Trafik seyahat süresinin yağmurlu hava koşulları ve alternatif güzergahları ile ilişkisinin SPSS üzerinde incelenmesi
Investigation of the relationship of traffic travel time with rainy weather conditions and alternative routes on SPSS
Yazar: MURAT AYYILDIZ
Danışman: DOÇ. DR. MURAT ERGÜN
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / İnşaat Mühendisliği Ana Bilim Dalı / Ulaştırma Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Ulaşım = Transportation ; İnşaat Mühendisliği = Civil Engineering ; İstatistik = Statistics
Dizin: Kara yolu ulaşımı = Land transportation ; Çok değişkenli istatistik = Multivariate statistic ; Çok değişkenli model = Multivariate model ; Çok değişkenli regresyon = Multivariate regression ; Çok değişkenli varyans analizi = Multivariate variance analysis ; Şehiriçi ulaşım = Urban transport

Özet:
Nüfus bakımından en yoğun şehirlerden biri olan İstanbul şehrinde trafik sorunları gündemini koruyan konular arasında yer almaktadır. Nüfus hareketliliğinin yüksek olduğu İstanbul'da kara yolu kullanıcılarının bir noktadan bir noktaya gidiş süresinin yani bir başka değişle seyahat süresinin sadece nüfus çokluğu ile açıklanamayacağı, birçok parametrenin etkisinin olabileceği düşünülmektedir. Seyahat süresi çeşitli parametrelere bağlı olarak değişebilmekte ve insanların trafikte geçirdiği zaman da bu değişime göre uzayıp kısalmaktadır. Bu değişimde hangi koşul ve parametrelerin etkili olduğu konusu araştırıldıkça İstanbul gibi büyük kentlerdeki trafiğin etkin bir yönetimi konusunda gelişim kaydedilecektir. Bu parametrelerin hangileri olduğuna istatistiksel yöntemler içeren analizlerle ulaşılabilecektir.

Bu çalışma çerçevesinde yağış miktarının, ve başlangıç-bitiş noktası aynı olan güzergahların kendi arasında birbirine olan etkisi irdelenmiştir. Ve bu parametrelerin hangilerinin hangi durumlarda daha önemli olduğunun analizi yapılması amacıyla çeşitli örneklem grupları üzerinde çalışılmıştır.

İBB Trafik Şube Müdürlüğünden ve Meteoroloji Genel Müdürlüğünden alınan ham veriler düzenlenip birbiriyle ilişkilendirilebilecek şekilde saatlik zaman dilimlerine indirgenmiştir. Çalışmanın kapsamlı ve sonuç odaklı olmasına adına İstanbul Avrupa Yakası'nda Beylikdüzü'nden yola çıkan araçların İstanbul Anadolu Yakası'nda Ataşehir'e varmasına dek geçen süreler kıtalar arası geçişe imkân sağlayan 15 Temmuz Şehitler Köprüsü, Fatih Sultan Mehmet Köprüsü, Avrasya Tüneli ve Yavuz Sultan Selim Köprüsü olmak üzere seçilen dört farklı güzergah için gruplandırma yapılmıştır. Ve bu gruplar yağmur koşullarına göre altı farklı örneklem setiyle incelenmiştir. Bu örneklem setleri; örneklem 1 (yağmurlu günler hafta içi sabah saatler, yağmurlu günler), örneklem 2 (yağmurlu günler ve yağmursuz ardışık günler hafta içi sabah saatler), örneklem 3 (yağmurlu günler hafta içi akşam saatler), örneklem 4 (yağmurlu günler ve yağmursuz ardışık günler hafta içi akşam saatler), örneklem 5 (yağmurlu günler hafta sonu öğle saatler) ve örneklem 6 (yağmurlu günler ve yağmursuz ardışık hafta sonları öğle saatler) olmak üzere altı örneklemden oluşmaktadır. 

İstanbul'un Avrupa Yakası'ndan yola çıkan bir aracın Anadolu Yakası'na ulaşma süresinin yağış ve diğer güzergahlara bağlı olarak değişip değişmediğinin analiz edilmesi için SPSS (Sosyal Bilimler İçin İstatistik Programı) programı kullanılarak sırasıyla normallik analizi, korelasyon analizi, R² analizi, tek yönlü varyans analizi ve çoklu regresyon analizi yapılmıştır. Yapılan analizlerlerin sonucunda bazı bulgulara ulaşılmıştır. Örneklem 1, örneklem 2, örneklem 3, örneklem 4 ve örneklem 6 grupları için yağmurun varlığı ile başlangıç ve bitiş noktaları aynı olan ulaşım rotalarının seyahat sürelerinin belirli bir ulaşım rotasının sahip olduğu seyahat süresi üzerinde istatistiksel olarak anlamlı bir ilişki kısmen bulunmaktadır. Örneklem 5 grubu için yağmurun varlığı ile başlangıç ve bitiş noktaları aynı olan ulaşım rotalarının seyahat sürelerinin belirli bir ulaşım rotasının sahip olduğu seyahat süresi üzerinde istatistiksel olarak anlamlı bir ilişki yoktur.

