Applying An Entropic Analysis to Locate Rapid Transit Lines in Sprawled Cities

Applying An Entropic Analysis to Locate Rapid Transit Lines in Sprawled Cities

Francisco A. Ortega Ramón Piedra-De-La-Cuadra Soly Ventura 

Higher Technical School of Architecture, Universidad de Sevilla, Spain

Page: 
626-637
|
DOI: 
https://doi.org/10.2495/SDP-V13-N4-626-637
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

Urban sprawl is a phenomenon that leads to an extensive use of motorized transport modes with negative environmental impacts such as congestion, time wasted in traffic jams, air and noise pollution and additional costs incurred by using non-renewable energy. Increasing the existing infrastructures is a decision, which often generates the installation of new urban settlements, whose degree of isolation is mitigated with a new increase in the demand for transport. This vicious circle can be broken by reducing the need of transport imposed by the urban model, which is only possible by bringing citizens closer to those services they demand. In the model of sprawled city, housing predominates as land use in the residential areas, where other complementary uses (such as commercial, cultural, institutional and industrial ones) are excluded in the urban development. When the urban districts don´t present enough complexity, an increase in traffic density between different zones into the city arises. Such forced mobility could be reduced if the functional diversity of the districts were greater, or if there was an urban rapid transit system connecting the areas that generate the greatest imbalances. To measure the complexity of the urban districts system, the Information Theory developed in the 1960s proposes the use of urban entropy. The paper addresses the problem of locating a rapid transit line (metro, tram, BRT) with the objective of maximize the functional diversity of the districts traversed by the alignment. In order to illustrate the proposed model a computational experience is carried out by using data from the metropolitan area of Seville (Spain).

Keywords: 

entropic analysis, rapid transit line, urban diversity

  References

[1] Bhatta, B., Analysis of urban growth and sprawl from remote sensing data. Advances in Geographic Information Science, pp. 17–36, 2000. https://doi.org/10.1007/978-3-642-05299-6_2

[2] Zhang, T., Community features and urban sprawl: the case of the Chicago metropolitan region. Land Use Policy, 18(3), pp. 221–232, 2001. https://doi.org/10.1016/s0264-8377(01)00018-7

[3] Mosammam, H.M., Nia, J.T., Khani, H., Teymouri, A. & Kazemi, M., Monitoring land use change and measuring urban sprawl based on its spatial forms. The Egyptian Journal of Remote Sensing and Space Science, 20(1), pp. 103–116, 2016. http://dx.doi.org/10.1016/j.ejrs.2016.08.002

[4] Rodrigue, J.-P., The geography of transport systems, Routledge: New York, 2017. Available at: https://people.hofstra.edu/geotrans/eng/ch6en/conc6en/ch6c4en.html. ISBN 978-1138669574. (Accessed 5 April 2017).

[5] Salat, S. & Bourdic, L., Urban complexity, scale hierarchy, energy efficiency and economic value creation. In WIT Transactions on Ecology and the Environment, Vol. 155, The Sustainable City VII: Urban Regeneration and Sustainability, Vol. 1 (Eds. M. Pacetti, G. Passerini, C.A. Brebbia, G. Latini) pp. 97–107, 2012.

[6] Burton, E., The compact city: Just or just compact? A preliminary analysis. Urban Studies, 37(11), pp. 1969–2001, 2000. https://doi.org/10.1080/00420980050162184

[7] CEC, Sustainable Urban Development in the European Union: A Framework for Action, Communication from the Commission COM (1998) 605 final, 28.10.98, Commission of the European Communities: Brussels, 1998.

[8] CEC, A new partnership for cohesion. Convergence, Competitiveness, Cooperation, Third Report on Economic and Social Cohesion, Commission of the European Communities: Brussels, 2004.

