Self-education of agents in the Multi-Airport Logistics System: A multiple cases study

Abstract

The Multiple Airports System (MAS) has been the topic of an increasing number of studies over recent years in the spatial and network analysis. However, there is relatively limited literature on the MAS in the perspective of logistics. This paper proposes the concept of the Multi-Airport Logistics System (MLS) by reviewing the related literature on MAS and comparing the performance of cargo and passengers transport in the U.S. MASs. Under the concept of the MLS, the boundary and the populations of the MLS are discussed by utilizing case studies on the MLS in the U.S., especially the Northeast MLS. Compared with existing studies, the concept of the MLS focus on the coordination of cargo development among airports and provide a framework for airports’ cargo development within the same region. By using Multiple Cases Study, we found that the agents gain experiences from the interaction with other agents and the environment, i.e. self-education of Agents by building blocks in the system.

Keywords

Multi-Airport Logistics System air cargo supply chain airport logistics self-education of agents

1 Introduction

Multiple Airports Region (MAR) refers to the area served with two or more airports, such as the Great London Area, includes five main airports [1]. Multiple Airports System (MAS) is the set of airports that serves airline traffic of a metropolitan area, which focus on the airports cooperation in the MAR [2]. The development of the MAS is an essential mechanism to help the world air transportation systems meeting future demand [3]. Therefore, the MAS have been the topic of an increasing number of studies over recent years in the spatial and network analysis.

Curiously, there is relatively limited literature on the MAS in the perspective of logistics. However, all-cargo carriers have conducted approximately two-thirds of the cargo activity, according to the National Plan of Integrated Airport Systems (2005–2009). The demand for discussion on the MAS in the perspective of logistics, therefore, has increased with the market maturation.

In this paper, we will discuss the MAS in the perspective of logistics and will put forward and discuss the idea of the Multi-Airport Logistics System (MLS). In comparison to former studies on the MAS, we compare the performance of cargo and passenger in the same MAS and take a closer look at the MLS. First, we review the related literature on the MAR, and the MAS. Second, we focus on the concept of the MLS. Finally, we present a three phases cases study to compare, identify, and investigate the MLSs. Based on the cases study; we discuss the difference of the MAS and the MLS, the boundary of the MLS, and the populations of the MLS. Finally, we frame some suggestions for future research.

2 Literature reviews and the concept of MLS

2.1 Literature reviews

Discussions on the MAR have been developed from the views of airports, airlines, and air passengers. Previous literature discussed the connotation, formation and the development of the MAS. However, research on the MAR focused on the perspective of cargo as a system is still relatively limited.

Related studies on the MAR can be traced back to 1920s, and focus on the discussion of air routes service between airports within the Los Angeles and the San Francisco Bay Area [4]. With the rapid growth in worldwide air traffic, maximum utilization of satellite, or secondary airports in metropolitan areas was introduced to meet the rapidly increasing demand for air service, and reduce aircraft congestion and flight delays. However, over the last 30 years, the proper management and development experience of the primary and secondary airports was poor worldwide, which was routinely marked by expensive mistakes, such as over-invest, overbuilding, and fail to anticipate the patterns of traffic distribution between airports [2].

According to the principal of affordable passengers’ access, Richard de Neufville illustrated a definition of the MAS based on “Metropolitan” [5]. The MAS is a way of sustainability for airport development and suggested that all the airports in the MAS need to find a way to overcome economic, financial and infrastructural problems as a whole, i.e. cooperation [6]. It was found that in the United States and Europe, the recent development of the MAS primarily involved the emergence of secondary airports. Due to the weak availability of airports, substantial growth of air traffic and weaker opposition to the construction of airports, the MAS in Asia have evolved through the building of new high capacity airports [7].

Recently, empirical studies in U.S., Europe, and Asia have revealed that the creation of the MAS can sustain the competitiveness of airports and a region [8]. Although, each airport aims to maximize its traffic volume by determining its strategies while being affected by those of other airports in the MAS [9], cooperation of airports in the MAS should be pay much more attention. The cooperation within the MAS focus on the co-operation air control and ground holding technology at several airports [10], and the organizations’ coordinated management of several airports [11].

