K- EN YAKIN KOMŞULAR VE LOJİSTİK REGRESYON MODELLERİ KULLANILARAK ÜNİVERSİTE ÖĞRENCİLERİNİN MİKRO-MOBİLİTE TERCİHLERİNİ ETKİLEYEN FAKTÖRLERİN KARŞILAŞTIRMALI ANALİZİ

Year 2024, Volume: 23 Issue: 46, 488 - 503, 27.12.2024

Mahmut Esad Ergin

https://doi.org/10.55071/ticaretfbd.1544658

Abstract

Paylaşımlı mikro-mobilite hizmetleri, dünya genelinde özellikle büyük şehirlerde hızla benimsenmiştir. Son zamanlarda, bireylerin sürdürülebilir bir ulaşım sistemini desteklemek amacıyla çevre dostu ulaşım modlarına geçiş yapmaları teşvik edilmektedir. Bu nedenle, literatürde, yol kullanıcılarının mikro-mobilite araçlarını kullanma eğilimleri ve potansiyelleri araştırılmaktadır. Bu çalışma, üniversite öğrencilerini hedef alarak, cinsiyet ve yolculuk süresi değişkenleri açısından ilk ve son kilometre (ilk ve son adım) yolculukları için mikro-mobilite araçlarını kullanma eğilimlerini analiz etmektedir. Çalışmada, makine öğrenmesi yaklaşımıyla k-En Yakın Komşu (kNN) ve Lojistik Regresyon (LR) algoritmaları kullanılmış ve karşılaştırılmıştır. Üniversite öğrencileri arasında mikro-mobilite araçlarının potansiyel kullanımını ölçmek amacıyla 150 öğrenciyle yüz yüze anket yapılmıştır. Sonuç olarak, LR modelinin doğruluk açısından kNN modelinden (sırasıyla 0,63 ve 0,43) daha iyi olduğu görülmüştür. Öte yandan, çalışmamıza katılan erkek öğrencilerin %51,82'si ve kadın öğrencilerin %62,50'si, yolculuklarının herhangi bir aşamasında mikro-mobilite araçlarını tercih etme eğiliminde olmadıklarını belirtmiş, potansiyel kullanıcılar için ana zorluğun “güvenlik” kriteri olduğu sonucuna ulaşılmıştır.

Keywords

Cinsiyet, k-en yakın komşu, lojistik regresyon, makine öğrenmesi, mikro-mobilite

A COMPARATIVE ANALYSIS OF THE FACTORS INFLUENCING UNIVERSITY STUDENTS' MICROMOBILITY PREFERENCES USING K-NEAREST NEIGHBORS AND LOGISTIC REGRESSION MODELS

Abstract

Shared micro-mobility services have swiftly become widely adopted in major urban centers globally. In particular, individuals are encouraged to transition to environmentally friendly modes of transportation to support a sustainable transportation system. For this reason, the tendencies and potential of individuals to use micro-mobility vehicles are being investigated. This paper focused on university students, analyzing their preferences for using micromobility vehicles, particularly for first-mile or last-mile trips in terms of gender and travel time variables. In the study, k-Nearest Neighbors (kNN) and Logistic Regression (LR) algorithms are used in machine learning approach and they were compared. A face-to-face survey was conducted with 150 students randomly to measure the potential use of micromobility vehicles among university students. As a result, LR model is better than kNN model according to the accuracy of the models, 0,63 and 0,43 respectively. On the other hand, 51,82% of male students and 62,50% of female students participating in our study reported that they are not inclined to prefer micromobility vehicles at any stage of their trips, and the main challenge for the potential users is safety.

