Araştırma Makalesi
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Estimating pedestrian travel demands based on neighborhood characteristics

Yıl 2023, Cilt: 13 Sayı: 2, 359 - 372, 15.04.2023
https://doi.org/10.17714/gumusfenbil.1099142

Öz

The importance given to transportation investments all over the world is increasing day by day. Therefore, effective planning plays an important role in both new transportation investments and improvement of the existing transportation system. In transportation planning, one of the main goals for sustainable urbanization and sustainable mobility is to provide a transportation system that gives priority to pedestrian and public transportation. In this context, in many developed and developing countries, especially pedestrian safety is prioritized and practices and studies are developed. Accordingly, the use of environment- and pedestrian-friendly transportation systems is being expanded. Pedestrian characteristics and behaviors should be examined first in order to develop practices and policies that encourage pedestrian travel. In this study, socio-economic and demographic characteristics that affect other-purpose (socializing, entertainment, shopping, banking, sports, etc.) pedestrian travel behaviors are investigated. In this context, characteristics of 50 neighborhoods and approximately 21000 household surveys were used. With the help of Multiple Linear Regression (MLR), Ridge Regression (RR) and Liu Regression methods, models explaining the changes in the frequency of pedestrian travel for other purposes were produced. Three methods were evaluated in terms of Mean Squares of Error (HKO), Akaike Infırmation Criteria (AIC) and Bayesian Information Criteria (BIC). All three criterias showed that the RR produced more successful model. According to HKO, the RR and Liu models were found to be approximately 35% and 27% more successful than the MLR model, respectively.

