Assessment of the Impacts of Built Environment Characteristics on Trip Generation Rates of Trans-regional Commercial Land Uses: A Case Study of Mashhad City
- Department of Civil Engineering, SR.C., Islamic Azad University, Tehran, Iran
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
Received: 2025-07-13
Revised: 2025-09-10
Accepted: 2025-10-13
Published in Issue 2026-03-31
Copyright (c) 2026 Ramin Ahooee, Abbas Babazadeh, Ali Naderan (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
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Abstract
Aims: This study aims to investigate the influence of built environment characteristics and physical attributes on trip generation rates for trans-regional commercial land uses. The primary goal is to identify which urban design and spatial factors most significantly affect traffic attraction in a metropolitan context, specifically within Mashhad, Iran.
Methodology: The research utilizes an extensive database of 6.8 million daily trips in Mashhad, focusing on 33 selected regional-scale commercial developments. Variables were categorized into physical attributes (e.g., land area, floor area) and built environment factors (e.g., road density, bus stop density, and shopping unit density). Data analysis was performed using descriptive statistics, correlation analysis to detect multicollinearity, and linear regression modeling to evaluate the relationships between independent variables and the average hourly trips generated.
Finding: The results indicate that trip generation is significantly influenced by four key variables: bus stop density, business unit density in the area, business unit density along streets, and total land area. Notably, the density of business units within the area emerged as the most influential positive factor. Conversely, business unit density along streets showed an inverse (negative) relationship with trip generation rates, suggesting that increased street-level density may lead to a reduction in certain types of vehicular trip attraction.
Conclusion: The study concludes that traditional trip estimation manuals, which often rely solely on physical building size, are insufficient for developing regions. Integrating built environment factors particularly public transport accessibility and local commercial density provides a more accurate forecast for traffic impact assessments. These findings offer urban planners and transportation engineers a localized framework for managing the traffic implications of large-scale commercial developments in metropolitan areas.
Keywords
- Trip Generation and Attraction,
- Built Environment Factors,
- Linear Regression,
- Metropolitan/Regional Commercial Land Uses
References
- Institute of Transportation Engineers. 3rd Ed. 2017;Institute of Transportation Engineers. https://www.ite.org/technical-resources/topics/trip-and-parking-generation/
- Al Shehab OM, Khedaywi TS. Trip and parking generation for shopping centers in Jordan. Institute of Transportation Engineers ITE Journal. 2018;88(2):45–9. https://www.ite.org/pub/?id=A0682B14-B039-382C-1E4E-344A32512F49
- Harazraah Consulting Engineers group. 2012.Iran: City of Mashhad.
- Xiao T, Lu H, Sun Z, Wang J. Trip generation prediction based on the convolutional neural network-multidimensional long-short term memory neural network model at grid cell scale. IEEE Access. 2021;9:79051–9. https://doi.org/10.1109/ACCESS.2021.3083683
- Doulabi S, Wilmot C, Stopher P, Antipova A. Contextual adjustment of the institute of transportation engineers trip generation rates for small and medium urban areas in Louisiana. Transportation Research Record. 2022;2676(8):44–55. https://doi.org/10.1177/03611981221082590
- Aboelenen KE, Mohammad AN, Elgaar MI, Choe P. Trip generation rates using household surveys in the state of Qatar. Journal of Traffic and Logistics Engineering 2021;9(1). https://www.jtle.net/uploadfile/2021/0618/20210618055 115753.pdf
- Altaher MG, Elsayed MA, Hassanin HD, Ibrahim AR. Trip Attraction Rates of Banking Services in Developing Countries' Cities. Civil Engineering Journal. 2023;9(2):343–55. https://doi.org/10.28991/CEJ-2023-09-02-07
- Anand A, George V. Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS. Civil Engineering Journal. 2022;8(9):1936–57. https://doi.org/10.28991/CEJ-2022-08-09-013