Keywords

Gender, k-nearest neighbors, logistic regression, machine learning, micro-mobility

References

• Adnan, M., Altaf, S., Bellemans, T., Yasar, A.-H., & Shakshuki, E. M. (2019). Last-mile travel and bicycle sharing system in small/medium sized cities: user’s preferences investigation using hybrid choice model. Journal of Ambient Intelligence and Humanized Computing, 10(12), 4721–4731. https://doi.org/10.1007/s12652-018-0849-5
• Alrefaei, A., & Ilyas, M. (2024). Using Machine Learning Multiclass Classification Technique to Detect IoT Attacks in Real Time. Sensors, 24(14). https://doi.org/10.3390/s24144516
• Campisi, T., Kuşkapan, E., Çodur, M. Y., & Dissanayake, D. (2024). Exploring the influence of socio-economic aspects on the use of electric scooters using machine learning applications: A case study in the city of Palermo. Research in Transportation Business & Management, 56, 101172. https://doi.org/10.1016/j.rtbm.2024.101172
• Cheng, W., Yang, J., Wu, X., Zhang, T., & Yin, Z. (2024). A Quantitative Study on Factors Influencing User Satisfaction of Micro-Mobility in China in the Post-Sharing Era. Sustainability, 16(4). https://doi.org/10.3390/su16041637
• Cho, S.-H., & Shin, D. (2022). Estimation of Route Choice Behaviors of Bike-Sharing Users as First- and Last-mile Trips for Introduction of Mobility-as-a-Service (MaaS). KSCE Journal of Civil Engineering, 26(7), 3102–3113. https://doi.org/10.1007/s12205-022-0802-1 Cochran, W.G. (1963). Sampling Techniques (3rd ed.), 75. John Wiley and Sons, Inc.
• Comi, A., Hriekova, O., & Nigro, M. (2024). Exploring road safety in the era of micro-mobility: evidence from Rome. Transportation Research Procedia, 78, 55–62. https://doi.org/10.1016/j.trpro.2024.02.008
• Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964
• Cubells, J., Miralles-Guasch, C., & Marquet, O. (2023). Gendered travel behaviour in micromobility? Travel speed and route choice through the lens of intersecting identities. Journal of Transport Geography, 106, 103502. https://doi.org/10.1016/j.jtrangeo.2022.103502
• Cunningham, P., & Delany, S. J. (2021). k-Nearest Neighbour Classifiers - A Tutorial. ACM Comput. Surv., 54(6). https://doi.org/10.1145/3459665
• Degele, J., Gorr, A., Haas, K., Kormann, D., Krauss, S., Lipinski, P., Tenbih, M., Koppenhoefer, C., Fauser, J., & Hertweck, D. (2018). Identifying E-Scooter Sharing Customer Segments Using Clustering. 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 1–8. https://doi.org/10.1109/ICE.2018.8436288
• Delbosc, A., & Thigpen, C. (2024). Who uses subsidized micromobility, and why? Understanding low-income riders in three countries. Journal of Cycling and Micromobility Research, 2, 100016. https://doi.org/10.1016/j.jcmr.2024.100016
• Dozza, M., Violin, A., & Rasch, A. (2022). A data-driven framework for the safe integration of micro-mobility into the transport system: Comparing bicycles and e-scooters in field trials. Journal of Safety Research, 81, 67–77. https://doi.org/10.1016/j.jsr.2022.01.007
• Ergin, M. E., & Tezcan, H. O. (2022). Joint Logit Model Approach to Analyze Soccer Spectators’ Arrival Time and Location Preferences for Interim Activities in Istanbul. International Journal of Engineering, Transactions A: Basics, 35(4). https://doi.org/10.5829/IJE.2022.35.04A.01
• Espino, R. (2023). Identifying Latent Variables for Active Cycling Mobility. An Application for University Students. Transportation Research Procedia, 71, 140–147. https://doi.org/10.1016/j.trpro.2023.11.068
• Forum, I. T. (2024). Safer Micromobility. 129. https://doi.org/10.1787/0d2e0dd5-en
• Gallego, A.-J., Calvo-Zaragoza, J., Valero-Mas, J. J., & Rico-Juan, J. R. (2018). Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation. Pattern Recognition, 74, 531–543. https://doi.org/10.1016/j.patcog.2017.09.038