Kaynakça

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  • Akçay, A., & Sarıözkan, S. (2015). Yumurta tavukçuluğunda gelirin Ridge Regresyon analizi ile tahmini. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 62(1), 69-74.
  • Albayrak, A. S. (2005). Çoklu Doğrusal Bağlantı Halinde Enküçük Kareler Tekniğinin Alternatifi Yanlı Tahmin Teknikleri ve Bir Uygulama. Uluslararası Yönetim İktisat ve İşletme Dergisi, 1(1), 105-126. https://doi.org/Retrieved from https://dergipark.org.tr/tr/pub/ijmeb/issue/54840/750869
  • Alpar, R. (1997). Uygulamalı çok değişkenli istatistiksel yöntemlere giriş-I. Bağırgan Yayımevi.
  • Alpu, Ö., & Şamkar, H. (2010). Liu Estimator based on an M Estimator. Turkiye Klinikleri Journal of Biostatistics, 2(2), 49-53.
  • Alpu, Ö., Şamkar, H., & Altan, E. (2010). Sağlam Ridge Regresyon Analizi ve Bir Uygulama. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(2).
  • Baran, P. K., Rodríguez, D. A., & Khattak, A. (2008). Space syntax and walking in a new urbanist and suburban neighbourhoods. Journal of Urban Design, 13(1), 5-28. https://doi.org/https://doi.org/10.1080/13574800701803498
  • Chang, J. S., Jung, D., Kim, J., & Kang, T. (2014). Comparative analysis of trip generation models: results using home-based work trips in the Seoul metropolitan area. Transportation Letters, 6(2), 78-88.
  • Clifton, K. J. (2004). Built Environment And Trip Generation for Non-Motorized Travel.
  • Craig, C. L., Brownson, R. C., Cragg, S. E., & Dunn, A. L. (2002). Exploring the effect of the environment on physical activity: a study examining walking to work. American journal of preventive medicine, 23(2), 36-43. https://doi.org/10.1016/s0749-3797(02)00472-5
  • de Almeida Guimarães, V., & Leal Junior, I. C. (2017). Performance assessment and evaluation method for passenger transportation: a step toward sustainability. Journal of Cleaner Production, 142, Part 1, 297-307. https://doi.org/http://dx.doi.org/10.1016/j.jclepro.2016.05.071
  • de Dios OrtÃozar, J., & Willumsen, L. G. (2011). Modelling transport. John Wiley & Sons.
  • Deb, S., Strawderman, L., DuBien, J., Smith, B., Carruth, D. W., & Garrison, T. M. (2017). Evaluating pedestrian behavior at crosswalks: Validation of a pedestrian behavior questionnaire for the US population. Accident Analysis & Prevention, 106, 191-201. https://doi.org/https://doi.org/10.1016/j.aap.2017.05.020
  • Delice, Y., Ozen, H., & Amirnazmiafshar, E. (2019). Suburban Passenger’s Mode Choice Behavior Based on Trip Purpose. International Journal of Management and Applied Science, 5(8).
  • Demirci, M. A. (2014). Ridge Regresyonda Sapma Parametresi k'nın Elde Edilmesinde Genetik Algoritma Yaklaşımı [Yüksek Lisans Tezi, 19 Mayıs Üniversitesi].
  • Frank, L. D. (1995). An analysis of relationships between urban form (density, mix, and jobs: housing balance) and travel behavior (mode choice, trip generation, trip length, and travel time).
  • Golob, T. F. (2000). A simultaneous model of household activity participation and trip chain generation. Transportation Research Part B: Methodological, 34(5), 355-376.
  • Greenwald, M. J. (2003). The road less traveled: New urbanist inducements to travel mode substitution for nonwork trips. Journal of Planning Education Research 23(1), 39-57. https://doi.org/10.1177/0739456X03256248
  • Gujarati, D. (2004). Basic Econometrics. United States Military Academy, West Point. In: Tata McGraw-Hill.
  • Hagberg, J., & Holmberg, U. (2017). Travel modes in grocery shopping. International Journal of Retail Distribution Management. https://doi.org/https://doi.org/10.1108/IJRDM-08-2016-0134
  • Herrero-Fernández, D., Parada-Fernández, P., Oliva-Macías, M., & Jorge, R. (2020). The influence of emotional state on risk perception in pedestrians: A psychophysiological approach. Safety Science, 130, 104857. https://doi.org/https://doi.org/10.1016/j.ssci.2020.104857
  • Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: applications to nonorthogonal problems. Technometrics, 12(1), 69-82.
  • Imdadullah, M., Aslam, M., & Altaf, S. (2017). liureg: A Comprehensive R Package for the Liu Estimation of Linear Regression Model with Collinear Regressors. R J., 9(2), 232. https://doi.org/https://doi.org/10.32614/RJ-2017-048
  • Jain, A., Casas, S., Liao, R., Xiong, Y., Feng, S., Segal, S., & Urtasun, R. (2020). Discrete residual flow for probabilistic pedestrian behavior prediction. 3rd Conference on Robot Learning, Osaka, Japan.
  • Kaçıranlar, S., & Sakallıoğlu, S. (2000). Liu Ve Temel Bileşenler Regresyon Tahmin Edicilerinin Birleştirilmesi. İstatistik Araştırma Sempozyumu, 27-29.
  • Kara, Ç., & Bilgiç, Ş. (2021a). Estimation of hospital trip characteristics in terms of transportation planning. Journal of Transport & Health, 20, 100987. https://doi.org/https://doi.org/10.1016/j.jth.2020.100987
  • Kara, Ç., & Bilgiç, Ş. (2021b). Hospital Trip Production and Attraction Modeling for Future Predictions. Journal of Urban Planning and Development, 147(4), 05021049. https://doi.org/https://doi.org/10.1061/(ASCE)UP.1943-5444.0000754
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Yaya yolculuk taleplerinin mahalle karakteristiklerine dayalı tahmini

Yıl 2023, Cilt: 13 Sayı: 2, 359 - 372, 15.04.2023
https://doi.org/10.17714/gumusfenbil.1099142