However, numerous literatures on the MAS only discussed passenger related issues, while limited literature addresses cargo [12]. Emphasis on the MAS, in the perspective of logistics, is still weak for one or more of the following reasons. (1) Almost 60% of air cargo moves in the belly of passenger flights [13]. Hence, air cargo was considered as an adjunct to passenger transportation. (2) The air passengers are independent selection and decision makers in the MAS. However, only freight forwarders and independent shippers are engaged in regular port selection [14]. In addition, independent shippers sometimes choose freighter forwarders with the only concern being the right place, right cargo condition, and right time at a suitable price. (3) Cargo handling is no more profitable than passenger handling. Airport’s aeronautical charges are often based only on aircraft weight. Therefore, airport developers and managers pay more attention to passenger handling than cargo [15]. Some work have inspired our research[24 –27].

2.2 Concept of the MLS

According to the literature review, we assume that the MAR in view of cargo development is the MLS. The MLS is an integrated complex formed by a set of significant airports, logistics parks, and Free/Foreign Trade Zones (FTZs), within all sectors of the air cargo supply chains in a city cluster.

This definition involves several important points:

It refers to a city cluster rather than a metropolitan region or a city. The city cluster is somewhat similar to the formation of the metropolis. However, the number of cities is different from a city cluster and a metropolitan area.

With the focus on the integrated air logistics market, the definition pays much more attention to the supply chain of air cargo carriers and their supply chain partners within the air cargo supply chain. The supply chain partners include manufacturers, air forwarders, airport ground handling companies, ground delivery companies, some other outsourcing companies, and relevant institutions and organizations [16].

The definition focuses on significant airports, related logistics parks, and FTZs. Typically, significant airports in the MLS are those airports which serve more than 10,000 tons of cargos a year, no matter how large the passenger volume. The logistics park is an indispensable part of the supply chain, with the function of distribution processing, package, and marking, etc. FTZ is a set of special customs supervision areas with an industrial park nearby, which usually are connect to the port by logistics and processing[17].

The integrated complex contains a considerable number of comparatively simple parts. Every part of the MLS is interacting with each other. It is hard to predict the emergent actions of the whole complex from the independent behaviors of each component in the complex.

The formation of the MLS is conducive to promote the complementary, sharing, interacting and coordinating activities. The MLS is an open, dynamic, and win-win cooperation network structure. Planners, operators, managers and customers of airports, logistics parks, and the FTZs are the nodes. The flows of cargo, capital, information, personnel, technology and other resources are the paths. Organizational protocols among the nodes are the operational guarantees. The cooperative innovations among the nodes are the momentums. The MLS can closely link original isolated nodes through various paths, which can enhance the efficiency of the entiresystem.

3 Multiple cases study

Multiple cases study is an effective method to analyze the MAS [3]. We carried out the multiple cases study to investigate three aspects: (1) the performance difference between cargo and passenger in the same MAR, (2) the spatial scope and population boundary of the MLS, (3) and the CAS features and mechanisms of the MLS. We gathered both quantitative and qualitative evidence from a broad range of originating sources to support the multiple cases study.

3.1 First phase: Comparing

The first phase of the multiple cases study is to identify the performance difference between cargo and passenger in the same MAR. Based on the research findings of [3], for the 14 U.S. MASs, we collected each airport’s annual passenger and cargo turnover volume from the Bureau of Transportation. We also used 1% and 20% traffic shares to classify the role of airports.

To discuss the difference of scope between the MAS and the MLS, we performed a correlation analysis between the distances and the traffic share. Figure 1 shows the relationship between the passenger traffic share of the U.S. MAS airports in 2014 and the distance to the central city.

3.2 Second phase: Identifying

The second stage of the multiple cases study was to identify the boundaries of the MLS. We paid much more attention to the airports with annual cargo traffic above 10,000 tons. We then collected the 2014 annual cargo (freight & mail) in metric tons for U.S. airports from Airport Council International-North America. Finally, we grouped these airports according to the spatial scope of the city clusters.

