Publications
101 results found
Cheong H-I, Macias JJE, Karamanis R, et al., 2023, Policy and strategy evaluation of ridesharing autonomous vehicle operation: a london case study, Transportation Research Record: Journal of the Transportation Research Board, Pages: 1-31, ISSN: 0361-1981
To understand the dynamics of an autonomous ridesharing transport mode from the perspectives of different stakeholders, a single model of such a system is essential, because this will enable policymakers and companies involved in the manufacture and operation of shared autonomous vehicles (SAVs) to develop user-centered strategies. The model needs to be based on real data, network, and traffic information and applied to real cities and situations, particularly those with complex public transportation systems. In this paper, we propose a new agent-based model for SAV deployment that enables the parametric assessment of key performance indicators from the perspective of potential SAV users, vehicle manufacturers, operators, and local authorities. This has been applied to a case study of three regions in London: central, inner, and outer. The results show there is no linear correlation between an increased ridesharing acceptance level and average trip duration. Without a fleet rebalancing algorithm, over 80% of SAVs’ energy expenditure is on picking up customers. By reducing pickup distance, SAVs could be a contender for a nonpersonal transportation system based on trip energy comparisons. The results provide a picture of future SAV systems for potential users and offer suggestions as to how operators can devise an optimal transportation strategy beyond the question of fleet size and how policymakers can improve the overall transport network and reduce its environmental impact based on energy consumption. As a result of its flexibility and parametric capability, the model can be utilized to inform any local authority how SAV services could be deployed in any city.
Ye Q, Feng Y, Macias JJE, et al., 2023, Adaptive road configurations for improved autonomous vehicle-pedestrian interactions using reinforcement learning, IEEE Transactions on Intelligent Transportation Systems, Vol: 24, Pages: 2024-2034, ISSN: 1524-9050
The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed to address this problem, but we lack an operational framework that can dynamically generate ROW plans for AVs and pedestrians in response to real-time demand. Based on microscopic traffic simulation, this study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians. Furthermore, the distributive learning algorithm outperforms its centralised counterpart regarding computational cost (49.55%), benchmark rewards (25.35%), best cumulative rewards (24.58%), optimal actions (13.49%) and rate of convergence. This novel road management technique could potentially contribute to the flow-adaptive and active mobility-friendly streets in the AVs era.
Hu S, Ye Y, Hu Q, et al., 2023, A Federated Learning-Based Framework for Ride-sourcing Traffic Demand Prediction, IEEE Transactions on Vehicular Technology, ISSN: 0018-9545
Accurate short-term ride-sourcing demand prediction is vital for transportation operations, planning, and policy-making. With the models developed from data based on individual ride-sourcing companies to the joint models with data from multiple ride-sourcing companies, the prediction performance of the proposed models is enhanced significantly. However, the privacy issues of these models become a problem. Raw data collected from individual companies could cause business concerns and data privacy issues. In this paper, we propose a Federated Learning (FL) based framework for traffic demand prediction (FedTDP), to solve this problem without sacrificing the prediction performance. In our framework, the model can encapsulate the spatial and temporal correlation of traffic demand data via LSTM and GCN respectively. Moreover, by associating FL with the spatio-temporal model, no raw data is uploaded to the centralized server, and only model parameters are required. Furthermore, a Shapley value-based reward mechanism is proposed to evaluate the contribution of ride-sourcing companies and can be used as a means to distribute rewards accordingly. Finally, a real-world case study of Hangzhou City, China, is conducted. More than 16 million real-world ride-sourcing requests collected from 8 ride-sourcing companies are used, covering most of the ride-sourcing travel demand across the city. The case study shows that the FL-based spatio-temporal model outperforms several well-established prediction models while preserving data privacy. It demonstrates the effectiveness and potential of our proposed framework. Some discussions related to the real-world implementations of the Shapley value-based reward mechanism are also given in the paper.
Xia Y, Liao C, Chen X, et al., 2023, Future reductions of China’s transport emissions impacted by changing driving behaviour, Nature Sustainability
This paper examines what individual drivers can contribute to reduce global vehicle emissions in their daily driving. Here we analyse vehicle emissions from the behavioural perspective with the aim of identifying ways in which drivers can reduce emissions by modifying their driving behaviour. We propose an indicator, the Standardized Driver Aggressiveness Index, to estimate the changes in private vehicle driving behaviour and perform estimates based on the real-world vehicular trajectory data collected from 2013 to 2021 in China. We then develop a forward-looking integrated assessment model to predict the extra vehicle emissions that would be induced by various types of car-following behaviour, for example, calm, neutral and aggressive behaviour. Our results indicate that by 2050, the cumulative emissions linked to driving behaviour that could be prevented will amount to 400.5 million tons of CO2. Our findings highlight the importance of considering behavioural changes as part of the solution to mitigate transport emissions, and underline the urgent need for interventions that can lead drivers to adopting more sustainable driving behaviour.
Chow AHF, Kuo Y-H, Angeloudis P, et al., 2022, Dynamic modelling and optimisation of transportation systems in the connected era, TRANSPORTMETRICA B-TRANSPORT DYNAMICS, Vol: 10, Pages: 801-802, ISSN: 2168-0566
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- Citations: 1
Ngu E, Parada L, Macias JJE, et al., 2022, Decentralised Multi-Agent Reinforcement Learning Approach for the Same-Day Delivery Problem, Transportation Research Record, Pages: 385-395
Same-day delivery (SDD) services have become increasingly popular in recent years. These have been usually modeled by previous studies as a certain class of dynamic vehicle routing problem (DVRP) where goods must be delivered from a depot to a set of customers in the same day that the orders were placed. Adaptive exact solution methods for DVRPs can become intractable even for small problem instances. In this paper, the same-day delivery problem (SDDP) is formulated as a Markov decision process (MDP) and it is solved using a parameter-sharing Deep Q-Network, which corresponds to a decentralised multi-agent reinforcement learning (MARL) approach. For this, a multi-agent grid-based SDD environment is created, consisting of multiple vehicles, a central depot, and dynamic order generation. In addition, zone-specific order generation and reward probabilities are introduced. The performance of the proposed MARL approach is compared against a mixed-integer programming (MIP) solution. Results show that the proposed MARL framework performs on par with MIP-based policy when the number of orders is relatively low. For problem instances with higher order arrival rates, computational results show that the MARL approach underperforms MIP by up to 30%. The performance gap between both methods becomes smaller when zone-specific parameters are employed. The gap is reduced from 30% to 3% for a 5 3 5 grid scenario with 30 orders. Execution time results indicate that the MARL approach is, on average, 65 times faster than the MIP-based policy, and therefore may be more advantageous for real-time control, at least for small-sized instances.
Zhang X, Angeloudis P, Demiris Y, 2022, ST CrossingPose: a spatial-temporal graph convolutional network for skeleton-based pedestrian crossing intention prediction, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 20773-20782, ISSN: 1524-9050
Pedestrian crossing intention prediction is crucial for the safety of pedestrians in the context of both autonomous and conventional vehicles and has attracted widespread interest recently. Various methods have been proposed to perform pedestrian crossing intention prediction, among which the skeleton-based methods have been very popular in recent years. However, most existing studies utilize manually designed features to handle skeleton data, limiting the performance of these methods. To solve this issue, we propose to predict pedestrian crossing intention based on spatial-temporal graph convolutional networks using skeleton data (ST CrossingPose). The proposed method can learn both spatial and temporal patterns from skeleton data, thus having a good feature representation ability. Extensive experiments on a public dataset demonstrate that the proposed method achieves very competitive performance in predicting crossing intention while maintaining a fast inference speed. We also analyze the effect of several factors, e.g., size of pedestrians, time to event, and occlusion, on the proposed method.
Ye Q, Feng Y, Qiu J, et al., 2022, Approximate optimum curbside utilisation for pick-up and drop-off (PUDO) and parking demands using reinforcement learning, 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, Pages: 2628-2633
With the uptake of automated transport, especially Pick-Up and Drop-Off (PUDO) operations of Shared Autonomous Vehicles (SAVs), the valet parking of passenger vehicles and delivery vans are envisaged to saturate our future streets. These emerging behaviours would join conventional on-street parking activities in an intensive competition for scarce curb resources. Existing curbside management approaches principally focus on those long-term parking demands, neglecting those short-term PUDO or docking events. Feasible solutions that coordinate diverse parking requests given limited curb space are still absent. We propose a Reinforcement Learning (RL) method to dynamically dispatch parking areas to accommodate a hybrid stream of parking behaviours. A partially-learning Deep Deterministic Policy Gradient (DDPG) algorithm is trained to approximate optimum dispatching strategies. Modelling results reveal satisfying convergence guarantees and robust learning patterns. Namely, the proposed model successfully discriminates parking demands of distinctive sorts and prioritises PUDOs and docking requests. Results also identify that when the demand-supply ratio situates at 2:1 to 4:1, the service rate approximates an optimal (83\%), and curbside occupancy surges to 80%. This work provides a novel intelligent dispatching model for diverse and fine-grained parking demands. Furthermore, it sheds light on deploying distinctive administrative strategies to the curbside in different contexts.
Zhang X, Feng Y, Angeloudis P, et al., 2022, Monocular visual traffic surveillance: a review, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 14148-14165, ISSN: 1524-9050
To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.
Ngu E, Parada L, Macias JJE, et al., 2022, Decentralised Multi-Agent Reinforcement Learning Approach for the Same-Day Delivery Problem, TRANSPORTATION RESEARCH RECORD, Vol: 2676, Pages: 385-395, ISSN: 0361-1981
Zhang K, Macias JJE, Paccagnan D, et al., 2022, The Competition and Inefficiency in Urban Road Last-Mile Delivery, Pages: 1473-1481, ISSN: 1548-8403
The last-mile delivery market is highly competitive and is saturated with numerous small operators. In this context, the fierce competition between operators, joint with the rapid increase in the demand for home-delivery, resulted in a significant increase in urban freight traffic further worsening congestion and pollution. To tackle these issues, previous research has studied the implementation of collaborative last-mile operations, with organisations sharing resources in the form of inventory space or transportation capacity. However, a common limitation of the proposed models is ignoring time windows and the effects of externalities such as network congestion. In this work, we propose a framework to quantify the efficiency loss in urban last-mile delivery system by comparing the solutions of a fully-decentralised and fully-centralised last-mile delivery problem. In doing so, we develop a Multi-depot Vehicle Routing Problem with Time Windows and Congestible Network that is solved using a bespoke Parallel Hybrid Genetic Algorithm that accounts for the non-linearities arising from modelling endogenous network congestion. The model is evaluated on a case study based on central London to assess the efficiency gaps of realistic last-mile delivery operations. When time window constraints are not included, our results show that the efficiency loss fluctuates the most with a small number of customers, while it stabilises to less than 15% for instances with over 100 customers. However, time windows could significantly exacerbate this issue, resulting in an additional 25% of efficiency loss.