Bu çalışma, trafik seyahat verilerinden oluşturulan modellerin, SPSS yazılımı kullanılarak belirlenen parametrelere göre istatistiksel yöntemlerle hipotezlerin test edilebileceğini ortaya koymuştur. Çalışmanın bulguları, araştırmacılar ve karar vericiler için değerli bilgiler sağlayabilir. Çeşitli veri setleri, modeller ve koşullar kullanılarak yapılan yeni analizlerle, trafik seyahat süresini etkileyen diğer faktörler de istatistiksel olarak tespit edilebilir. Yeni parametrelerin belirlenmesi, trafik süresi ve zaman yönetimi konusunda etkili yeni çözümlerin geliştirilmesine katkı sunabilir.

Abstract:
Traffic congestion remains a persistent challenge in Istanbul, a bustling metropolis characterized by its dense population and high levels of mobility. Despite efforts to address this issue, it continues to feature prominently on the city's agenda. The complexity of managing traffic in Istanbul stems from the fact that travel time, a crucial metric for commuters, cannot be solely attributed to population density. Rather, it is influenced by a myriad of factors, each playing a role in shaping the urban commute experience. These factors include but are not limited to road infrastructure, weather conditions, time of day, and route choices. Understanding the interplay between these variables is essential for devising effective traffic management strategies. By conducting thorough investigations into the conditions and parameters that impact travel time, cities like Istanbul can take significant strides towards improving their transportation systems. Statistical analysis serves as a valuable tool in this endeavor, allowing policymakers and urban planners to identify key variables and develop targeted interventions aimed at alleviating congestion and enhancing the overall efficiency of the city's road network.

Within the scope of this comprehensive study, an in-depth analysis was conducted to explore the intricate interplay between rainy weather, precipitation levels, and route selection, all of which converge to influence travel dynamics in Istanbul's bustling urban landscape. Through meticulous examination, the study aimed to discern the relative significance of these variables and their impact on travel time variability. To achieve this, a diverse array of sample groups was meticulously constructed, each representing distinct scenarios and conditions prevalent in the city's dynamic transportation ecosystem.

By scrutinizing various sample groups, ranging from rainy days to weekdays and weekends, the study sought to unravel the nuanced relationships between different parameters and their effects on travel time. The goal was to identify which variables exerted a more pronounced influence under specific circumstances, thereby providing valuable insights for effective traffic management strategies. Through rigorous analysis and statistical modeling, the study aimed to shed light on the complex dynamics of travel behavior in response to changing weather conditions and route options.

By delving into the multifaceted interactions between rainy weather, precipitation levels, and route choices, the study aimed to arm policymakers and urban planners with actionable insights to optimize transportation systems and alleviate congestion in Istanbul and other major cities facing similar challenges. Through a combination of empirical data analysis and statistical techniques, the study aimed to pave the way for more informed decision-making and strategic interventions aimed at enhancing the efficiency and reliability of urban transportation networks.

The meticulous process of data compilation and refinement began with the collection of raw data from reputable sources, including the IMM Traffic Branch Directorate and the General Directorate of Meteorology. These datasets, originating from different sources, were harmonized to ensure uniformity and compatibility, facilitating meaningful correlations between variables. By aligning the data to the same time periods, researchers could effectively correlate traffic conditions with meteorological factors, laying the groundwork for a comprehensive analysis.

To ensure the study's comprehensiveness and efficacy, a structured methodology was employed to measure travel time from Beylikdüzü on the European Side to Ataşehir on the Anatolian Side of Istanbul. This measurement was conducted across key intercontinental passages, including the 15 July Martyrs Bridge, Fatih Sultan Mehmet Bridge, Eurasia Tunnel, and Yavuz Sultan Selim Bridge. The Yavuz Sultan Selim Bridge, which is newer than the others, was included in the analysis to provide a holistic view of the travel options offered to users in this study.

