Effects of Bypass in Small and Non-congested Cities: A Case Study of the City Badajoz

Keywords: transport planning, traffic model, origin-destination matrix, Badajoz

Abstract

Small cities with less than 200,000 inhabitants do not usually suffer from chronic congestion problems. However, private vehicles are used excessively, making it necessary to implement measures to encourage further use of public transport and pedestrian mobility to make it more sustainable. Bypasses improve level of service (LOS) by removing cars from the city center, leading to significant reductions in overall travel time. Most studies so far have been conducted in large cities suffering chronic congestion problems, so the aim of this research is to analyze the effects of bypasses in small and non-congested cities through the construction of a traffic model in Badajoz (Spain), starting with the allocation of the origin-destination travel matrix derived from surveys and traffic counts conducted at the southern and eastern accesses. The traffic model describes the mobility in potentially-capturable future southern traffic relationships and allows insights into different alternatives in the construction of a new high LOS road. This research concludes that small cities with no chronic congestion problems should plan bypasses as close as possible to the city, since they are the most economical, produce greater traffic capture, greater time savings, and eliminate the largest number of CO2 emissions from the urban center. The more distant alternatives have a higher LOS, however, these are longer and more expensive solutions that also capture less traffic and thus eliminate less CO2 emissions.

Author Biographies

Juan Francisco Coloma, Universidad de Extremadura

Assistant Professor

Department of Construction

Marta Garcia, Universidad de Extremadura (Spain)

Associate Professor

Department of Construction

Raúl Guzmán, Universidad de Extremadura (Spain)

Assistant Professor

Department of Construction

References

Ramanathan V, Feng Y. Air pollution, greenhouse gases and climate change: Global and regional perspectives. Atmospheric Environment. 2009; 43(1): 37-50. doi:http://dx.doi.org/10.1016/j.atmosenv.2008.09.063

Emberger G. Low carbon transport strategy in Europe–A critical review. International Journal of Sustainable Transportation; 2015. doi: http://dx.doi.org/10.1080/15568318.2015.1106246.

Energy Information Administration. International Energy Outlook 2013. No. DOE/EIA-0484(2013). Washington D.C: U.S. Department of Energy. Available online: https://www.eia.gov/outlooks/ieo/pdf/0484(2013).pdf , accessed 26/06/2017.

Beljatynskij A, Kuzhel N, Prentkovskis O, Bakulich O and Klimenko I. The criteria describing the need for highway reconstruction based on the theory of traffic flows and repay time. Transport. 2009; 24(4): 308-317.

Laurinavičius A, Miškinis D, Vaiškūnaitė R, Laurinavičius A. Analysis and evaluation of the effect of studded tyres on road pavement and environment (III). The Baltic Journal of Road and Bridge Engineering. 2010; 5(3): 169-176.

Leipus L, Butkus D, Janusevicius T. Research on motor transport produced noise on gravel and asphalt roads/Autotransporto keliamo triuksmo zvyrkeliuose ir asfaltuotuose keliuose tyrimas/Transporta plusmas radita troksna izpete uz grants un asfalta celiem/Liiklusvahendite tekitatud mura uuring kruus-ja asfaltkatetel. The Baltic journal of road and bridge engineering. 2010; 5(3): 125-125.

Vitkūnas R, Meidutė I. Evaluation of bypass influence on reducing air polution in Vilnius city. Transport. 2011; 26(1): 43-49.

Junta de Extremadura. Resolución de 6 de octubre de 2014, del Consejero, por la que se aprueba el expediente de información pública y se aprueba definitivamente el estudio informativo de "Ronda Sur de Badajoz", Diario Oficial de Extremadura (DOE), 9 de octubre de 2014, nº 195, p. 30.469-30.471. Mérida, Spain. Available in doe.gobex.es/pdfs/doe/2016/1290o/16040111.pdf, accessed 21/09/2017.

Patriksson M. Urban traffic planning. The traffic assignment Problem. Models & Methods. New York, 2015; p. 3-6. Dover Publications, Inc. ISBN: 978-0-486-78790-9.

Cascetta E. Transportation systems analysis: models and applications (Vol. 29). Springer Science & Business Media; 2009. ISBN: 978-0-387-75856-5.

Novačko L, Šimunović L, Krasić D. Estimation of Origin-Destination Trip Matrices for Small Cities. PROMET-Traffic&Transportation. 2014; 26(5): 419-428.

Ortúzar JD, Willumsen LG. Estimación de modelos a partir de aforos de tráfico. Modelos de transporte. Santander: Ibeas-Portilla A, Dell’Olio L (traductores). PUbliCan, Ediciones de la Universidad de Cantabria, 2008; p. 577-604. ISBN 978-84-8102-512-5.

Ministerio de Fomento. Datos históricos de tráfico desde 1960 en las estaciones de aforo. Madrid; 2015. Available in http://www.fomento.es/MFOM/LANG_CASTELLANO/DIRECCIONES_GENERALES/CARRETERAS/TRAFICO_VELOCIDADES/DATOS_HISTORICOS/, accessed 21/09/2017.

Junta de Extremadura. AFOEX. Mérida; 2015. Available in http://fomento.gobex.es/fomento/live/informacion-ciudadano/carreteras/AforosExtremadura/AFOEX15.zip, accessed 27/10/2016.

Willumsen LG. Simplified transport models based on traffic counts. Transportation. 1981; 10: 257-278. doi: http://dx.doi.org/10.1007/BF00148462.

Bernardin Jr VL, Trevino S, Slater G, Gliebe J. Simultaneous Travel Model Estimation from Survey Data and Traffic Counts. Transportation Research Record: Journal of the Transportation Research Board. 2015; 2(2494): 69-76.

