USABILITY OF LARGE URBAN FACILITIES IN SPATIAL TRANSFORMATION - CASE STUDY OF REGIONAL SHOPPING CENTERS IN ISTANBUL

Due to the dynamic nature of the urban development in developing countries in parallel to rapidly changing economic, social and technological environments, decisions based on master plans do usually fail. Therefore, spatial transformation is the number one prerequisite to create more livable cities in countries where land use and location decisions do greatly divert from master plans that ill-fully represent the nature of urban development in rapidly changing environment. It is very unfortunate that like many developing countries, central government as well as local governments in Turkey have adopted this approach which is totally inappropriate to their changing environment due to rapid urbanization. In middle and low income economies, urbanization has increased by an average of 3.5 and 3.7% per annum, respectively, compared with an average of 1.5% per annum in the industrialized countries (the rate in Turkey was 4.35% from 1965 to 1985). The percent of urban population in the largest city in Turkey, Istanbul, was 24% in 1980 compared to 18% in 1960. The population of Istanbul was 11.2 million in 2000 compared to 11.3 million of Paris and 11.1 million of Osaka, Kobe (World Development Report by World Bank, 1984). In the periphery of the metropolitan city of Istanbul, there are numerous neighborhoods and urban centers hat need spatial transformation or renewal for the betterment of urban space. Renewal was defined as clearance and redevelopment until the mid-1960s. This approach for the urban betterment was changed in the 1970s by establishing legal ground via improvement and development plans. In contrast to this, in parallel to the radical changes in economic policies in the 1980s, renewal policy for the problematic locations in large urban areas were again equaled regeneration, and spatial transformations were made for the capitalization of global interests in the name of urban rent by transformation projects (Dündar, 2001). The former— improvement and development plans— failed due to the reason said in the beginning. The latter— transformation projects— have found limited application (Portakal Çiçeði, Dikmen Vadisi, Zafer Plaza transformation projects and some others) due to two great limitations: finances and public acceptance towards transformation projects. To overcome these obstacles in general, some approaches are developed, such as ÝHT-ÝHTr-Real-estate planning tools, master plans for earthquakes and natural disasters (Istanbul Metropolitan City), KED Model (Çelikhan et al., 2004). However, these approaches have not found widespread application yet due to necessary legal changes they require and most importantly the finances needed for the transformations desired in urban areas. Under the economic and social conditions in developing countries, what expected from ideal transformation approaches are to create financial tools during the process and to offer the urban rent to land owners primarily in order to speed up the transformation process towards the desired direction by creating voluntarily participation at the utmost level and to reduce the legal problems due to the introduction of new developments and land use planned by the transformation projects to be applied. This study is originated from the idea that large urban developments attract new land uses and users to their proximity or repel current land uses and users around them. This process can be seen as a “voluntarily transformation” process. Since large shopping centers or malls are built in almost every largely populated urban area all over the world in the last 20-30 years due to new shopping habits and global capital investments, we studied the effects of large shopping malls on land use in their proximity as being large developments they create urban transformation process in their proximity, as a case study in Istanbul, Turkey. To support our approach, Dennis at al. (2002) interestingly reported in their study in Northern London that the fist step in urban renovation is to renovate retail shopping and shopping centers. In this context, we performed user surveys in residential and commercial areas as well as at real estate agents in the proximity two large shopping centers; namely, Akmerkez (Etiler, Beþiktaþ) and Tepe-Nautilus (Acýbadem, Kadýköy) in Istanbul. In addition, in the study areas the data on land use changes provided by State Statistics Institute of Turkey have been examined. It is concluded the shopping centers stimulated urban transformation on real estates in their close proximity, and in time they created transformations from residential to commercial within their primary influence boundaries, and beyond those up to a certain distance they became an attractive zone for residential use.

Multicriteria sustainability evaluation of transport networks for selected European countries

As an essential economic activity, transportation has complex interactions with the environment and society. Since the concept of sustainable development has become one of the top priorities for nations, there has been a growing interest in evaluating the performance of transport systems with respect to sustainability issues. The main purpose of this study is to introduce a decision making framework to assess the sustainability of the transport networks in a multidimensional setting and a technique to identify non-compromise alternatives. We also propose an elucidation technique to identify according to which criteria a system needs to be improved and how much improvement is required to attain a certain level of sustainability. The proposed methods are applied to a set of selected European countries within a case study

Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed

Model for Estimating Increased Ridership Caused by Integration of Two Urban Transit Modes: Case Study of Metro and Bus-Minibus Transit Systems, Istanbul, Turkey

A simple, practical tool based on integration (basically, shortening) of competing bus and minibus transit services is provided for estimating increased ridership on metro lines. A simple model is developed to estimate the increased ridership in the metro line of Istanbul, Turkey (7.9 km long and being extended), caused by integration with bus and minibus transit lines, and the benefits related to the intermodal integration are presented. To estimate the effectiveness of the integration action, ridership information in all modes before and after the integration was gathered and analyzed. It was found that the ridership-over-capacity (RoC) ratio in the metro lines was quite low (0.28) before the integration. On December 24, 2001, a partial integration was deployed by the bus operator of the metropolitan city when 11 bus and three minibus routes were integrated to the metro, which yielded significant increase in RoC (56%). The study also considered possible options for the development of a full intermodal plan. In this regard, existing bus and minibus transit lines were examined, and 33 bus and three minibus routes were recommended for integration. It was found that such integration would increase RoC from 0.28 to 0.76 without any significant increase in operating cost. Also, the city and private bus and minibus operators would save an estimated of $4.8 million a year from cuts in operating these services along the metro route. It was also estimated that the integration would lower the amount of bus traffic in downtown Taksim and Mecidiyeköy by approximately 25% and 38%, respectively

Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed