111 results found
Liu J, Han K, Chen X, et al., 2019, Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data, Transportation Research Part C: Emerging Technologies, Vol: 106, Pages: 145-165, ISSN: 0968-090X
© 2019 Elsevier Ltd Vehicle trajectory data collected via GPS-enabled devices have played increasingly important roles in estimating network-wide traffic, given their broad spatial-temporal coverage and representativeness of traffic dynamics. This paper exploits taxi GPS data, license plate recognition (LPR) data, and geographical information for reconstructing the spatial and temporal patterns of urban traffic emissions. Vehicle emission factor models are employed to estimate emissions based on taxi trajectories. The estimated emissions are then mapped to spatial grids of urban areas to account for spatial heterogeneity. To extrapolate emissions from the taxi fleet to the whole vehicle population, we use Gaussian process regression (GPR) models supported by geographical features to estimate the spatially heterogeneous traffic volume and fleet composition. Unlike previous studies, this paper utilizes the taxi GPS data and LPR data to disaggregate vehicle and emission characteristics through space and time in a large-scale urban network. The results of a case study in Hangzhou, China, reveal high-resolution spatio-temporal patterns of traffic flows and emissions, and identify emission hotspots. This study provides an accessible means of inferring the environmental impact of urban traffic with multi-source urban data that are now widely available in urban areas.
Li S, Xu R, Han K, 2019, Demand-oriented train services optimization for a congested urban rail line: integrating short turning and heterogeneous headways, Transportmetrica A: Transport Science, Vol: 15, Pages: 1459-1486, ISSN: 2324-9935
This paper focuses on the demand-oriented passenger train scheduling problem for a congested urban rail line, considering uneven spatial and temporal demand distributions. A passenger-train interaction framework is developed to dynamically assign passengers to capacitated trains. A mixed integer nonlinear programming model that combines heterogeneous headways and short turning as an integrated strategy (HH-ST) is proposed with the aim of jointly minimizing passenger waiting time and operational costs, as well as balancing train loads. A two-stage genetic algorithm based on an integer coding approach is proposed to solve this problem. The proposed HH-ST strategy is compared with alternative strategies, namely ST alone, HH alone and regular schedule, through a real-world case study of Shanghai Metro Line 9. The results show that the HH-ST strategy provides a better trade-off between users’ and operators’ cost than other strategies, thus achieving a better match between transport capacity and passenger demand.
Han K, Eve G, Friesz TL, Computing Dynamic User Equilibria on Large-Scale Networks with Software Implementation, Networks and Spatial Economics, ISSN: 1566-113X
Friesz TL, Han K, 2018, The mathematical foundations of dynamic user equilibrium, Transportation Research Part B: Methodological, ISSN: 0191-2615
This paper is pedagogic in nature, meant to provide researchers a single reference for learning how to apply the emerging literature on differential variational inequalities to the study of dynamic traffic assignment problems that are Cournot-like noncooperative games. The paper is presented in a style that makes it accessible to the widest possible audience. In particular, we apply the theory of differential variational inequalities (DVIs) to the dynamic user equilibrium (DUE) problem. We first show that there is a variational inequality whose necessary conditions describe a DUE. We restate the flow conservation constraint associated with each origin-destination pair as a first-order two-point boundary value problem, thereby leading to a DVI representation of DUE; then we employ Pontryagin-type necessary conditions to show that any DVI solution is a DUE. We also show that the DVI formulation leads directly to a fixed-point algorithm. We explain the fixed-point algorithm by showing the calculations intrinsic to each of its steps when applied to simple examples.
Munoz-Mendez F, Klemmer K, Han K, et al., Community structures, interactions and dynamics in London’s bicycle sharing network, The 7th International Workshop on Pervasive Urban Applications (PURBA 2018), Publisher: ACM
We apply a novel clustering technique to London’s bikesharing network, deriving distinctive behavioral patterns and assessing community interactions and spatio-temporal dynamics. The analyses reveal self- contained, interconnected and hybrid clusters that mimic London’s physical structure. Exploring changes over time, we find geographically isolated and specialized communities to be relatively consistent, while the remaining system exhibits volatility. We increase understanding of the collective behavior of the bikesharing users.
