Investigation of factors influencing the transportation mode preferences of academic staff: Ege University case

Abstract

University campus transportation plans should focus on reducing private vehicle trips, similar to strategies used in urban areas. This not only promotes an environmentally sustainable campus transportation system but also ensures safer transportation for non-motorized modes. In this context, it becomes crucial to identify the factors that influence mode choice. In this paper, by establishing a binary logit model, we estimate the factors influencing the choice of using the car mode for commute trips among the academic staff of Ege University, Türkiye. The most significant variables found in this study are age, academic title, and trip durations for both car and public transportation. While sex is typically expected to play a significant role in mode choice models, it was found to be insignificant for academic staff in this study. A hypothetical scenario revealed that reducing the final walk durations from public transportation stops by half decreases the average public transportation trip duration from 38.08 minutes to 33.88 minutes, resulting in a 5% shift from private transportation to public transportation. These findings are informative and provide a basis for implementing measures to reduce car trips, thereby promoting a green university campus.

Keywords

• University campus transportation plan
• Demand modeling
• Binary logit
• Green campus.

Akademik personelin ulaşım türü tercihlerini etkileyen etmenlerin araştırılması: Ege Üniversitesi örneği

Öz

Üniversite kampüs ulaşım planları, kentsel alanlarda kullanılan stratejilere benzer şekilde özel ulaşım yolculuklarını azaltmaya odaklanmalıdır. Böylece çevresel olarak sürdürülebilir bir kampüs ulaşım sistemi teşvik edilmekle birlikte motorsuz türler için daha güvenli bir ulaşım sistemi sağlanmış olur. Bu bağlamda, tür seçimini etkileyen faktörlerin belirlenmesi büyük önem kazanmaktadır. Bu çalışmada Ege Üniversitesi akademik personelinin iş yolcuklarında özel ulaşım türünü kullanma tercihini etkileyen faktörlerin belirlenmesi amacıyla ikili logit model kurulmuştur. Çalışma sonucunda en etkili etmenlerin yaş, akademik unvan, özel ve toplu taşıma için yolculuk süreleri olduğu görülmüştür. Türel seçim modellerinde cinsiyetin anlamlı bir parametre olması beklenirken, bu çalışmada akademik personel için anlamlı olmadığı görülmüştür. Duraklardan varış noktasına yürüme sürelerinin yarıya düştüğü, buna bağlı olarak ortalama toplu taşıma yolculuk süresinin 38.08 dakikadan 33,88 dakikaya düştüğü bir hipotetik bir senaryoda, logit model kullanılarak özel ulaşımdan toplu taşımaya %5'lik bir kayma gerçekleşeceği hesaplanmıştır. Bu bulgular, özel taşıt yolculuklarını azaltmaya yönelik tedbirlerin uygulanması ve böylece yeşil bir üniversite kampüsünün teşvik edilmesi için bir temel sağlayabilecektir.

Anahtar Kelimeler

• Üniversite kampüsü ulaşım planı
• Talep modellemesi
• İkili logit
• Yeşil kampüs

Kaynakça

1. [1] Arslan T, Murat YŞ. “Kent ulaşımında organizasyonel sorunların değerlendirilmesi: Antakya örneği”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 17(1), 33-41, 2011.
2. [2] Aldrete-Sanchez R, Shelton J, Cheu RL. “Integrating the Transportation System with a University Campus Transportation Master Plan: A Case Study”. Texas Transportation Institute, Texas, USA, 125, 2010.
3. [3] Balsas CJL. “Sustainable transportation planning on college campuses”. Transport Policy, 10(1), 35-49, 2003.
4. [4] Asadi-Shekari Z, Moeinaddini M, Zaly Shah M. “A pedestrian level of service method for evaluating and promoting walking facilities on campus streets”. Land Use Policy, 38, 175-193, 2014.
5. [5] Morar T, Bertolini L. “Planning for pedestrians: A way out of traffic congestion”. Procedia - Social and Behavioral Sciences, 81, 600-608, 2013.
6. [6] Brown J, Hess DB, Shoup D. “Unlimited access”. Transportation, 28, 233-267, 2001.
7. [7] Maricopa Association of Governments. “Arizona State University Travel Survey and Model Update: Final Report”. Arizona, USA, 177, 2015.
8. [8] Abiola OS, Ayodeji JD. “Travel demand model for a typical Nigeria university”. International Journal of Civil & Environmental Engineering IJCEE-IJENS, 12(3), 89-99, 2012.