We chose 24 airports as the basis nodes for the MLS (see Table 1). There are 33 airports nodes in the non-power law part of the distribution in the U.S. air transportation (flight) networks [3]. According to the historical cargo data of the airports, the cargo traffic of LGA and the DCA are usually below 10,000 tons a year. More than 77% of the other 31 airports, or 24 airports, had more than 100,000 tons of annual cargo traffic in 2014.

We carried out a space-time cluster analysis to identify the spatial scope of the MLS (see Fig. 2). Truck and rail distances in the range of less than 800 km are already competing with air transportation [18]. We then preformed a truck driving time analysis on these 24 airports by using GIS software: ArcGIS Online. We set a one-hour to five-hour truck driving time range for the study.

Next step, we performed an industry-density analysis by space-time cluster with a selection of businesses in industry by North American Industry Classification System (NAICS) Code. We also compared the business, spending, and the population densities of the airports with more than 200,000 tons of annual cargo traffic in 2014. In addition, we performed a gravity analysis. Figure 3 shows the results of the density analysis.

The second phase of multiple cases studies resulted in six clusters of airports in the U.S. (see Fig. 4). These six MLSs are the Seattle MLS, California MLS, Texas MLS, Florida MLS, Great Lakes and Ohio River Region MLS, and the Northeast MLS. The California MLS was combined with the San Francisco Bay Area and the LOS Area. The Great Lakes and Ohio River Region MLS was combination of the Great Lakes Area and the Ohio River Area. In addition, the Texas MLS is centered in the DFW Area, and the Florida MLS is centered in the MIA Area.

3.3 Third phase: Investigating

The third phase of the multiple cases study is to identify the features and the mechanisms of the MLS by focusing on a particular case. We focused on the airport clusters that combined their competitive development. This resulted in the selection of specific MLS cases for a variety of reasons as follows:

First, we chose the MLSs, including airports, with multiple levels of cargo traffic. For example, there are two main airports in the Seattle 3-hour-truck-driving radius. Cargo traffic levels at these airports are the same, and the hinterlands of these airports are relatively independent. Therefore, we ruled out the airports in Seattle Area and Florida State in this phase.

Second, we focused on the MLSs based on airports with more than 5% of the cargo traffic share. There were 114 airports in the U.S. with more than 10,000 tons of cargo turnover volume in 2014. These 114 airports account about 72% of the total ACI North America member airports. According to the Pareto principle, we assumed that these airports were significant airports in the MLS. On one hand, the airports with more than 5% cargo traffic share contained both the primary and the secondary airports. These airports were determined to be representative airports within the MLS. By further restricting our study to those airports with a 5% share of the cargo traffic, reduced the scope of our analysis. As a result, feasibility our case study was significantly improved.

Finally, to highlight the concept of the MLS, we chose the Northeast MLS for further analysis and investigation. The MLS that serves the Northeast Region is mainly composed of New York/Kennedy Airport (JFK), New Jersey/Newark Airport (EWR), Pennsylvania/Philadelphia Airport (PHL), Massachusetts/Boston Airport (BOS), Washington, D.C. Dulles Airport (IAD), and several others (see Fig. 5).

The Northeast MLS is the most complex one in the U.S. The top three airports, JFK, EWR and PHL, have a market share of about 70% of the whole cargo traffic of this region. Moreover, the three-hour-drive radius of these three airports covers about 87% of the market share of the total cargo traffic in the MLS. Most of these airports interact with each other within a two hours’ drive radius. We collected data and information from the website of these airports, the Google, the Google Map, and we took a site investigation of these airports.

4 Discussions on the cases

4.1 Difference of the MLS and the MAS

We find that the performance of cargo and passenger transportation in the same MAR has essential differences in the first phase study on the multiple cases. Therefore, we separated the discussion on cargo and passenger transportation by using the MLS and the MAS. There are mainly three differences, i.e., the structure difference, the role difference, and the composition difference.