Tian Y, Wan L, Han K, et al., 2018, Optimization of terminal airspace operation with environmental considerations, Transportation Research Part D: Transport and Environment, Vol: 63, Pages: 872-889, ISSN: 1361-9209
The rapid growth in air traffic has resulted in increased emission and noise levels in terminal areas, which brings negative environmental impact to surrounding areas. This study aims to optimize terminal area operations by taking into account environmental constraints pertaining to emission and noise. A multi-objective terminal area resource allocation problem is formulated by employing the arrival fix allocation (AFA) problem, while minimizing aircraft holding time, emission, and noise. The NSGA-II algorithm is employed to find the optimal assignment of terminal fixes with given demand input and environmental considerations, by incorporating the continuous descent approach (CDA). A case study of the Shanghai terminal area yields the following results: (1) Compared with existing arrival fix locations and the first-come-first-serve (FCFS) strategy, the AFA reduces emissions by 19.6%, and the areas impacted by noise by 16.4%. AFA and CDA combined reduce the emissions by 28% and noise by 38.1%; (2) Flight delays caused by the imbalance of demand and supply can be reduced by 72% (AFA) and 81% (AFA and CDA) respectively, compared with the FCFS strategy. The study demonstrates the feasibility of the proposed optimization framework to reduce the environmental impact in terminal areas while improving the operational efficiency, as well as its potential to underpin sustainable air traffic management.
Yin J, Hu M, Ma Y, et al., 2018, Airport taxi situation awareness with a macroscopic distribution network analysis, Networks and Spatial Economics, ISSN: 1566-113X
This paper proposes a framework for airport taxi situation awareness to enhance the assessment of aircraft ground movements in complex airport surfaces. Through a macroscopic distribution network (MDN) of arrival and departure taxi processes in a spatial-temporal domain, we establish two sets of taxi situation indices (TSIs) from the perspectives of single aircraft and the whole network. These TSIs are characterized into five categories: aircraft taxi time indices (ATTIs), surface instantaneous flow indices (SIFIs), surface cumulative flow indices (SCFIs), aircraft queue length indices (AQLIs), and slot resource demand indices (SRDIs). The coverage of the TSIs system is discussed in detail based on the departure and arrival reference aircraft. A real-world case study of Shanghai Pudong airport demonstrates significant correlations among some of the proposed TSIs such as the ATTIs, SCFIs and AQLIs. We identify the most crucial influencing factors of the taxi process and propose two new metrics to assess the taxi situation at the aircraft and network levels, by establishing taxi situation assessment models instead of using two systems of multiple TSIs. The findings can provide significant references to decision makers regarding airport ground movements for the purposes of air traffic scheduling and congestion control in complex airports.
Pu J, Liu C, Zhao J, et al., 2018, Vulnerability Assessment of Metro Systems Based on Dynamic Network Structure, 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 525-537, ISSN: 0302-9743
Invulnerable metro systems are essential for the safety and efficiency of urban transportation services. Therefore, it is of significant interest to systematically assess the vulnerability of metro systems. To this end, in this paper, we assess the vulnerability of metro systems with a data-driven framework in which dynamic travel patterns are considered. Specifically, we use effective attack strategies based on the topology structure of metro networks. The network structure depends on not only connectivity among metro stations but also dynamic passenger flow patterns. Thus, two data-driven metrics, satisfaction rate (SR) and satisfaction rate with path cost (SRPC), are proposed to quantify the vulnerability of metro networks after our attack strategies. Finally, we conduct experiments on Shanghai metro system. The results indicate that the metro system is vulnerable to malicious attacks while it shows strong robustness to random failures. Our results also highlight weak-points and bottlenecks in the system, which may bear practical managerial implications for policymakers to improve the reliability and robustness of the metro systems and the public transportation services.