9. [9] Garikapati VM, You D, Pendyala RM, Patel T, Kottommannil J, Sussman A. “Design, development, and implementation of a university travel demand modeling framework”. Transportation Research Record: Journal of the Transportation Research Board, 2563(1), 105-113, 2016.
10. [10] Hamad K, Obaid L. “Tour-based travel demand forecasting model for a university campus”. Transport Policy, 117, 118-137, 2022.
11. [11] Kim J, Lee B. “Campus commute mode choice in a college town: An application of the integrated choice and latent variable (ICLV) model”. Travel Behaviour and Society, 30, 249-261, 2023.
12. [12] Altıntaşı O, Tüydeş Yaman H. “Utilization of RFID data to evaluate characteristics of private car commuters in Middle East Technical University campus”. Pamukkale University Journal of Engineering Sciences, 22(3), 171-177, 2016.
13. [13] Tyrinopoulos Y, Antoniou C. “Factors affecting modal choice in urban mobility”. European Transport Research Review, 5(1), 27-39, 2013.
14. [14] Burian J, Zajíčková L, Ivan I, Macků K. “Attitudes and motivation to use public or individual transport: A case study of two middle-sized cities”. Social Sciences, 7(6), 83, 2018.
15. [15] Silvestri A, Foudi S, Galarraga I. “How to get commuters out of private cars? Exploring the role of perceived social impacts in mode choice in five European countries”. Energy Research & Social Science, 92, 102811, 2022.
16. [16] Puan OC, Hassan YAH, Mashros N, Idham MK, Hassan NA, Warid MNM, et al. “Transportation mode choice binary logit model: A case study for Johor Bahru city”. IOP Conference Series: Materials Science and Engineering, 527(1), 012066, 2019.
17. [17] Abuhamoud MAA, Rahmat RAOK, Ismail A. “Modeling of transport mode in Libya: A binary logit model for government transportation encouragement”. Australian Journal of Basic and Applied Sciences, 5(5), 1291-1296, 2011.
18. [18] Chauhan V, Suman HK, Bolia NB. “Binary logistic model for estimation of mode shift into Delhi metro”. The Open Transportation Journal, 10(1), 124-136, 2016.
19. [19] Basbas S, Tetou V, Politis I. “Ordinal and binary logistic logit models for examination of behavioral, infrastructure and perception factors influencing biking”. WIT Transactions on the Built Environment, 130, 573-584, 2013.
20. [20] Cochran WG. Sampling Techniques. 3rd ed. New York, USA, John Wiley & Sons, Inc., 1977.
21. [21] Bartlett JE, Kotrlik JW, Higgins CC. “Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research”. Information Technology, Learning, and Performance Journal, 19(1), 43-50, 2001.
22. [22] Google. “Google Directions API”. https://developers.google.com/maps/documentation/directions (10.02.2024).
23. [23] ESHOT. “Nasıl Giderim”. https://eshot.gov.tr/tr/NasilGiderim (10.02.2024).
24. [24] Washington SP, Karlaftis MG, Mannering F. Statistical and Econometric Methods for Transportation Data Analysis. 1st ed. New York, USA, Chapman and Hall/CRC, 2003.
25. [25] Heinen E, Chatterjee K. “The same mode again? An exploration of mode choice variability in Great Britain using the national travel survey”. Transportation Research Part A: Policy and Practice, 78, 266-282, 2015.
26. [26] Kwan M, Kotsev A. “Gender differences in commute time and accessibility in Sofia, Bulgaria: A study using 3D geovisualisation”. The Geographical Journal, 181(1), 83-96, 2015.
27. [27] Best H, Lanzendorf M. “Division of labour and gender differences in metropolitan car use”. Journal of Transport Geography, 13(2), 109-121, 2005.
28. [28] Al-Fouzan SA. “Using car parking requirements to promote sustainable transport development in the Kingdom of Saudi Arabia”. Cities, 29(3), 201-211, 2012.
29. [29] Shoup DC. The High Cost of Free Parking. 1st ed. New York, USA, Routledge, 2005.