282 results found
Goldbeck N, Angeloudis P, Ochieng W, Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models, Reliability Engineering and System Safety, ISSN: 0951-8320
Critical infrastructure systems are becoming increasingly interdependent, which can exacerbate the impacts of disruptive events through cascading failures, hindered asset repairs and network congestion. Current resilience assessment methods fall short of fully capturing such interdependency effects as they tend to model asset reliability and network flows separately and often rely on static flow assignment methods. In this paper, we develop an integrated, dynamic modelling and simulation framework that combines network and asset representations of infrastructure systems and models the optimal response to disruptions using a rolling planning horizon. The framework considers dependencies pertaining to failure propagation, system-of-systems architecture and resources required for operating and repairing assets. Stochastic asset failure is captured by a scenario tree generation algorithm whereas the redistribution of network flows and the optimal deployment of repair resources are modelled using a minimum cost flow approach. A case study on London’s metro and electric power networks shows how the proposed methodology can be used to assess the resilience of city-scale infrastructure systems to a local flooding incident and estimate the value of the resilience loss triangle for different levels of hazard exposure and repair capabilities.
Koudis GS, Hu SJ, Majumdar A, et al., 2018, The impact of single engine taxiing on aircraft fuel consumption and pollutant emissions, Aeronautical Journal, Vol: 122, Pages: 1967-1984, ISSN: 0001-9240
Optimisation of aircraft ground operations to reduce airport emissions can reduce resultant local air quality impacts. Single engine taxiing (SET), where only half of the installed number of engines are used for the majority of the taxi duration, offers the opportunity to reduce fuel consumption, and emissions of NOX, CO and HC. Using 3510 flight data records, this paper develops a model for SET operations and presents a case study of London Heathrow, where we show that SET is regularly implemented during taxi-in. The model predicts fuel consumption and pollutant emissions with greater accuracy than previous studies that used simplistic assumptions. Without SET during taxi-in, fuel consumption and pollutant emissions would increase by up to 50%. Reducing the time before SET is initiated to the 25th percentile of recorded values would reduce fuel consumption and pollutant emissions by 7–14%, respectively, relative to current operations. Future research should investigate the practicalities of reducing the time before SET initialisation so that additional benefits of reduced fuel loadings, which would decrease fuel consumption across the whole flight, can be achieved.
Yin J, Ma Y, Hu Y, et al., Dynamic runway configurations and flexible arrival/departure tradeoffs in metroplex airports, IEEE/AIAA 37th Digital Avionics Systems Conference, Publisher: IEEE
Runway system is central to airport capacity. Its inefficient ultilization has been identified as a major source of airport congestions. This paper analyzes the patterns of demand- capacity imbalance and design a series of flexible strategies for air traffic demand management (ATDM), and then optimize runway configurations in metroplex (i.e. multi-airport system) airports, under a set of tradeoff settings for arrival and departure priorities. An optimization model with 4 imbalance cases, 11 tradeoff scenarios and 2 configuration strategies, are proposed to minimize the flight holding cost and the number of adjusted flights. The proposed evolutionary algorithm can obtain close-to optimal results with a very low computational cost. A case study of the Shanghai metroplex airports shows that, compared with the traditional static strategy, the proposed dynamic strategy can significantly reduce the number of adjusted flights. The proposed framework in this paper can be applied on pre-tactical (i.e. one-day planning) as well as tactical (i.e. 2-h rolling horizon) levels, to keep the balance between high demand and limited capacity through flexible ATDM options.
Ainalis D, Achurra-Gonzalez P, Gaudin A, et al., Ultra-Capacitor based kinetic energy recovery system for heavy goods vheicles, 15th International Symposium on Heavy Vehicle Transport Technology
The Climate Change Act 2008 commits the UK to reduce the Greenhouse Gas emissions by 80% by 2050 relative to 1990 levels. While Heavy Goods Vehicles and buses contribute about 4% of the total Greenhouse Gas emissions in the UK, these emissions only decrease by 10% between 1990 and 2015. Urban areas are particularly susceptible to emissions and can have a significant impact upon the health of residents. For Heavy Goods Vehicles, braking losses are one of the most significant losses. A Kinetic Energy Recovery System can help reduce these emissions, and increase fuel efficiency by up to 30 %. This paper describes an InnovateUK funded project aimed at evaluating the technical and economic feasibility of a retrofitted Kinetic Energy Recovery System on Heavy Goods Vehicles through an operational trial, controlled emissions and fuel tests, and numerical modelling. A series of preliminary results using a numerical vehicle model is compared with operational data, along with simulations comparing the fuel efficiency of a Heavy Goods Vehicle with and without the KERS.