Yin J, Hu Y, Ma Y, et al., Machine learning techniques for taxi-out time prediction with a macroscopic network topology, 37th AIAA/IEEE Digital Avionics Systems Conference (DASC), Publisher: IEEE
Accurate prediction of taxi-out time is essential for enhancing airport performance and flight efficiency. In this paper, we apply machine learning techniques to predict the taxi- out time of departure aircraft at Shanghai Pudong International Airport. The exploration of historical data reveals several relevant influencing factors of taxi-out time as well as their correlations. We formulate an extensive system of predictors for our machine learning approach, based on a macroscopic network topology from an aggregate view. The predictors can be divided into 4 categories; namely surface instantaneous flow indices (SIFIs), surface cumulative flow indices (SCFIs), aircraft queue length indices (AQLIs) and slot resource demand indices (SRDIs). Three machine learning methods: linear regression (LR), support vector machines (SVM) and random forest (RF) are formulated using one-day and one-month training samples, and applied to new test dataset to validate the prediction performance. Computational results show that the training RF model using one-month sample significantly outperform other models in terms of prediction accuracy. The proposed methodology can bring significant benefits to analyzing airport ground movement performance and support the activities of airport decision making.
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.
Wang Y, Szeto WY, Han K, et al., 2018, Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications, Transportation Research Part B: Methodological, Vol: 111, Pages: 370-394, ISSN: 0191-2615
The fact that road transportation negatively affects the quality of the environment and deteriorates its bearing capacity has drawn a wide range of concerns among researchers. In order to provide more realistic traffic data for estimations of environmental impacts, dynamic traffic assignment (DTA) models have been adopted in transportation planning and traffic management models concerning environmental sustainability. This review summarizes and examines the recent methodological advances of DTA models in environmentally sustainable road transportation applications including traffic signal control concerning vehicular emissions and emission pricing. A classification of emission estimation models and their integration with DTA models are accordingly reviewed as supplementary to the existing reviews. Finally, a variety of future research prospects of DTA for environmentally sustainable road transportation research are discussed. In particular, this review also points out that at present the research about DTA models in conjunction with noise predictive models is relatively deficient.
Fu Z, Jia Q, Chen J, et al., 2018, A fine discrete field cellular automaton for pedestrian dynamics integrating pedestrian heterogeneity, anisotropy, and time-dependent characteristics, Transportation Research Part C: Emerging Technologies, Vol: 91, Pages: 37-61, ISSN: 0968-090X
This paper proposes a discrete field cellular automaton (CA) model that integrates pedestrian heterogeneity, anisotropy, and time-dependent characteristics. The pedestrian movement direction, moving/staying, and steering are governed by the transfer equations. Compared with existing studies on fine-discretized CA models, the proposed model is advantageous in terms of flexibility, higher spatial accuracy, wider speed range, relatively low computational cost, and elaborated conflict resolution with synchronous update scheme. Three different application scenarios are created by adjusting the definite conditions of the model: (1) The first one is a unidirectional pedestrian movement in a channel, where a complete jam in the high-density region is observed from the proposed model, which is missing from existing floor field CA models. (2) The second one is evacuation from a room, where the evacuation time is independent of the discretization factor, which is different from previous work. (3) The third one is an ascending evacuation through a 21-storey stair system, where pedestrians move with constant speed or with fatigue. The evacuation time in the latter case is nearly twice of that in the former.