- Rith M, Fillone A, Biona JBM. The impact of socioeconomic characteristics and land use patterns on household vehicle ownership and energy consumption in an urban area with insufficient public transport service–A case study of metro Manila. Journal of Transport Geography. 2019;79:102484. https://doi.org/10.1016/j.jtrangeo.2019.102484
- Faghih-Imani A, Eluru N. Determining the role of bicycle sharing system infrastructure installation decision on usage: Case study of montreal BIXI system. Transportation Research Part A: Policy and Practice. 2016;94:685–98. https://doi.org/10.1016/j.tra.2016.10.024
- Newman PW, Kenworthy JR. The land use—transport connection: An overview. Land use policy. 1996;13(1):1–22. https://doi.org/10.1016/0264-8377(95)00027-5
- Hua-bing D, Jin-chuan C, Ji-fu G. Research on trip generation rate of office buildings in Beijing. Journal of Transportation Systems Engineering and Information Technology. 2007;7(3):90. https://www.tseit.org.cn/EN/article/advancedSearchResu lt.do
- Karambakhsh S. Automatic Methodology for Multi-modal Trip Generation with Roadside LiDAR. 2023. https://www.proquest.com/openview/e1b8bcb976dedded b9c72f7c36cb6d4c/1?pq-origsite=gscholar&cbl=18750&diss=y
- Lawson CT, Holguín-Veras J, Sánchez-Díaz I, Jaller M, Campbell S, Powers EL. Estimated generation of freight trips based on land use. Transportation Research Record. 2012;2269(1):65–72. https://doi.org/10.3141/2269-08
- Weinberger R, Ricks K, Schrieber J, Cohen L, Symmetra Design L. Trip generation data collection in urban areas. 2014. https://rosap.ntl.bts.gov/view/dot/28280
- Shafizadeh K, Lee R, Niemeier D, Parker T, Handy S. Evaluation of operation and accuracy of available smart growth trip generation methodologies for use in California. Transportation Research Record. 2012;2307(1):120–31. https://doi.org/10.3141/2307-13
- Handy S, Shafizadeh KR, Schneider R. California smart-growth trip generation rates study. 2013. https://nacto.org/wp-content/uploads/smart_growth_trip_generation_rates_ha ndy.pdf
- Weinberger R, Dock S, Cohen L, Rogers JD, Henson J. Predicting travel impacts of new development in America's major cities: Testing alternative trip generation models. Transportation Research Record. 2015;2500(1):36–47. https://doi.org/10.3141/2500-05
- Cavill N. Physical activity and health in Europe: evidence for action. 2006. https://books.google.com/books?hl=en&lr=&id=nXuzD wAAQBAJ&oi=fnd&pg=PR6&dq=Physical+activity+and+health+in+Europe:+evidence+for+action
- De Palma A, Rochat D. Mode choices for trips to work in Geneva: an empirical analysis. Journal of Transport Geography. 2000;8(1):43–51. https://doi.org/10.1016/S0966-6923(99)00026-5
- Donald IJ, Cooper SR, Conchie SM. An extended theory of planned behaviour model of the psychological factors affecting commuters' transport mode use. Journal of environmental psychology. 2014;40:39–48. https://doi.org/10.1016/j.jenvp.2014.03.003
- Weliwitiya H, Rose G, Johnson M. Bicycle train intermodality: Effects of demography, station characteristics and the built environment. Journal of Transport Geography. 2019;74:395–404. https://doi.org/10.1016/j.jtrangeo.2018.12.016
- Feng Y, Fullerton D, Gan L. Vehicle choices, miles driven, and pollution policies. Journal of Regulatory Economics. 2013;44:4–29. https://link.springer.com/article/10.1007/s11149-013-9221-z
- Bhat CR, Sen S, Eluru N. The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use. Transportation Research Part B: Methodological. 2009;43(1):1–18. https://doi.org/10.1016/j.trb.2008.06.009
- Wang X, Shao C, Yin C, Zhuge C. Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China. Int J Environ Res Public Health. 2018;15(9):1868. https://doi.org/10.3390/ijerph15091868
- Spissu E, Pinjari AR, Pendyala RM, Bhat CR. A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel. Transportation. 2009;36:403–22. https://doi.org/10.1007/s11116-009-9208-x
- Jiang Y, Gu P, Chen Y, He D, Mao Q. Influence of land use and street characteristics on car ownership and use: Evidence from Jinan, China. Transportation Research Part D: Transport and Environment. 2017;52:518–34. https://doi.org/10.1016/j.trd.2016.08.030
- Carse A, Goodman A, Mackett RL, Panter J, Ogilvie D. The factors influencing car use in a cycle-friendly city: the case of Cambridge. J Transp Geogr. 2013;28(100):67–74. https://doi.org/10.1016/j.jtrangeo.2012.10.013