Öz

Tüm dünyada ulaştırma yatırımlarına verilen önem her geçen gün artmaktadır. Bu nedenle, etkin planlama gerek yeni ulaşım yatırımlarında gerekse mevcut ulaşım sisteminin iyileştirilmesinde önemli bir rol oynamaktadır. Ulaşım planlamasında, sürdürülebilir kentleşme ve sürdürülebilir hareketliliğin sağlanması için temel hedeflerden biri; yaya ve toplu taşıma öncelikli bir ulaşım sisteminin sağlanması olarak gösterilmektedir. Bu kapsamda birçok gelişmiş ve gelişmekte olan ülkede, özellikle yaya güvenliği ön planda tutulup, bunlarla ilgili uygulama ve çalışmalar geliştirilerek, çevre ve yaya dostu ulaşım sistemlerinin kullanımı yaygınlaştırılmaktadır. Yaya olarak seyahate teşvik edici uygulamalar ve politikalar geliştirilebilmek için öncelikle yaya karakteristikleri ve davranışları incelenmelidir. Bu çalışmada, diğer (sosyalleşme, eğlence, alışveriş, banka, spor, vb.) amaçlı yaya yolculuk davranışlarını etkileyen sosyo-ekonomik ve demografik karakteristikler araştırılmaktadır. Bu kapsamda 50 mahalleye ait karakteristikler ve toplamda yaklaşık 21000 hane halkı anketi kullanılmıştır. Çoklu Doğrusal Regresyon (ÇDR), Ridge Regresyonu (RR) ve Liu Regresyonu yöntemleriyle diğer amaçlı yaya yolculuk sıklığındaki değişimleri öngören modeller üretilmiştir. Üç yöntem, Hata Kareler Ortalaması (HKO), Akaike Bilgi Kriteri (ABK) ve Bayes Bilgi Kriteri (BBK) açısından değerlendirilmiştir. Her üç başarı ölçütü de RR’nin daha başarılı model ürettiğini göstermiştir. HKO’ya göre, RR ve Liu modellerinin ÇDR modeline kıyasla, sırasıyla yaklaşık %35 ve %27 daha başarılı olduğu tespit edilmiştir.

