Case Study In Istanbul Research Articles

Quantifying thermo-physiological stress exposure during extreme events: Developing a morphological case study in Istanbul

This study aimed to determine thermal comfort conditions during Extreme Heat Events (EHE), particularly focus on Very Hot Day and Heat Wave Event in Istanbul. The analysis was based on a comprehensive dataset of hourly temperature records collected from six meteorological stations over the period 1991–2021. Subsequently, Land Surface Temperature (LST) was mapped on the basis of EHE days. Finally, specific areas with different street morphologies values from the referenced LST were obtained, enabling the evaluation of heat stress for the EHE day and for the period between 2006 and 2021. The study identified August 5th, 2021 as the day of extreme heat stress in Istanbul. LST obtained for August 5th was divided into three groups: low (23.3°C), moderate (26°C), and high (29.3 - 30°C), and findings regarding heat stress were obtained through thermal indices. Physiologically Equivalent Temperature (PET) values on August 5th, 2021, were significantly higher than the 15-year average, showing a shift from moderate and slight heat stress to strong and extreme heat stress levels. PET values indicated strong heat stress, especially around noon, reaching up to 40.5°C in lower LST, and extreme heat stress level of 45.7°C in the afternoon in higher LST. Extreme thermal conditions were generally observed on the right side of streets oriented between 105° and 165° in both regions.

The Impact of Innovative Transportation Services on Travel Behaviors: A Case Study of Istanbul

There has been a paradigm shift in urban mobility around the developments in information and communication technologies, zero emission targets to reduce transport-related emissions. Hence, traditional transportation services are replaced by sustainable, shared, smart, innovative mobility services. It is crucial to understand what extent these services, which are increasingly used in the cities and have potential to become a part of the main transportation system of the city rather than serving as an alternative mode of transportation, affect people's travel behaviors and daily activity preferences to be aware of the trends in urban mobility, passenger and freight mobility. By this study, new mobility services such as micromobility, car and ride sharing services used in Istanbul examined in the context of (1) their effect on travel behavior (2) the relationship between people's demographic, socioeconomic status and new transportation service use (3) the conditions where the adoption of these services are higher. In this study, survey was conducted with a sample of 404 people in Istanbul and the results were interpreted using both explanatory analysis methods and the chi-square independence test as a statistical method. As a result of the study, questions such as what is the relationship between the demographics, travel behaviors and the use of new mobility services, the effect of these services on the total trips, transportation modes replaced by them, in which situations they are preferred or not and under what conditions those who do not use this service can use them were answered

Optimizing Distribution Center Locations for Disaster Relief: A Multi-Objective Model and Case Study in Istanbul

Optimizing Distribution Center Locations for Disaster Relief: A Multi-Objective Model and Case Study in Istanbul

Investigating the Relationship between Urbanization and Air Pollution Using Google Earth Engine Platform: A Case Study of Istanbul

Rapid population growth in megacities such as Istanbul has led to various effects, such as industrialization, urbanization, loss of green areas, increasing vehicle traffic, and higher consumption of fossil fuels. These reasons, along with many other environmental factors, contribute to the rise of air pollution in urban life. This study aimed to examine the relationship between urbanization and air pollution in Istanbul. For this purpose, land cover maps covering Istanbul province were produced using Landsat-5 (TM), Landsat-8 (OLI), and Sentinel-2 (MSI) images for the years 1996 to 2021 at three-year intervals on the Google Earth Engine platform. Land cover for classification purposes was divided into five different classes: forest, water surface, urban area, and bare land, and classified using a random forest machine learning algorithm. To examine the impact of this urban area growth on air pollution, in the second step of the study, the column number density values of Sentinel 5P (TROPOMI) data for SO2, NO2, CO, and O3 gases for 2019, 2020, and 2021 were analyzed. The averages of the data from 39 air pollution monitoring stations across Istanbul were also examined. According to this classification, the urban area expanded from 491 km2 in 1996 to 1222 km2 by 2021. Considering the total surface area of Istanbul province, the urban area, which was 9% in 1996, reached 23% by 2021. The TROPOMI values were calculated as follows: the average column number density values for SO2, NO2, CO, and O3 were 0.0003538 mol/m², 0.0339514 mol/m², 0.0000984 mol/m², and 0.1453515 mol/m², respectively. Similarly, the gas concentrations of SO2, NO2, CO, and O3 measured from the ground stations were calculated as 6.603 µ/m3, 786,815 µ/m3, 43.763 µ/m3 and 45.773 µ/m3, respectively. Correlations between urbanization and TROPOMI values revealed a positive correlation of 0.39, 0.02, and 0.80 for SO2, NO2, and CO gases, while a negative correlation of 0.25 was found for O3 gas. The study also examined correlations between TROPOMI and ground station measurements, resulting in positive correlations of 0.55, 0.66, and 0.16 for SO2, NO2, and CO gases, respectively, while a negative correlation of 0.05 was found for O3 gas. Based on these findings, among the air pollutants studied both through TROPOMI and ground station data, the highest correlation was observed for CO gas.

