كاربرد كلان-داده ها در نقد توسعه مبتني بر حمل و نقل عمومي

Using Big Data in critique of Transit Oriented Development

امروزه با پيشرفت فناوري اطلاعات و تكنولوژي ديجيتال، حجم بالايي از داده ها با عناوين كلان-داده و داده هاي بهنگام ، در اختيار متخصصان علوم مختلف مهندسي و نيز علوم اجتماعي قرار گرفته كه از طريق آن امكان ارزيابي نظريه هاي موجود با قابليت تعميم-پذيري فراهم شده است. يكي از اين رويكردها در شهرسازي، توسعه مبتني بر حمل و نقل عمومي است كه بر اساس سفرسازي كاربري ها و ارقام موجود حاصل از مدل هاي حمل و نقل شهري، پيشنهادهاي برنامه ريزي كاربري زمين و تغييرات تراكم را مطرح مي سازد. در اين پژوهش از تفسير كلان-داده ها براي اعتبارسنجي معيارهاي تجربي مربوط به توسعه مبتني بر حمل و نقل عمومي و برنامه ريزي حمل و نقل شهري در خصوص سفرسازي كاربري ها استفاده مي شود. بدين منظور الگوي ترافيكي كاربري آموزش عالي با روش ارزيابي همبستگي تحليل مي گردد. با به كارگيري نگرش شهرسازي در پالايش كلان-داده هاي ترافيكي موجود و در دسترس براي دانشگاه ايالتي كاليفرنيا در شهر لس آنجلس، الگوي ترافيكي سفرهاي تحصيلي استخراج مي شود. نتايج ارزيابي الگوي سفرهاي تحصيلي نشان مي دهد، توليد سفر بر ارقام سفرسازي كاربري ها استوار نبوده، بلكه تابعي از ترافيك كل شهر است. اين نتايج، طراحي و برنامه ريزي كاربري زمين را كه بر-اساس مديريت تقاضا در مدل هاي حمل و نقل شهري و به خصوص توسعه شهري مبتني بر حمل و نقل عمومي انجام مي شود، با ترديد جدي مواجه مي سازد. يافته هاي اين پژوهش، مثالي از كاربرد كلان-داده ها به عنوان يكي از دستاوردهاي به روز فناوري اطلاعات در مطالعات شهرسازي است.

Nowadays, researchers can access a large pool of data provided by high technology methods. Big data can be used to evaluate historical theories and methods. Unfortunately, this new data has been ignored in urban planning models. There are various hypotheses in urban studies that can be tested by real data. One of the known models in urban planning is Transit Oriented Development or TOD which is used for land use planning and density changes in a sustainable framework. TOD is one of the alternatives in urban development for sustainable city which tries to create a connection between land use and transportation. In this paper, we use big data analysis to validate this model and existing hypothesis of TOD as well as transportation planning in trip generation. Here, traffic patterns for California State University (CSU) in Los Angeles (LA) are compared in relation with activity times for the university because big data for real time data series for city of Los Angeles is available. CSULA is located in especial location within a residential neighbourhood and there is not any other attractive land use in proximity. Unlike data mining and database techniques and before using advanced methods and algorithms by IT professionals, urban planning approach is taken for data cleaning of big data in the City of Los Angeles. This approach is focused on the time of activity for students. Schools are closed on weekends and also in break times along summer. Time is a criterion for data cleaning before analysis. Therefore, size of big data is reduced and academic trip patterns are derived by SQL in Oracle database. Findings show that trip generation is not related to land use activity and type, but is more related to general pattern of traffic flow in the city. In other words, planners and engineers, especially in Transit Oriented Development, should avoid focusing on trip generation standards alone. TOD is blamed to be a sustainable development and such a blunder must not happen in a sustainable solution. This method can be applied to different type of land uses and the result would be interpreted in other locations such as Tehran Metropolis. It is very important to avoid unsustainable development for Tehran metropolis, despite the fact that the solutions are called Transit Oriented Development by new BRT infrastructure regardless of urban spatial structure. Results are a good example for using big data in social sciences and urban planning. Urban planners must be updated with new technology to use real data instead of history data.