國立虎尾科技大學 |

Applying Computational Intelligence Techniques to Forecast Traffic Flow Using Traffic Sensor Data & Weather Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Applying Computational Intelligence Techniques to Forecast Traffic Flow Using Traffic Sensor Data & Weather Data./
作者:	Clavon, Danielle N.
面頁冊數:	1 online resource (92 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Contained By:	Dissertation Abstracts International79-02B(E).
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355472790

Applying Computational Intelligence Techniques to Forecast Traffic Flow Using Traffic Sensor Data & Weather Data.
Clavon, Danielle N.

Applying Computational Intelligence Techniques to Forecast Traffic Flow Using Traffic Sensor Data & Weather Data. - 1 online resource (92 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (D.Engr.)

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355472790Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Applying Computational Intelligence Techniques to Forecast Traffic Flow Using Traffic Sensor Data & Weather Data.
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504 $a Includes bibliographical references
520 $a 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.
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