Does Parking Type Preference Behavior Differ According to Whether It Is Paid or Free? A Case Study in Istanbul, Türkiye

Parking behavior depends on drivers’ choice of parking type and willingness to pay for parking. Generally, the parking type refers to off-street and on-street parking facilities. The main factors affecting the preference for parking types are driver, vehicle, travel, and parking characteristics. Understanding drivers’ parking type preference behavior and accurately modeling drivers’ tendencies helps develop sustainable parking management policies. This study examines the parking preferences of drivers in Istanbul with binary logit models according to whether they pay for parking. The results of the models show that the number of factors influencing parking type preference is higher for free parking than for paid parking, including driver, vehicle, travel, and parking characteristics. Moreover, some factors in the models affect drivers’ parking type preferences differently for paid and free parking. Namely, low-income individuals tend to use on-street parking when parking is free and off-street parking when it is paid. Conversely, individuals who drive small-size vehicles prefer off-street parking for free parking and on-street parking for paid parking. Individuals who prefer off-street parking for free parking expect shorter walking distances to the final destination and parking duration. On the contrary, individuals who choose on-street parking for paid parking anticipate shorter walking distances to the final destination and parking duration.

Exploring the Potential Use of Social Media Platforms and Decision Support Tools in Disaster Management: A Case Study in Istanbul, Türkiye

Exploring the Potential Use of Social Media Platforms and Decision Support Tools in Disaster Management: A Case Study in Istanbul, Türkiye

A novel seismic vulnerability assessment for the urban roadway by using interval valued fermatean fuzzy analytical hierarchy process

AbstractSeismic activity poses significant challenges to urban road infrastructure, often resulting in road closures due to the combined effects of damaged buildings and affected road networks. In contrast, the resilience of roads is crucially important for all kinds of relief activities after an earthquake in this context, this study outlines a methodological framework for assessing the vulnerability of urban road infrastructure to seismic activity. By integrating various criteria within an Interval-valued Fermatean fuzzy Analytic Hierarchy Process framework, the approach offers a comprehensive analysis of vulnerability, considering both quantitative and qualitative factors. This method is a weighting method that has not been used before in MCDM studies in the field of earthquakes. A risk factor is obtained for each road section by using this comprehensive analysis of the vulnerability. This integrated approach considers the interplay between damaged buildings, road networks, and disaster response mechanisms, thereby enhancing the ability to anticipate and respond to seismic events effectively. The study conducts a case study in Istanbul, Turkey, a seismic-prone area, to validate the effectiveness of the proposed methodology. Key findings indicate that the approach can identify and quantify vulnerabilities within the transport network, enabling the identification of high-risk areas for necessary mitigation measures. Moreover, the methodology’s validity is confirmed through a validation study in Gölbaşı district, Adıyaman, Türkiye, which experienced severe damage during earthquakes on 6 February 2023 earthquakes. By providing a structured and comprehensive vulnerability analysis, the research aims to contribute to the resilience of urban infrastructure, particularly in earthquake-prone regions.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

Site Selection of Medical Waste Disposal Facilities Using the Interval-Valued Neutrosophic Fuzzy EDAS Method: The Case Study of Istanbul