Kaynakça

  • Adegoke, A. S., Adewuyi, E., Ayinde, K., & Lukman, A. F. (2016). A comparative study of some robust ridge and liu estimators. Science World Journal, 11(4), 16-20.
  • Akçay, A., & Sarıözkan, S. (2015). Yumurta tavukçuluğunda gelirin Ridge Regresyon analizi ile tahmini. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 62(1), 69-74.
  • Albayrak, A. S. (2005). Çoklu Doğrusal Bağlantı Halinde Enküçük Kareler Tekniğinin Alternatifi Yanlı Tahmin Teknikleri ve Bir Uygulama. Uluslararası Yönetim İktisat ve İşletme Dergisi, 1(1), 105-126. https://doi.org/Retrieved from https://dergipark.org.tr/tr/pub/ijmeb/issue/54840/750869
  • Alpar, R. (1997). Uygulamalı çok değişkenli istatistiksel yöntemlere giriş-I. Bağırgan Yayımevi.
  • Alpu, Ö., & Şamkar, H. (2010). Liu Estimator based on an M Estimator. Turkiye Klinikleri Journal of Biostatistics, 2(2), 49-53.
  • Alpu, Ö., Şamkar, H., & Altan, E. (2010). Sağlam Ridge Regresyon Analizi ve Bir Uygulama. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(2).
  • Baran, P. K., Rodríguez, D. A., & Khattak, A. (2008). Space syntax and walking in a new urbanist and suburban neighbourhoods. Journal of Urban Design, 13(1), 5-28. https://doi.org/https://doi.org/10.1080/13574800701803498
  • Chang, J. S., Jung, D., Kim, J., & Kang, T. (2014). Comparative analysis of trip generation models: results using home-based work trips in the Seoul metropolitan area. Transportation Letters, 6(2), 78-88.
  • Clifton, K. J. (2004). Built Environment And Trip Generation for Non-Motorized Travel.
  • Craig, C. L., Brownson, R. C., Cragg, S. E., & Dunn, A. L. (2002). Exploring the effect of the environment on physical activity: a study examining walking to work. American journal of preventive medicine, 23(2), 36-43. https://doi.org/10.1016/s0749-3797(02)00472-5
  • de Almeida Guimarães, V., & Leal Junior, I. C. (2017). Performance assessment and evaluation method for passenger transportation: a step toward sustainability. Journal of Cleaner Production, 142, Part 1, 297-307. https://doi.org/http://dx.doi.org/10.1016/j.jclepro.2016.05.071
  • de Dios OrtÃozar, J., & Willumsen, L. G. (2011). Modelling transport. John Wiley & Sons.
  • Deb, S., Strawderman, L., DuBien, J., Smith, B., Carruth, D. W., & Garrison, T. M. (2017). Evaluating pedestrian behavior at crosswalks: Validation of a pedestrian behavior questionnaire for the US population. Accident Analysis & Prevention, 106, 191-201. https://doi.org/https://doi.org/10.1016/j.aap.2017.05.020
  • Delice, Y., Ozen, H., & Amirnazmiafshar, E. (2019). Suburban Passenger’s Mode Choice Behavior Based on Trip Purpose. International Journal of Management and Applied Science, 5(8).
  • Demirci, M. A. (2014). Ridge Regresyonda Sapma Parametresi k'nın Elde Edilmesinde Genetik Algoritma Yaklaşımı [Yüksek Lisans Tezi, 19 Mayıs Üniversitesi].
  • Frank, L. D. (1995). An analysis of relationships between urban form (density, mix, and jobs: housing balance) and travel behavior (mode choice, trip generation, trip length, and travel time).
  • Golob, T. F. (2000). A simultaneous model of household activity participation and trip chain generation. Transportation Research Part B: Methodological, 34(5), 355-376.
  • Greenwald, M. J. (2003). The road less traveled: New urbanist inducements to travel mode substitution for nonwork trips. Journal of Planning Education Research 23(1), 39-57. https://doi.org/10.1177/0739456X03256248
  • Gujarati, D. (2004). Basic Econometrics. United States Military Academy, West Point. In: Tata McGraw-Hill.
  • Hagberg, J., & Holmberg, U. (2017). Travel modes in grocery shopping. International Journal of Retail Distribution Management. https://doi.org/https://doi.org/10.1108/IJRDM-08-2016-0134
  • Herrero-Fernández, D., Parada-Fernández, P., Oliva-Macías, M., & Jorge, R. (2020). The influence of emotional state on risk perception in pedestrians: A psychophysiological approach. Safety Science, 130, 104857. https://doi.org/https://doi.org/10.1016/j.ssci.2020.104857
  • Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: applications to nonorthogonal problems. Technometrics, 12(1), 69-82.
  • Imdadullah, M., Aslam, M., & Altaf, S. (2017). liureg: A Comprehensive R Package for the Liu Estimation of Linear Regression Model with Collinear Regressors. R J., 9(2), 232. https://doi.org/https://doi.org/10.32614/RJ-2017-048