Sidiropoulos S, Majumdar A, Han K, 2018, A framework for the optimization of terminal airspace operations in Multi-Airport Systems, Transportation Research Part B: Methodological, Vol: 110, Pages: 160-187, ISSN: 0191-2615
Major cities like London, New York, and Tokyo are served by several airports, effectively creating a Multi-Airport System (MAS), or Metroplex. The operations of individual Metroplex airports are highly interdependent, rendering their efficient management rather difficult. This paper proposes a framework for the design of dynamic arrival and departure routes in MAS Terminal Maneuvering Areas, which fundamentally changes the operation in MAS airspaces for much improved efficiency when compared to the current situation. The framework consists of three components. The first presents a new procedure for characterizing dynamic arrival and departure routes based on the spatio-temporal distributions of flights. The second component is a novel Analytic Hierarchy Process (AHP) model for the prioritization of the dynamic routes, which takes into account a set of quantitative and qualitative attributes important for MAS operations. The third component is a priority-based method for the positioning of terminal waypoints as well as the design of three-dimensional, conflict-free terminal routes. Such a method accounts for the AHP-derived priorities while satisfying the minimal separation and aircraft maneuverability constraints. The developed framework is applied to a case study of the New York Metroplex, using aircraft trajectories during a heavy traffic period on typical day of operation in the New York Terminal Control Area in November 2011. The proposed framework is quantitatively assessed using the AirTOp fast-time simulation model. The results suggest significant improvements of the new design over the existing one, as measured by several key performance indicators such as travel distance, travel time, fuel burn, and controller workload. The operational feasibility of the framework is further validated qualitatively by subject matter experts from the Port Authority of New York and New Jersey, the operator of the New York Metroplex.
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.
Han K, Yao T, Jiang C, et al., 2017, Lagrangian-based Hydrodynamic Model for Traffic Data Fusion on Freeways, Networks and Spatial Economics, Vol: 17, Pages: 1071-1094, ISSN: 1566-113X
This paper conducts a comprehensive study of the Lagrangian-based hydrodynamic model with application to highway state estimation. Our analysis is motivated by the practical problems of freeway traffic monitoring and estimation using multi-source data measured from mobile devices and fixed sensors. We conduct rigorous mathematical analysis on the Hamilton-Jacobi representation of the Lighthill-Whitham-Richards model in the transformed coordinates, and derive explicit and closed-form solutions with piecewise affine initial, boundary, and internal conditions, based on the variational principle. A numerical study of the Mobile Century field experiment demonstrates some unique features and the effectiveness in traffic estimation of the Lagrangian-based model.
Han K, Friesz TL, 2017, Continuity of the Effective Delay Operator for Networks Based on the Link Delay Model, Networks and Spatial Economics, Vol: 17, Pages: 1095-1110, ISSN: 1566-113X
This paper is concerned with a dynamic traffic network performance model, known as dynamic network loading (DNL), that is frequently employed in the modeling and computation of analytical dynamic user equilibrium (DUE). As a key component of continuous-time DUE models, DNL aims at describing and predicting the spatial-temporal evolution of traffic flows on a network that is consistent with established route and departure time choices of travelers, by introducing appropriate dynamics to flow propagation, flow conservation, and travel delays. The DNL procedure gives rise to the path delay operator, which associates a vector of path flows (path departure rates) with the corresponding path travel costs. In this paper, we establish strong continuity of the path delay operator for networks whose arc flows are described by the link delay model (Friesz et al., Oper Res 41(1):80–91, 1993; Carey, Networks and Spatial Economics 1(3):349–375, 2001). Unlike the result established in Zhu and Marcotte (Transp Sci 34(4):402–414, 2000), our continuity proof is constructed without assuming a priori uniform boundedness of the path flows. Such a more general continuity result has a few important implications to the existence of simultaneous route-and-departure-time DUE without a priori boundedness of path flows, and to any numerical algorithm that allows convergence to be rigorously analyzed.