In recent years, as a result of the increasing demand for health services, medical waste (MW) generated from health facilities has increased significantly. Problems that threaten the environment and public health may arise as a result of inadequate medical waste management (MWM), especially in densely populated metropolitan areas. Therefore, it is very important that the disposal process of waste is carried out in a way that minimizes harm to human health and the environment. MW disposal site selection is among the most important decisions that local governments make. These decisions have many conflicting and similar criteria and alternatives. However, decision-makers may experience significant uncertainty when evaluating the alternatives. This study adopts the interval-valued neutrosophic (IVN) fuzzy EDAS method for the evaluation of MW disposal facility siting alternatives in Istanbul. This approach is used to evaluate potential sites based on a comprehensive, hierarchical criteria framework designed to address data uncertainty and inconsistency common in multi-criteria decision-making (MCDM) scenarios. Within the scope of the study, six main criteria (distance settlement area, social acceptance, costs, environmental impacts, infrastructure facilities, and disaster and emergency) and nineteen sub-criteria are meticulously analyzed. Considering the geographical location and dense urban texture of Istanbul, the study emphasizes the criteria related to distance to residential areas, logistics costs, and potential disaster risks. Among the identified criteria, land costs, topographical features, proximity to landfills, and distance to high-voltage lines are emphasized as the least important criteria. This study, which evaluated various alternatives, identified Pendik, located on the Anatolian side of Istanbul, as the most suitable site for MW disposal due to its minimal risk. The study also compares the four main alternatives and highlights their relative strengths and weaknesses.

The Impact of the COVID-19 Pandemic on Online Grocery Supply Chain Management: A Case Study in Istanbul

The COVID-19 pandemic has already crippled normal life all over the world. Its negative impact not only changed the human health system tragically but also disrupted the global economic system. One negative result was ended up in the global food supply chain. As the lockdown times have suspended the manufacturing and logistic activities, therefore, the customers have experienced unimaginable chaos in the shopping markets. Moreover, the purchasing habit of the consumers has remarkably changed compared to pre-pandemic. To meet this new demand pattern, many grocery retailers have tried to adapt to the new normal. While before COVID-19 offline grocery purchasing was popular, after the pandemic, online service got tremendous attention in market. In this study, online grocery supply chain management during the COVID-19 in Istanbul is considered. The aim is to find out how online grocery companies will serve more efficiently during the pandemics and which factors have more effect on the customer’s satisfaction. To do so, first, three popular grocery retailers in Istanbul were selected. Then, a related survey was designed to understand the consumer experience as doing online grocery shopping in COVID-19. Unsurprisingly, a result shows that 60% of the respondents did online shopping every 3-4 days in one week, and the delivery time is the most important factor for the customers. Then, the SWOT analyses were performed accordingly, and the related strategies were summarized. Finally, several managerial implications were given to may improve the company’s online services in COVID-19 and post COVID-19 in Turkey.

Integrated optimization of facility location, casualty allocation and medical staff planning for post-disaster emergency response

PurposeDisaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.Design/methodology/approachThis study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.FindingsUsing a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.Originality/valueThis study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

A Different Nested Logit Model Structure Consistent with Random Utility Theory for Various Freight Types – A Case Study for Istanbul

Freight transport significantly contributes to urban traffic but is often overlooked by decision-makers compared to passenger transport. Conventional transportation modelling studies often use aggregate approaches for freight transport, undoubtedly due to the difficulty of data collection. However, the nature of freight transportation is much more complex. For this reason, examining the determinants of freight vehicle preferences with discrete approaches is crucial for the contributions that can be put forward, especially in local studies. To address this apparent gap in the study of local disaggregated approaches to freight transport, we present a discrete modelling-based methodology to investigate the factors that determine freight vehicle preferences for shippers and senders. The estimated nested logit model is constructed with the RU2 approach, the second part of random utility theory, thus avoiding the theoretical inconsistencies that arise when generic coefficients are used. As a result, the model structures provided satisfactory results compared to the literature. It was revealed that the factors affecting freight vehicle choice preferences were influenced by packaging preferences and differed according to freight groups. This local study is the first nested logit study for freight modelling in Istanbul and it is aimed to shed light on future national studies.

