Modelling the Impact of Built Environment, Geographical Scales and Latent Constructs On Individual Travel Behaviour

La relaci��n entre la estructura urbana y la movilidad ha sido estudiada desde hace m��s de 70 a��os. El entorno urbano incluye m��ltiples dimensiones como por ejemplo: la estructura urbana, los usos de suelo, la distribuci��n de instalaciones diversas (comercios, escuelas y zonas de restauraci��n, parking, etc.). Al realizar una revisi��n de la literatura existente en este contexto, se encuentran distintos an��lisis, metodolog��as, escalas geogr��ficas y dimensiones, tanto de la movilidad como de la estructura urbana. En este sentido, se trata de una relaci��n muy estudiada pero muy compleja, sobre la que no existe hasta el momento un consenso sobre qu�� dimensi��n del entorno urbano influye sobre qu�� dimensi��n de la movilidad, y cu��l es la manera apropiada de representar esta relaci��n. Con el prop��sito de contestar estas preguntas investigaci��n, la presente tesis tiene los siguientes objetivos generales: (1) Contribuir al mejor entendimiento de la compleja relaci��n estructura urbana y movilidad. y (2) Entender el rol de los atributos latentes en la relaci��n entorno urbano y movilidad. El objetivo espec��fico de la tesis es analizar la influencia del entorno urbano sobre dos dimensiones de la movilidad: n��mero de viajes y tipo de tour. Vista la complejidad de la relaci��n entorno urbano y movilidad, se pretende contribuir al mejor entendimiento de la relaci��n a trav��s de la utilizaci��n de 3 escalas geogr��ficas de las variables y del an��lisis de la influencia de efectos inobservados en la movilidad. Para el an��lisis se utiliza una base de datos conformada por tres tipos de datos: (1) Una encuesta de movilidad realizada durante los a��os 2006 y 2007. Se obtuvo un total de 943 encuestas, en 3 barrios de Madrid: Chamber��, Pozuelo y Algete. (2) Informaci��n municipal del Instituto Nacional de Estad��stica: dicha informaci��n se encuentra enlazada con los or��genes y destinos de los viajes recogidos en la encuesta. Y (3) Informaci��n georeferenciada en Arc-GIS de los hogares participantes en la encuesta: la base de datos contiene informaci��n respecto a la estructura de las calles, localizaci��n de escuelas, parking, centros m��dicos y lugares de restauraci��n. Se analiz�� la correlaci��n entre e intra-grupos y se modelizaron 4 casos de atributos bajo la estructura ordinal logit. Posteriormente se eval��a la auto-selecci��n a trav��s de la estimaci��n conjunta de las elecciones de tipo de barrio y n��mero de viajes. La elecci��n del tipo de barrio consta de 3 alternativas: CBD, Urban y Suburban, seg��n la zona de residencia recogida en las encuestas. Mientras que la elecci��n del n��mero de viajes consta de 4 categor��as ordinales: 0 viajes, 1-2 viajes, 3-4 viajes y 5 o m��s viajes. A partir de la mejor especificaci��n del modelo ordinal logit. Se desarroll�� un modelo joint mixed-ordinal conjunto. Los resultados indican que las variables ex��genas requieren un an��lisis exhaustivo de correlaciones con el fin de evitar resultados sesgados. ha determinado que es importante medir los atributos del BE donde se realiza el viaje, pero tambi��n la informaci��n municipal es muy explicativa de la movilidad individual. Por tanto, la percepci��n de las zonas de destino a nivel municipal es considerada importante. En el contexto de la Auto-selecci��n (self-selection) es importante modelizar conjuntamente las decisiones. La Auto-selecci��n existe, puesto que los par��metros estimados conjuntamente son significativos. Sin embargo, s��lo ciertos atributos del entorno urbano son igualmente importantes sobre la elecci��n de la zona de residencia y frecuencia de viajes. Para analizar la Propensi��n al Viaje, se desarroll�� un modelo h��brido, formado por: una variable latente, un indicador y un modelo de elecci��n discreta. La variable latente se denomina ���Propensi��n al Viaje���, cuyo indicador en ecuaci��n de medida es el n��mero de viajes; la elecci��n discreta es el tipo de tour. El modelo de elecci��n consiste en 5 alternativas, seg��n la jerarqu��a de actividades establecida en la tesis: HOME, no realiza viajes durante el d��a de estudio, HWH tour cuya actividad principal es el trabajo o estudios, y no se realizan paradas intermedias; HWHs tour si el individuo reaiza paradas intermedias; HOH tour cuya actividad principal es distinta a trabajo y estudios, y no se realizan paradas intermedias; HOHs donde se realizan paradas intermedias. Para llegar a la mejor especificaci��n del modelo, se realiz�� un trabajo importante considerando diferentes estructuras de modelos y tres tipos de estimaciones. De tal manera, se obtuvieron par��metros consistentes y eficientes. Los resultados muestran que la modelizaci��n de los tours, representa una ventaja sobre la modelizaci��n de los viajes, puesto que supera las limitaciones de espacio y tiempo, enlazando los viajes realizados por la misma persona en el d��a de estudio. La propensi��n al viaje (PT) existe y es espec��fica para cada tipo de tour. Los par��metros estimados en el modelo h��brido resultaron significativos y distintos para cada alternativa de tipo de tour. Por ��ltimo, en la tesis se verifica que los modelos h��bridos representan una mejora sobre los modelos tradicionales de elecci��n discreta, dando como resultado par��metros consistentes y m��s robustos. En cuanto a pol��ticas de transporte, se ha demostrado que los atributos del entorno urbano son m��s importantes que los LOS (Level of Service) en la generaci��n de tours multi-etapas. la presente tesis representa el primer an��lisis emp��rico de la relaci��n entre los tipos de tours y la propensi��n al viaje. El concepto Propensity to Travel ha sido desarrollado exclusivamente para la tesis. Igualmente, el desarrollo de un modelo conjunto RC-Number of trips basado en tres escalas de medida representa innovaci��n en cuanto a la comparaci��n de las escalas geogr��ficas, que no hab��a sido hecha en la modelizaci��n de la self-selection. The relationship between built environment (BE) and travel behaviour (TB) has been studied in a number of cases, using several methods - aggregate and disaggregate approaches - and different focuses ��� trip frequency, automobile