- Cervero R, Duncan M. Walking, bicycling, and urban landscapes: evidence from the San Francisco Bay Area. Am J Public Health. 2003;93(9):1478–83. https://doi.org/10.2105/ajph.93.9.1478
- Frank LD, Schmid TL, Sallis JF, Chapman J, Saelens BE. Linking objectively measured physical activity with objectively measured urban form: findings from SMARTRAQ. Am J Prev Med. 2005;28(2 Suppl 2):117–25. https://doi.org/10.1016/j.amepre.2004.11.001
- de Nazelle A, Nieuwenhuijsen MJ, Anto JM, Brauer M, Briggs D, Braun-Fahrlander C, et al. Improving health through policies that promote active travel: a review of evidence to support integrated health impact assessment. Environ Int. 2011;37(4):766–77. https://doi.org/10.1016/j.envint.2011.02.003
- Al Razib MS, Rahman FI. Determination of trip attraction rates of shopping centers in Uttara Area, Dhaka. American Journal of Management Science and Engineering. 2017;2(5):150–5. https://doi.org/10.11648/j.ajmse.20170205.19
- George P, Kattor GJ. Forecasting trip attraction based on commercial land use characteristic. International Journal of Research in Engineering and technology. 2013;2(9). https://ijret.org/volumes/2013v02/i09/IJRET2013020907 2.pdf
- George P, Kattor GJ, Malik kva. prediction of trip attraction based on commercial land use characteristics. International Journal of Innovative Research in Science, Engineering and Technology. 2013;2:352–9. https://www.ijirset.com/upload/2013/special/environmen tal/47_PREDICTION.pdf
- Mamun M, Rahman S, Rahman M, Aziz Y, Raihan M. Determination of trip attraction rates of shopping centers in Dhaka city. 2nd International Conference on Advances in Civil Engineering. 2014:913–7. https://doi.org/10.13140/RG.2.1.2955.7522
- Sasidhar K, Vineeth Y, Subbarao S. Trip attraction rates of commercial land use: a case study. Indian Journal of Science and Technology. 2016;9(30):1–5. https://indjst.org/download-article.php?Article_Unique_Id=INDJST7688&Full_Text_Pdf_Download=True
- Uddin M, Hasan MR, Ahmed I, Das P, Uddin MA, Hasan T. A comprehensive study on trip attraction rates of shopping centers in dhanmondi area. International Journal of Civil & Environmental Engineering. 2012;12(4):12–6. https://www.researchgate.net/publication/282995205_A_ Comprehensive_Study_on_Trip_Attraction_Rates_of_S hopping_Centers_in_Dhanmondi_Area
- Shahri M, Ghannadi M. Explanatory analyses of work trip generation using mixed geographically weighted regression (mgwr). ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023;10:707–14. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-707-2023
- Rezaei M, Soltani A, Kashkooli HN. Predicting active travel durations in Tehran: A multilayer perceptron approach. Journal of Urban Mobility. 2025;7:100126. https://doi.org/10.1016/j.urbmob.2025.100126
- Masoumi H. Residential location choice in Istanbul, Tehran, and Cairo: the importance of commuting to work. Sustainability. 2021;13(10):5757. https://doi.org/10.3390/su13105757
- Alinaghipour M, Pourramzan I, Molaei Hashjin N. Explaining Environmental Livability of Rural Settlements around Rasht Metropolis. Human Geography Research. 2021;53(1):1–22. https://doi.org/10.22059/jhgr.2018.255494.1007676
- Karimani F, Manesh MS. Importance Monopoly and Importance Competition on the Urban Network Infrastructure (Case study: Public transportation lane No. 10 and 96 of Mashhad bus system). Creative City Design. 2019;2(2):56–66. https://oiccpress.com/crcd/article/view/7618
- Abu–Eisheh S, Ghanim MS, Dodeen A. Trip generation model for a developing city in an emerging country. Transportation Research Interdisciplinary Perspectives. 2024;24:101048. https://doi.org/10.1016/j.trip.2024.101048
- Rakić M, Bogdanović V, Garunović N, Simeunović M, Stević Ž, Radović Stojčić D. The Forecasting Model of the Impact of Shopping Centres in Urban Areas on the Generation of Traffic Demand. Applied Sciences. 2024;14(19):8759. https://doi.org/10.3390/app14198759
- Xie Y, Loesch B, Bhuyan Z, Logozzo F, Chen D, Liu BB. Developing Massachusetts-Specific Trip Generation Models for Land Use Projects. 2023. https://rosap.ntl.bts.gov/view/dot/72776
- Sohu V, Papaioannou D, HASEGAWA N. Transit-oriented development and accessibility: case studies from Southeast Asian cities. 2023. https://trid.trb.org/View/2289127
- Transportation Network Management and Engineering Organization. In: persian) ttsoMci, editor.: 18th transportation statistics of Mashhad city (in persian). Mashhad Municipality; 2022.