The effects of technological developments in transportation vehicles on air pollution mitigation of metropolitan cities: A case study of Istanbul

This study investigates the impact of transportation vehicles on air pollution emissions in Istanbul, a metropolitan city in Türkiye. The TIMES (The Integrated MARKAL-EFOM System) Model has been used to determine the current level of air pollutants coming from the transportation sector, make future estimates, and assess the impact of various scenarios on air pollution emissions. Air pollutants such as carbon monoxide, nitrogen oxides, sulfur oxides, particulate matter, and non-methane volatile organic compounds are included in the model.The amount of air pollution emissions discussed in this research were 74, 68, 13, 5, and 2 kt for NOx, CO, VOC, SO2, and PM, respectively, in the base year of 2016. In 2055, these emissions have shifted to 190, 98, 26, 8, and 5 kt, respectively. This means that emissions are predicted to increase between 1.4 and 2.6 times. According to model results, individual measures could decrease potential air pollution emissions for 2055 by up to 13 %. When all of the actions done within the pollutants of the study are combined, the total amount of emissions has decreased by 30.2 %, 24.3 %, 18.8 %, 5.3 % and 21.4 % for NOx, CO, VOC, SO2, and PM, respectively. This research emphasizes how critical it is to address metropolitan areas' transportation-related air pollution. The number of such studies dealing with air pollution parameters using the TIMES Model is very few, and it is expected that this study will create important outputs for similar studies.

Understanding the Spatial Relationship between Large Investment Decisions and Plan Changes: A Case Study of Istanbul

Understanding the Spatial Relationship between Large Investment Decisions and Plan Changes: A Case Study of Istanbul

Last mile delivery routing problem using autonomous electric vehicles

This paper presents a study on the application of Autonomous Delivery Vehicles (ADVs) in last-mile delivery for urban logistics. Specifically, we focus on a routing problem using multi-stop ADVs, with the goal of minimizing route and vehicle usage costs while satisfying several constraints associated with load and battery capacities, maximum route duration for ADVs, and maximum walking distance for customers. We refer to this problem as the Autonomous Delivery Vehicle Routing Problem (ADVRP) and present its mixed-integer linear programming formulation. Due to the NP-hardness of the problem, we propose a two-phase metaheuristic approach that first clusters customers and determines stopping locations for the ADVs, followed by a phase that determines the optimal routes for the ADVs using hybrid variable neighborhood search and simulated annealing. To evaluate our proposed solution methodology, we conduct computational experiments on various related Vehicle Routing Problems (VRPs) from the literature and newly generated ADVRP instances. The results show that the proposed two-phase metaheuristic approach can produce high-quality solutions with minimal computational effort while outperforming an exact solver in 26 medium- and large-sized instances of ADVRP and reaching optimal solutions in most VRP instances and related problems. Furthermore, we conduct sensitivity analyses on selected problem parameters and present a case study in Istanbul, Turkey to provide managerial insights for implementing ADVs in urban logistics.

On the identification of most appropriate green roof types for urbanized cities using multi-tier decision analysis: A case study of Istanbul, Turkey

Green roofs (GRs) have gained considerable attention in recent years as a sustainable solution for densely urbanized cities to mitigate the negative impacts of urbanization on the environment. Hence, this research proposed a hybrid multi-criteria decision-making scheme aiming to determine the best-matched GR formation for metropolitan city of Istanbul. Therefore, fuzzy AHP and TOPSIS were adopted for weighting a total of 16 criteria and performing binary prioritization of intensive GRs (IGRs) and extensive GRs (EGRs) for the focalized region, respectively. Two GR types were examined based on four major dimensions, i.e., financial, technical, knowledge, and environmental aspects. The results highlighted the vitality of financial (35.01%) and environmental (34.91%) facets, followed by the technical (21.83%) dimension, whereas knowledge (8.24%) was the least significant aspect. Also, initial and operational costs (global weight: 17.37%) and stormwater retention capacity (global weight: 16.98%) outperformed their counterparts. Combining the criteria weight computed and the average scores of each factor with regard to the city further illustrated that EGR (CCi: 0.7442) was found as the optimal GR configuration for Istanbul. The key findings of this research aimed to contribute to the growing GR literature by exploring the paradigms regarding the sustainability and resilience of urbanized cities.

