Valor de la información del tiempo de viaje por carretera

II Premio Joven Investigador, otorgado por el comité organizador del X Congreso de Ingeniería del Transporte (Granada, 2012)De un tiempo a esta parte, un objetivo común de muchos gestores de tráfico alrededor de todo el mundo es proporcionar información en tiempo real del tiempo de viaje previsto en las infraestructuras que gestionan. A estos efectos se han invertido muchos recursos en la monitorización intensiva que se requiere para obtener este tipo de información, por ejemplo en las redes metropolitanas de autopistas. Las tecnologías utilizadas y las precisiones conseguidas pueden ser múltiples y variadas, aunque todas ellas afectadas por la incertidumbre intrínseca al objetivo perseguido: una previsión a corto término. Estos esfuerzos responden al hecho que el tiempo de viaje previsto es la información de tráfico que más valora el usuario. No obstante, se desconoce como cuantificar el valor de esta información y como varía en función de la precisión proporcionada. Este conocimiento permitiría realizar análisis coste–beneficio que ayudarían a la toma de decisiones en relación a la tecnología a utilizar, la precisión necesaria, la selección de ámbitos rentables, o en analizar la disponibilidad al pago por esta información. El presente artículo pretende rellenar este vacío. Se propone una metodología que cuantifica el valor de la información del tiempo de viaje. Este valor será mayor cuanto mayor sea la reducción de la incertidumbre que provoca y cuanto mayor sea el coste de esta incertidumbre. Por lo tanto el valor de la información dependerá del usuario y su conocimiento previo de la infraestructura, de la fiabilidad de la propia infraestructura, del motivo del viaje, del momento en el que recibe la información, de las posibles alternativas de actuación disponibles y de la precisión de la información recibida. Se presenta un modelo probabilístico, basado en la teoría de la utilidad aleatoria, que partiendo de estas variables proporciona el valor de la información para un usuario concreto en una determinada infraestructura. Agregando estos valores para una determinada composición de conductores se puede obtener el valor global del sistema. En el artículo se presenta un ejemplo de aplicación de la metodología para el acceso norte por autopista a la ciudad de Barcelona.Peer ReviewedAward-winningPostprint (published version

Estimate freeway travel time reliability under recurring and nonrecurring congestion

Travel time and its reliability are considered as intuitive measure of service quality by transportation agencies. Moreover, highly reliable travel times allow for arriving at work or other destinations on time in the context of personal travel and facilitate just-in-time logistics services in freight operations. Travel times are the result of the traffic congestion. By considering different impact factors and shortcoming of the sensing technologies, this dissertation proposed methods for travel time and its reliability estimation. First of all, this dissertation presented a method to estimate corridor-level travel times based on data collected from roadside radar sensors, considering spatially correlated traffic conditions. Link-level and corridor-level travel time distributions are estimated using these travel time estimates and compared with the ones estimated based on probe vehicle data. The maximum likelihood estimation is used to estimate the parameters of Weibull, gamma, normal, and lognormal distributions. According to the log likelihood values, lognormal distribution is the best fit among all the tested distributions. Corridor-level travel time reliability measures are extracted from the travel time distributions. The proposed travel time estimation model can well capture the temporal pattern of travel time and its distribution. Second, a travel time reliability measure estimation method is proposed by incorporating standstill distance and time headway distributions in car-following models. The method is based on simplified two-component travel time distribution. By using Monte Carlo simulation, the speed-density region under congested condition and the travel time reliability measures can be generated. The results shows that the speed-density region derived from the steady-state Pipes model encloses most of the field data. Moreover, the proposed method estimate travel time reliability measures more precisely and faster, compared with using VISSIM simulation. Finally, a work zone travel time estimation approach is proposed in this dissertation. First, the impact of work zone on capacity is investigated. For the work zone capacity prediction framework, the predicted upper bound of capacity is close to the maximum 15-min flow rate. Moreover, based on the predicted capacity, density at capacity and free flow speed, work zone travel times are estimated by using the modified segment speed estimation model from the study of Newman. The estimated travel times roughly followed the pattern of the INRIX travel times. Moreover, the travel time reliability indices are estimated directly from the estimated travel times. The result shows that the travel time reliability indices based on estimated travel times are close to the indices based on INRIX travel times

Algorithm combining point and interval detector data to estimate highway travel times

The dissertation is divided into three interconnected parts. The first part presents a link travel time estimation algorithm that is based on the use of robust statistic able to exclude the impact of outliers. Outliers in travel time measurements are vehicles whose shortened or extended travel times are not caused by the traffic conditions, but are the result of individual behavior of such vehicle. As the adequate information on travel times is the one of personal cars, the influence of other vehicle categories should be eliminated from the samples which is not feasible with the use of existing link travel time estimation algorithms. The second part of the dissertation presents a method to estimate the value of travel time based on speed extrapolations from point measurements. The method is able to determine whether a speed variation represents a random fluctuation due to individual driver’s behavior and should therefore be smoothed or is a consequence of a change in traffic conditions as a result of a shock wave and should therefore be kept as it is in order to provide prompt response of the algorithm. By extrapolating the speed, the value of travel time from point speed measurements is obtained. In the third part of the dissertation, a data fusion algorithm is presented, combining point and interval detector data to estimate highway travel times also taking into account qualititative measurements of traffic flow. The purpose of travel time data fusion from different sources is to overcome on one hand the spatial inaccuracy of indirect travel time estimation from point speed measurements and on the other hand to overcome the information delay of the direct travel time measurements. By combining both data sources, a short-term travel time prediction is achieved, as the input for the travel time information system