Abstract

Walking considered as one of the safest modes of

travel available, sustainable to human society as well as

environmentally beneficial. In this context, the aim of this

research is to investigate pedestrians’ traffic gap acceptance

and crossing decision for Mid-block Street crossing in urban

areas in Malaysia. Pedestrian crossing behaviour at

Rughaya Street has been examined in terms of the decision

to cross or not the street and size of traffic gaps accepted by

pedestrian, as well as the related contributing factors. A

field study was conducted to collect the data of pedestrians’

decisions under real mix traffic Condition using video

camera on a typical unsignalized urban street section. JPEG

files were obtained from video recording by using Snapshot

Wizard software. The data extracted included traffic

characteristics such as traffic size, traffic speed, etc.

pedestrian individual characteristics such as gender, in

addition to individual behavior such as waiting, frequency

of attempt, etc.). Furthermore, The extracted data were

used to develop and examine a pedestrian gap acceptance

model based on A lognormal regression model and binary

logistic model by SPSS (22) in order to validate the impact

of various parameters on the size of traffic gaps accepted by

pedestrians as well as the effect on the decision of

pedestrians to cross the street or not. So that the effect of

the gap accepted available and of other factors on the

decision of pedestrians to cross the street or not is examined.

These results indicate that the data set for this particular

location has a majority of male pedestrians which were

insignificant variables in both models moreover a lognormal

regression results shows that accepted gaps size depend on

traffic size, crossing distance, speed of approaching vehicle

and time spent by pedestrian at the curb waiting for a

suitable gap size to start crossing. The BL model performs

well for the reason that it captures the pedestrian decision

making process with traffic taking the relevant attributes

into consideration. According to the coefficients of BL

regression analysis equation we noticed that the illegal

parking, traffic size, traffic waiting time and gap size are the

vital attributes for the Pedestrian gap acceptance model..

Index Terms: Pedestrian crossing, gap acceptance, crossing

decision, multiple linear regression, binary logistic

regression.

Abstract

In many cities today, owning a car has become an important and

dominant mode of transport. Increasing dominance of own cars as a mode

of transport is due to its inherent advantages associated with its usage. In

Tripoli, Libya due to rapid increase in car usage associated with poor public

transport system has resulted in an increase of traffic congestion, accidents

and lack of parking spaces. This study aims to identify the factors

preventing owning cars from shifting to public transport system (PT). And

to develop model shift from owning cars to public transport in order to

formulate policies to achieve this. A policy to enhance public transport

system usage and control car ownership simultaneously has become

necessary for solving traffic congestion problems. A questionnaire survey

was carried out on users of transport in Tripoli such as own car, taxis,

microbuses and coaches drivers (n = 900). The probability of car drivers

shifting to public transport was examined based on scenario of several

options such as reduction in public transport travel time and travel cost.

Reduction in total travel time and cost for public transport mode emerged as

the most important elements in attracting car users towards using public

transport system. Logistic regression technique has been used to analyze the

factors that impact users to switch their trips to public transportation

alternatives. Statistical Package for Social Science (SPSS) version 19 and

Excel 2007 software were used to analyze the questionnaire in this study

Key words: public transport, car ownership, logistic regression, mode

choice and traffic congestion.