Heterogeneous urban traffic data and their integration through kernel-based interpolation

This paper presents collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. In this paper, the data fusion algorithm is developed by using a kernel based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space time resolution, with different level of accuracy, and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test-bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London. This paper contributes to analysis and management of urban transport facilities

Cost functions for mainline train operations and their application to timetable optimization

This paper discusses a set of cost functions for timetabling mainline train services. Mainline train services are generally heterogeneous which consist of passenger and freight trains, slow and express services, domestic and international connections, etc. The feasibility of a timetable is subject to a number of factors including availability of trains and crew, infrastructure capacity, and travel demand. With the complex nature of modern railway systems and the heterogeneity of rail traffic, deriving satisfactory train service schedules for passengers, train operators, and infrastructure manager is always a challenge. The cost functions presented here are used as indicators for evaluating different performance associated with the corresponding timetable. The performances of interest include carbon, capacity, cost, and customer satisfaction. These four performance indicators are also identified as the ’4C’ criteria by the railway industry in Great Britain. These 4C criteria are set in order to address the need to improve customer satisfaction (e.g. by providing more punctual service) and operational capacity, while decreasing operational cost and carbon emission. The authors also demonstrate the application of these cost functions into an optimization framework which derives optimal timetable for heterogeneous train services. The method is applied to Brighton Main Line in south-east England as a case study. The results reveal that overall performance of the railway systems can be achieved by re-scheduling and re-sequencing the train services through the optimization framework, while this may have to come at the expense of slow and local train services if the optimization is not properly formulated

Quantitative Approaches to Resilience in Transport Networks

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Modelling urban traffic dynamics based upon the variational formulation of kinematic waves

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Identification of critical combination of vulnerable links in transportation networks – a global optimisation approach

This paper presents a global optimisation framework for identifying the most critical combination of vulnerable links in a transportation network. The problem is formulated as a mixed-integer non-linear programme with equilibrium constraints, aiming to determine the combination of links whose deterioration would induce the most increase in total travel cost in the network. A global optimisation solution method applying a piecewise linearisation approach and range-reduction technique is developed to solve the model. From the numerical results, it is interesting and counterintuitive to note that the set of most vulnerable links when simultaneous multiple-link failure occurs is not simply the combination of the most vulnerable links with single-link failure, and the links in the critical combination of vulnerable links are not necessarily connected or even in the neighbourhood of each other. The numerical results also show that the ranking of vulnerable links will be significantly affected by certain input parameters

Dynamic spatial weight matrix and localised STARIMA for network modelling

Various statistical model specifications for describing spatiotemporal processes have been proposed over the years, including the space–time autoregressive integrated moving average (STARIMA) and its various extensions. These model specifications assume that the correlation in data can be adequately described by parameters that are globally fixed spatially and/or temporally. They are inadequate for cases in which the correlations among data are dynamic and heterogeneous, such as network data. The aim of this article is to describe autocorrelation in network data with a dynamic spatial weight matrix and a localized STARIMA model that captures the autocorrelation locally (heterogeneity) and dynamically (nonstationarity). The specification is tested with traffic data collected for central London. The result shows that the performance of estimation and prediction is improved compared with standard STARIMA models that are widely used for space–time modeling. En los últimos años, se han propuesto diversas especificaciones de modelado estadístico para describir procesos espacio-temporales. Esto incluye el modelo espacio-temporal autorregresivo integrado de media móvil (STARIMA) y sus varios derivados. Estas especificaciones de modelo asumen que la correlación de los datos puede ser adecuadamente descrita por parámetros que se fijan a nivel global en el espacio y/o tiempo. Dichos parámetros son inadecuados para los casos en los que las correlaciones entre los datos son dinámicas y heterogéneas, como en el contexto de los datos de la red. El objetivo de este artículo es describir la autocorrelación en los datos de red con una matriz de ponderación espacial dinámica y un modelo STARIMA localizado (LSTARIMA) que captura la autocorrelación local (heterogeneidad) de forma dinámica (no estacionariedad). La especificación del modelo es evaluada con datos de tráfico recolectados en el centro de Londres. Los resultados demuestran que los rendimientos de estimación y predicción mejoran con el método propuesto en comparación con los modelos STARIMA estándar que son ampliamente utilizados para el modelado de espacio-temporal

Scheduling and routing freight trains with multiple cost functions

This study presents a multi-objective optimization model for effectively scheduling and routing freight trains with minimum disruption caused to surrounding train traffic given the existing infrastructure capacity. Most existing timetabling policies around the world favor passenger train operations over the freight ones. The objective of the study is to facilitate freight operations with the proposed methodology. The optimization model is formulated here as a mixed integer program (MIP) which captures simultaneous scheduling and routing options of trains. The optimizer is applied to a real case scenario on the Brighton Main Line (BML) in southeast England. Given the network configuration, the optimizer is shown to be able to schedule and (re-)route requested freight trains with minimized additional costs induced to the system. The authors also examine selected scenario with marginal cost analysis and find that the train allocation process produced by the optimization model is somehow similar to existing practice in small scale applications, while the proposed algorithm is more systematic, generalizable to large-scale applications and multiple cost functions