MATEC Web Conf.
Volume 308, 20202019 8th International Conference on Transportation and Traffic Engineering (ICTTE 2019)
|Number of page(s)||4|
|Section||Road Safety and Risk Management|
|Published online||12 February 2020|
Modelling Pedestrian Crossing Behaviour based on Human Factor
1 Centre of Town and Regional Planning Studies, Universiti Teknologi MARA, Malaysia
2 Centre of Surveying Science and Geomatics, Universiti Teknologi MARA, Malaysia
a Corresponding author: firstname.lastname@example.org
Research on pedestrian crossing behaviour in urban areas is extensive and has contributed to very useful insights into the role of road, traffic and pedestrian characteristics on the crossing decisions of pedestrians, their compliance with traffic rules and the related safety. However, human factors are rarely incorporated in pedestrian crossing behaviour research. The objective of this research is to analyse the development of pedestrian crossing choice models on the basis of road traffic and human factors. For that purpose, a questionnaire was distributed to 663 respondents among pedestrians in the Shah Alam district. The respondents were asked to fill in a questionnaire about their travel motivations, ability characteristics, risk perceptions and preferences with respect to walking and road crossing, as well as their opinion on drivers, etc. From the modelling analysis, the results showed that there is a significant relationship between Human Factor and Crossing Behaviour; there were two components of Human Factor that influenced pedestrian crossing behaviour to emerge, namely a “risk-taker” and a “rule-follower”. Based on ‘path coefficient’ of Human Factor analysis, this study concludes that a ‘risk-taker’ component contributed more to Crossing Behaviour. The findings of this research can be used to evaluate the implementation of new pedestrian crossings and a redesign of existing pedestrian crossing environments.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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