Publications
339 results found
Yu Y, Han K, Ochieng W, 2020, Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing, Transportation Research Part C: Emerging Technologies, Vol: 114, Pages: 59-83, ISSN: 0968-090X
This paper presents a doubly dynamic day-to-day (DTD) traffic assignment model with simultaneous route-and-departure-time (SRDT) choices while incorporating incomplete and imperfect information as well as bounded rationality. Two SRDT choice models are proposed to incorporate imperfect travel information: One based on multinomial Logit (MNL) model and the other on sequential, mixed multinomial/nested Logit model. These two variants, serving as base models, are further extended with two features: bounded rationality (BR) and information sharing. BR is considered by incorporating the indifference band into the random utility component of the MNL model, forming a BR-based DTD stochastic model. A macroscopic model of travel information sharing is integrated into the DTD dynamics to account for the impact of incomplete information on travelers’ SRDT choices. These DTD choice models are combined with within-day dynamics following the Lighthill-Whitham-Richards (LWR) fluid dynamic network loading model. Simulations on large-scale networks (Anaheim) illustrate the interactions between users’ adaptive decision making and network conditions (including local disruption) with different levels of information availability and user behavior. Our findings highlight the need for modeling network transient and disequilibriated states, which are often overlooked in equilibrium-constrained network design and optimization. The MATLAB package and computational examples are available at https://github.com/DrKeHan/DTD.
Sun R, Wang G, Fan Z, et al., 2020, An Integrated Urban Positioning Algorithm Using Matching, Particle Swam Optimized Adaptive Neuro Fuzzy Inference System and a Spatial City Model, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, Vol: 69, Pages: 4842-4854, ISSN: 0018-9545
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- Citations: 8
Elhajj M, Ochieng WY, 2020, Urban bus positioning: Location based services and high level system architecture, Case Studies on Transport Policy, Vol: 8, Pages: 12-21, ISSN: 2213-624X
Today’s urban transport systems are dominated by private vehicles, which are significant contributors to traffic congestion and pollution. This is expected to increase as the urban population grows, predicted to account for about 68% of the world’s population by 2050. In comparison to private cars, transport systems dominated by buses produce lower traffic congestion and emissions. Therefore, improvements in bus operation activities most of which require information on bus location (i.e. location based services) should facilitate urban transport sustainability.However, to date there is no agreement globally on the location based services, their location requirements and technologies to deliver significant improvement in bus operations. Therefore, this paper creates for the first time, a comprehensive list of bus operation services and specifies the performance requirements. These are considered together with challenging spatio-temporal characteristics of the urban environment to specify a high-level location determination system architecture for urban bus operations. The services, their requirements, standards and positioning system architecture are essential for the formulation of appropriate policies, regulation, service provision, and development and procurement of urban bus positioning systems.
Sun R, Zhang W, Zheng J, et al., 2020, GNSS/INS Integration with Integrity Monitoring for UAV No-fly Zone Management, REMOTE SENSING, Vol: 12
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- Citations: 17
Escribano Macias J, Angeloudis P, Ochieng W, 2020, Optimal hub selection for rapid medical deliveries using unmanned aerial vehicles, Transportation Research Part C: Emerging Technologies, Vol: 110, Pages: 56-80, ISSN: 0968-090X
Unmanned Aerial Vehicles (UAVs) are being increasingly deployed in humanitarian response operations. Beyond regulations, vehicle range and integration with the humanitarian supply chain inhibit their deployment. To address these issues, we present a novel bi-stage operational planning approach that consists of a trajectory optimisation algorithm (that considers multiple flight stages), and a hub selection-routing algorithm that incorporates a new battery management heuristic. We apply the algorithm to a hypothetical response mission in Taiwan after the Chi-Chi earthquake of 1999 considering mission duration and distribution fairness. Our analysis indicates that UAV fleets can be used to provide rapid relief to populations of 20,000 individuals in under 24 h. Additionally, the proposed methodology achieves significant reductions in mission duration and battery stock requirements with respect to conservative energy estimations and other heuristics.
Goldbeck N, Angeloudis P, Ochieng W, 2020, Optimal supply chain resilience with consideration of failure propagation and repair logistics, Transportation Research Part E: Logistics and Transportation Review, Vol: 133, Pages: 1-20, ISSN: 1366-5545
The joint optimisation of investments in capacity and repair capability of production and logistics systems at risk of being damaged is an important aspect of supply chain resilience that is not sufficiently addressed by state-of-the-art modelling approaches. Furthermore, logistical issues of procuring repair resources impact speed of recovery but are not considered in most existing models. This paper presents a novel multi-stage stochastic programming model that optimizes pre-disruption investment decisions, as well as post-disruption dynamic adjustment of supply chain operations and allocation of repair resources. A case study demonstrates how the method can quantify the effects of pooling repair resources.