  • Jain, A., Casas, S., Liao, R., Xiong, Y., Feng, S., Segal, S., & Urtasun, R. (2020). Discrete residual flow for probabilistic pedestrian behavior prediction. 3rd Conference on Robot Learning, Osaka, Japan.
  • Kaçıranlar, S., & Sakallıoğlu, S. (2000). Liu Ve Temel Bileşenler Regresyon Tahmin Edicilerinin Birleştirilmesi. İstatistik Araştırma Sempozyumu, 27-29.
  • Kara, Ç., & Bilgiç, Ş. (2021a). Estimation of hospital trip characteristics in terms of transportation planning. Journal of Transport & Health, 20, 100987. https://doi.org/https://doi.org/10.1016/j.jth.2020.100987
  • Kara, Ç., & Bilgiç, Ş. (2021b). Hospital Trip Production and Attraction Modeling for Future Predictions. Journal of Urban Planning and Development, 147(4), 05021049. https://doi.org/https://doi.org/10.1061/(ASCE)UP.1943-5444.0000754
  • Kejian, L. (1993). A new class of blased estimate in linear regression. Communications in Statistics-Theory and Methods, 22(2), 393-402. https://doi.org/https://doi.org/10.1080/03610929308831027
  • Khisty, C. J., & Arslan, T. (2005). Possibilities of steering the transportation planning process in the face of bounded rationality and unbounded uncertainty. Transportation Research Part C: Emerging Technologies, 13(2), 77-92. https://doi.org/http://dx.doi.org/10.1016/j.trc.2005.04.003
  • Kibria, B., & Banik, S. (2020). Some ridge regression estimators and their performances. Journal of Modern Applied Statistical Methods, 15(1). https://doi.org/10.22237/jmasm/1462075860
  • Kim, N. S., & Susilo, Y. O. (2013). Comparison of pedestrian trip generation models. Journal of Advanced Transportation, 47(4), 399-412. https://doi.org/https://doi.org/10.1002/atr.166
  • Kroeger, L., Heinitz, F., & Winkler, C. (2018). Operationalizing a spatial differentiation of trip generation rates using proxy indicators of accessibility. Travel Behaviour and Society, 11, 156-173.
  • Küçük, A. (2019). Doğrusal regresyonda Ridge, Liu ve LASSO tahmin edicileri üzerine bir çalışma [Yüksek Lisans Tezi, Hacettepe Üniversitesi].
  • Liu, X.-Q. (2011). Improved Liu estimator in a linear regression model. Journal of Statistical Planning and Inference, 141(1), 189-196. https://doi.org/doi.org/10.1016/j.jspi.2010.05.030
  • Muniz, G., Kibria, B., & Shukur, G. (2012). On developing ridge regression parameters: a graphical investigation. Department of Mathematics and Statistics. 10.
  • Muniz, G., & Kibria, B. G. (2009). On some ridge regression estimators: An empirical comparisons. Communications in Statistics—Simulation and Computation®, 38(3), 621-630. https://doi.org/https://doi.org/10.1080/03610910802592838
  • Olvera, L. D., Plat, D., & Pochet, P. (2003). Transportation conditions and access to services in a context of urban sprawl and deregulation. The case of Dar es Salaam. Transport Policy, 10(4), 287-298. https://doi.org/https://doi.org/10.1016/S0967-070X(03)00056-8
  • Olvera, L. D., Plat, D., & Pochet, P. (2008). Household transport expenditure in Sub-Saharan African cities: measurement and analysis. Journal of Transport Geography, 16(1), 1-13. https://doi.org/https://doi.org/10.1016/j.jtrangeo.2007.04.001
  • Öğüt, K. S., Tezcan, H. O., Sarısoy, G., Terzi, F., Gerçek, H., & Gedizlioğlu, E. (2017). Eskişehir Ulaşım Ana Plani Sonuç Raporu.
  • Özlem, A., ŞAMKAR, H., & ALTAN, E. (2010). Sağlam ridge regresyon analizi ve bir uygulama. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 25(2), 137-148.
  • Pabayo, R., Gauvin, L., & Barnett, T. A. (2011). Longitudinal changes in active transportation to school in Canadian youth aged 6 through 16 years. Pediatrics, 128(2), e404-e413. https://doi.org/10.1542/peds.2010-1612
  • Papagiannakis, A., Baraklianos, I., & Spyridonidou, A. (2018). Urban travel behaviour and household income in times of economic crisis: Challenges and perspectives for sustainable mobility. Transport Policy, 65, 51-60. https://doi.org/https://doi.org/10.1016/j.tranpol.2016.12.006
  • Portet, S. (2020). A primer on model selection using the Akaike Information Criterion. Infectious Disease Modelling, 5, 111-128. https://doi.org/https://doi.org/10.1016/j.idm.2019.12.010
  • Primerano, F., Taylor, M. A., Pitaksringkarn, L., & Tisato, P. (2008). Defining and understanding trip chaining behaviour. Transportation, 35(1), 55-72. https://doi.org/https://doi.org/10.1007/s11116-007-9134-8