Ye Q, Feng Y, Candela E, et al., 2022, Spatial-temporal flows-adaptive street layout control using reinforcement learning, Sustainability, Vol: 14, ISSN: 2071-1050
Complete streets scheme makes seminal contributions to securing the basic public right-of-way (ROW), improving road safety, and maintaining high traffic efficiency for all modes of commute. However, such a popular street design paradigm also faces endogenous pressures like the appeal to a more balanced ROW for non-vehicular users. In addition, the deployment of Autonomous Vehicle (AV) mobility is likely to challenge the conventional use of the street space as well as this scheme. Previous studies have invented automated control techniques for specific road management issues, such as traffic light control and lane management. Whereas models and algorithms that dynamically calibrate the ROW of road space corresponding to travel demands and place-making requirements still represent a research gap. This study proposes a novel optimal control method that decides the ROW of road space assigned to driveways and sidewalks in real-time. To solve this optimal control task, a reinforcement learning method is introduced that employs a microscopic traffic simulator, namely SUMO, as its environment. The model was trained for 150 episodes using a four-legged intersection and joint AVs-pedestrian travel demands of a day. Results evidenced the effectiveness of the model in both symmetric and asymmetric road settings. After being trained by 150 episodes, our proposed model significantly increased its comprehensive reward of both pedestrians and vehicular traffic efficiency and sidewalk ratio by 10.39%. Decisions on the balanced ROW are optimised as 90.16% of the edges decrease the driveways supply and raise sidewalk shares by approximately 9%. Moreover, during 18.22% of the tested time slots, a lane-width equivalent space is shifted from driveways to sidewalks, minimising the travel costs for both an AV fleet and pedestrians. Our study primarily contributes to the modelling architecture and algorithms concerning centralised and real-time ROW management. Prospective applications out o
Escribano J, Chang H, Angeloudis P, 2022, Integrated Path Planning and Task Assignment Model for On-Demand Last-Mile UAV-Based Delivery, Editors: DeArmas, Ramalhinho, Voss, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 198-213, ISBN: 978-3-031-16578-8
Ye Q, Feng Y, Han J, et al., 2021, A reinforcement learning-based adaptive control model for future street planning an algorithm and a case study, Doha, Qatar, 57th ISOCARP World Planning Congress, Publisher: ISOCARP, Pages: 1-13
With the emerging technologies in Intelligent Transportation System (ITS), the adaptive operation of road space is likely to be realised within decades. An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency. However, there is a lack of effective controlling techniques for these adaptive street infrastructures. To fill this gap in existing studies, we formulate this control problem as a Markov Game and develop a solution based on the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street parking areas in real-time. Integrated with the SUMO traffic simulator, this model was evaluated using the road network of the South Kensington District against three cases of divergent traffic conditions: pedestrian flow rates, AVs traffic flow rates and parking demands. Results reveal that our model can achieve an average reduction of 3.87% and 6.26% in street space assigned for on-street parking and vehicular operations. Combined with space gained by limiting the number of driving lanes, the average proportion of sidewalks to total widths of streets can significantly increase by 10.13%.
Mijic A, Whyte J, Myers R, et al., 2021, Reply to a discussion of 'a research agenda on systems approaches to infrastructure' by david elms, Civil Engineering and Environmental Systems: decision making and problem solving, Vol: 38, Pages: 295-297, ISSN: 0263-0257
Liu Q, Hu S, Angeloudis P, et al., 2021, Dynamic wireless power transfer system for electric-powered connected and autonomous vehicle on urban road network, IET INTELLIGENT TRANSPORT SYSTEMS, Vol: 15, Pages: 1153-1166, ISSN: 1751-956X
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- Citations: 3
Mijic A, Whyte J, Fisk D, et al., 2021, The Centre for Systems Engineering and Innovation – 2030 vision and 10-year celebration
The 2030 vision of the Centre is to bring Systems Engineering and Innovation to Civil Infrastructure by changing how cross-sector infrastructure challenges are addressedin an integrated way using principles of systems engineering to maximise resilience, safety and sustainability in an increasingly complex world.We want to better understand the environmental and societal impacts of infrastructure interventions under uncertainty. This requires a change in current approaches to infrastructure systems engineering: starting from the natural environmentand its resources, encompassing societaluse of infrastructure and the supporting infrastructure assets and services.We argue for modelling that brings natural as well as built environments within the system boundaries to better understand infrastructure and to better assess sustainability. We seethe work as relevant to both the academic community and to a wide range of industry and policy applications that are working on infrastructure transition pathways towards fair, safe and sustainable society.This vision was developed through discussions between academics in preparation for the Centre for Systems Engineering and Innovation (CSEI) 10 years celebration. These rich discussions about the future of the Centre were inspired by developing themes for a celebration event, through which we have summarised the first 10 years of the Centre’s work and our vision for the future and identified six emerging research areas.
