Suchergebnisse

PurposeTravel is usually not valued in and of itself, but for the activities it allows people to partake in. Therefore, if change occurs in either the activities people perform, or in the means they use to perform them, the demand for travel is likely to change accordingly. Technologies have the potential to accommodate the activities people need or want to perform and how they perform them. The purpose of this conceptual paper is to increase the understanding of the complex relations between technologies developing outside the transport domain, social practices and travel, and the uncertainties that can result from these linkages. As such it draws attention to the interconnectivity of transport with other domains (e.g. healthcare, retail, leisure).Design/methodology/approachThe relations between non‐transport technologies, social practices and travel are largely unintended and/or unanticipated. This study therefore utilised notions developed elsewhere of the mechanisms through which unintended consequences materialize. With these notions in mind, some selected examples of past, present and possible future technologies expose the possible indirect influences they can have on travel demand, thereby developing the conceptual understanding of these linkages.FindingsIf policies are being developed to limit, change, or reduce people's travel then non‐transport technologies may thwart those policy ambitions in serious ways or be realised in unexpected and surprising forms.Research limitations/implicationsThere appears precious little (quantitative) evidence of data that captures the relations between technologies, social practices and travel.Originality/valueThis study is one of the first to examine the indirect impacts of technological developments occurring outside the transport domain on travel demand.

Spain's transport infrastructure policy has become a paradigmatic case of oversupply and of mismatch with demand. The massive expansion of the country's transport infrastructure over the last decade has not been a response to demand bottlenecks or previously identified needs. For this reason, the intensity of use today on all interurban modes of transport in Spain falls well below that of other EU countries. This paper analyzes the institutional and regulatory factors that have permitted this policy, allowing us to draw lessons from the Spanish case that should help other countries avoid the pitfalls and shortcomings of Spanish policy.

A now substantial body of literature finds that land use and urban form have a statistically significant, albeit relatively modest, effect on travel behavior. Some scholars have suggested that various built-environment characteristics influence travel more in concert than when considered in isolation. Yet few previous studies have combined built-environment measures to create holistic descriptions of the overall character of neighborhoods, and fewer still have related these neighborhoods to residents' travel decisions. To address this gap in the literature, we develop a typology of seven distinct neighborhood types by applying factor analysis and then cluster analysis to a set of 20 variables describing built-environment characteristics for most census tracts in the United States. We then include these neighborhood types in a set of multivariate regression models to estimate the effect of neighborhood type on the travel behavior of neighborhood residents, controlling for an array of personal and household characteristics. We find relatively little variation in the number of daily trips among neighborhood types, but there is substantial neighborhood variation in both person miles of travel and mode choice. Travel by residents of one particular neighborhood type is notably distinguished from all others by a very low number of miles traveled, little solo driving, and high transit use. However, this neighborhood type is found almost exclusively in just a few very large metropolitan areas, and its replicability is uncertain.

