This paper introduces a methodology to assess the level of vulnerability of road transport networks. A new technique based on fuzzy logic and exhaustive search optimisation is used to combine vulnerability attributes with different weights into a single vulnerability index for network links, which may be used to measure the impact of disruptive events. The network vulnerability index is then calculated using two different aggregations: an aggregated vulnerability index based on physical characteristics and an aggregated vulnerability index based on operational characteristics. The former uses link physical properties such as its length and the number of lanes, whilst the latter reflects aspects of the network flow. The application of the methodology on a synthetic network (based on Delft city, Netherland) demonstrates the ability of the technique to estimate variation in the level of vulnerability under different scenarios. The method also allows exploration of how variation in demand and supply impact on overall network vulnerability, providing a new tool for decision makers to understand the dynamic nature of vulnerability under various events. The method could also be used as an evaluation tool to gauge the impact of particular policies on the level of vulnerability for the highway network and highlight weaknesses in the network.

This paper presents two redundancy indices for road traffic network junctions and also an aggregated network redundancy index. The proposed redundancy indices could be implemented to identify optimal design alternatives during the planning stage of the network junctions whereas the aggregated network redundancy index could assess the best control and management policies under disruptive events. Furthermore, effective measures of network redundancy are important to policy makers in understanding the current resilience and future planning to mitigate the impacts of greenhouse gases. The proposed junction indices cover the static aspect of redundancy, i.e. alternative paths, and the dynamic feature of redundancy reflected by the availability of spare capacity under different network loading and service level. The proposed redundancy indices are based on the entropy concept, due to its ability to measure the system configuration in addition to being able to model the inherent uncertainty in road transport network conditions. Various system parameters based on different combinations of link flow, relative link spare capacity and Relative Link Speed (RLS) were examined. However, the two redundancy indices developed from the combined RLS and relative link spare capacity showed strong correlation with junction delay and volume capacity ratio of a synthetic road transport network of Delft city. Furthermore, the developed redundancy indices responded well to demand variation under the same network conditions and supply variations. Another case study on Junction 3A in M42 motorway near Birmingham demonstrated that the developed redundancy index is able to reflect the impact of the Active Traffic Management (ATM) scheme introduced in 2006.

This paper presents the case for IT transformation and big data for safety risk management on the GB railways. This paper explains why the interest in data driven safety solutions is very high in the railways by describing the drivers that shape risk management for the railways. A brief overview of research projects in the Big Data Risk Analysis (BDRA) programme supports the case and helps understand the research agenda for the transformation of safety and risk on the GB railways. The drivers and the projects provide insight in the current research needs for the transformation and explains why safety researchers have to broaden their skill set to include digital skills and potentially even programming. The case for IT transformation of risk management systems is compelling and the paper describes just the tip of the iceberg of opportunities opening up for safety analysis that, after all, depends on data.

This paper proposes a model to assess train passing a red signal without authorization, a SPAD. The approach is based on Big Data techniques so that many types of data may be integrated, or even added at a later date, to get a richer view of these complicated events. The proposed approach integrates multiple data sources using a graph database. A four-steps data modeling approach for safety data model is introduced. The steps are problem formulation, identification of data points, identification of relations and calculation of the safety indicators. A graph database was used to store, manage and query the data, whereas R software was used to automate the data upload and post-process the results. A case study demonstrates how indicators have extracted that warning in the case that the SPAD safety envelope is reduced. The technique is demonstrated with a case study that focuses on the detection of SPADs and safety distances for SPADs. The latter provides indicators for to assess the severity of near-SPAD incidents.

This paper proposes a fuzzy logic model for assessing the mobility of road transport networks from a network perspective. Two mobility attributes are introduced to account for the physical connectivity and road transport network level of service. The relative importance of the two mobility attributes has been established through the fuzzy inference reasoning procedure that was implemented to estimate a single mobility indicator. The advantage of quantifying two mobility attributes is that it improves the ability of the mobility indicator developed to assess the level of mobility under different types of disruptive events.A case study of real traffic data from seven British cities shows a strong correlation between the proposed mobility indicator and the Geo distance per minute, demonstrating the applicability of the proposed fuzzy logic model. The second case study of a synthetic road transport network for Delft city illustrates the ability of the proposed network mobility indicator to reflect variation in the demand side (i.e. departure rate) and supply side (i.e. network capcity and link closure). Overall, the proposed mobility indicator offers a new tool for decision makers in understanding the dynamic nature of mobility under various disruptive events.