Li J, Zhang K, Shen L, et al., 2021, A Domain Adaptation Framework for Short-term Traffic Prediction, IEEE Intelligent Transportation Systems Conference (ITSC), Publisher: IEEE, Pages: 3564-3569, ISSN: 2153-0009
Anastasiadis E, Angeloudis P, Ainalis D, et al., 2021, On the selection of charging facility locations for EV-based ride-hailing services: a computational case study, Sustainability, Vol: 13, ISSN: 2071-1050
The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs with the objective of minimising the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can produce solutions for large problem instances. Our results refer to a representative set of the total data for Chicago and indicate that nearly 180 CSs need to be installed to handle the demand of a TNC fleet of 3000 vehicles.
Candela E, Feng Y, Mead D, et al., 2021, Fast Collision Prediction for Autonomous Vehicles using a Stochastic Dynamics Model, IEEE Intelligent Transportation Systems Conference (ITSC), Publisher: IEEE, Pages: 211-216, ISSN: 2153-0009
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- Citations: 1
Ye Q, Stebbins SM, Feng Y, et al., 2020, Intelligent management of on-street parking provision for the autonomous vehicles era, 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, Pages: 1-7, ISSN: 2153-0009
The increasing degree of connectivity between vehicles and infrastructure, and the impending deployment of autonomous vehicles (AV) in urban streets, presents unique opportunities and challenges regarding the on-street parking provision for AVs. This study develops a novel simulation-optimisation approach for intelligent curbside management, based on a metaheuristic technique. The hybrid method balances curb lanes for driving or parking, aiming to minimise the average traffic delay. The model is tested using an idealised grid layout with a range of flow rates and parking policies. Results demonstrate delay decreased by 9%-27% from the benchmark case. Additionally, the traffic delay distribution shows the trade-offs between expanding road capacity and minimising traffic demand through curb management, indicating the interplay between curb parking and traffic management in the AV era.
Whyte J, Mijic A, Myers RJ, et al., 2020, A research agenda on systems approaches to infrastructure, Journal of Civil Engineering and Environmental Systems, Vol: 37, Pages: 214-233, ISSN: 1029-0249
At a time of system shocks, significant underlying challenges are revealed in current approaches to delivering infrastructure, including that infrastructure users in many societies feel distant from nature. We set out a research agenda on systems approaches to infrastructure, drawing on ten years of interdisciplinary work on operating infrastructure, infrastructure interventions and lifecycles. Research insights and directions on complexity, systems integration, data-driven systems engineering, infrastructure life-cycles, and the transition towards zero pollution are summarised. This work identifies a need to better understand the natural and societal impacts of infrastructure interventions under uncertainty. We argue for a change in current approaches to infrastructure: starting from the natural environment and its resources, encompassing societal use of infrastructure and the supporting infrastructure assets and services. To support such proposed new systems approaches to infrastructure, researchers need to develop novel modelling methods, forms of model integration, and multi-criteria indicators.
Escribano Macias J, Goldbeck N, Hsu P-Y, et al., 2020, Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles, OR SPECTRUM, Vol: 42, Pages: 1089-1125, ISSN: 0171-6468
Unmanned aerial vehicles (UAVs) have been increasingly viewed as useful tools to assist humanitarian response in recent years. While organisations already employ UAVs for damage assessment during relief delivery, there is a lack of research into formalising a problem that considers both aspects simultaneously. This paper presents a novel endogenous stochastic vehicle routing problem that coordinates UAV and relief vehicle deployments to minimise overall mission cost. The algorithm considers stochastic damage levels in a transport network, with UAVs surveying the network to determine the actual network damages. Ground vehicles are simultaneously routed based on the information gathered by the UAVs. A case study based on the Haiti road network is solved using a greedy solution approach and an adapted genetic algorithm. Both methods provide a significant improvement in vehicle travel time compared to a deterministic approach and a non-assisted relief delivery operation, demonstrating the benefits of UAV-assisted response.