[9] Vickerman, R., Spiekermann, K. & Wegener, M., Accessibility and economic development in Europe. Regional Studies, 33(1), pp. 1–15, 1999. https://doi.org/10.1080/00343409950118878

[10] Martín, J.C., Gutiérrez, J. & Román, C., Data Envelopment Analysis (DEA) index to measure the accessibility impacts of new infrastructure investments: the case of the high-speed train corridor Madrid–Barcelona–French border. Regional Studies, 38(6), pp. 697–712, 2004. https://doi.org/10.1080/003434042000240987

[11] López, E., Gutiérrez, J. & Gómez, G., Measuring regional cohesion effects of largescale transport infrastructure investments: an accessibility approach. European Planning Studies, 16(2), pp. 277–301, 2008. https://doi.org/10.1080/09654310701814629

[12] López, E. & Monzón, A., Integration of sustainability issues in strategic transportation planning: a multi-criteria model for the assessment of transport infrastructure plans. Computer-Aided Civil and Infrastructure Engineering, 25, pp. 440–451, 2010. https://doi.org/10.1111/j.1467-8667.2010.00652.x

[13] Gendreau, M., Laporte, G. & Mesa, J. A., Locating rapid transit lines. Journal of Advanced Transportation, 29, pp. 145–162, 1995. https://doi.org/10.1002/atr.5670290202

[14] Musso, A. & Vuchic, V.R., Characteristic of metro networks and methodology for their evaluation. Transportation Research Record, 1162, pp. 22–33, 1988.

[15] Laporte, G., Mesa, J.A. & Ortega, F., Assessing topological configurations for rapid transit networks. Studies in Locational Analysis, 7, pp. 105–121, 1994.

[16] Laporte, G., Mesa, J.A. & Ortega, F., Assessing the efficiency of rapid transit configurations. TOP, 5, pp. 95–104, 1997. https://doi.org/10.1007/bf02568532

[17] Dufourd, H., Gendreau, M. & Laporte, G., Locating a transit line using tabu search. Location Science, 4, pp. 1–19, 1996. https://doi.org/10.1016/s0966-8349(96)00008-3

[18] Bruno, G., Gendreau, M. & Laporte, G., A heuristic for the location of a rapid transit line. Computers & Operations Research, 29, pp. 1–12, 2002. https://doi.org/10.1016/s0305-0548(00)00051-4

[19] Laporte, G., Mesa, J.A., Ortega, F.A. & Perea, F., Planning rapid transit networks. Socio-Economic Planning Sciences, 45, pp. 95–104, 2011. https://doi.org/10.1016/j.seps.2011.02.001

[20] Shannon, C.E., A mathematical theory of communication. Bell System Technical Journal, 27, pp. 379–423, 1948. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

[21] Margalef, R., Teoría de los sistemas ecológicos, Publicacions de la Universitat de Barcelona. Barcelona, 1991. 

[22] Yeh, A.G.O. & Xia, L., Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogrammetric Engineering & Remote Sensing, 67(1), pp. 83–90, 2001.

[23] Coward, A. & Salingaros, N., The information architecture of cities. Journal of Information Science, 30(2), pp. 107–118, 2004. https://doi.org/10.1177/0165551504041682

[24] Sierra, M., Towards an ecology of the form. Game theory and urban sustainability in the information age, Doctoral dissertation, Universidad de Sevilla, 2009.

[25] Bascuñán-Walker, F., Bordones-Gana, D. & Reyes-Fernández, J., Efectos de la entropía urbana en el coste energético del trasporte. Urbano, 14(23), pp. 20–27, 2011. (Universidad del Bío Bío, Concepción, Chile.)

[26] Mesa, J.A. & Ortega, F.A., Park-and-ride station catchment areas in metropolitan rapid transit systems, M. Pursula and J. Niittymäki (eds.), Mathematical Methods on Optimization in Transportation Systems, (pp. 81–93) Kluwer Academic Publishers, Dordrecht, Netherlands, 2001.

[27] Pisinger, D. & Toth, P., Knapsack problems, in: D-Z. Du & P. Pardalos (Eds.), Handbook of Combinatorial Optimization, Vol. 1, Kluwer Academic Publishers, Dordrecht, pp. 299–428, 1998.

[28] Laporte, G., Mesa, J.A. & Ortega, F., Determinación del Alineamiento de Máxima Cobertura de Viaje en el Diseño de Sistemas Urbanos de Transporte (in Spanish). Proceedings of the V Congreso de Ingeniería del Transporte, pp. 2179–2188, 2002, Santander, Spain.