• Geeksforgeeks. (2024). Logistic Regression in Machine Learning. Retrieved September 1, 2024 from https://www.geeksforgeeks.org/understanding-logistic-regression/
• Guan, X., van Lierop, D., An, Z., Heinen, E., & Ettema, D. (2024). Shared micro-mobility and transport equity: A case study of three European countries. Cities, 153, 105298. https://doi.org/10.1016/j.cities.2024.105298
• Hensher, D. A., Wei, E., Liu, W., & Balbontin, C. (2024). Profiling future passenger transport initiatives to identify the growing role of active and micro-mobility modes. Transportation Research Part A: Policy and Practice, 187, 104172. https://doi.org/10.1016/j.tra.2024.104172
• Hong, D., Jang, S., & Lee, C. (2023). Investigation of shared micromobility preference for last-mile travel on shared parking lots in city center. Travel Behaviour and Society, 30, 163–177. https://doi.org/10.1016/j.tbs.2022.09.002
• Ignaccolo, M., Inturri, G., Cocuzza, E., Giuffrida, N., Le Pira, M., & Torrisi, V. (2022). Developing micromobility in urban areas: network planning criteria for e-scooters and electric micromobility devices. Transportation Research Procedia, 60, 448–455. https://doi.org/10.1016/j.trpro.2021.12.058
• Jaber, A., Ashqar, H., & Csonka, B. (2024). Determining the Location of Shared Electric Micro-Mobility Stations in Urban Environment. Urban Science 8(2). https://doi.org/10.3390/urbansci8020064
• Ji, Y., Fan, Y., Ermagun, A., Cao, X., Wang, W., & Das, K. (2017). Public bicycle as a feeder mode to rail transit in China: The role of gender, age, income, trip purpose, and bicycle theft experience. International Journal of Sustainable Transportation, 11(4), 308–317. https://doi.org/10.1080/15568318.2016.1253802
• Li, Q., Zhang, E., Luca, D., & Fuerst, F. (2024). The travel pattern difference in dockless micro-mobility: Shared e-bikes versus shared bikes. Transportation Research Part D: Transport and Environment, 130, 104179. https://doi.org/10.1016/j.trd.2024.104179
• Maalouf, M. (2011). Logistic regression in data analysis: an overview. International Journal of Data Analysis Techniques and Strategies, 3(3), 281–299. https://doi.org/10.1504/IJDATS.2011.041335
• Mahesh, B. (2019). Machine Learning Algorithms -A Review. In International Journal of Science and Research (IJSR), 9. https://doi.org/10.21275/ART20203995
• NABSA. (2022). 4th Annual Shared Micromobility State of the Industry Report. https://doi.org/10.7922/G20R9MRM
• NACTO. (2023). Shared Micromobility in the U.S. and CANADA. Retrieved September 1, 2024 from https://nacto.org/publication/shared-micromobility-in-2022/
• Özdemir, P. (2023). University students; perspectives on micromobility: An evaluation based on e-scooters TT - Üni̇versi̇te öğrenci̇leri̇ni̇n mi̇kromobi̇li̇teye bakış açıları: E-scooterlar açısından bi̇r değerlendi̇rme. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 6(2), 223–237. https://doi.org/10.51513/jitsa.1257000
• Portal, M. (n.d.). Mobility Portal Europe. Retrieved July 30, 2024, from https://mobilityportal.eu/270-million-users-chose-to-use-shared-micromobility-services-in-europe/#:~:text=While the demand for shared,by 17.7%25 compared to 2021.
• Reck, D. J., & Axhausen, K. W. (2021). Who uses shared micro-mobility services? Empirical evidence from Zurich, Switzerland. Transportation Research Part D: Transport and Environment, 94, 102803. https://doi.org/10.1016/j.trd.2021.102803
• Regulski, K., Opaliński, A., Swadźba, J., Sitkowski, P., Wąsowicz, P., & Kwietniewska-Śmietana, A. (2024). Machine Learning Prediction Techniques in the Optimization of Diagnostic Laboratories’ Network Operations. In Applied Sciences, 14(6). https://doi.org/10.3390/app14062429
