MACROSCOPIC MODELING OF THE IMPACTS OF THE STANDARD GAUGE RAILWAY LINE ON THE PERFORMANCE OF THE NORTHERN ROAD TRANSPORT CORRIDOR
DOI:
https://doi.org/10.11113/mjce.v29.15590Keywords:
Impacts of freight, macro-scopic modelling, travel time, speed, volume to capacity ratio and emissions.Abstract
With a predicted rapid international and regional trade within the region, the Northern road transport corridor will have to deal with the challenges associated with additional traffic growth without compromising on travel time, safety and environmental concerns. Towards this, the Kenyan government embarked on the construction of the Standard Gauge Railway Line (SGRL) in order to improve the Northern Corridor network performance so as to enhance the logistics competitiveness along the corridor. The paper presents the findings of an ongoing research into the impacts of freight traffic on roadway capacity related elements such as travel time, speed, volume to capacity ratios as well as ozone emissions on the corridor. Under the plausible economic growth regime, the SGRL will only divert a maximum of 20% of freight demand from the road network and is expected to reduce capacity on the network, reduce travel time, minimize emissions concentrations and improve the entire network reliability. However, this will depend on the rail modal split capacity, and as such the excess capacity will only be handled by the road network which in turn will result into capacity constraints beyond the 2025. Further, the SGRL will result in substantial reduction of Ozone concentration along the network up to the forecast year 2020 only, beyond which the Ozone concentration on some sections of the corridor will begin to exceed the acceptable WHO standards of 100μg/m3.References
Andersson, J., (2007). Forecasting Swedish GDP Growth.†Master’s Thesis. Lund University.
Ben-Akiva, M., Meersman, H. and Van de Voorde, E. (eds.) (2013). Freight Transport Modelling: 1st ed ed., Emerald: Bingley, UK.
Bergheim, S., (2008). Long-Run Growth Forecasting. Berlin: Springer
Brockwell, P. J. and Davis, R. A., (2002). Introduction to Time Series and Forecasting. Springer. New York.
FHWA (2007). Federal Highway Administration, Freight Analysis Framework. Developed by Center for Transportation Analysis Oak Ridge National Laboratory 2360 Cherahala Boulevard.
Krugman, P. and Obstfeld, M., (2006). International Economics: Theory and Policy. Sixth Edition, New York: Addison Wesley.
Marcellino, M. M., Stock, J. H. and Watson, M. W., (2005). A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series. University of Bocconi Working Paper Series 285.
Ortúzar, J. D. and Willumsen L.G., (2011). Modeling Transport, 4th edition, John Willey and Sons, Chichester U.K.
Robertson, J. C. and Tallman, E. W., (1999). Vector Autoregressions: Forecasting and Reality. Federal Reserve Bank of Atlanta Economic Review. 84(4): 4-18.
UK Highways Agency, (1992). The Design Manual for Roads and Bridges, Volume 12.
WHO, (2006). World Health Organization (WHO) Air quality guidelines for particulate matter ozone, nitrogen dioxide and sulfur dioxide. Global update 2005 - Summary of risk assessment. WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland.