Bonsall PW, O'Flaherty CA. Observational traffic surveys. Transport planning and traffic engineering 4th edition. London: Arnold, 1997; p. 232-251. ISBN 0-340-66279-4.

Viegas JM, Guedes-Gomes F. Codificación de orígenes y destinos de viajes y zonas variables. Ingeniería de Tránsito y Transporte: actas del X Congreso Panamericano. Santander: Ministerio de Fomento; 1998. Cataloged in WorldCat http://www.worldcat.org/title/ingenieria-de-transito-y-transporte-actas-del-x-congreso-panamericano-santander-21-al-24-de-septiembre-de-1998/oclc/625616084.

Chang KT, Khatib Z, Ou Y. Effects of zoning structure and network detail on traffic demand modeling. Environment and Planning B: Planning and design. 2002; 29(1): 37-52.

Lederman R, Wynter L. Real-time traffic estimation using data expansion. Transport Research B. 2011; 45:1062-1079. doi: http://dx.doi.org/10.1016/j.trb.2011.05.024.

Kang-Tsung C, Zaher K, Yanmei O. Effects of zoning structure and network detail on traffic demand modelling. Environment and Planning B: Planning and Design. 2002; 29:37-52. doi https://doi.org/10.1068/b2742.

Cascetta E, Russo F. Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance. Transportation. 1997; 24: 271–293. doi: http://dx.doi.org/10.1023/A:1004968411792.

Russo F, Vitetta A. Reverse assignment: calibrating link cost functions and updating demand from traffic counts and time measurements. Inverse Problems in Science and Engineering. 2011; 19(7): 921-950.

Shrewsbury JS. Calibration of trip distribution by generalised linear models. NZ Transport Agency research report 473. New Zealand; 2012. ISBN 978-0-478-39404-7

Burrell JE. Multiple route assignment and its application to capacity restraint. Proceedings of Fourth International Symposium on the Theory of Traffic Flow. Karlsruhe, Germany; 1968.

Dial RB. A probabilistic multipath traffic assignment model which obviates path enumeration. Transportation Research. 1971; 5: 83-111. Cataloged in WordCat http://worldcat.org/isbn/1840647973.

Ortuzar JD. Nested logit models for mixed-mode travel in urban corridors. Transport Research A. 1983; 17A(4): 283-299. doi: https://doi.org/10.1016/0191-2607(83)90092-4.

McFadden D. Conditional Logit Analysis of Qualitative Choice Behavior. Frontiers in Econometrics. New York, Academic Press, 1974; p.105-145. Available in https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf.

Dissanayake D, Morikawa T. Household travel behavior in developing countries: Nested logit model of vehicle ownership, mode choice, and trip chaining. Transportation Research Record: Journal of the Transportation Research Board. 2002; 1805:45-52.

European Environment Agency. Corinair, 2016. EMEP/EEA air pollutant emission inventory guidebook–2016. Available on line: https://www.eea.europa.eu/publications/emep-eea-guidebook-2016, accessed 21/09/2017.

Instituto para la Diversificación y ahorro de la Energía. Consumo de Carburante y Emisiones de CO2 en Coches Nuevos; 2017. Available online: http://coches.idae.es/portal/BaseDatos/MarcaModelo.aspx , accessed 13/06/2017.

Wardrop JG. Some theoretical aspects of road traffic research. Proceedings of the institution of Civil Engineers. Part II. 1952; 1: 325-362.

Thamizh-Arasan V, Koshy RZ. Methodology for modeling highly heterogeneous traffic flow. Journal of transportation engineering. 2005; 131(7): 544-551.

Yang H, Sasaki T, Lida Y, Asakura Y. Estimation of origin-destination matrices from link traffic counts on congested networks. Transport Research Part B. 1992; 26 B(6): 417-134. doi: http://dx.doi.org/10.1016/0191-2615(92)90008-K

Frederix R, Viti F, Tampère C. Dynamic Origin-Destination estimation in congested networks: theoretical findings and implications in practice. Transportmetrica. 2013; 9(6): 494-513.

Robillard P. Estimating the O-D matrix from observed link volumes .Transport Research. 1975; 9:123-128. doi: http://dx.doi.org/10.1016/0041-1647(75)90049-0.

Horberg P. Estimation of parameters in models for traffic prediction: a non-linear regression approach. Transport Research. 1976; 10:263-265. doi: http://dx.doi.org/10.1016/0041-1647(76)90059-9.

Wills MJ. A flexible gravity-opportunities model for trip distribution. Transport research B. 1986; 20B (2): 89-111. doi: http://dx.doi.org/10.1016/0191-2615(86)90001-9.

Tamin OZ, Willumsen LG. Transport demand model estimation from traffic counts. Transportation. 1989; 16:3-26. doi: http://dx.doi.org/10.1007/BF00223044.

Hagen-Zanker A, Jin Y. A new method of adaptive zoning for spatial interaction models. Geographical Analysis. 2012; 44(4): 281-301.

McLynn JM, Woronka T. Passenger demand and modal split models. US Northeast Corridor Transportation Project. 1969, Report 230.

Murray-Tuite P, Wolsho, B. Evacuation transportation modeling: An overview of research, development, and practice. Transportation Research Part C: Emerging Technologies. 2013; 27: 25-45.

Published
2018-09-10
How to Cite
1.
Coloma JF, Garcia M, Guzmán R. Effects of Bypass in Small and Non-congested Cities: A Case Study of the City Badajoz. Promet - Traffic&Transportation. 2018;30(4):479-8. DOI: 10.7307/ptt.v30i4.2748
Section
Articles