Travel time variability or distribution is very important to travel time reliability studies in transportation systems. This study aimed at developing a multivariate regression model for estimating travel times for dynamic highway networks in Akure Metropolis. The independent variables for the model are Traffic volume, density, speed of vehicles, and traffic flow while the dependent response variable is the Travel time. The estimated travel time was compared with the observed travel time from the real field data and the estimation using the regression model reveals a significant level of accuracy. Also, it was discovered that traffic volume, speed, density, and flow were highly correlated with travel time. The result analyzed using descriptive statistics in the SPSS software environment reveals an R2 value of 0.998, thereby indicating that the independent variables accounted for 99% of travel time in the study area. The Hypothesis tested at 95% confidence level using ANOVA unveils that there is no significant difference between the observed and estimated travel time model. The Mean Absolute Percentage Error (MAPE) of 0.049 shows that the model performed very well and was very efficient for analyzing the probabilistic relation between travel time and the independent variables. The study recommends the use of the developed travel time model for estimating travel time within the study area.
Adebayo, J. O Olugbenga and E. O. Enobong, “Predictive Modelling of Traffic Flow in Akure, Nigeria: Unsignalized Intersections in Focus”. Journal of Urban and Environmental Engineering, Vol. 10, No. 2. Pp. 270-278, 2016
S. A. Ajayi, H. A. Quadri and R. O. Sani, “Predictive Modeling of Travel Time on Major Roads in Akure, Nigeria”. Proceedings of Nigerian Building and Road Research Institute (NBRRI) Conference on Sustainable Development Goals and Nigeria Construction Industry, 2018
A. Bhaskar, E. Chung, M. Kuwahara, O. de Mouzon, and A. –G. Dumont, “A Urban Network Travel Time Estimation from Stop-line Loop Detector Data and Signal Controller Data”. In: Urban Transport XIII Urban Transport and the Environment in the 21st Century. (Brebbia, C. A. (ed.)), 96, Wessex Institute of Technology, UK. WIT Transactions on the Built Environment, 2007
P. B. Fils, “Modeling Travel Time and Reliability on Urban Arterials for Recurrent Conditions”. University of South Florida, USA, 2012
P. Jayakrishna, C. Steven, and B. Athanassios, “Estimation of Bus Arrival Times Using APC Data” Journal of Public Transportation, Vol. 7, No. 1, 2004
R. John and V. Z. Erik, “A Simple and Effective Method for Predicting Travel Times on Freeways” IEEE Transactions on Intelligent Transportation Systems 5-3(3):200 – 207. October 2004. DOI: 10.1109/TITS.2004.833765·
S. Krishna, K. Aathira, K. Bhimaji and J. Gaurang, “Travel Time Estimation Modelling under Heterogeneous Traffic: A Case Study of Urban Traffic Corridor in Surat, India” Research article of Periodica Polytechnica (Transportation Engineering) https://doi.org/10.3311/PPtr.10847, 2018
E. E. Okoko, “A Predictive Modeling of Spatial Interaction Pattern in the Transport System in Akure” Unpublished PhD Thesis, Federal University of Technology, Akure, 2002
J. O. Olusina, “Modelling Traffic Congestion using Analytic Hierarchy Process in a Geomatics Environment” LAP LAMBERT Academic Publishing, Heinrich-BockingStr.6-8, 66121 Saarbrucken, Deutschland/Germany. ISBN: 978-3-84544138-2, 2013
A. O. Owolabi, “Paratransit Modal Choice in Akure, Nigeria”. Applications of Behvioural Models. Institution of Transportation Engineers Journal, 79, 54-58, 2009
M. Satyakumar, R. Anil and B. Sivakumar, “Travel Time Estimation and Prediction using Mobile Phones: A Cost Effective Method for Developing Countries” Civil Engineering Dimension 16(1): pp 33-39, 2014
V. P. Sisiopiku, N. M. Rouphail, A. Santiago, “Analysis of Correlation between Arterial Travel Time and Detector Data from Simulation and Field Studies” Transportation Research Record. 1457, pp. 166–173, 1994
Transportation Research Board “Signalized intersection” Highway Capacity Manual, Fifth edition. National Research Council, Washington, D.C., U.S.A, 2000
X. Y. Zhan, S. Hasan, S. V. Ukkusuri and C. Kamga, C. “Urban Link Travel Time Estimation using Large-Scale Taxi Data with Partial Information” Transportation Research Part C, 33, 37-49, 2013.
This work is licensed under a Creative Commons Attribution 4.0 International License.
The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.
Submission of the manuscript represents that the manuscript has not been published previously and is not considered for publication elsewhere.