Sun R, Wang G, Zhang W, et al., 2020, A gradient boosting decision tree based GPS signal reception classification algorithm, APPLIED SOFT COMPUTING, Vol: 86, ISSN: 1568-4946
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- Citations: 76
Shang W-L, Chen Y, Ochieng WY, 2020, Resilience Analysis of Transport Networks by Combining Variable Message Signs With Agent-Based Day-to-Day Dynamic Learning, IEEE Access, Vol: 8, Pages: 104458-104468
Yin J, Ma Y, Tian W, et al., 2020, Impact Analysis of Demand Management on Runway Configuration in Metroplex Airports, IEEE Access, Vol: 8, Pages: 66189-66212
Yang L, van Dam KH, Majumdar A, et al., 2019, Integrated design of transport infrastructure and public spaces considering human behavior: A review of state-of-the-art methods and tools, FRONTIERS OF ARCHITECTURAL RESEARCH, Vol: 8, Pages: 429-453, ISSN: 2095-2635
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- Citations: 23
Huang M, Ochieng WY, Nie H, et al., 2019, Main Wheel Prerotation and Ground Taxi Driven by Electric Taxi System, Journal of Aerospace Engineering, Vol: 32, ISSN: 0893-1321
Goldbeck N, Angeloudis P, Ochieng W, 2019, Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models, Reliability Engineering and System Safety, Vol: 188, Pages: 62-79, 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.
Sun R, Hsu L-T, Xue D, et al., 2019, GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System, JOURNAL OF NAVIGATION, Vol: 72, Pages: 685-701, ISSN: 0373-4633
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- Citations: 27
Ye B, Yong T, Shortle J, et al., 2019, Sensitivity analysis of potential capacity and safety of flow corridor to self-separation parameters, AERONAUTICAL JOURNAL, Vol: 123, Pages: 56-78, ISSN: 0001-9240
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- Citations: 2
Wang H, Gu C, Ochieng WY, 2019, Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data, JOURNAL OF ADVANCED TRANSPORTATION, ISSN: 0197-6729
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- Citations: 6
Yin J, Ma Y, Hu Y, et al., 2018, 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.
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.
Psyllou E, Majumdar A, Ochieng W, 2018, A Review of Navigation Involving General Aviation Pilots Flying under Visual Flight Rules, JOURNAL OF NAVIGATION, Vol: 71, Pages: 1130-1142, ISSN: 0373-4633
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- Citations: 4
Ainalis D, Achurra-Gonzalez P, Gaudin A, et al., 2018, Ultra-Capacitor based kinetic energy recovery system for heavy goods vehicles, 15th International Symposium on Heavy Vehicle Transport Technology, Publisher: International Forum for 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, 2018, AIAA Integrated Trajectory-Location-Routing for Rapid Humanitarian Deliveries using Unmanned Aerial Vehicles, 2018 Aviation Technology, Integration, and Operations Conference
Macias JJE, Angeloudis P, Ochieng W, 2018, Integrated trajectory-location-routing for rapid humanitarian deliveries using unmanned aerial vehicles
Unmanned Aerial Vehicles have the potential to provide an economical solution to the challenges of post-disaster land-based relief operations. Beyond regulatory concerns, technical and particularly airspace integration limitations inhibit their deployment in practice. To address these issues and ensure uninterrupted optimal operations, we present a novel approach consisting of an integrated trajectory-location-routing algorithm that seeks to determine the optimal location of supporting infrastructure in the distribution supply chain. Unique to this approach is the consideration of dynamic obstacle avoidance and variable battery consumption relationships. An approximate algorithm based on a bi-level Large Neighbourhood Search is used to obtain close to optimal solutions under reasonable runtime. Results show that fleets of small UAVs could quickly distribute relief supplies to affected population groups with minimal reliance on ground infrastructure.
Sun R, Zhang Y, Ye B, et al., 2018, A Required Navigation Performance Based Approach to Monitor the Accuracy and Integrity Performance of UAVs for Delivery Applications, 9th China Satellite Navigation Conference (CSNC) - Location, Time of Augmentation, Publisher: SPRINGER, Pages: 675-688, ISSN: 1876-1100
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- Citations: 3
Chatzimichailidou MM, Martinetti A, Majumdar A, et al., 2018, Wheel maintenance in rolling stock: safety challenges in the defect detection process, International Journal of System of Systems Engineering, Vol: 8, Pages: 387-387, ISSN: 1748-0671
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.
Kyriakidis M, Majumdar A, Ochieng WY, 2017, The human performance railway operational index—a novel approach to assess human performance for railway operations, Reliability Engineering and System Safety, Vol: 170, Pages: 226-243, ISSN: 0951-8320
Human error and degraded human performance are associated with more than 80% of all railway accidents worldwide. Research on human performance and human reliability has highlighted the importance of the contextual factors associated with human errors, known as performance shaping factors (PSFs). A major shortcomings of current Human Reliability Analysis techniques, which employ qualitative and quantitative methods for assessing the human contribution to risk, lies with their little capability to model the dependencies among PSFs and to quantify their impact on human performance. This paper presents a novel approach to assess human performance accounting for the dependencies among the relevant PSFs, referred to as Human Performance Railway Operational Index (HuPeROI). The HuPeROI is developed on the integration of the Analytic Network Process and Success Likelihood Index Methodology, using the insights of 52 front-line, managerial and human factors railway personnel, and was demonstrated in three different types of railway operations: regional, high-speed and underground. Findings show that the HuPeROI can be efficiently used to assess operators’ performance as function of the quality of the relevant R-PSFs. Regulatory bodies and other stakeholders can implement the framework within their safety management systems to improve safety of railway operations.
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, 2017, A Dynamic Network Flow Model for Interdependent Infrastructure and Supply Chain Networks with Uncertain Asset Operability, International Conference on Computational Logistics
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