Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests
The present research investigates a range of factors affecting autonomous vehicle (AV) acceptance of Greek citizens through a questionnaire distributed to 563 respondents. Following the extraction of descriptive statistics, self-organizing maps (SOMs) were employed to meaningfully categorize and agg...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
|
| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S038611122300047X |
| _version_ | 1797378719696814080 |
|---|---|
| author | Apostolos Ziakopoulos Christina Telidou Apostolos Anagnostopoulos Fotini Kehagia George Yannis |
| author_facet | Apostolos Ziakopoulos Christina Telidou Apostolos Anagnostopoulos Fotini Kehagia George Yannis |
| author_sort | Apostolos Ziakopoulos |
| collection | DOAJ |
| description | The present research investigates a range of factors affecting autonomous vehicle (AV) acceptance of Greek citizens through a questionnaire distributed to 563 respondents. Following the extraction of descriptive statistics, self-organizing maps (SOMs) were employed to meaningfully categorize and aggregate questions pertaining to four main pillars of the questionnaire, which are conceptually relevant namely: (i) how several factors affect general car choices of respondents, (ii) what the respondents perceived that AVs would offer, (iii) how much they agreed with stated expected technology and efficiency-oriented AV traits and (iv) how they believe several factors affect driving behavior overall. A Random Forest (RF) algorithm was applied to classify the AV acceptance decisions of a training subset of the respondents, and was subsequently assessed on a test subset. SOM results indicate that participants can be meaningfully separated into two SOM cluster groups for pillars (i), (ii) and (iv), while pillar (iii) yielded separations into three SOM cluster groups. RF feature importance calculation indicated a number of affecting variables; the five most contributing ones are: distance covering capabilities of AVs was a major factor affecting acceptance decisions, followed (by a wide margin) by responder opinions on whether the principles and conscience of drivers can be replaced by an AI navigator without reducing safety levels, while the algorithm itself conducted successful classification to about 80% of test cases. Present results can be used to anticipate AV penetration levels based on sample characteristics and to improve AV traits in cases where higher AV penetration is sought. |
| first_indexed | 2024-03-08T20:11:45Z |
| format | Article |
| id | doaj.art-0e39bb5d625141f5beefd09107e4aa58 |
| institution | Directory Open Access Journal |
| issn | 0386-1112 |
| language | English |
| last_indexed | 2024-03-08T20:11:45Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj.art-0e39bb5d625141f5beefd09107e4aa582023-12-23T05:19:58ZengElsevierIATSS Research0386-11122023-12-01474499513Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random ForestsApostolos Ziakopoulos0Christina Telidou1Apostolos Anagnostopoulos2Fotini Kehagia3George Yannis4National Technical University of Athens, Department of Transportation Planning and Engineering, 5 Iroon Polytechniou St., GR-15773 Athens, Greece; Corresponding author.School of Civil Engineering, Division of Transportation and Construction Management, Highway Laboratory, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, GreeceSchool of Civil Engineering, Division of Transportation and Construction Management, Highway Laboratory, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, GreeceSchool of Civil Engineering, Division of Transportation and Construction Management, Highway Laboratory, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, GreeceNational Technical University of Athens, Department of Transportation Planning and Engineering, 5 Iroon Polytechniou St., GR-15773 Athens, GreeceThe present research investigates a range of factors affecting autonomous vehicle (AV) acceptance of Greek citizens through a questionnaire distributed to 563 respondents. Following the extraction of descriptive statistics, self-organizing maps (SOMs) were employed to meaningfully categorize and aggregate questions pertaining to four main pillars of the questionnaire, which are conceptually relevant namely: (i) how several factors affect general car choices of respondents, (ii) what the respondents perceived that AVs would offer, (iii) how much they agreed with stated expected technology and efficiency-oriented AV traits and (iv) how they believe several factors affect driving behavior overall. A Random Forest (RF) algorithm was applied to classify the AV acceptance decisions of a training subset of the respondents, and was subsequently assessed on a test subset. SOM results indicate that participants can be meaningfully separated into two SOM cluster groups for pillars (i), (ii) and (iv), while pillar (iii) yielded separations into three SOM cluster groups. RF feature importance calculation indicated a number of affecting variables; the five most contributing ones are: distance covering capabilities of AVs was a major factor affecting acceptance decisions, followed (by a wide margin) by responder opinions on whether the principles and conscience of drivers can be replaced by an AI navigator without reducing safety levels, while the algorithm itself conducted successful classification to about 80% of test cases. Present results can be used to anticipate AV penetration levels based on sample characteristics and to improve AV traits in cases where higher AV penetration is sought.http://www.sciencedirect.com/science/article/pii/S038611122300047XAutomated vehiclesPerception analysisInformation miningSelf-Organizing MapsRandom Forest |
| spellingShingle | Apostolos Ziakopoulos Christina Telidou Apostolos Anagnostopoulos Fotini Kehagia George Yannis Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests IATSS Research Automated vehicles Perception analysis Information mining Self-Organizing Maps Random Forest |
| title | Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests |
| title_full | Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests |
| title_fullStr | Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests |
| title_full_unstemmed | Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests |
| title_short | Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests |
| title_sort | perceptions towards autonomous vehicle acceptance information mining from self organizing maps and random forests |
| topic | Automated vehicles Perception analysis Information mining Self-Organizing Maps Random Forest |
| url | http://www.sciencedirect.com/science/article/pii/S038611122300047X |
| work_keys_str_mv | AT apostolosziakopoulos perceptionstowardsautonomousvehicleacceptanceinformationminingfromselforganizingmapsandrandomforests AT christinatelidou perceptionstowardsautonomousvehicleacceptanceinformationminingfromselforganizingmapsandrandomforests AT apostolosanagnostopoulos perceptionstowardsautonomousvehicleacceptanceinformationminingfromselforganizingmapsandrandomforests AT fotinikehagia perceptionstowardsautonomousvehicleacceptanceinformationminingfromselforganizingmapsandrandomforests AT georgeyannis perceptionstowardsautonomousvehicleacceptanceinformationminingfromselforganizingmapsandrandomforests |