ABSTRACT. Transport is a strategic sector for the economy and has a strong impact on economic growth and welfare, but also produces several negative externalities due to, among other causes, the excessive use of cars. Both the economic impact and the negative externalities associated with transport have generated an increased interest among researchers in the field of transport economics. In order to evaluate the balance between positive and negative effects of transport, policy evaluation studies are needed. This thesis focuses on the application of novel methods in transport demand analysis which are useful in the evaluation of transport policies. The thesis is divided in four chapters which contribute to the scientific development of the field. Chapter 1 focuses on the aggregated transport demand. Using alternative approaches, we examine the concepts of β, σ and club convergence in road transport CO2 emissions per capita of a sample of 23 European Union countries over the period 1990-2014. We also estimate dynamic panel data models with interaction terms in order to explain the factors determining the evolution of the emissions and the effect of a set of variables on the speed of convergence. Our results show, first, a reduction in the disparities of emission levels, and a conditional convergence process during the period under study; second, the evidence that this process is conditioned by factors such as economic activity, fuel price or annual average distance travelled by cars. Further, some of these variables appear to have a significant effect on the speed of convergence, a result that may have significant implications for the cross-country impact of the European policies on climate change currently in place. The next three chapters focus on the disaggregate transport demand, specifically on the individual travel mode choice, by using different applications of discrete choice models. We conduct surveys on Revealed Preferences (RP) and Stated Preferences (SP) and estimate different specifications of discrete choice models. The case study of Chapters 2 and 3 is a new tramline implementation in Tenerife, Canary Islands (Spain) where we analyse how the individual preferences change with the introduction of the new mode. We build a novel panel data with information about transport choices of the same group of individuals (college students). Just before the implementation of the tramline, we collect information about RP of transport mode choices and about SP in a simulated scenario with the tram as a hypothetical alternative. Two years after the tram started operating, we gather information about RP to ascertain the impact of the new tramline in the student mobility patterns. With this information, we estimate several panel mixed logit models with error components. The main objective of Chapter 2 is to evaluate the effect of using partial information on the estimation of the Values of Travel Time Savings (VTTS). We conclude that the estimation of the VTTS changes when comparing the results obtained with models that only consider information before or after the tramline implementation with that obtained with a panel data approach using all the information simultaneously. Further, we obtain a better statistical fit to data and, according to previous evidence in our study context, more reasonable values of travel time using a panel data approach. Our results suggest that when a new transport mode is implemented, the VTTS obtained with models than only consider prior or later periods of time can be underestimated and hence lead to wrong valuations of the benefits associated with the new alternative, even when stated preferences are used to anticipate changes in the user preferences. The purpose of Chapter 3 is to analyse the influence of past behaviour on the current transport mode choices. To do this, we examine the inertia effect, a factor usually not considered in discrete choice models of travel demand. Around the implementation of new transport modes, the majority of studies on inertia have relied on combining RP and SP obtained prior to the implementation and measuring the inertia as the effect that the real choices(RP) have on the choices in the hypothetical scenarios (SP). In our case, we find a significant inertia effect only between the previous and posterior implementation RP observations, which increases the probability of choosing the car once the tram starts running. However, we do not find inertia effect on the previous implementation RP-SP information, hence taking into account only this information might have led to wrong conclusions about the effect of the transport policy. Furthermore, we compare models with and without inertia and conclude that the models with inertia provide better fit to data, smaller direct car choice elasticities and increasing asymmetric effects between the car and public transport crosschoice elasticities. Lastly, Chapter 4 adopts a novel methodological approach to estimate the recreational value of a natural site. To calculate this value, estimations of the visitor values of travel time are needed. In the recreational demand literature, the most common approach for the calculation of the values of travel time has been the use of different proportions of the wage rate. However, criticisms of this method abound because in a recreational trip the relevant measure is the opportunity cost of leisure time rather than work time. In this chapter, we obtain the value of travel time through the trade-off between time and money considered by the tourist visitors when choosing the transport mode to access the natural site, and we present the first calculation of the recreational value of the Teide National Park. Specifically, using a revealed preference survey, we estimate mixed logit models accounting for random preference heterogeneity, derive travel time values and incorporate them into a zonal travel cost model. This approach allows us to estimate different time values depending on transport mode and stage of the trip and shows that the use of discrete choice models instead of the wage rate approach has a strong impact on the recreational value calculated. ; El transporte es un sector estratégico para la economía y tiene un fuerte impacto sobre el crecimiento y el bienestar, pero también genera numerosas externalidades negativas producidas, entre otras causas, por el uso excesivo del coche. Tanto su impacto económico como los problemas que ocasiona han generado un fuerte interés entre los investigadores en el campo de la economía del transporte. Para poder establecer un balance entre efectos positivos y negativos del transporte, son necesarios estudios de evaluación de políticas. Esta tesis se centra en la aplicación de métodos novedosos en el análisis de la demanda de transporte que son útiles en la evaluación de políticas de transporte. La tesis se divide en cuatro ensayos que contribuyen al desarrollo científico de este campo. El Capítulo 1 se focaliza en la demanda agregada de transporte. Usando técnicas alternativas, se examinan los conceptos de β, σ y club convergencia en las emisiones