice Analysis,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2677, no. 7, pp. 545-557, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Yu Gu, and Anthony Chen, “Modeling Mode Choice of Customized Bus Services with Loyalty Subscription Schemes in Multi-Modal Transportation Networks,” Transportation Research Part C: Emerging Technologies, vol. 147, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Lujin Zhao et al., “Using Conjoint Analysis to Incorporate Heterogeneous Preferences into Multimodal Transit Trip Simulations,” System Engineering, vol. 26, no. 4, pp. 438-448, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Zhan Zhao, and Yuebing Liang, “A Deep Inverse Reinforcement Learning Approach to Route Choice Modeling with Context-Dependent Rewards,” Transportation Research Part C: Emerging Technologies, vol. 149, pp. 1-37, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Yutong Shen, Yuelong Wu, and Baozhen Yao, “Research on Heterogeneous Traveler Travel Mode Choices with Differences under a Mixed Traffic Environment,” Sensors, vol. 23, no. 13, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[52] Seungkyu Ryu, and Minki Kim, “Solving a Multi-Class Traffic Assignment Model with Mixed Modes,” Applied Science, vol. 12, no. 7, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[53] N. Dheeraj Kumar et al., “Modeling Work Trip Mode Choice in Post-Metro Bengaluru City,” ECS Transactions, vol. 107, no. 1, 2022.
[Google Scholar] [Publisher Link]
[54] Khaled Assi et al., “Mode Choice Behavior Modeling: A Synergy by Hybrid Neural Network and Fuzzy Logic System,” Arabian Journal for Science and Engineering, vol. 47, pp. 5255-5269, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Chunyan Tang et al., “Modeling Limited-Stop Bus Corridor Services with Fare Payment Mode Choice and Trip Purpose Consideration,” Computational Intelligence and Neuroscience Computational Intelligence and Neuroscience, vol. 2022, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[56] Aleksander Purba, “Attitudes and Perceptions on Travel Mode Choice in an Emerging Urban Area of Indonesia: The Case of Southern Sumatra,” International Journal of GEOMATE, vol. 23, no. 98, pp. 10-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[57] Lingjuan Chen et al., “Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data,” Sustainability, vol. 14, no. 22, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[58] Seungkyu Ryu, “Mode Choice Change under Environmental Constraints in the Combined Modal Split and Traffic Assignment Model,” Sustainability, vol. 13, no. 7, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[59] Xinyuan Chen et al., “Modeling a Distance-Based Preferential Fare Scheme for Park-and-Ride Services in a Multimodal Transport Network,” Sustainability, vol. 13, no. 5, pp. 1-14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[60] Shin-Hyung Cho, and Seung-Young Kho, “Exploring Route Choice Behaviours Accommodating Stochastic Choice Set Generations,” Journal of Advanced Transportation, vol. 2021, pp. 1-14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[61] Xiaowei Li et al., “Influence of Weather Conditions on the Intercity Travel Mode Choice: A Case of Xi’an,” Computational Intelligence and Neuroscience, vol. 2021, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[62] Marek Drliciak et al., “Traffic Volumes as a Modal Split Parameter,” Sustainability, vol. 12, no. 24, pp. 1-21, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[63] Xingang Li et al., “Modal Split and Commuting Patterns on a Bottleneck-Constrained Highway with Peak-Only Bus Lane,” Transportation Planning and Technology, vol. 43, no. 8, pp. 821-850, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[64] Jianhui Wu et al., “Guidance Optimization of Travelers’ Travel Mode Choice Based on Fuel Tax Rate and Bus Departure Quantity in Two-Mode Transportation System,” Journal of Advanced Transportation, vol. 2020, pp. 1-10, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[65] Phattarasuda Witchayaphong et al., “Influential Factors Affecting Travelers’ Mode Choice Behavior on Mass Transit in Bangkok, Thailand,” Sustainability, vol. 12, no. 22, pp. 1-18, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[66] Bowen Hou, Shuzhi Zhao, and Huasheng Liu, “A Combined Modal Split and Traffic Assignment Model with Capacity Constraints for Siting Remote Park-and-Ride Facilities,” IEEE Access, vol. 8, pp. 80502-80517, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[67] Kyoungok Kim, “Effects of Weather and Calendar Events on Mode-Choice Behaviors for Public Transportation,” Journal of Transportation Engineering, Part A: Systems, vol. 146, no. 7, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Sunduck Suh, Chang-Ho Park, and Tschangho John Kim, “A Highway Capacity Function in Korea: Measurement and Calibration,” Transportation Research Part A: General, vol. 24, no. 3, pp. 177-186, 1990.
