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Identifying Multimodal Conflicts wit...

ProQuest Information and Learning Co.

Identifying Multimodal Conflicts with Machine Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Identifying Multimodal Conflicts with Machine Learning./
作者:	Hui, Nancy.
面頁冊數:	1 online resource (137 pages)
附註:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
標題:	Transportation. -
電子資源:	click for full text (PQDT)
ISBN:	9780355461121

Identifying Multimodal Conflicts with Machine Learning.
Hui, Nancy.

Identifying Multimodal Conflicts with Machine Learning. - 1 online resource (137 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.A.S.)--University of Toronto (Canada), 2017.

Includes bibliographical references

This study explores the efficacy of using machine learning classifiers in traffic conflict identification. Quantitative conflict identification methods are largely designed through observation of motorized vehicles only, and can report erroneous results when applied to non-motorized modes. Through video recordings of Bloor Street before and after an installation of bicycle lanes, the use of conflict analysis in identifying hazardous behaviour on multimodal streets is affirmed, and a dataset of conflict and non-conflict events is constructed. Six machine learning classifiers are trained on the dataset: three classifiers were trained using only conflict indicators, and three classifiers were trained using the full set of explanatory variables. Machine learning techniques were found to be more effective in conflict identification than traditional threshold-based identification techniques, and user mode, speed, and acceleration may influence interaction severity. Combining machine learning with the results of automated video processing has particular promise in the field of conflict analysis.

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

Mode of access: World Wide Web

ISBN: 9780355461121Subjects--Topical Terms:

558117
Transportation.
Index Terms--Genre/Form:

554714
Electronic books.

Identifying Multimodal Conflicts with Machine Learning.
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500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Advisers: Eric J. Miller; Matthew Roorda.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2017.
504 $a Includes bibliographical references
520 $a This study explores the efficacy of using machine learning classifiers in traffic conflict identification. Quantitative conflict identification methods are largely designed through observation of motorized vehicles only, and can report erroneous results when applied to non-motorized modes. Through video recordings of Bloor Street before and after an installation of bicycle lanes, the use of conflict analysis in identifying hazardous behaviour on multimodal streets is affirmed, and a dataset of conflict and non-conflict events is constructed. Six machine learning classifiers are trained on the dataset: three classifiers were trained using only conflict indicators, and three classifiers were trained using the full set of explanatory variables. Machine learning techniques were found to be more effective in conflict identification than traditional threshold-based identification techniques, and user mode, speed, and acceleration may influence interaction severity. Combining machine learning with the results of automated video processing has particular promise in the field of conflict analysis.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Transportation. $3 558117
650 4 $a Civil engineering. $3 561339
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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