Karamanis R, Anastasiadis E, Stettler M, et al., 2020, Vehicle redistribution in ride-sourcing markets using convex minimum cost flows
Ride-sourcing platforms often face imbalances in the demand and supply ofrides across areas in their operating road-networks. As such, dynamic pricingmethods have been used to mediate these demand asymmetries through surge pricemultipliers, thus incentivising higher driver participation in the market.However, the anticipated commercialisation of autonomous vehicles couldtransform the current ride-sourcing platforms to fleet operators. The absenceof human drivers fosters the need for empty vehicle management to address anyvehicle supply deficiencies. Proactive redistribution using integer programmingand demand predictive models have been proposed in research to address thisproblem. A shortcoming of existing models, however, is that they ignore themarket structure and underlying customer choice behaviour. As such, currentmodels do not capture the real value of redistribution. To resolve this, weformulate the vehicle redistribution problem as a non-linear minimum cost flowproblem which accounts for the relationship of supply and demand of rides, byassuming a customer discrete choice model and a market structure. Wedemonstrate that this model can have a convex domain, and we introduce an edgesplitting algorithm to solve a transformed convex minimum cost flow problem forvehicle redistribution. By testing our model using simulation, we show that ourredistribution algorithm can decrease wait times up to 50% and increase vehicleutilization up to 8%. Our findings outline that the value of redistribution iscontingent on localised market structure and customer behaviour.
Hsu P-Y, Aurisicchio M, Angeloudis P, et al., 2020, Understanding and visualizing schedule deviations in construction projects using fault tree analysis, Engineering, Construction and Architectural Management, Vol: 27, Pages: 2501-2522, ISSN: 0969-9988
Delays in construction projects are both disruptive and expensive. Thus, potential causes of schedule deviation need to be identified and mitigated. In previous research, delay factors were predominantly identified through surveys administered to stakeholders in construction projects. Such delay factors are typically considered individually and presented at the same level without explicitly examining their sequence of occurrence and inter-relationships. In reality, owing to the complex structure of construction projects and long execution time, non-conformance to schedule occurs by a chain of cascading events. An understanding of these linkages is important not only for minimising the delays but also for revealing the liability of stakeholders. To explicitly illustrate the cause–effect and logical relationship between delay factors and further identify the primary factors which possess the highest significance toward the overall project schedule delay, the fault tree analysis (FTA) method, a widely implemented approach to root cause problems in safety-critical systems, has been systematically and rigorously executed.
Otero Arenzana A, Escribano Macias JJ, Angeloudis P, 2020, Design of hospital delivery networks using Unmanned Aerial Vehicles, Transportation Research Record: Journal of the Transportation Research Board, Vol: 2674, Pages: 405-418, ISSN: 0361-1981
Unmanned aerial vehicles (UAVs) are being increasingly implemented in a range of applications. Their low payload capacity and ability to overcome congested road networks enables them to provide fast delivery services for urgent high-value low-volume cargo. This work investigates the economic viability of integrating UAVs into urban hospital supply chains. In doing so, a strategic model that determines the optimal configuration of supporting infrastructure for urgent UAV delivery between hospitals is proposed. The model incorporates a tailored facility location algorithm that selects an optimal number of hubs given a set of candidates and determines the number of UAVs required to fulfill total demand. The objective is to minimize the total cost of implementation, computed as the sum of generalized, battery, vehicle, and hub establishment costs. The model is applied to a case study based on the establishment of a UAV delivery network for deliveries between National Health Service (NHS) hospitals in London. A baseline scenario is also developed using current NHS vehicles for delivery. Results demonstrate that UAV-based delivery provides significant reductions in operational costs compared with the baseline. Furthermore, the analysis indicates the location of hubs is more significant to the solution optimality than any increase in range or payload.
Karamanis R, Anastasiadis E, Angeloudis P, et al., 2020, Assignment and pricing of shared rides in ride-sourcing using combinatorial double auctions, IEEE Transactions on Intelligent Transportation Systems, Vol: 22, Pages: 5648-5659, ISSN: 1524-9050
Transportation Network Companies employ dynamic pricing methods at periods of peak travel to incentivise driver participation and balance supply and demand for rides. Surge pricing multipliers are commonly used and are applied following demand and estimates of customer and driver trip valuations. Combinatorial double auctions have been identified as a suitable alternative, as they can achieve maximum social welfare in the allocation by relying on customers and drivers stating their valuations. A shortcoming of current models, however, is that they fail to account for the effects of trip detours that take place in shared trips and their impact on the accuracy of pricing estimates. To resolve this, we formulate a new shared-ride assignment and pricing algorithm using combinatorial double auctions. We demonstrate that this model is reduced to a maximum weighted independent set model, which is known to be APX-hard. A fast local search heuristic is also presented, which is capable of producing results that lie within 10% of the exact approach for practical implementations. Our proposed algorithm could be used as a fast and reliable assignment and pricing mechanism of ride-sharing requests to vehicles during peak travel times.