The dataset was further stratified into four distinct routes, each representing a unique combination of roadways and bridges, including the Selim Bridge. These routes served as the basis for six different sample sets, meticulously crafted to capture various scenarios and conditions prevalent in Istanbul's diverse traffic landscape. Sample sets were determined based on factors such as weekday morning and evening rush hours during rainy weather conditions, as well as weekend afternoon rush hours. This comprehensive approach ensured that a broad spectrum of traffic conditions was represented, allowing for a nuanced analysis of the impact of rain on travel time variability.

Sample sets were carefully designed to encompass a range of scenarios, from rainy days during weekday rush hours to consecutive days with and without rain, as well as weekend afternoons. By examining these scenarios in isolation and in combination, researchers aimed to tease out the subtle interactions between rain conditions and traffic patterns, providing valuable insights for traffic management and urban planning initiatives.

Through the systematic arrangement of data and the meticulous design of sample sets, the study aimed to provide a robust foundation for understanding the complex dynamics of travel time variability in Istanbul. By elucidating the impact of rain on travel behavior across different routes and scenarios, the findings of the study would inform evidence-based decision-making and contribute to the development of targeted interventions aimed at improving the efficiency and reliability of urban transportation networks. To delve deeper into the intricate relationship between travel time, rainfall, and route selection, a sophisticated statistical analysis technique known as multiple regression analysis was employed. This method, conducted using the powerful SPSS (Statistical Package for the Social Sciences) software, allowed researchers to examine how the time taken for a vehicle to traverse from the European Side to the Anatolian Side of Istanbul varied based on various factors, including rainfall and the choice of routes.

Multiple regression analysis enables researchers to assess the extent to which one or more independent variables, such as rainfall and route selection, collectively predict or influence a dependent variable, in this case, travel time. By systematically analyzing the data collected from the diverse sample sets and routes, researchers could discern the relative importance of each variable and their combined impact on travel time variability.

Through these rigorous statistical analysis processes, it was aimed to reveal the basic patterns and relationships that determine travel time dynamics in Istanbul's complex urban environment. The study can contribute to new studies that will positively affect travel time and related traffic by identifying factors that have a major impact on travel time.

The utilization of multiple regression analysis within the SPSS framework represents a sophisticated approach to data analysis, providing researchers with valuable insights into the multifaceted nature of travel behavior and the complex interplay of factors that shape urban mobility patterns. By leveraging the analytical power of SPSS, researchers could derive actionable insights to inform evidence-based decision-making and drive positive change in the management of urban transportation systems.

Normality analysis, correlation analysis, R square analysis, ANOVA, and multiple regression analysis were conducted on the data groups. The analyses revealed several findings. For sample groups 1, 2, 3, 4, and 6, a statistically significant relationship was found between the presence of rain and the travel times on routes with the same starting and ending points, impacting the travel time of a specific route. However, for sample group 5, no statistically significant relationship was found between the presence of rain and the travel times on routes with the same starting and ending points.

In the Pearson correlation analyses of the routes, no significant correlation was found with the total amount of precipitation in the morning hours on weekdays. However, the total precipitation in the evening hours on weekdays reached a significant level. On weekends, it was observed that rainfall was significant on rainy days for the routes using the July 15 and FSM Bridges, but this significance was lost when consecutive rainless weekend days were included in the sample.

Multiple regression analyses indicated that the effect of morning precipitation on weekdays was not significant. If the analysis was confined to rainy weekday evenings, it was observed that an increase in precipitation significantly affected only the travel time on the route using the YSS Bridge. On weekday evenings with rain and consecutive rainless weekdays, rainfall significantly affected the travel time on the route using the 15 July Martyrs Bridge. All variables lost their significance on rainy weekends. On rainy weekends and consecutive rainless weekend days, only the travel times of alternative routes influenced each other. Conversely, on weekdays, the travel times of nearby routes generally influenced each other, whereas on weekends, the travel times of routes using distant bridges influenced each other. Notably, only the travel time on the route using the 15 July Martyrs Bridge influenced the travel time on the route using the Eurasia Tunnel.

The tendency of people to travel on different routes and at random times on weekends may have caused the impact area to expand or lose significance on rainy days. It can be inferred that the effects on the routes are more significant since weekday mornings and evenings typically correspond to commuting times. The fact that weekday mornings are not affected by rain might be due to fixed work start times, while weekday evenings are affected by rain due to variable home arrival times.

The study revealed that the models created from traffic travel data can be tested with statistical methods according to the parameters determined using SPSS software. The study's findings may provide valuable information for researchers and policy makers. With new analyzes using various data sets, models and conditions, other factors affecting traffic travel time can also be statistically identified. Determining new parameters can contribute to the development of new effective solutions for traffic duration and time management.