Han K, Graham D, Ochieng W, 2018, M20/A20 Congestion Prediction with Post-Brexit Border Delays, M20/A20 Congestion Prediction with Post-Brexit Border Delays
This research was commissioned by the BBC Inside Out South East program. It aims to quantify the congestion impact on M20/A20 of potential check time increase at Port of Dover and Eurotunnel (in Folkestone) in a post-Brexit scenario. We focus on a 40-mile segment of the M20/A20 motorway between Maidstone and Dover, with local access to Ashford and Folkestone. We consider outbound lorries and passenger vehicles that use the ferry and tunnel to cross the Straight of Dover, as well as traffic with local origins and destinations. Traffic simulations were conducted with assumptions regarding the check times at Dover and Eurotunnel for both current and post-Brexit scenarios. The impact of vehicle queuing at these locations was assessed in terms of queue length, travel time, and disruption to local traffic. The findings show that even one or two minutes of extra check times at the borders are accompanied by a dramatic increase of congestion on the motorways as well as local streets, with queues extending up to 30 miles from Dover/Eurotunnel towards Maidstone and travel time approaching 5 hours in peak times.
Escribano Macias J, Angeloudis P, Ochieng W, AIAA Integrated Trajectory-Location-Routing for Rapid Humanitarian Deliveries using Unmanned Aerial Vehicles, 2018 Aviation Technology, Integration, and Operations Conference
Sun R, Cheng Q, Xue D, et al., 2017, GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application., Sensors, Vol: 17, ISSN: 1424-2818
The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision.
Sun R, Xie F, Xue D, et al., 2017, A novel rear-end collision detection algorithm based on GNSS fusion and ANFIS, Journal of Advanced Transportation, Vol: 2017, ISSN: 0197-6729
Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate.
Ali BS, Ochieng WY, Zainudin R, 2017, An analysis and model for Automatic Dependent Surveillance Broadcast (ADS-B) continuity, GPS SOLUTIONS, Vol: 21, Pages: 1841-1854, ISSN: 1080-5370
Sun R, Cheng Q, Wang G, et al., 2017, A novel online data-driven algorithm for detecting UAV navigation sensor faults, Sensors (Basel, Switzerland), Vol: 17, ISSN: 1424-2818
The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.
Goldbeck N, Angeloudis P, Ochieng W, A Dynamic Network Flow Model for Interdependent Infrastructure and Supply Chain Networks with Uncertain Asset Operability, International Conference on Computational Logistics
Qin F, Sun R, Ochieng WY, et al., 2017, Integrated GNSS/DR/road segment information system for variable road user charging, Transportation Research Part C: Emerging Technologies, Vol: 82, Pages: 261-272, ISSN: 0968-090X
Road User Charging (RUC) is designed to reduce congestion and collect revenue for the maintenance of transportation infrastructure. In order to determine the charges, it is important that appropriate Road User Charging Indicators (RUCI) are defined. This paper focusses on Variable Road User Charging (VRUC) as the more dynamic and flexible compared to Fixed Road User Charging (FRUC), and thus is a better reflection of the utility of the road space. The main issues associated with VRUC are the definition of appropriate charging indicators and their measurement. This paper addresses the former by proposing a number of new charging indicators, considering the equalization of the charges and marginal social cost imposed on others. The measurement of the indicators is addressed by a novel data fusion algorithm for the determination of the vehicle state based on the integration of Global Navigation Satellite Systems (GNSS) with Dead Reckoning (DR) and road segment information. Statistical analyses are presented in terms of the Required Navigation Performance (RNP) parameters of accuracy, integrity, continuity and availability, based on simulation and field tests. It is shown that the proposed fusion model is superior to positioning with GPS only, and GPS plus GLONASS, in terms of all the RNP parameters with a significant improvement in availability.
Ochieng W, 2017, Integrated method for the UAV navigation sensor anomaly detection, IET Radar, Sonar and Navigation, Vol: 11, Pages: 847-853, ISSN: 1751-8784
© 2016, The Institution of Engineering and Technology. The rapid development of unmanned aerial vehicles (UAVs) has made great progress for its widespread uses in military and civilian applications in recent years. On-board integrated navigation sensors are essential for UAV flight control systems in that they must operate with robustness and reliability. To achieve this, timely and effectively anomaly detection capabilities for the estimated UAV status from the integrated navigation sensors are required to ensure the UAV flight safety. Extraction of the anomaly information from the real-time navigation sensors and designing a robust and reliable anomaly detection algorithm are major issues for the UAV navigation sensor anomaly detection. This study introduces a novel integrated algorithm for detecting UAV on-board navigation sensor anomaly, by combining particle filter (PF) estimated state residuals with fuzzy inference system (FIS) decision system. The residual information is obtained based on the difference between the collected Global Positioning System measurements and high accuracy PF estimates. The indicators derived from the PF residuals are further made as inputs for the FIS system to output the different anomaly levels. The simulation and filed test results have demonstrated the effectiveness and efficiency of the proposed anomaly detection method in terms of timeliness, recall and precision.