- Moghadam SS, Mofrad SS. Urban sprawl trend analysis using statistical and remote sensing approach Case Study: Mashhad City. Creative City Design. 2018;1(1):1–8. https://oiccpress.com/crcd/article/view/7588
- Gehrke SR, Wang L. Operationalizing the neighborhood effects of the built environment on travel behavior. Journal of transport geography. 2020;82:102561. https://doi.org/10.1016/j.jtrangeo.2019.102561
- Mitchell Hess P, Vernez Moudon A, Logsdon MG. Measuring land use patterns for transportation research. Transportation research record. 2001;1780(1):17–24. https://doi.org/10.3141/1780-03
- Guo JY, Bhat CR. Operationalizing the concept of neighborhood: Application to residential location choice analysis. Journal of transport geography. 2007;15(1):31–45. https://doi.org/10.1016/j.jtrangeo.2005.11.001
- Manaugh K, Kreider T. What is mixed use? Presenting an interaction method for measuring land use mix. Journal of Transport and Land use. 2013;6(1):63–72. https://www.jstor.org/stable/26202648?seq=5
- Bamney A, Gupta N, Jashami H, Megat-Johari M-U, Savolainen P. An analysis of changes in county-level travel behavior considering COVID-19–related travel restrictions, immunization patterns, and political leanings. Journal of Transportation Engineering, Part A: Systems. 2022;148(11):04022096. https://ascelibrary.org/doi/abs/10.1061/JTEPBS.0000748
- Zhao Z, Koutsopoulos HN, Zhao J. Detecting pattern changes in individual travel behavior: A Bayesian approach. Transportation research part B: methodological. 2018;112:73–88. https://doi.org/10.1016/j.trb.2018.03.017
- de Oliveira LK, Herédia RT, Bertoncini BV, de Oliveira RLM. Freight trip generation to buildings under construction: a comparative analysis with linear regression and generalised linear regression. Transportes. 2020:28–42. https://doi.org/10.14295/transportes.v28i5.1885
- Zenina N, Borisov A. Regression analysis for transport trip generation evaluation. Information Technology and Management Science. 2013:89–94. https://doi.org/10.2478/itms-2013-0014
- Noland RB, Smart MJ, Guo Z. Bikeshare trip generation in New York City. Transportation Research Part A: Policy and Practice. 2016:164–81. https://doi.org/10.1016/j.tra.2016.08.030
- Ramos ÉMS, Bergstad CJ, Nässén J. Understanding daily car use: Driving habits, motives, attitudes, and norms across trip purposes. Transportation research part F: traffic psychology and behaviour. 2020:306–15. https://doi.org/10.1016/j.trf.2019.11.013
- Calvo F, Eboli L, Forciniti C, Mazzulla G. Factors influencing trip generation on metro system in Madrid (Spain). Transportation Research Part D: Transport and Environment. 2019;67:156–72. https://doi.org/10.1016/j.trd.2018.11.021
- Field A. Discovering statistics using IBM SPSS statistics: Sage publications limited; 2024.
- Gelman A, Carlin J. Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspect Psychol Sci. 2014;9(6):641–51. https://doi.org/10.1177/1745691614551642
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