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A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors in the "smart" city of Santander, Spain. The sensor network is segmented in four different regions, and then survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is presented.

Metro networks provide efficient transportation services to large numbers of travelers in urban areas around the World; any unexpected operational disruption can lead to rapid degradation of the provided level of service by a city's public transportation system. In such instances, quick and efficient substitution of services is necessary for accommodating metro passengers including the widely used practice of "bridging" metro stations using bus services. Despite its widespread application, bus bridging is largely done ad-hoc and not as part of an integrated optimization procedure. In this paper we propose a methodological framework for planning and designing an efficient bus bridging network. Furthermore, we offer a set of structured steps and optimization models and algorithms for handling bus bridging problems.

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