use, and vehicle miles travelled and so on. Definitely, travel is generated by the need to undertake activities and obtain services, and there is a general consensus that urban components affect TB. However researches are still needed to better understand which components of the travel behaviour are affected most and by which of the urban components. In order to fill the gap in the research, the present dissertation faced two main objectives: (1) To contribute to the better understanding of the relationship between travel demand and urban environment. And (2) To develop an econometric model for estimating travel demand with urban environment attributes. With this purpose, the present thesis faced an exhaustive research and computation of land-use variables in order to find the best representation of BE for modelling trip frequency. In particular two empirical analyses are carried out: 1. Estimation of three dimensions of travel demand using dimensions of urban environment. We compare different travel dimensions and geographical scales, and we measure self-selection contribution following the joint models. 2. Develop a hybrid model, integrated latent variable and discrete choice model. The implementation of hybrid models is new in the analysis of land-use and travel behaviour. BE and TB explicitly interact and allow richness information about a specific individual decision process For all empirical analysis is used a data-base from a survey conducted in 2006 and 2007 in Madrid. Spatial attributes describing neighbourhood environment are derived from different data sources: National Institute of Statistics-INE (Administrative: municipality and district) and GIS (circular units). INE provides raw data for such spatial units as: municipality and district. The construction of census units is trivial as the census bureau provides tables that readily define districts and municipalities. The construction of circular units requires us to determine the radius and associate the spatial information to our households. The first empirical part analyzes trip frequency by applying an ordered logit model. In this part is studied the effect of socio-economic, transport and land use characteristics on two travel dimensions: trip frequency and type of tour. In particular the land use is defined in terms of type of neighbourhoods and types of dwellers. Three neighbourhood representations are explored, and described three for constructing neighbourhood attributes. In particular administrative units are examined to represent neighbourhood and circular ��� unit representation. Ordered logit models are applied, while ordinal logit models are well-known, an intensive work for constructing a spatial attributes was carried out. On the other hand, the second empirical analysis consists of the development of an innovative econometric model that considers a latent variable called ���propensity to travel���, and choice model is the choice of type of tour. The first two specifications of ordinal models help to estimate this latent variable. The latent variable is unobserved but the manifestation is called ���indicators���, then the probability of choosing an alternative of tour is conditional to the probability of latent variable and type of tour. Since latent variable is unknown we fit the integral over its distribution. Four ���sets of best variables��� are specified, following the specification obtained from the correlation analysis. The results evidence that the relative importance of SE variables versus BE variables depends on how BE variables are measured. We found that each of these three spatial scales has its intangible qualities and drawbacks. Spatial scales play an important role on predicting travel demand due to the variability in measures at trip origin/destinations within the same administrative unit (municipality, district and so on). Larger units will produce less variation in data; but it does not affect certain variables, such as public transport supply, that are more significant at municipality level. By contrast, land-use measures are more efficient at district level. Self-selection in this context, is weak. Thus, the influence of BE attributes is true. The results of the hybrid model show that unobserved factors affect the choice of tour complexity. The latent variable used in this model is propensity to travel that is explained by socioeconomic aspects and neighbourhood attributes. The results show that neighbourhood attributes have indeed a significant impact on the choice of the type of tours either directly and through the propensity to travel. The propensity to travel has a different impact depending on the structure of each tour and increases the probability of choosing more complex tours, such as tours with many intermediate stops. The integration of choice and latent variable model shows that omitting important perception and attitudes leads to inconsistent estimates. The results also indicate that goodness of fit improves by adding the latent variable in both sequential and simultaneous estimation. There are significant differences in the sensitivity to the latent variable across alternatives. In general, as expected, the hybrid models show a major improvement into the goodness of fit of the model, compared to a classical discrete choice model that does not incorporate latent effects. The integrated model leads to a more detailed analysis of the behavioural process. Summarizing, the effect that built environment characteristics on trip frequency studied is deeply analyzed. In particular we tried to better understand how land use characteristics can be defined and measured and which of these measures do have really an impact on trip frequency. We also tried to test the superiority of HCM on this field. We can concluded that HCM shows a major improvement into the goodness of fit of the model, compared to classical discrete choice model that does not incorporate latent effects. And consequently, the application of HCM shows the importance of LV on the decision of tour complexity. People are more elastic to built environment attributes than level of services. Thus, policy implications must take place to develop more mixed areas, work-places in combination with commercial retails.