Zhang X, Zhan X, Feng S, et al., 2017, An Analytical Model for BDS B1 Spreading Code Self-Interference Evaluation Considering NH Code Effects, SENSORS, Vol: 17, ISSN: 1424-8220
The short spreading code used by the BeiDou Navigation Satellite System (BDS) B1-I or GPS Coarse/Acquistiion (C/A) can cause aggregately undesirable cross-correlation between signals within each single constellation. This GPS-to-GPS or BDS-to-BDS correlation is referred to as self-interference. A GPS C/A code self-interference model is extended to propose a self-interference model for BDS B1, taking into account the unique feature of the B1-I signal transmitted by BDS medium Earth orbit (MEO) and inclined geosynchronous orbit (IGSO) satellites—an extra Neumann-Hoffmann (NH) code. Currently there is no analytical model for BDS self-interference and a simple three parameter analytical model is proposed. The model is developed by calculating the spectral separation coefficient (SSC), converting SSC to equivalent white noise power level, and then using this to calculate effective carrier-to-noise density ratio. Cyclostationarity embedded in the signal offers the proposed model additional accuracy in predicting B1-I self-interference. Hardware simulator data are used to validate the model. Software simulator data are used to show the impact of self-interference on a typical BDS receiver including the finding that self-interference effect is most significant when the differential Doppler between desired and undesired signal is zero. Simulation results show the aggregate noise caused by just two undesirable spreading codes on a single desirable signal could lift the receiver noise floor by 3.83 dB under extreme C/N0 (carrier to noise density ratio) conditions (around 20 dB-Hz). This aggregate noise has the potential to increase code tracking standard deviation by 11.65 m under low C/N0 (15–19 dB-Hz) conditions and should therefore, be avoided for high-sensitivity applications. Although the findings refer to Beidou system, the principle weakness of the short codes illuminated here are valid for other satellite navigation systems.
Sidiropoulos S, Han K, Majumdar A, et al., 2016, Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty, Transportation Research Part C - Emerging Technologies, Vol: 75, Pages: 212-227, ISSN: 0968-090X
Multi-Airport Systems (MAS), or Metroplexes, serve air traffic demand in cities with two or more airports. Due to the spatial proximity and operational interdependency of the airports, Metroplex airspaces are characterized by high complexity, and current system structures fail to provide satisfactory utilization of the available airspace resources. In order to support system-level design and management towards increased operational efficiency in such systems, an accurate depiction of major demand patterns is a prerequisite. This paper proposes a framework for the robust identification of significant air traffic flow patterns in Metroplex systems, which is aligned with the dynamic route service policy for the effective management of Metroplex operations. We first characterize deterministic demand through a spatio-temporal clustering algorithm that takes into account changes in the traffic flows over the planning horizon. Then, in order to handle uncertainties in the demand, a Distributionally Robust Optimization (DRO) approach is proposed, which takes into account demand variations and prediction errors in a robust way to ensure the reliability of the demand identification. The DRO-based approach is applied on pre-tactical (i.e. one-day planning) as well as operational levels (i.e. 2-h rolling horizon). The framework is applied to Time Based Flow Management (TBFM) data from the New York Metroplex. The framework and results are validated by Subject Matter Experts (SMEs).
Anvari B, Angeloudis P, Ochieng WY, 2016, A multi-objective GA-based optimisation for holistic Manufacturing, transportation and Assembly of precast construction, Automation in Construction, Vol: 71, Pages: 226-241, ISSN: 0926-5805
Resource scheduling of construction proposals allows project managers to assess resource requirements, provide costs and analyse potential delays. The Manufacturing, transportation and Assembly (MtA) sectors of precast construction projects are strongly linked, but considered separately during the scheduling phase. However, it is important to evaluate the cost and time impacts of consequential decisions from manufacturing up to assembly. In this paper, a multi-objective Genetic Algorithm-based (GA-based) searching technique is proposed to solve unified MtA resource scheduling problems (which are equivalent to extended Flexible Job Shop Scheduling Problems). To the best of the authors' knowledge, this is the first time that a GA-based optimisation approach is applied to a holistic MtA problem with the aim of minimising time and cost while maximising safety. The model is evaluated and compared to other exact and non-exact models using instances from the literature and scenarios inspired from real precast constructions.
