國立虎尾科技大學 |

Language: English

Back

Identifying Multimodal Conflicts wit...

ProQuest Information and Learning Co.

Identifying Multimodal Conflicts with Machine Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Identifying Multimodal Conflicts with Machine Learning./
Author:	Hui, Nancy.
Description:	1 online resource (137 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
Subject:	Transportation. -
Online resource:	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.
LDR:02335ntm a2200349Ki 4500 001 918810
005 20181106104111.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355461121
035 $a (MiAaPQ)AAI10604157
035 $a (MiAaPQ)toronto:15893
035 $a AAI10604157
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Hui, Nancy. $3 1193236
245 1 0 $a Identifying Multimodal Conflicts with Machine Learning.
264 0 $c 2017
300 $a 1 online resource (137 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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
690 $a 0709
690 $a 0543
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Toronto (Canada). $b Civil Engineering. $3 1179797
773 0 $t Masters Abstracts International $g 57-02(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10604157 $z click for full text (PQDT)

based on 0 review(s)

Multimedia

Reviews

Add a review and share your thoughts with other readers

Export

pickup library