• Roig-Costa, O., Miralles-Guasch, C., & Marquet, O. (2024). Shared bikes vs. private e-scooters. Understanding patterns of use and demand in a policy-constrained micromobility environment. Transport Policy, 146, 116–125. https://doi.org/10.1016/j.tranpol.2023.11.010
• Rymarczyk, T., Kozłowski, E., Kłosowski, G., & Niderla, K. (2019). Logistic Regression for Machine Learning in Process Tomography. Sensors, 19(15). https://doi.org/10.3390/s19153400
• Sadeghi, M., Aghabayk, K., & Quddus, M. (2024). A hybrid Machine learning and statistical modeling approach for analyzing the crash severity of mobility scooter users considering temporal instability. Accident Analysis & Prevention, 206, 107696. https://doi.org/10.1016/j.aap.2024.107696
• Sarker, M. A. A., Asgari, H., Chowdhury, A. Z., & Jin, X. (2024). Exploring Micromobility Choice Behavior across Different Mode Users Using Machine Learning Methods. Multimodal Transportation, 100167. https://doi.org/10.1016/j.multra.2024.100167
• Scikit-learn. (n.d.-a). KNeighborsClassifier. Retrieved September 1, 2024 from https://scikit-learn.org/stable/modules/generated/sklearn.neighbors. KNeighborsClassifier.html
• Scikit-learn. (n.d.-b). LogisticRegression. Retrieved September 1, 2024 from https://scikit-learn.org/stable/modules/generated/sklearn.linear_model. LogisticRegression.html
• Sun, B., Garikapati, V., Wilson, A., & Duvall, A. (2021). Estimating energy bounds for adoption of shared micromobility. Transportation Research Part D: Transport and Environment, 100, 103012. https://doi.org/10.1016/j.trd.2021.103012
• Tait, C., Beecham, R., Lovelace, R., & Barber, S. (2022). Is cycling infrastructure in London safe and equitable? Evidence from the cycling infrastructure database. Journal of Transport & Health, 26, 101369. https://doi.org/10.1016/j.jth.2022.101369
• Trevor, Hastie; Tibshirani, Robert; Friedman, J. (2019). The Elements of Statistical Learning (2nd Ed.). Springer New York, NY. https://doi.org/10.1007/978-0-387-84858-7
• Tzouras, P. G., Mitropoulos, L., Karolemeas, C., Stravropoulou, E., Vlahogianni, E. I., & Kepaptsoglou, K. (2024). Agent-based simulation model of micro-mobility trips in heterogeneous and perceived unsafe road environments. Journal of Cycling and Micromobility Research, 2, 100042. https://doi.org/10.1016/j.jcmr.2024.100042
• Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1), 6256. https://doi.org/10.1038/s41598-022-10358-x
• Wang, Y., Pan, Z., & Dong, J. (2022). A new two-layer nearest neighbor selection method for kNN classifier. Knowledge-Based Systems, 235, 107604. https://doi.org/10.1016/j.knosys.2021.107604
• Wang, Y., Pan, Z., & Pan, Y. (2020). A Training Data Set Cleaning Method by Classification Ability Ranking for the $k$ -Nearest Neighbor Classifier. IEEE Transactions on Neural Networks and Learning Systems, 31(5), 1544–1556. https://doi.org/10.1109/TNNLS.2019.2920864
• Wolnowska, A. E., & Kasyk, L. (2024). Study of the Demand for Ecological Means of Transport in Micromobility: A Case of Bikesharing in Szczecin, Poland. Sustainability, 16(9). https://doi.org/10.3390/su16093620
• Yin, Z., Rybarczyk, G., Zheng, A., Su, L., Sun, B., & Yan, X. (2024). Shared micromobility as a first- and last-mile transit solution? Spatiotemporal insights from a novel dataset. Journal of Transport Geography, 114, 103778. https://doi.org/10.1016/j.jtrangeo.2023.103778
• Zhang, C., Du, B., Zheng, Z., & Shen, J. (2023). Space sharing between pedestrians and micro-mobility vehicles: A systematic review. Transportation Research Part D: Transport and Environment, 116, 103629. /https://doi.org/10.1016/j.trd.2023.103629