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Predictive Modeling of Travel Time on Major Roads in Akure, Nigeria.

Ajayi, S.A and Quadri, H.A. and Sani, R. O. (2018) Predictive Modeling of Travel Time on Major Roads in Akure, Nigeria. In: Proceedings of NBRRI International Conf. on Sustainable Development Goals & Nigerian Construction Industry,, JUNE, 2018, Online.

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Abstract

Large cities in developing countries are characterized by growth in automobile ownership, insufficient transportation infrastructure and service development. These cities often suffer from congestion, poor mobility and accessibility, significant economic waste, adverse environmental impact and safety problems. This paper focuses on identification of travel time characteristics and other traffic parameters and to develop a predictive model for travel time on Akure major roads. Data on travel time were collected for vehicles during the morning and evening peak periods using floating car technique. The data was analyzed using Statistical Packages for Social Sciences (SPSS) and fitted into Multiple Regression model to establish a relationship between the Travel Time and other road traffic parameters. Travel time (Tt) was modeled as a function of section length (X1), number of intersections (X2), pedestrian/ economic activities (X3), Traffic volume (X4), enforcement agency (X5) and road width (X6). The Coefficient of multiple determination R2 was 0.702 which means that there is 70.2% of the dependent variable (travel time) in the forward direction as explained (accounted) by the independent variables and 72.2% in the opposite direction. The result revealed that section length, pedestrian economic activity and traffic volume were all significant at 5% level and has a positive relationship with travel time in both forward and reverse direction. The model identifies the impact of these traffic parameters on travel time and recommend measures for improvement.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Congestion, Floating car technique, Travel Time, Traffic Parameters, Regression model.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TE Highway engineering. Roads and pavements
Divisions: Faculty of Engineering, Science and Mathematics > School of Civil Engineering and the Environment
Depositing User: Patricia Nwokealisi
Date Deposited: 16 Oct 2024 13:12
Last Modified: 16 Oct 2024 13:12
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/18514

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