Sun R, Han K, Hu J, et al., An integrated algorithm based on BeiDou/GPS/IMU and its application for anomalous driving detection., ION GNSS+
Recent years have seen a booming of safety-related Intelligent Transportation System (ITS) applications, which have placed increasingly stringent requirements on the performance of Global Navigation Satellite Systems (GNSS). Examples include lane control, collision avoidance, and intelligent speed assistance. Detecting the lane level anomalous driving behavior is crucial for these safety critical ITS applications. The two major issues associated with the lane-level irregular driving identification are (1) accessibility to high accuracy positioning and vehicle dynamic parameters, and (2) extraction of anomalous driving behavior from these parameters. This paper introduces an integrated algorithm for detecting lane-level anomalous driving. Lane-level high accuracy vehicle positioning is achieved by fusing GPS and Beidou feeds with Inertial Measurement Unit (IMU) using Unscented Particle Filter (UPF). Anomalous driving detection is achieved based on the application of a newly designed Fuzzy Inference System. Computer simulation and real-world field test demonstrate the advantage of the proposed approach over existing ones from previous studies.
Shang W, Han K, Ochieng W, et al., 2016, Agent-based day-to-day traffic network model with information percolation, Transportmetrica A-Transport Science, Vol: 13, Pages: 38-66, ISSN: 2324-9935
This paper explores the impact of travel information sharing on road networks using a two-layer, agent-based, day-to-day traffic network model. The first layer (cyber layer) represents a conceptual communication network where travel information is shared among drivers. The second layer (physical layer) captures the day-to-day evolution in a traffic network where individual drivers seek to minimize their own travel costs by making route choices. A key hypothesis in this model is that instead of having perfect information, the drivers form individual groups, among which travel information is shared and utilized for routing decisions. The formation of groups occurs in the cyber layer according to the notion of percolation, which describes the formation of connected clusters (groups) in a random graph. We apply the novel notion of percolation to capture the disaggregated and distributed nature of travel information sharing. We present a numerical study on the convergence of the transport network, when a range of percolation rates are considered. The findings suggest a positive correlation between the percolation rate and the speed of convergence, which is validated through statistical analysis. A sensitivity analysis is also presented which shows a bifurcation phenomenon with regard to certain model parameters.
Goldbeck N, Angeloudis P, Ochieng W, 2016, Analysing the resilience of metro systems under consideration of interdependencies: A combined Dynamic Bayesian Network and network flow approach, 14th World Conference on Transport Research (WCTR)
Ali BS, Schuster W, Ochieng WY, 2016, Evaluation of the Capability of Automatic Dependent Surveillance Broadcast to Meet the Requirements of Future Airborne Surveillance Applications, Journal of Navigation, ISSN: 0373-4633
Automatic Dependent Surveillance Broadcast (ADS-B) Out supports various ground applications including Air Traffic Control (ATC) surveillance in radar airspace, non-radar airspace and on the airport surface. In addition, the capability of aircraft to receive ADS-B Out messages from other aircraft within their coverage (ADS-B In) enables enhanced airborne surveillance applications. The requirements of the application vary depending on its safety-criticality. More stringent applications will require higher levels of performance. It is therefore critical that the ADS-B system performance is measured against the most stringent application it is designed for. This paper reviews the various enhanced airborne surveillance applications and the required ADS-B information to support them. It identifies the ADS-B based applications required for Air Traffic Management (ATM) modernisation under the SESAR/NextGen programs. It discusses existing ADS-B Out versions and their capabilities. A mapping exercise is undertaken to assess the credibility of the ADS-B system performance to support the functionalities and requirements of the various enhanced airborne surveillance applications and establish those that require further research and development, highlighting some of the key challenges.
Anvari B, Majumdar A, Ochieng W, Mixed traffic modelling involving pedestrian dynamics for integrated street designs: A review, PED2016: 8th International Conference on Pedestrian and Evacuation Dynamics
Sidiropoulos S, Han K, Majumdar A, et al., 2016, Identifying significant traffic flow patterns in Multi-Airport Systems Terminal Manoeuvring Areas under uncertainty, 16th AIAA Aviation Technology, Integration, and Operations Conference
Sun R, Han K, Hu J, et al., 2016, Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements, Transportation Research Part C: Emerging Technologies, Vol: 69, Pages: 193-207, ISSN: 1879-2359
There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimetre/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.