[CrossRef] [Google Scholar] [Publisher Link]
[69] Heinz Spiess, “Technical Note- Conical Volume-Delay Functions,” Transportation Science, vol. 24, no. 2, pp. 87-167, 1990.
[CrossRef] [Google Scholar] [Publisher Link]
[70] United States. Bureau of Public Roads, Traffic Assignment Manual: For Application with a Large, High Speed Computer, U.S. Department of Commerce, Bureau of Public Roads, Office of Planning, Urban Planning Division, vol. 37, 1964.
[Google Scholar] [Publisher Link]
[71] Andy H.F. Chow, “Trip Assignment–A Literature Review,” California PATH, UC Berkeley, Report, pp. 1-24, 2007.
[Google Scholar]
[72] Krishna Saw, B.K. Katti, and G. Joshi, “Literature Review of Traffic Assignment: Static and Dynamic,” International Journal of Transpotation Engineering, vol. 2, no. 4, pp. 339-347, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[73] Yuhan Zhou, and Hani S. Mahmassani, “Faster Convergence of Integrated Activity-Based Models in Dynamic Multimodal Transit Assignment Using Macroscopic Road Congestion Estimation,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2678, no. 8, pp. 716-730, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[74] Ryuichi Tani, Teppei Kato, and Kenetsu Uchida, “Multiclass Traffic Assignment Model Considering Heterogeneous Stochastic Headways of Autonomous Vehicles and Human-Driven Vehicles,” International Journal of Intelligent Transportation Systems Research, vol. 22, pp. 761-773, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[75] Ting Wang et al., “Multimodal Traffic Assignment Considering Heterogeneous Demand and Modular Operation of Shared Autonomous Vehicles,” Transportation Research Part C: Emerging Technologies, vol. 169, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[76] Behzad Bamdad Mehrabaniet et al., “A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles,” Transportmetrica A: Transport Science, vol. 21, no. 2, pp. 1-24, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[77] S. Zhao, L. Liu, and P. Zhao, “Traffic Assignment in Urban Transportation Network Problem with Emission Constraints in China: A Cooperative Game Theory,” Environmental Science and Pollution Research, vol. 30, pp. 69274-69288, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[78] Seungkyu Ryu, Anthony Chen, and Songyot Kitthamkesorn, “A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function,” Journal of Advanced Transportation, vol. 2021, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[79] Xing Zeng et al., “A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network,” ISPRS International Jounal of Geo-Information, vol. 10, no. 3, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[80] Haizheng Zhang et al., “Improving the Accuracy and Efficiency of Online Calibration for Simulation-Based Dynamic Traffic Assignment,” Transportation Research Part C: Emerging Technologies, vol. 128, pp. 1-26, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[81] Zainab Salam Al-Duhaidahawi et al., “Traffic Assignment of Al-Kufa City Using TransCAD,” IOP Conference Series: Materials Science and Engineering: 3rd International Conference on Recent Innovations in Engineering (ICRIE 2020), Duhok, Iraqi, vol. 978, pp. 1-16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[82] Mundher Seger, and Lajos Kisgyörgy, “Uncertainty Quantification of the Traffic Assignment Model,” Periodica Polytechnica Civil Engineering, vol. 64, no. 4, pp. 1181-1201, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[83] Junxiang Xu, Jingni Guo, and Jin Zhang, “Research on Traffic Assignment Model and Algorithm Based on Cumulative Prospect Theory under Uncertain Factors,” Transportation Research Record: Journal of the Transportation Research Board, vol. [CrossRef] [Google Scholar] [Publisher Link]
[84] Huijun Sun et al., “Day-to-Day Evolution Model Based on Dynamic Reference Point with Heterogeneous Travelers,” Networks and Spatial Economics, vol. 20, pp. 935-961, 2020.
[CrossRef] [Google Scholar] [Publisher Link]