عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
شماره کتابشناسی ملی
شماره
TL49613
زبان اثر
زبان متن نوشتاري يا گفتاري و مانند آن
انگلیسی
عنوان و نام پديدآور
عنوان اصلي
Applying Computational Intelligence Techniques to Forecast Traffic Flow Using Traffic Sensor Data & Weather Data
نام عام مواد
[Thesis]
نام نخستين پديدآور
Danielle N. Clavon
نام ساير پديدآوران
Islam, Muhammad F.
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
The George Washington University
تاریخ نشرو بخش و غیره
2018
مشخصات ظاهری
نام خاص و کميت اثر
92
يادداشت کلی
متن يادداشت
Committee members: Eggstaff, Justin; Mazzuchi, Thomas A.; Rackley, Daphne; Sarkani, Shahram
یادداشتهای مربوط به نشر، بخش و غیره
متن يادداشت
Place of publication: United States, Ann Arbor; ISBN=978-0-355-47279-0
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
D.Engr.
نظم درجات
Engineering Management
کسي که مدرک را اعطا کرده
The George Washington University
امتياز متن
2018
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Traffic congestion is becoming a major problem in metropolitan areas across the globe. One useful way to attempt to mitigate traffic congestion is being able to forecast traffic flow. Traffic flow forecast must be accurate because of the critical part it plays in the development of intelligent transportation systems and SMART City initiatives for metropolitan areas. Many cities are in the process of deploying various technologies that range from traffic cameras to traffic signal cameras to improve the current state of traffic congestion as part of one of their SMART City initiatives. The era of Big Data for a number of cities is on the rise through all the new collection channels, which makes it critical to have statistical methods in place on how to interpret and analyze the new data. This praxis will focus on multivariate analysis. Sacramento, as well as other cities in California, will serve as a proxy for this praxis to illustrate the methodology. The praxis is designed to serve as a potential framework for other cities to adopt. The forecasts will be divided into two sections; AM Peak and PM Peak time. In order to aid in decreasing traffic congestions, an Artificial Neural Network was created to forecast traffic flow. The proposed methodology uses Levenberg Marquardt (LM) backpropagation for the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) architecture. The dataset was collected from January 1, 2015, to August 31, 2016. The following variables were used for this study: flow, temperature, humidity, visibility, and speed. The results of the analysis proved that deploying NARX to forecast traffic flow is beneficial and provides an accurate forecast measured by Mean Absolute Percentage Error that ranges from 5% to 13% for the cities studied for this praxis. Therefore, the proposed methodology in the praxis can be applied to different cities in an effort to support their efforts of having the ability to forecast traffic flow to decrease congestion.
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Computer Engineering; Transportation; Systems science
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Applied sciences;Social sciences;Traffic flow;Traffic sensor data;Weather data
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام اشخاص تاييد نشده
Alvi, Muzna Fatima
نام شخص - ( مسئولیت معنوی درجه دوم )
مستند نام اشخاص تاييد نشده
Islam, Muhammad F.
شناسه افزوده (تنالگان)
تقسيم فرعي
Engineering Management
مستند نام تنالگان تاييد نشده
The George Washington University
شماره دستیابی
شماره بازیابی
1964722816; 10635715
دسترسی و محل الکترونیکی
نام الکترونيکي
مطالعه متن کتاب وضعیت انتشار
فرمت انتشار
p
اطلاعات رکورد کتابشناسی
نوع ماده
[Thesis]
کد کاربرگه
276903
اطلاعات دسترسی رکورد
سطح دسترسي
a
تكميل شده
Y