Yang L, Yin S, Han K, et al., 2017, Fundamental diagrams of airport surface traffic: Models and applications, Transportation Research Part B: Methodological, Vol: 106, Pages: 29-51, ISSN: 0191-2615
This paper reveals and explores the flow characteristics of airport surface network on both mesoscopic and macroscopic levels. We propose an efficient modeling approach based on the cell transmission model for simulating the spatio-temporal evolution of flow and congestion on taxiway and apron networks. The existence of link-based fundamental diagram that expresses the functional relationship between link density and flow is demonstrated using empirical data collected in Guangzhou Baiyun airport. The proposed CTM-based network model is shown to be an efficient and accurate method capable of supporting air traffic prediction and decision support. In addition, using both CTM-based simulation and empirical data, we further reveal the existence of an aggregate relationship between traffic density and runway throughput, which is referred to as macroscopic fundamental diagram (MFD) in the literature of road traffic. The MFD on the airport surface is analyzed in depth, and utilized to devise several robust off-block control strategies under uncertainties, which are shown to significantly outperform existing off-block control methods.
Li S, Xu R, Han K, et al., Optimizing train service plans to coordinate transport capacity for urban rail transit lines, Transportation Research Board 97th Annual Meeting
In view of big passenger flow volume and high passenger risk at transfer stations during the peak period, this paper studied the coordination method of urban rail transit network transportation organization from the perspective of capacity matching. The change law of passenger flow was analyzed, and the calculation methods of train remaining carrying capacity, waiting passenger demand and the largest number of people gathered on the platform were determined. The concept of capacity coordination degree (CCD) was proposed, used to describe the matching degree between traffic demand and transport capacity of each line. Based on this, taking the optimal comprehensive CCD of the transfer station as the goal, the first train departure time and train departure interval as decision variables, and guarantee of passenger safety within station as the main constraint, a nonlinear integer programming model of train service plans collaborative optimization was established, and the genetic algorithm was designed. A case study of a two-line intersecting network was carried out. The results show that, after the use of capacity coordination scheme, the total number of running trains increases by only 1, the number of remaining passengers reduces by 68.44%, comprehensive CCD is closer to 1, and the largest number of people gathered in big passenger flow directions decreases by 11.77% and 19.68%, respectively. Transport supply can better meet the passenger demand in all directions, effectively improving the interests of both passengers and operators.
Ma L, Chen Q, Han K, et al., A tale of two stations: Analyzing metro ridership with big data, Transportation Research Board 97th Annual Meeting
This paper presents a multi-dimensional case study of the Beijing metro system. In particular, we examine two non-transfer stations, Zaoying and Jiangtai, which are on the same metro line in central Beijing. Multi-source and heterogeneous data are integrated to analyze and diagnose the drastically different metro ridership at the two stations. These include transit smart card data, taxi GPS data, network data, Point of Interest data, demographic data, online second-hand property price data, cell phone signalling data, and bike sharing data. The different utilization of metro system at these two locations is attributed to a number of factors pertaining to transportation infrastructure, built environment, demographic composition, commuting patterns, and connectivity of multi-modal transit networks. The findings suggest the importance of local accessibility of the metro stations as well as its connectivity with the rest of the transit system, in order to maximize the transport capability of the metro system. Our analysis also highlights the benefit of collecting and analyzing fine-granularity data in order to identify key bottlenecks and inefficiencies in the transportation system, as conventional macroscopic transportation planning data do not sufficiently capture the local accessibility and mobility in an urban environment.
Yin J, Hu M, Ma Y, et al., Spatial-temporal topology and performance analysis of airport taxi network, Transportation Research Board 97th Annual Meeting
This paper proposes a spatial-temporal topology from a macroscopic view to analyze the performance of airport taxi network operations. Through a macroscopic modelling of arrival and departure aircraft taxi processes in the airport taxi network, we establish a system of taxi network performance indicators (TNPIs) consisting 5 categories and 26 indicators, which includes the surface instantaneous flow indicators (SIFIs), surface cumulative flow indicators (SCFIs), aircraft queue length indicators (AQLIs), slot resource demand indicators (SRDIs) and aircraft taxi time indicators (ATTIs). Then, we analyze the correlation among different TNPIs. By identifying the key factors affecting aircraft taxi time such as takeoff and landing queue length, we provide models for predicting aircraft taxi time based on multiple regression analysis. The real-world case study in Shanghai Pudong airport demonstrates significant correlations among some of the proposed TNPIs, and the results also show the significantly improved accuracy of the proposed prediction models over some conventional models, which brings significant benefits to analyze the performance of airport taxi network and support decision making in airport operations.
