DOI : https://doi.org/10.18517/ijaseit.1.6.124

Logistic Regression Models to Forecast Travelling Behaviour in Tripoli City

Amiruddin Ismail (1), Adel Ettaieb Elmloshi (2)
(1) Sustainable Urban Transport Research Centre, Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
(2) Sustainable Urban Transport Research Centre, Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
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How to cite (IJASEIT) :
Ismail, Amiruddin, and Adel Ettaieb Elmloshi. “Logistic Regression Models to Forecast Travelling Behaviour in Tripoli City”. International Journal on Advanced Science, Engineering and Information Technology, vol. 1, no. 6, Dec. 2011, pp. 618-23, doi:10.18517/ijaseit.1.6.124.
Citation Format :
Transport modes are very important to Libyan’s Tripoli residents for their daily trips. However, the total number of own car and private transport namely taxi and micro buses on the road increases and causes many problems such as traffic congestion, accidents, air and noise pollution. These problems then causes other related phenomena to the travel activities such as delay in trips, stress and frustration to motorists which may affect their productivity and efficiency to both workers and students. Delay may also increase travel cost as well inefficiency in trips making if compare to other public transport users in some Arabs cities. Switching to public transport (PT) modes alternatives such as buses, light rail transit and underground train could improve travel time and travel costs. A transport study has been carried out at Tripoli City Authority areas among own car users who live in areas with inadequate of private transport and poor public transportation services. Analyses about relation between factors such as travel time, travel cost, trip purpose and parking cost have been made to answer research questions. Logistic regression technique has been used to analyse these factors that influence users to switch their trips mode to public transport alternatives.

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