Resilience Assignment Framework using System Dynamics and Fuzzy Logic.

This paper is concerned with the development of a conceptual framework that measures the resilience of the transport network under climate change related events. However, the conceptual framework could be adapted and quantified to suit each disruption’s unique impacts. The proposed resilience framework evaluates the changes in transport network performance in multi-stage processes; pre, during and after the disruption. The framework will be of use to decision makers in understanding the dynamic nature of resilience under various events. Furthermore, it could be used as an evaluation tool to gauge transport network performance and highlight weaknesses in the network. In this paper, the system dynamics approach and fuzzy logic theory are integrated and employed to study three characteristics of network resilience. The proposed methodology has been selected to overcome two dominant problems in transport modelling, namely complexity and uncertainty. The system dynamics approach is intended to overcome the double counting effect of extreme events on various resilience characteristics because of its ability to model the feedback process and time delay. On the other hand, fuzzy logic is used to model the relationships among different variables that are difficult to express in numerical form such as redundancy and mobility

A network mobility indicator using a fuzzy logic approach

This paper introduces a methodology to assess the mobility of a road transport network from the 3 network perspective. In this research, the mobility of the road transport network is defined as the 4 ability of the road transport network to connect all the origin-destination pairs within the network with 5 an acceptable level of service. Two mobility attributes are therefore introduced to assess the physical 6 connectivity and the road transport network level of service. Furthermore, a simple technique based 7 on a fuzzy logic approach is used to combine mobility attributes into a single mobility indicator in 8 order to measure the impact of disruptive events on road transport network functionality. 9 The application of the proposed methodology on a hypothetical Delft city network shows the ability of the technique to estimate variation in the level of mobility under different scenarios. The method allows the study of demand and supply side variations on overall network mobility, providing a new tool for decision makers in understanding the dynamic nature of mobility under various events. The method can also be used as an evaluation tool to gauge the highway network mobility level, and to highlight weaknesses in the network

An operational indicator for network mobility using fuzzy logic.

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 capacity 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

The resilience of road transport networks redundancy, vulnerability and mobility characteristics

This thesis is concerned with the development of a composite resilience index for road transport networks. The index employs three characteristics, namely redundancy, vulnerability and mobility, measuring resilience at network junction, link and origin-destination levels, respectively. Various techniques have been adopted to quantify each characteristic and the composite resilience index as summarised below. The redundancy indicator for road transport network junctions is 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 were examined. The developed redundancy indicator covers 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 vulnerability indicator for road transport network links is developed by combining vulnerability attributes (e.g. link capacity, flow, length, free flow and traffic congestion density) with different weights using a new methodology based on fuzzy logic and exhaustive search optimisation techniques. Furthermore, the network vulnerability indicators are calculated using two different aggregations: an aggregated vulnerability indicator based on physical characteristics and the other based on operational characteristics. The mobility indicator for road transport networks is formulated from two mobility attributes reflecting the physical connectivity and level of service. The combination of the two mobility attributes into a single mobility indicator is achieved by a fuzzy logic approach. Finally, the interdependence of the proposed characteristics is explored and the composite resilience index is estimated from the aggregation of the three characteristics indicators using two different approaches, namely equal weighting and principal component analysis methods. Moreover, the impact of real-time travel information on the proposed resilience characteristics and the composite resilience index has been investigated. The application of the proposed methodology on a synthetic road transport network of Delft city (Netherlands) and other real life case studies shows that the developed indicators for the three characteristics and the composite resilience index responded well to traffic load change and supply variations. The developed composite resilience index will be of use in various ways; first, helping decision makers in understanding the dynamic nature of resilience under different disruptive events, highlighting weaknesses in the network and future planning to mitigate the impact of disruptive events. Furthermore, each developed indicator for the three characteristics considered can be used as a tool to assess the effectiveness of different management policies or technologies to improve the overall network performance or the daily operation of road transport networks

Driver Competence Performance Indicators Using OTMR

The current practice for assessing driver competence performance is in-cab riding by driver managers. However, this paper investigates whether real-world driving data extracted from on-train monitoring recorders data (OTMR) can be used to assess the driver performance. A number of indicators were used to evaluate the drivers’ performance. These include: their use of the emergency bypass switch, the driver's reminder appliance as well as the driver’s reaction time. A study case illustrated the applicability of OTMR data to estimate the proposed indicators, which suggests that the indicators can be useful in the driver management system in addition to the current indicators. Furthermore, the proposed indicators could be used to tailor the driver training schemes up to their individual needs and evaluate their effectiveness. They could even be used for improving driver competence performance and reducing crash involvement by revealing potentially detrimental driving performance

Integrating data to support SPAD management

A stop signal passed without authority (aka SPAD) is one of the most serious types of incidents in railways since they potentially cause derailments or collisions. SPADs are complex incidents that have been usually analysed as human factors incidents. Human errors of train drivers such as slips or lapses have been prevalent in SPAD incident investigations. In the big data era, alternatives to the traditional methods can be used to support SPAD analysis of whatever kind. Railway systems produce a huge amount of data from a variety of data sources that can be used to get a better understanding of the factors involved in SPADs. This paper describes a first trial within the Big Data Risk Analysis program (BDRA) in order to combine unstructured data from SMIS/IFCS text records with structured data from of railway signals in order to support the SPAD management

The evaluation of redundancy for road traffic networks.

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 were examined. However, the two redundancy indices developed from the combined relative link speed 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 scheme introduced in 2006

Operational Safety Indicators Using Real Train Driving Data

This paper explores the utilization of On Train Data Recorders (OTDR) in monitoring train safety systems use. Train Protection and Warning System (TPWS), Emergency Bypass Switch (EBS) and the Driver's Reminder Appliance (DRA) were used as examples of train safety systems. Using OTDR data to monitor safety systems has to potential to improve compliance with the Rule Book, especially if data can be collected and analysed in real-time. Identification of the deviation from recommended rules that may have safety implications may be useful in the pre-incident investigation

A Big Data modeling approach with graph databases for SPAD risk

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

A Composite Resilience Index for Road Transport Networks

This paper is concerned with the development of a composite index for the resilience of road transport networks under disruptive events. The index employs three resilience characteristics, namely redundancy, vulnerability and mobility. Two different approaches, i.e. equal weighting and principal component analysis, are adopted to conduct the aggregation. In addition, the impact of the availability of real-time travel information for travellers on the three resilience characteristics and the composite resilience index is described. The application of the index on a synthetic road transport network of Delft city (Netherlands) shows that it responds well to traffic load changes and supply variations. The composite resilience index could be of use in various ways including supporting decision makers in understanding the dynamic nature of resilience under different disruptive events, highlighting weaknesses in the network and in assisting future planning to mitigate the impacts of disruptive events