A decision support system for total airport operations management and planning

The airport planning and decision making process exhibits various trade‐offs and complications due to the large number of stakeholders having different, and sometimes conflicting, objectives regarding the assessment of airport performance. As a result, the airport performance assessment necessitates the use of advanced modelling capabilities and decision support systems or tools in order to capture the multifaceted aspects, interests and measures of airport performance like capacity, delays, safety, security, noise and cost‐effectiveness. Presently, airport decision makers lack decision support tools able to provide an integrated view of total airport (both airside and landside) operations and analyse at a reasonable effort and decision‐oriented manner the various trade‐offs involved among different airport performance measures. The objective of this paper is twofold: (i) to describe the decision‐oriented modelling framework and development process of a decision support system for total airport operations management and planning, and (ii) to demonstrate the decision support capabilities and basic modelling functionalities of the proposed system. Copyright © 2010 John Wiley & Sons, Ltd.     

Knowledge based decision support in airport terminal design

The state of the art in airport terminal design has essentially remained unchanged since the 1970s, relying on the use of detailed simulation together with empirical and statistical formulae for sizing the terminal. Air transport has altered considerably since then, with increased use of air travel for both business and leisure, together with the increased use of ‘hub’ terminals in which the terminal acts as an interchange stop on a journey involving several flights. The use of simulation involves a detailed analysis of the terminal design under one set of conditions. To change the design or operating conditions involves a complete resimulation using the new design parameters which is time consuming and computationally expensive.

By using a knowledge based approach, a system can be provided which has the flexibility and speed required to explore the consequences of implementing design decisions in a variety of conditions, together with the ability to use and alter facility sizing methodologies to reflect current and future design guidelines.     

Development of efficient stop planning optimization process for high‐speed rail systems

The Taiwan High Speed Rail (THSR) has recently added three additional stations to its original network. Although the three additional stations can improve accessibility to the system, these new stations can present difficulties in the transportation planning process, particularly for planning of train stops. The additional stations may benefit some passengers, but may also lengthen the travel time for the other passengers. Therefore, the main challenge faced by THSR is finding an efficient way to design appropriate stopping patterns. Past studies on stop planning usually adopted meta‐heuristics or decomposition methods to solve this complex problem. Although these solution techniques can improve solution efficiency, none of them can guarantee the optimality of the solution and capture the transfer movement of different stopping patterns. In this research, we proposed an innovative network structure to address complex stop planning problems for high‐speed rail systems. Given its special network structure, two binary integer programming models were developed to simultaneously form and determine the optimal stopping patterns for real‐world THSR stop planning problems. An optimization process was also developed to accurately estimate the station transfer time corresponding to the variation in stopping patterns and passenger flow. Results of the case studies suggest that the proposed binary integer programming models exhibit superior solution quality and efficiency over existing exact optimization models. Consequently, using this stop planning optimization process can help high‐speed rail system planners in designing optimal stopping patterns that correspond to passenger demand. Copyright © 2017 John Wiley & Sons, Ltd.     

The changing role of decision support instruments in integrated infrastructure planning: lessons from the Sustainability Check

This article draws lessons about recent innovations in decision support for coping with challenges in integrated infrastructure planning strategies. After setting up a conceptual framework for the scope of analysis and the use of information in infrastructure planning, the empirical section explores the introduction of early-stage sustainability assessment tools. Data collection draws on experiences gained in the Netherlands with a new tool: ‘Sustainability Check’. We conclude that such instruments have a number of capacities that address the challenges of area-oriented planning: (a) bringing together information about the comprehensive value of alternatives, (b) facilitating the generation of alternatives, (c) addressing institutional fragmentation by learning about referential frames, and (d) adding contextual perspectives to the ‘hard’ outcomes of conventional tools. We also conclude that tools such as Sustainability Check should not be seen as a replacement for conventional decision support tools, but rather as complementary to them.     

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.     

A spatial decision support system for retail plan generation and impact assessment

Current geographic information systems typically offer limited analytical capabilities and lack the flexibility to support spatial decision making effectively. Spatial decision support systems aim to fill this gap. Following this approach, this paper describes an operational system for integrated land-use and transportation planning called Location Planner. The system integrates a wide variety of spatial models in a flexible and easy-to-use problem solving environment. Users are able to construct a model out of available components and use the model for impact analysis and optimization. Thus, in contrast to existing spatial decision support systems, the proposed system allows users to address a wide range of problems. The paper describes the architecture of the system and an illustrative application. Furthermore, the potentials of the system for land-use and transportation planning are discussed.     

Flexible strategic planning of transport systems

Abstract

This paper presents a decision support methodology for long-range planning of transport systems that exhibits strategic flexibility and stochastic system parameters. Unlike one-off strategic decisions, flexible decisions should be dynamically reformulated with time. The proposed methodology is based on the construction of a tree structure of multiple interlinked tactical planning problems, each associated with a scenario in the tree, where problems under scenarios at intermediate dates incorporate in their formulation the solution of the corresponding problems associated with past (future) connected scenarios. The resulting tree structure of interconnected planning decisions becomes a strategic-tactical decision support system that allows managers to formulate suitable flexible strategic decisions that mitigate the consequences associated with downside scenarios while taking advantage of the upside opportunities. The methodology is applied to the planning of a fleet deployment through charter contracts where contract prices depend on both market behavior and the duration of the contract itself.     