Yang L, Yin S, Hu M, et al., Empirical study of air traffic dynamics using coupled network modeling and non-linear analysis, Transportation Research Board 97th Annual Meeting
Yang L, Yin S, Hu M, et al., 2017, Empirical exploration of air traffic and human dynamics in terminal airspaces, Transportation Research Part C: Emerging Technologies, Vol: 84, Pages: 219-244, ISSN: 0968-090X
We propose a multi-layer network approach to model and analyze air traffic terminal networks, which are viewed as complex, task-critical, techno-social systems with numerous interactions among airspaces, procedures, aircraft, and air traffic controllers (ATCOs). Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) are developed to represent critical physical and operational characteristics. Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from ATCOs, respectively. Furthermore, a set of analytical metrics, including network variables, complex network attributes, controllers’ cognitive complexity, and chaos metrics, are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of “ATCOs-flow” interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in the conflict system and human behavioral system when traffic switches to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.
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
Han K, 2017, Framework for Real-Time Traffic Management with Case Studies, Transportation Research Record, Vol: 2658, Pages: 35-43, ISSN: 0361-1981
This paper focuses on real-time traffic management facilitated by modern telecommunication technologies and advanced real-time optimization algorithms. The discussion begins with a recent European project that provides a real-time decision support system for the reduction of traffic congestion and emissions. The work ow and techniques involved therein are explained and issues and potential gaps identi ed. A more generic real-time decision-making framework based on decision rules and distributionally robust optimization is then introduced. The paper illustrates the wide applicability and unique advantages of such a framework with case studies on responsive signal control, use of an adaptive variable message sign, and air traffic management.
Song W, Han K, Wang Y, et al., 2017, Statistical metamodeling of dynamic network loading, Transportation Research Part B: Methodological, ISSN: 0191-2615
Dynamic traffic assignment models rely on a network performance module known as dynamic network loading (DNL), which expresses flow propagation, flow conservation, and travel delay at a network level. The DNL defines the so-called network delay operator, which maps a set of path departure rates to a set of path travel times (or costs). It is widely known that the delay operator is not available in closed form, and has undesirable properties that severely complicate DTA analysis and computation, such as discontinuity, non-differentiability, non-monotonicity, and computational inefficiency. This paper proposes a fresh take on this important and difficult issue, by providing a class of surrogate DNL models based on a statistical learning method known as Kriging. We present a metamodeling framework that systematically approximates DNL models and is flexible in the sense of allowing the modeler to make trade-offs among model granularity, complexity, and accuracy. It is shown that such surrogate DNL models yield highly accurate approximations (with errors below 8%) and superior computational efficiency (9 to 455 times faster than conventional DNL procedures such as those based on the link transmission model). Moreover, these approximate DNL models admit closed-form and analytical delay operators, which are Lipschitz continuous and infinitely differentiable, with closed-form Jacobians. We provide in-depth discussions on the implications of these properties to DTA research and model applications.
Chen D, Hu M, Zhang H, et al., 2017, A network based dynamic air traffic flow model for en route airspace system traffic flow optimization, Transportation Research Part E - Logistics and Transportation Review, Vol: 106, Pages: 1-19, ISSN: 1366-5545
This study proposes a mesoscopic dynamic air traffic model based on a dynamic network for en route airspaces by characterizing the dynamics and distribution of traffic speed. Based on this model, we solve a flow optimization problem for enforcing capacity constraints with the minimum operational cost using a dual decomposition method. A case study of an en route airspace in Shanghai demonstrates the accuracy of the proposed model in successfully capturing the flow dynamics, as well as the effectiveness of the proposed optimization framework to reduce en route delays by balancing the dynamic traffic demand and airspace capacity.
