Crossing Behaviour of Pedestrians Along Urban Streets in Malaysia

Walid A Al Bargi,Joewono Prasetijo,Basil David Daniel,Siti Naquiyah Mohamad Nor,Munzilah Md Rohani,M.J Zainorizuan,O Mohamad Hanifi,A Mohd Shalahuddin,L Alvin John Meng Siang,L Yee Yong,R Siti Nazahiyah

doi:10.1051/matecconf/201710308003

Abstract

Road crossings are considered as an unavoidable part of walking in which the desirable route of pedestrians interacts with vehicles. These interactions may expose the pedestrians to risks or delays. In Malaysia, road accident statistics show that pedestrian casualties are fairly high. Inappropriate gap acceptance when pedestrians cross roads is a main contributing element to this situation. In this context, the purpose of this study was to develop realistic models for pedestrian road crossing behaviour using the regression technique for mid-block street crossing. A choice model was produced to capture the decision making process of pedestrians whereas rejected or accepted vehicular gaps was based on the discrete choice theory. Gap acceptance data under real mix traffic conditions was collected using video camera on a typical unsignalised two lane one way urban street section in the city center of Kuala Lumpur, Malaysia. The lognormal regression model developed for the crossing behaviour model shows that traffic speed, pedestrian waiting time, gender, crossing distance, age group, frequency of attempts and pedestrian number are the significant factors which are able to predict 77.0% of variance or changes in accepted gap size at 0.05 significance level. Higher traffic speed, lower waiting time, being a male, wider crossing distance, older age group, lower frequency of attempts and higher number of pedestrian were found to influence pedestrians to accept a bigger gap size. The binary logistic regression developed for the crossing choice model was found to be influenced by traffic speed, driver yield, pedestrian number and age group. Furthermore, lower traffic speed, willingness of drivers to slow down, more pedestrian crossings at the same time and a younger age group lead to a higher chance or probability of crossing roads. The model was validated again using 100 isolated samples and an accuracy of 98% was obtained compared to the calibrated model which yielded an accuracy of 98.9%.

Highlights

The concept of “pedestrian” can have many different meanings
Apart from that, this study discovered that traffic speed, pedestrian gender, crossing distance, age group and pedestrian number showed a positive impact on the log accepted gap size with a positive regression weight, B
The calibrated model showed an overall accuracy of 98.9% while the validation model showed an accuracy of 98% which show that the model can be used to predict the crossing choice of pedestrians based on 4 significant predictors namely traffic speed, driver yield, pedestrian number, and physical age group

Summary

Introduction

The concept of “pedestrian” can have many different meanings. The Concise Oxford dictionary defines it as “a person walking in the street and not travelling in a vehicle”.Crossing facility users are always at risk when they make their daily trips. Pedestrians usually have to wait for a long time before finding a volunteer who stops for them to allow them to cross safely otherwise they are forced to wait until there are no more vehicles. This phenomenon may be caused by a lack of awareness on the rule of the right of way in such situations [2]. An increase in traffic volume leads to smaller accepted gaps These gaps are often described by estimating the means of liner regression modeling or by probability distributions. It can be declared that the minimum accepted gap has been estimated to be 2 seconds while the mean accepted gap has been estimated to be 8 seconds [17]

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Methods

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Conclusion