In pursuit of improved rural-urban governance: an investigation of multi-level stakeholder analysis

ABSTRACT This paper aims to examine the distinct conditions and implications of a multi-level stakeholder analysis for the governance of urban-rural intersections through a case study in Istanbul, adopting an advanced stakeholder analysis from UN-Habitat. Based on a case study of the landscape identity of Istanbul's rural settlements, the study offers multi-level definitions of the 331 stakeholders detected, power-influence matrices, and strategies that may foster balanced multi-level urban-rural governance practices in a major metropolitan area. The findings highlight the multiplicity of levels, sectors, resources, and the actors’ differing capacities and interests to influence rural-urban governance decisions as well as their implementation.

A spatial multi-criteria decision-making model for planning new logistic centers in metropolitan areas

The logistics center concept has been discussed in the literature for over four decades. Logistics centers simplify the logistics network and have many advantages, such as lower transportation costs, an economy of scale, and integrated service capabilities. We propose a spatial multi-criteria decision-making model for new logistic centers in metropolitan areas. The first focus of the study is identifying the logistic concerns, defining the factors affecting the replacement decisions and determining the weights of the factors in metropolitan areas with many expert opinions. The second focuses on spatial analysis to locate new logistics centers serving urban areas. We present a case study in Istanbul, the most populous metropolis in Europe, to demonstrate the applicability and exhibit efficacy of the method proposed in this study. Outputs of the study pointed out where the convenient places are to locate new logistics centers.

Numerical and Economic Analysis of Hydronic-Heated Anti-Icing Solutions on Underground Park Driveways

Snow and ice forming on the entrance and exit driveways of underground car parks of buildings brings serious difficulties and risks in safe parking for vehicles in winter. Even though traditional methods such as chemical salt and snow plowing reduce slippery conditions on driveways, they also result in infrastructure- and environment-related damages. Hydronic heating is an alternative way to prevent snow and ice forming; thereby, the hydronic heating driveway (HHD) is a promising technique for energy-efficient and environment-friendly solutions. This study presents a time-dependent three-dimensional numerical heat transfer model for HHD applications with realistic boundary conditions and meteorological data in the MATLAB environment. After developing the numerical heat transfer model, the model is applied to a case study in Istanbul, Turkey and followed by an economic comparison with the commercial electrically-heated driveways (EHD) method that is applied in two different ways; applying the electric cables in (i) whole driveway and (ii) only tire tracks. Different escalation rates in natural gas and electricity, hot fluid inlet temperature, air temperature, and the number of parallel pipes are the main parameters in the case study. Results show that the decrease in pipe spacing drops the investment cost term but it needs a higher supplied fluid temperature for anti-icing, and therefore the operating cost term increases. Among other cases was the number of parallel pipes, with 50 being the most economically feasible solution for all air temperatures ranging from 0 °C to −10 °C. The economic comparison shows that the EHD with only tire tracks has the minimum total cost as it significantly decreased both the operating and investment cost terms. In case of an anti-icing requirement on the whole road surface, the HHD system was found to be preferable to the EHD whole driveway scenario at air temperatures of 0 °C and −5 °C, while it is more beneficial only for the high electricity escalation rates at the ambient temperature of −10 °C.

A semi-desirable location and sizing model for hydrogen fuel storage areas: A case study for Istanbul

Due to their sustainable nature and high energy efficiency, hydrogen fuel vehicles (HFVs) gradually replace gasoline internal combustion engine vehicles. The increasing number of HFVs brings out the need for installing hydrogen fuel storage areas (HFSAs) that serve as local suppliers for the fuel retail stations. In this study, we consider the HFSA locating and sizing problem for the Anatolian side of Istanbul and pose it as a bi-objective semi-desirable facility location problem which incorporates both social and transshipment costs as push and pull objectives. Next, by adopting a weighted goal programming framework, we minimize the total weighted unwanted deviations from each goal for years between 2021 and 2030 while accounting for several organizational and governmental rules. Our modelling approach is also supported by a structured demand prediction methodology which accounts for the HFV market penetration rates and other parameters such as predicted population, number of HFVs on road, etc. Having two conflicting objectives, we adopt a posteriori approach and generate a representative set of non-dominated solutions to provide decision support to planners and investors. Finally, we perform a sensitivity analysis to account for the uncertainty in the hydrogen fuel demands of districts by defining an uncertainty set for the demand of each district. Sensitivity analysis reveals how and how much change in the district demands modify the most preferred solution.