Song W, Han K, Wang Y, et al., 2017, Statistical metamodeling of dynamic network loading, Transportation Research Procedia, Vol: 23, Pages: 263-282, ISSN: 2352-1465
Dynamic traffic assignment models rely on a network performance module known as dynamic network loading(DNL), which expresses the dynamics of flow propagation, flow conservation, and travel delay at a network level. The DNL defines the so-called network delay operator,which maps a set of path departure rates to a set of path travel times. It is widely known that the delay operator is not available in closed form, and has undesirable properties that severely complicate DTA analysis and computation, such as discontinuity, non-differentiability, non-monotonicity, and computational inefficiency. This paper proposes a fresh take on this important and difficult problem, by providing a class of surrogate DNL models based on a statistical learning method known as Kriging.We present a metamodeling framework that systematically approximates DNL models and is flexible in the sense of allowing the modeler to make trade-offs among model granularity, complexity, and accuracy. It is shown that such surrogate DNL models yield highly accurate approximations (with errors below 8%) and superior computational efficiency (9 to 455 times faster than conventional DNL procedures). Moreover, these approximate DNL models admit closed-form and analytical delay operators, which are Lipschitz continuous and infinitely differentiable, while possessing closed-form Jacobians. The implications of these desirable properties for DTA research and model applications are discussed in depth.
Liu J, Li K, Yin M, et al., 2017, Optimizing key parameters of ground delay program with uncertain airport capacity, Journal of Advanced Transportation, Vol: 2017, ISSN: 0197-6729
The Ground Delay Program (GDP) relies heavily on the capacity of the subject airport, which, due to its uncertainty, adds to the difficulty and suboptimality of GDP operation. This paper proposes a framework for the joint optimization of GDP key parameters including file time, end time, and distance. These parameters are articulated and incorporated in a GDP model, based on which an optimization problem is proposed and solved under uncertain airport capacity. Unlike existing literature, this paper explicitly calculates the optimal GDP file time, which could significantly reduce the delay times as shown in our numerical study. We also propose a joint GDP end-time-and-distance model solved with genetic algorithm. The optimization problem takes into account the GDP operational efficiency, airline and flight equity, and Air Traffic Control (ATC) risks. A simulation study with real-world data is undertaken to demonstrate the advantage of the proposed framework. It is shown that, in comparison with the current GDP in operation, the proposed solution reduces the total delay time, unnecessary ground delay, and unnecessary ground delay flights by 14.7%, 50.8%, and 48.3%, respectively. The proposed GDP strategy has the potential to effectively reduce the overall delay while maintaining the ATC safety risk within an acceptable level.
Yu C, Ma W, Han K, et al., 2017, Optimization of vehicle and pedestrian signals for isolated intersections, Transportation Research Part B - Methodological, Vol: 98, Pages: 135-153, ISSN: 0191-2615
In most traffic signal optimization problems, pedestrian traffic at an intersection receives minor consideration compared to vehicular traffic, and usually in the form of simplistic and exogenous constraints (e.g., minimum green time). This could render the resulting signal timings sub-optimal especially in dense urban areas with significant pedestrian traffic, or when two-stage pedestrian crosswalks are present. This paper proposes a convex (quadratic) programming approach to optimize traffic signal timings for an isolated intersection with one- and two-stage crosswalks, assuming undersaturated vehicular traffic condition. Both vehicle and pedestrian traffic are integrated into a unified framework, where the total weighted delay of pedestrians and vehicles at different types of crosswalks (i.e. one- or two-stage) is adopted as the objective function, and temporal and spatial constraints (e.g. signal phasing plan and spatial capacity of the refuge island) are explicitly formulated. A case study demonstrates the impacts of incorporating pedestrian delay as well as geometric and spatial constraints (e.g., available space on the refuge island) in the signal optimization. A further analysis shows that a two-stage crosswalk may outperform a one-stage crosswalk in terms of both vehicle and pedestrian delays in some circumstances.
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).
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