  • Pulugurtha, S. S., & Repaka, S. R. (2008). Assessment of models to measure pedestrian activity at signalized intersections. Transportation Research Record, 2073(1), 39-48. https://doi.org/https://doi.org/10.3141/2073-05
  • Rasouli, A., Kotseruba, I., & Tsotsos, J. K. (2017). Understanding pedestrian behavior in complex traffic scenes. IEEE Transactions on Intelligent Vehicles, 3(1), 61-70. https://doi.org/10.1109/TIV.2017.2788193
  • Rathert, T. Ç., Üçkardeş, F., Narinç, D., & Aksoy, T. (2011). Comparision of principal component regression with the least square method in prediction of internal egg quality characteristics in japanese quails. Kafkas Universitesi Veteriner Fakultesi Dergisi, 17(5).
  • Resmî Gazete. (2018). Karayolları Trafik Kanunu İle Bazı Kanunlarda Değişiklik Yapılması Hakkında Kanun. Resmî Gazete: Resmî Gazete
  • Revelle, W., & Revelle, M. W. (2015). Procedures for Psychological, Psychometric, and Personality Research. The comprehensive R archive network, 337, 338.
  • Ridel, D., Rehder, E., Lauer, M., Stiller, C., & Wolf, D. (2018). A literature review on the prediction of pedestrian behavior in urban scenarios. 21st International Conference on Intelligent Transportation Systems (ITSC),
  • Schneider, R. J., Arnold, L. S., & Ragland, D. R. (2009). Pilot model for estimating pedestrian intersection crossing volumes. Transportation Research Record, 2140(1), 13-26. https://doi.org/https://doi.org/10.3141/2140-02
  • Shay, E., Fan, Y., Rodríguez, D. A., & Khattak, A. (2006). Drive or walk? Utilitarian trips within a neotraditional neighborhood. Transportation research record, 1985(1), 154-161. https://doi.org/https://doi.org/10.1177/036119810619850011
  • Sietchiping, R., Permezel, M. J., & Ngomsi, C. (2012). Transport and mobility in sub-Saharan African cities: An overview of practices, lessons and options for improvements. Cities, 29(3), 183-189. https://doi.org/https://doi.org/10.1016/j.cities.2011.11.005
  • Sorensen, H., Bogomolova, S., Anderson, K., Trinh, G., Sharp, A., Kennedy, R., Wright, M. (2017). Fundamental patterns of in-store shopper behavior. Journal of Retailing and Consumer Services, 37, 182-194. https://doi.org/https://doi.org/10.1016/j.jretconser.2017.02.003
  • Suel, E., & Polak, J. W. (2017). Development of joint models for channel, store, and travel mode choice: Grocery shopping in London. Transportation Research Part A: Policy and Practice, 99, 147-162.
  • Tarı, R. (2010). Ekonometri, Genişletilmiş 6.
  • Thisted, R. (1976). Ridge regression, minimax estimation, and empirical Bayes methods. [PhD thesis, Stanford University].
  • Topal, M., Eyduran, E., Yağanoğlu, A. M., Sönmez, A., & Keskin, S. (2010). Çoklu Doğrusal Bağlantı Durumunda Ridge ve Temel Bileşenler Regresyon Analiz Yöntemlerinin Kullanımı. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 41(1), 53-57.
  • Ullah, M. I., Aslam, M., & Altaf, S. (2018). lmridge: A Comprehensive R Package for Ridge Regression. R J., 10(2), 326.
  • Üçkardeş, F., Ercan, E., Narinç, D., & Aksoy, T. (2012). Japon bıldırcınlarında yumurta ak indeksinin ridge regresyon yöntemiyle tahmin edilmesi. Akademik Ziraat Dergisi, 1(1), 11-20.
  • Watanabe, S. (2013). A widely applicable Bayesian information criterion. Journal of Machine learning research, 14(27), 867-897.
  • Wells, H. L., McClure, L. A., Porter, B. E., & Schwebel, D. C. (2018). Distracted pedestrian behavior on two urban college campuses. Journal of community health, 43(1), 96-102. https://doi.org/https://doi.org/10.1007/s10900-017-0392-x
  • Yu, B., Zhang, J., & Li, X. (2017). Dynamic life course analysis on residential location choice. Transportation Research Part A: Policy and Practice. https://doi.org/http://dx.doi.org/10.1016/j.tra.2017.01.009
Toplam 63 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Çağdaş Kara 0000-0002-2520-6561

Yayımlanma Tarihi 15 Nisan 2023
Gönderilme Tarihi 5 Nisan 2022
Kabul Tarihi 21 Şubat 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 2

Kaynak Göster

APA Kara, Ç. (2023). Yaya yolculuk taleplerinin mahalle karakteristiklerine dayalı tahmini. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 13(2), 359-372. https://doi.org/10.17714/gumusfenbil.1099142