Recogida de datos y modelización de la demanda de transporte en la República Dominicana: Revisión de literatura
Issue | Vol. 1 Núm. 1 (2018): Ciencia, Ingenierías y Aplicaciones |
DOI | |
Publicado | ene 1, 2018 |
Estadísticas |
Resumen
La apertura de un nuevo modo de transporte genera cambios en la movilidad de los viajeros en su área de servicio. Sin embargo, la omisión de efectos de hábitos e inercia puede producir resultados sesgados en la predicción de la demanda. Los viajeros reaccionan diferente en cuanto a la selección de modos de transporte según sean sus hábitos de selección modal más o menos fuertes. Igualmente, la literatura ha demostrado que el comportamiento pasado es la mejor predicción del comportamiento futuro. Por tanto, el principal objetivo del presente documento es revisar la literatura correspondiente a la modelización de la demanda de viajeros entre distintos modos de transporte de la ciudad de Santo Domingo. Igualmente, se incluye una breve reseña sobre el uso de nuevas tecnologías, tales como los GPS y smartphones para la recogida de datos, y un análisis de accesibilidad. Finalmente, se describen los modelos de demanda para analizar futuras políticas de transporte urbano en la República Dominicana.
Bernardi, S., Puello, L.L.P. & Geurs, K.T. (submitted). The evaluation of Dutch cycling patterns: evidence from smartphone data. Journal of Transport and Land Use.
Bhat, C. & Koppelman, F. (1999). A retrospective and prospective survey of time-use research. Transportation, 26(2): 119-139.
Bhat, C., Srinivasan, S. (2005). A multidimensional mixed ordered-response model for analyzing weekend activity participation. Transportation Research Part B: Methodological, 39(3): 255278.
Boarnet, M., Sarmiento, S. Can land-use policy really affect travel behaviour? A study of the link between non-work travel and landuse characteristics. Urban Studies, 35(7): 1155-1169.
Cardozo, O., Gutiérrez Puebla, J. & García Palomares, J.C. Influencia de la morfología urbana en la demanda de transporte público. Geofocus, (10)4.
Cervero, R. & Kockelman, K. (1997a). Travel Demand and the 3 Ds: Density, Diversity and Design. Transportation Research Part D, Vol. 2, 199-219.
Cervero, R. & Kockelman, K. (1997b). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199-219.
Cervero, R., Murphy, S., Ferrell, C., Goguts, N. & Tsai, Y.H. (2004). Transit-Oriented Development in America: Experiences, Challenges and Prospects. In, vol. TCRP Report 102. Transportation Research Board, Washington D.C.: National Research Council.
Cherchi, E. Modelling individual preferences, state of the art, recent advances and future directions. In: 12th International Conference on Travel Behaviour Research (IATBR), Jaipur, India, 13-18 December, 2009.
De Souza, F., La Paix Puello, L., Brussel, M., Orrico, R. & van Maarseveen, M. (2017). Modelling the potential for cycling in access trips to bus, train and metro in Rio de Janeiro. Transportation Research Part D: Transport and Environment, 56, 55-67.
Delmelle, E.C. & Casas, I. Evaluating the spatial equity of bus rapid transit-based accessibility patterns in a developing country: The case of Cali, Colombia. Transport Policy, 20(0): 36-46.
Dieleman, F.M., Dijst, M. & Burghouwt, G. Urban form and travel behaviour: micro-level household attributes and residential context. Urban Studies, 39(3): 507-527.
Domencich, T. & McFadden, D. (1975). Urban travel demand: a behavioral analysis / Thomas A. Domencich and Daniel McFaden. Vol. Book, Whole. Amsterdam: North-Holland, New York: American Elservier.
El-Geneidy, A.M., Tétreault, P.R. Surprenant-Legault, J. (2018) Pedestrian access to transit: Identifying redundancies and gaps using a variable service area analysis. Automated Vehicles Symposium. San Francisco, CA.
Ewing, R. (1993). Transportation service standards-as if people matter. Transportation Research Record 1400, 10-17.
Friedman, B., Gordon, S.P. & Peers, J.B. (1994). Effect of neotraditional neighborhood design on travel characteristics. In Transportation Research Record: Journal of the Transportation Research Board, No. 1446, Transportation Research Board of the National Academies, Washington, D.C.,1466, 63-70.
García-Palomares, J.C. Urban sprawl and travel to work: the case of the metropolitan area of Madrid. Journal of Transport Geography, 18(2): 197-213.
Geurs, K., Bok, M.d. & Zondag, B. (2012). Accessibility benefits of integrated land use and public transport policy plans in the Netherlands. In: Geurs, K.T., Krizek, K. & Reggiani, A. (eds.) Accessibility Analysis and Transport Planning: Challenges for Europe and North America.
Geurs, K. & Wee, B.v. (2013). Accessibility: perspectives, measures and applications. In: B. van Wee, J.A.A., D. Banister (ed). The Transport System and Transport Policy: An Introduction.