de CO2 per cápita de transporte por carretera para una muestra de 23 países europeos en el periodo 1990-2014. Con el objetivo de explicar los factores que determinan la evolución de las emisiones y el efecto de estos factores en la velocidad de convergencia, se estiman modelos de paneles de datos dinámicos con términos de interacción. Los resultados muestran, para el periodo temporal considerado, primero, una reducción en la disparidad de los niveles de emisiones acompañado de un proceso de convergencia condicional y, segundo, la evidencia de que este proceso está condicionado por factores tales como la actividad económica, el precio del combustible o la distancia promedio anual recorrida por los coches. Además, algunas de estas variables tienen un efecto significativo sobre la velocidad de convergencia, un resultado que puede tener implicaciones sobre el impacto entre países de las políticas contra el cambio climático que se están llevando a cabo en Europa actualmente. Los siguientes tres capítulos se focalizan en el análisis de la demanda a nivel desagregado, particularmente en la elección individual de modo de transporte. Se construyen encuestas de Preferencias Reveladas (PR) y Preferencias Declaradas (PD) y se estiman diferentes especificaciones de modelos de elección discreta. Los Capítulos 2 y 3 tienen como caso de estudio la implementación de un tranvía en Tenerife, Islas Canarias (España), donde se analizan cómo cambian las preferencias de los individuos con la introducción del nuevo modo. Se construye un panel de datos novedoso con información de las elecciones de modo de transporte de un mismo grupo de individuos (estudiantes universitarios). Justo antes de la implementación, se recogió información sobre PR de la elección de modo y sobre PD en un escenario simulado donde el tranvía aparecía como una alternativa de transporte hipotética. Dos años después de que el tranvía estuviera operando, se recogió nuevamente información de PR, permitiendo conocer el impacto del tranvía sobre los patrones de movilidad de los estudiantes. Con esta información, se estiman diversos modelos logit mixtos de datos de panel con componentes de error. El principal objetivo del Capítulo 2 es evaluar el efecto que tiene sobre las estimaciones del Valor Subjetivo de los ahorros de Tiempo de Viaje (VSTV) el uso parcial de la información. Se comprueba que la estimación del VSTV cambia cuando se comparan los resultados obtenidos con modelos que sólo consideran información de antes o de después de la implementación del tranvía con los obtenidos utilizando un panel de datos que considera toda la información de forma simultánea. Además, con los modelos de datos de panel se obtiene un mejor ajuste y valores del tiempo más acordes con la evidencia previa referida a nuestro contexto de estudio. Estos resultados sugieren que los modelos que sólo consideran información previa o posterior a la implementación de un nuevo modo de transporte pueden subestimar los valores del tiempo. Por tanto, la valoración de los beneficios derivados de la nueva alternativa podría ser errónea, incluso cuando se utilizan preferencias declaradas para anticipar cambios en las preferencias de los usuarios. El objetivo del Capítulo 3 es analizar la influencia del comportamiento pasado sobre las elecciones actuales de modo de transporte. Para ello, se analiza el denominado efecto inercia, un efecto poco considerado en los modelos de elección discreta de modo de transporte. La mayoría de los estudios sobre inercia que analizan la implementación de nuevos modos utilizan únicamente información sobre PR y PD previa a la implementación, y analizan la inercia como el efecto que las elecciones reales (PR) tienen sobre las elecciones en los escenarios hipotéticos (PD). En nuestro caso, se encuentra un efecto inercia significativo sólo entre las elecciones reales (PR) previas y posteriores a la implementación del tranvía, que incrementa la probabilidad de elegir el coche una vez que este nuevo modo está en funcionamiento. Sin embargo, no se encuentra inercia entre la combinación de datos de PR-PD previa a la implementación, por lo que considerar únicamente estos datos podría haber llevado a conclusiones erróneas sobre el efecto de la política. Además, se concluye que los modelos que consideran el efecto inercia tienen un mejor ajuste a los datos en comparación a los que no la consideran, así como menores elasticidades directas de elección del coche y mayores efectos asimétricos con respecto a las elasticidades cruzadas de la elección del coche y del transporte público. Por último, en el Capítulo 4 se propone un enfoque metodológico novedoso para estimar el valor de uso recreativo de un espacio natural. Para estimar ese valor es necesario disponer de estimaciones sobre el valor del tiempo de viaje de los visitantes. En la literatura de demanda recreativa, el enfoque más habitual para calcular el valor del tiempo de viaje ha sido el uso de distintas proporciones de la tasa salarial. No obstante, las críticas a este enfoque abundan debido a que en un viaje recreativo la medida relevante es el coste de oportunidad del tiempo de ocio y no el del tiempo de trabajo. En este capítulo, se obtiene el valor del tiempo mediante el trade-off entre tiempo y dinero que realizan los turistas cuando eligen el modo de transporte en el que acceden al espacio natural y se calcula por primera vez el valor de uso recreativo del Parque Nacional del Teide. Específicamente, usando una encuesta de preferencias reveladas, se estiman modelos logit mixtos que tienen en cuenta las preferencias heterogéneas, se derivan los valores del tiempo y se incorporan en un modelo de coste de viaje zonal. Se muestra que el uso de modelos de elección discreta frente a una aproximación de tasa salarial tiene un fuerte impacto sobre el valor de uso recreativo calculado y, además, permite estimar distintos valores del tiempo según modo de transporte y etapa del viaje.

"This book brings together conceptual and empirical insights to explore the interconnections between social networks based on Information and Communication Technologies (ICT) and travel behaviour in urban environments. Over the past decade, rapid development of ICT has led to extensive social impacts and influence on travel and mobility patterns within urban spaces. A new field of research of digital social networks and travel behaviour is now emerging. This book presents state-of-the-art knowledge cutting-edge research, and integrated analysis methods from the fields of social networks, travel behaviour and urban analysis. It explores the challenges related to the question of how we can synchronize among social networks activities, transport means, intelligent communication/information technologies and the urban form. This innovative book encourages multidisciplinary insights and fusion among three disciplines of social networks, travel behaviour and urban analysis. It offers new horizons for research and will be of interest to students and scholars studying mobilities, transport studies, urban geography, urban planning, the built environment, and urban policy"--