Xue Y, Feng S, Ochieng WY, et al., 2016, The Improvement of the Positioning Accuracy in Search and Rescue with Two Satellites, 7th China Satellite Navigation Conference (CSNC), Publisher: Springer Verlag (Germany), Pages: 255-261, ISSN: 1876-1100
The efficiency of search and rescue (SAR) heavily relies on the positioning accuracy, so high positioning accuracy is very important in the procedure of SAR, especially when searched and rescued object (SARO) dropped into the place where the number of the visible satellites is very small. It is necessary to know the position of the SARO using as less satellites as possible because of the signal sheltered. Combining with the principle of time and frequency difference of arrival (TDOA and FDOA, respectively) and differential technology of positioning error correction in global positioning system (GPS), pseudorange differential positioning method basing on TDOA and FDOA is put forward in the procedure of SAR with two satellites. By choosing the proper reference object (RO), the pseudorange correction of RO is used to correct pseudorange of the SARO so that more accurate position of SARO is solved. Finally, simulation results show that the positioning accuracy can be improved and can precede 5 km after differential by selecting RO and SARO which are within 1000 km apart for pseudorange differential technology.
Nascimento FAC, Majumdar A, Ochieng WY, et al., 2016, Fundamentals of safety management: The Offshore Helicopter Transportation System Model, Safety Science, Vol: 85, Pages: 194-204, ISSN: 0925-7535
Anvari B, Bell MGH, Angeloudis P, et al., 2016, Calibration and validation of a shared space model: case study, Transportation Research Record, Vol: 2588, Pages: 43-52
Damy S, Majumdar A, Ochieng WY, 2016, GNSS-based High Accuracy Positioning for Railway Applications, 47th Annual Precise Time and Time Interval Systems and Applications Meeting (PTTI) / International Technical Meeting of the-Institute-of-Navigation, Publisher: Institute of Navigation, Pages: 1003-1014
The railway industry is facing a global increase in travel and freight demand, which requires extra capacity. Providing such capacity is limited by the current positioning systems used for railway traffic management. The past decade has seen a growing interest in GNSSbased positioning solutions for railway applications due to their global coverage, low cost and interoperability with existing systems. However, the railway operational environment presents a number of challenges to GNSS due to track side buildings, stations and tunnels which attenuate or block signals and generate multipath. This paper reviews the wide range of railway applications that can benefit from a GNSS-based enhanced positioning function, along with the existing requirements. It reviews the different positioning techniques that can be used and the different error sources the system has to deal with, including multipath. Finally it compares the effect of different multipath mitigation weighting techniques including elevation weighting, C/N0 weighting, residualbased weighting and multipath-based weighting. A novel adaptive weighting method based on the railway track geometry is introduced also and is compared to the current methods.
Goldbeck N, Angeloudis P, Ochieng W, 2016, Joint Vulnerability Analysis of Urban Rail Transit and Utility Networks, Transportation Research Board 95th Annual Meeting, Publisher: Transportation Research Board
As climate change is expected to increase the frequency of extreme weather events, cities around the world develop strategies to improve their disaster resilience. A key issue is the protection of critical urban infrastructure systems, such as transport networks. Rail transit networks are particularly exposed to flood risks and additional vulnerabilities arise from interdependencies with other infrastructure systems. This paper aims to improve modelling techniques that help to understand the conditions under which cascading failure can occur in interdependent urban infrastructure systems. Building on existing network flow models, a novel method for the coupling of networks is introduced, using binary connector variables and mixed integer linear programming. The coupling is modelled as additional commodity demand that is induced in one network depending on the commodity flows in another network. An example problem consisting of a rail transit network, a control system, an electric power network and a water supply network illustrates the practicability of the proposed modelling technique.
Zhang X, Zhang Z, Ochieng W, et al., 2016, A Reverse Approach to Antenna Specifications for London Buses Next-generation Positioning System, 29th International Technical Meeting of The-Satellite-Division-of-the-Institute-of-Navigation (ION GNSS+), Publisher: INST NAVIGATION, Pages: 1927-1936, ISSN: 2331-5911
Nascimento FAC, Majumdar A, Ochieng WY, et al., 2015, Nighttime offshore helicopter operations: a survey of risk levels per phase of flight, flying recency requirement and visual approach technique, AERONAUTICAL JOURNAL, Vol: 119, Pages: 1475-1498, ISSN: 0001-9240
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