A dispatching decision support system for countering delay propagation in intermodal logistics networks

This paper specifies a dispatching decision support system devoted to managing intermodal logistics operations while countering delay and delay propagation. When service disruptions occur within a logistics network where schedule coordination is employed, a dispatching control model determines through an optimization process whether each ready outbound vehicle should be dispatched immediately or held to wait for some late incoming vehicles. Decisions should consider potential missed-connection costs that may occur not only at the next transfer terminals but also at hubs located further downstream. Numerical examples and a sensitivity analysis with different slack time settings for attenuating delay propagation are presented.     

A decision support system for port selection

Abstract

A number of studies have been carried out on the factors determining port choice, derived from the perspectives of shippers, carriers or both. Recently, some studies using multi-criteria analysis, more specifically Saaty's analytical hierarchy process (AHP), have been undertaken to address port competitiveness and port selection by shipping lines. Based on a review of the literature on port selection, this article proposes a decision support system (DSS) for port selection using AHP methodology. The proposed DSS is web-based and thus it can be accessed by more decision makers and data collection can be carried out faster. Moreover, AHP addresses the issue of how to structure a complex decision problem, identify its criteria, measure the interaction among them and finally synthesise all the information to arrive at priorities, which depict preferences. AHP is able to assist port managers in obtaining a detailed understanding of the criteria and address the port selection problem utilising multi-criteria analysis. This article presents the architecture and the port selection procedure of the web-based DSS, and then illustrates three different cases. It shows how technology advancement can bring positive effects of strategic planning to shipping firms.     

Local environmental impact assessment as decision support for the introduction of electromobility in urban public transport systems

The urban transport sector offers a noteworthy potential for the reduction of national greenhouse gas emissions as well as local pollutant emissions such as nitrogen oxides and particulate matter if electric drive systems are increasingly used. Owing to the fact that electric busses are still in the development phase, higher investment costs have evolved for public transport providers. Hence, decision making about where to introduce electric bus lines is mainly characterized by economic as well as technological considerations. The integration of local or regional ecological aspects is often neglected. An interdisciplinary approach was applied to the bus network of an urban public transport provider. By combining spatial-analytical techniques and statistical methods, the local environmental relief potential of electric busses has been evaluated. The results show that due to their specific line characteristics and the frequency of service, two bus lines out of 28 are particularly suitable for the introduction of electromobility in Dresden, Germany. The presented scientific work contributes to the extension of environmental assessments and decision making tools by including the spatial dimension of environmental impacts. It increases the practical relevance, especially for management decisions of political and entrepreneurial stakeholders by providing a sensible decision basis for local or regional infrastructure projects.     

A new composite decision support framework for strategic and sustainable transport appraisals

This paper concerns the development of a new decision support framework for the appraisal of transport infrastructure projects. In such appraisals there will often be a need for including both conventional transport impacts as well as criteria of a more strategic and/or sustainable character. The proposed framework is based on the use of cost-benefit analysis featuring feasibility risk assessment in combination with multi-criteria decision analysis and is supported by the concept of decision conferencing. The framework is applied for a transport related case study dealing with the complex decision problem of determining the most attractive alternative for a new fixed link between Denmark and Sweden – the so-called HH-connection. Applying the framework to the case study made it possible to address the decision problem from an economic, a strategic, and a sustainable point of view simultaneously. The outcome of the case study demonstrates the decision making framework as a valuable decision support system (DSS), and it is concluded that appraisals of transport projects can be effectively supported by the use of the DSS. Finally, perspectives of the future modelling work are given.     

Conflict‐free time‐based trajectory planning for aircraft taxi automation with refined taxiway modeling

To mitigate airport congestion caused by increasing air traffic demand, the trajectory‐based surface operations concept has been proposed to improve surface movement efficiency while maintaining safety. It utilizes decision support tools to provide optimized time‐based trajectories for each aircraft and uses automation systems to guide surface movements and monitor their conformance with assigned trajectories. Whether the time‐based trajectories can be effectively followed so that the expected benefits can be guaranteed depends firstly on whether these trajectories are realistic. So, this paper first deals with the modeling biases of the network model typically used for taxi trajectory planning via refined taxiway modeling. Then it presents a zone control‐based dynamic routing and timing algorithm upon the refined taxiway model to find the shortest time taxi route and timings for an aircraft. Finally, the presented algorithm is integrated with a sequential planning framework to continuously decide taxi routes and timings. Experimental results demonstrate that the solution time for an aircraft can be steadily around a few milliseconds with timely cleaning of expired time windows, showing potential for real‐time decision support applications. The results also show the advantages of the proposed methodology over existing approaches. Copyright © 2015 John Wiley & Sons, Ltd.     

Pick‐up locations and bus allocation for transit‐based evacuation planning with demand uncertainty

This paper develops a decision‐support model for transit‐based evacuation planning under demand uncertainty. Demand uncertainty refers to the uncertainty associated with the number of transit‐dependent evacuees. A robust optimization model is proposed to determine the optimal pick‐up points for evacuees to assemble, and allocate available buses to transport the assembled evacuees between the pick‐up locations and different public shelters. The model is formulated as a mixed‐integer linear program and is solved via a cutting plane scheme. The numerical example based on the Sioux Falls network demonstrates that the robust plan yields lower total evacuation time and is reliable in serving the realized evacuee demand. Copyright © 2012 John Wiley & Sons, Ltd.     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.