Geurs, K.T., & Van Wee, B. (2004a). Accessibility evaluation of landuse and transport strategies: review and research directions. Journal of Transport geography, 12: 127-140.
Geurs, K.T. & Van Wee, B. (2004b). Land-use/transport interaction models as tools for sustainability impact assessments of transport investments: review and research directions. European Journal of Transport and Infrastructure Research, 4(3): 333355.
Gutiérrez, J., Cardozo, O.D. & García-Palomares, J.C. Transit ridership forecasting at station level: an approach based on distance-decay weighted regression. Journal of Transport Geography, 19(6): 1081-1092.
Handy, S., Cao, X. & Mokhtarian, P. (2005). Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transportation Research Part D, 10(6), 427-444.
Hanson, S. (1982). The determinants of daily travel-activity patterns: relative location and sociodemographic factors. Urban Geography, 3(3): 179-202.
Howell, A. & Páez, A. Urban Geography, Household Context, and Car Ownership: A case Study in Hamilton, Ontario. Transport Research Board.
Kitamura, R., Mokhtarian, P.L. & Daidet, L. A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation, 24(2): 125-158.
96
La Paix, L. (2010). Relación entre la generación de viajes y la densidad urbana: caracterización de la movilidad con modelos probit ordenados. In: IX Conference of Transport Engineering (CIT), 2010.
La Paix, L. (2012). Modelling the Impact of Built Environment, Geographical Scales and Latent Constructs on Individual Travel Behaviour. Doctoral Thesis. Universidad Politécnica de Madrid.
La Paix, L., Bierlaire, M., Cherchi, E. & Monzón, A. (2010). How urban environment affects travel behaviour? Integrated Choice and Latent Variable Model for Travel Schedules. In: Book of Selected Papers of 2º International Choice Modelling Conference, vol. Stephane Hess and Andrew Daly. London, U.K.: Emerald.
La Paix, L., Monzón, A. & Cherchi, E. (2010). Location effects and Trip generation: evidence from Madrid Metropolitan Area. In: XII World Conference on Transport Research, 2010.
La Paix, L., Monzón, A., Cherchi, E. (2012). Modelling the relationship between urban environment and travel behaviour: policy and indicators. In: MOBIL.TUM 2012 International Scientific Conference on Mobility and Transport, vol. Munich, Germany. Munich.
La Paix Puello, L., Chowdhury, S. & Geurs, K. Using panel data for modelling duration dynamics of outdoor leisure activities. Journal of Choice Modelling.
La Paix Puello, L. & Geurs, K. Modelling observed and unobserved factors in cycling to railway stations: application to transit-oriented-developments in the Netherlands. European Journal of Transport Infrastructure Research, 15(1): 27-50.
La Paix Puello, L. & Geurs, K. Integration of unobserved effects in generalised transport access costs of cycling to railway stations. European Journal of Transport and Infrastructure Research, 16(2): 385-405.
La Paix Puello, L., Olde-Kalter, M.-J., Geurs, K.T. (2017). Measurement of non-random attrition effects on mobility rates using trip diaries data. Transportation Research Part A: Policy and Practice, 106, 51-64.
McFadden, D. The measurement of urban travel demand. Journal of Public Economics, 3: 303-328.
McFadden, D. (1981). Econometric Models of Probabilistic Choice. In: Manski, C.F., McFadden, D. (eds.) Structural analysis of discrete data with economic applications, 198-272. Cambridge: MIT Press.
Monzón, A., La Paix, L., Delgado, M.A. & Fernández, A. (2008). Influencia de la localización en los patrones de movilidad metropolitana; análisis comparado según tipologia de encuesta. Estudios de Construcción y Transportes, 108(0), 203-210.
Paez, A., Scott, D., Potoglou, D., Kanaroglou, P. & Newbold, K.B. Elderly mobility: Demographic and spatial analysis of trip making in the Hamilton CMA, Canada. Urban Studies, 44(1): 123-146.
Pushkarev, B. & Zupan, J.M. (1977). Public transportation and land use policy. vol. Book, Whole. Indiana: University Pr.
Thomas, T., Puello, L.L.P. & Geurs, K. Intrapersonal mode choice variation: evidence from a four-week smartphone-based travel survey in the Netherlands. Journal of Transport Geography. (in press).
White Mountain Survey, C. (1991). White Mountain Survey Company, City of Portsmouth Traffic/Trip Generation Study, vol. unpublished. White Mountain Survey Company, City of Portsmouth Traffic/Trip Generation Study., vol. Book, WholeOssippeee, New Hampshire.
Yáñez, M.F., Mansilla, P. & de Dios Ortúzar, J. The Santiago Panel: Measuring the effects of implementing Transantiago. Transportation, 37(1): 125-149.
- Resumen visto - 710 veces
- PDF descargado - 335 veces
- HTML descargado - 255 veces
Descargas
Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Copyright
© Ciencia, Ingenierías y Aplicaciones, 2018
Afiliaciones
Lissy La Paix Puello
University of Twente