Yu J, Stettler MEJ, Angeloudis P, et al., 2020, Urban network-wide traffic speed estimation with massive ride-sourcing GPS traces, Transportation Research Part C: Emerging Technologies, Vol: 112, Pages: 136-152, ISSN: 0968-090X
The ability to obtain accurate estimates of city-wide urban traffic patterns is essential for the development of effective intelligent transportation systems and the efficient operation of smart mobility platforms. This paper focuses on the network-wide traffic speed estimation, using trajectory data generated by a city-wide fleet of ride-sourcing vehicles equipped with GPS-capable smartphones. A cell-based map-matching technique is proposed to link vehicle trajectories with road geometries, and to produce network-wide spatio-temporal speed matrices. Data limitations are addressed using the Schatten p-norm matrix completion algorithm, which can minimize speed estimation errors even with high rates of data unavailability. A case study using data from Chengdu, China, demonstrates that the algorithm performs well even in situations involving continuous data loss over a few hours, and consequently, addresses large-scale network-wide traffic state estimation problems with missing data, while at the same time outperforming other data recovery techniques that were used as benchmarks. Our approach can be used to generate congestion maps that can help monitor and visualize traffic dynamics across the network, and therefore form the basis for new traffic management, proactive congestion identification, and congestion mitigation strategies.
Escribano-Macias JJ, Angeloudis P, Han K, 2020, Optimal design of Rapid evacuation strategies in constrained urban transport networks, Transportmetrica A: Transport Science, Vol: 16, Pages: 1079-1110, ISSN: 2324-9935
Large-scale evacuations constitute common life-saving exercises that are activated in many disaster response campaigns. Their effectiveness is often inhibited by traffic congestion, disrupted and imperfect coordination mechanisms, and the poor state of the underlying transportation networks. To address this problem, this paper presents a hybrid simulation-optimisation methodology to optimise evacuation response strategies through demand staging and signal phasing. We introduce a pre-planning model that evaluates evacuation policies, using a low-level dynamic traffic assignment model that captures the effects of congestion, queuing and vehicle spillback. Optimal strategies are determined using derivative-free optimisation algorithms, applied to an evacuation problem based on a benchmark dataset. The effects of varying the number of activated paths and the frequency of departure under different network conditions are observed. Our analysis indicates that combined departure time scheduling and signal phasing is a promising method to improve evacuation efficiency when compared to a worst-case benchmark scenario.
Hsu PY, Aurisicchio M, Angeloudis P, 2020, Optimal logistics planning for modular construction using multi-stage stochastic programming, Pages: 245-252, ISSN: 2352-1457
The modular construction method has been adopted extensively by the construction sector for pursuing higher building quality and better project efficiency. However, the employment of this new construction method has not only altered the definition of construction supply chains, but also poses new challenges to the logistics system which has conventionally focused on raw material transportation. This challenge is exacerbated in the transport and inventory aspects when the project is executed in urban settings, owing to the frequent traffic congestion, crowded environment, as well as the bulkiness and delicacy of finished modules. This study develops a multi-stage stochastic programming model for identifying the optimal supply chain configuration for the modular construction method. Site demand is considered to be stochastic, forcing project managers to make several operational decisions at multiple time points during project execution. The developed model can provide the best production, transportation and inventory plans, as well as the most favourable initial inventory preparation schemes. Furthermore, we have proven that the implementation of multi-stage stochastic programming model can yield more economical and risk-averse solutions than the two-stage stochastic programming approach.
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