Abstract

This paper explores the inter-connectedness of the evolution of transportation networks and land use through the application of a Markov Chain mode. The model investigates how individual cells, with both land use and transportation network attributes, change over time. While this paper does not consider land use density directly, it does not consider land use type, and as cells change, it can be concluded that some form of development is likely to be occurring. The next section of the paper outlines the Markov Chain model and a discussion of the Twin Cities data used in the study follows. The subsequent section develops the transition probability matrices used by the Markov Chain. Those matrices are analyzed in order to understand the empirical regularities that appear in the data. They are then applied to both assess the predictive ability of the Markov Chain model (comparing what the model would predict with what actually happened), and then applying the mode to forecast future changes the Twin Cities area. The paper concludes with some suggestions for future research. Document type: Part of book or chapter of book

Abstract

Probabilistic model checking mainly concentrates on techniques for reasoning about the probabilities of certain path properties or expected values of certain random variables. For the quantitative system analysis, however, there is also another type of interesting performance measure, namely quantiles. A typical quantile query takes as input a lower probability bound p and a reachability property. The task is then to compute the minimal reward bound r such that with probability at least p the target set will be reached before the accumulated reward exceeds r. Quantiles are well-known from mathematical statistics, but to the best of our knowledge they have not been addressed by the model checking community so far. In this paper, we study the complexity of quantile queries for until properties in discrete-time finite-state Markov decision processes with non-negative rewards on states. We show that qualitative quantile queries can be evaluated in polynomial time and present an exponential algorithm for the evaluation of quantitative quantile queries. For the special case of Markov chains, we show that quantitative quantile queries can be evaluated in time polynomial in the size of the chain and the maximum reward. Document type: Part of book or chapter of book

Abstract

Maritime transport covers the immense majority of the world’s international trade, particularly for Spain, where it represents a 74% of the total transported goods. Most of this transport is made via containers, because of its versatility. Its drawback is that very specific infrastructure and large spaces are needed for its management, and these are not fast to build nor cheap. In addition, the inability to manage containers if the infrastructure is under work or has reached his maximum capacity may translate into important economic losses. To prevent this from happening it is mandatory to plan in advance, and that means accept some predictions of the future to act accordingly. There is a wide range of accepted forecasting methods, because none is good in every situation, and predicting container throughput is not an exception. In this project, we will discuss the validity of Markov Chains when it comes to forecast maritime goods exchange, more precisely the total annual volume of TEUs. As real cases, we will take the data of 4 important Spanish ports (Algeciras, Barcelona, Tarragona and Valencia) from 1973 to 2015 and use it to create a prediction model. We will use the Markov property to create predictions and carry out Monte Carlo simulations to sample them. Over the sections we will change the input data and the parameters of the model to better understand the behavior of Markov chains when applied in such area. To have a reference, we will perform a witness forecast by means of a linear regression. An accuracy measure is planned for each of the models to assess a comparison between them, and find which one is more precise, and thus more valid. El transporte marítimo abarca la inmensa mayoría del comercio internacional en todo el mundo, y para España en particular, donde representa el 74% del total transportado. La mayor parte de este transporte se realiza en contenedores, por lo versátiles que resultan. Lo malo de este sistema es que se necesita una infraestructura específica y mucho espacio para poder procesarlos, lo cual tarda tiempo en construirse e implica grandes inversiones. Además, el hecho de no poder recibir contenedores por remodelaciones en la infraestructura o esta ha llegado a su máximo puede traducirse en cuantiosas pérdidas económicas. Para evitar que esto ocurra es de vital importancia planear de antemano, lo cual implica aceptar una predicción y actuar en consecuencia. Existe un amplio abanico de métodos predictivos, esto se debe a que ninguno de ellos es válido en todas las situaciones posibles, y en el ámbito del transporte marítimo no es una excepción. En este Proyecto discutiremos la validez de las cadenas de Márkov a la hora de prever el intercambio de mercancías por vía marítima, más concretamente el número total de TEUs por año. Como estudiar casos reales tomaremos los datos de 4 importantes puertos españoles (Algeciras, Barcelona, Tarragona and Valencia) desde 1973 hasta 2015 y los utilizaremos para probar el modelo. Utilizaremos la propiedad de Márkov para crear las predicciones y llevaremos a cabo simulaciones Monte Carlo con el fin de muestrearlas. A través de los diferentes apartados iremos cambiando los datos y los parámetros del modelo para entender mejor el comportamiento de las cadenas de Márkov cuando se aplican en esta área en particular. Para tener una referencia, utilizaremos como experimento testigo una predicción realizada con una regresión lineal. Está planeada también una medida de la precisión de cada uno de los modelos, para poder así compararlos y evaluar cuál es el que mejor se ajusta a la realidad, y por lo tanto es el más valido.

Abstract

We investigate the hypothesis that long-term memory in populations of agents can lead to counterproductive emergent properties at the system level. Our investigation is framed in the context of a discrete, one-dimensional road-traffic congestion model: we investigate the influence of simple cognition in a population of rational commuter agents that use memory to optimise their departure time, taking into account congestion delays on previous trips. Our results differ from the well-known minority game in that crowded slots do not carry any explicit penalty. We use Markov chain analysis to uncover fundamental properties of this model and then use the gained insight as a benchmark. Then, using Monte Carlo simulations, we study two scenarios: one in which "myopic” agents only remember the outcome (delay) of their latest commute, and one in which their memory is practically infinite. We show that there exists a trade-off, whereby myopic memory reduces congestion but increases uncertainty, while infinite memory does the opposite. We evaluate the performance against the optimal distribution of departure times (i.e. where both delay and uncertainty are minimised simultaneously). This optimal but unstable distribution is identified using a genetic algorithm. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Abstract

Between 1900 and 2000, the length of paved roads in the United States increased from 240 km to 6,400,000 km (Peat 2002, BTS 2002) with virtually 100% of the U.S. population having almost immediate access to paved roadways. Similarly, in 1830 there were 37 km of railroad in the United States, but by 1920 total track mileage had increased more than ten-thousand times to 416,000 km miles, however since then, rail track mileage has shrunk to about 272,000 km (Garrison 1996, BTS 2002). The growth (and decline) of transport networks obviously affects the social and economic activities that a region can support; yet the dynamics of how such growth occurs is one of the least understood areas in transport, geography, and regional science. This is revealed time and again in the long-range planning efforts of metropolitan planning organizations (MPOs), where transport network changes are treated exclusively as the result of top-down decision-making. Changes to the transport network are rather the result of numerous small decisions (and some large ones) by property owners, firms, developers, towns, cities, counties, state department of transport districts, MPOs, and states in response to market conditions and policy initiatives. Understanding how markets and policies translate into facilities on the ground is essential for scientific understanding and improving forecasting, planning, policy-making, and evaluation. Document type: Part of book or chapter of book