In some MASs, the structures of the cargo and passenger traffic share are different. Figure 6 indicates that the MASs of New York, Miami, Chicago, and Houston, which have more concentrated cargo traffic shares.

Some airports have much higher cargo shares and have much more important cargo role in the MASs. The cargo share of IAD in the Washington D.C MAS was 71%, while the passenger share was only 33% in 2014. ONT, MHT, and PIE also had a more significant cargo traffic share in 2014 (see Fig. 7).

Some significant airports in the MASs have less than 1% cargo traffic. These airports do not fit well in the MLS; such as the Norfolk MAS (see Fig. 8).

Some airports are not in the passenger MASs, but have a significant cargo traffic share. For example, the Dallas MAS, where there are two significant airports in passenger transportation; DFW, and DAL. Fort Worth Alliance Airport (AFW) is not in the MAS, but its share cargo traffic is about 15% of the region.

4.2 Spatial scope of the MLS

Hansen illustrated that the airport located more conveniently to the market has an initial advantage stemming from its greater accessibility than the other airports in the MAS. Also, the service attributes of an airport in the MAS directly related to airport traffic volume [19].

The studies on the formation of the MAS mainly focused on the discussion of air passengers’ airport choice. Although the literature on airport choice, from cargo shippers’ perspective is limited, there are many studies on the factors determining port choice by marine port cargo users. It seems reasonable to assume that from a cargo shippers’ point of view, there is ultimately a relationship between airport selection and marine port selection. The results from marine port selection studies, therefore, provide some basis for airport selection by ‘cargo shippers’.

Shippers typically choose the airfreight forwarders for cargo shipment. Forwarders compare the different modes of transportation for shipment, such as trains, trucks, air, and marine, to achieve the most profit margins at the right place and the right time [20]. Airport selection by cargo shippers is an ultimately an issue related to marine port choice. In port selection, the hinterland is larger than in the passenger market [21], and the selection activities center on economic gravity and industry clusters [22]. These conclusions provide a basis for our discussion, therefore, on MAR in view of cargo transportation.

The spatial scope of the MLS is different from that of the MAS. Travelers’ airport choice decisions form the spatial ranges of the MAS. The industry density and the accessibility created the spatial scope of the MLS.

On one hand, the traffic share of airports has no significant relationship with the distance from the airport to the central city. An airport far from the central city may attract more passengers relying on its advantages of airfare or access. In the U.S. MASs, the average distance from an airport to the central city is 30.9 km. The nearest airport to the central city is BOS, which is right in the central of its MAS. The furthest airport to the central city is FNT, which is 86.9 km from its city central. The distance from the central city to 87% of the primary passenger airports in the MAS is below the average. Approximately 61% of the secondary airports in the MAS are relatively far away from their central city. The standardized coefficient between distances and the passenger traffic share is 0.57, therefore, which means the correlation is low (see Table 2).

The other hand, the tolerable access distance of cargo is even further than it of the passengers. Typically, passengers do not drive 800 km away to take a flight, but cargo does not complain about the distance. Within the range of 800–1000 km, trucks can be considered as the extension of the flight routes, especially for the international flights [18]. It is reasonable to expand the spatial scope of the MLS from a metropolitan area to a city cluster with closely related industries. As such, 75% of all the U.S. businesses are within one day’s drive from IND. In addition, IAD has conducted its air cargo market strategies in five levels, i.e. local, regional, primary road feeder area, secondary road feeder area, and all other areas (see in Fig. 9).

4.3 Population of agents in the MLS

In the MLS, individuals, firms, and organizations are related to the supply chain of air cargo agents, which constitute the nodes in the network. Choi, et al depicted that an agent may represent an individual, a project team, a division, or an entire organization [23]. Figure 10 shows the agents, types of agents, and the population of agents of the MLS.

Agents are the individual “actors” and have the ability to interact with others and their complex surrounding. The actions of individuals, firms, and organizations affect the course of events that occur in a MLS. However, cargo does not qualify as an agent in the MLS because it is not responsive to these actors.

There are primarily five types of agents in the MLS. The first type is the agent group of manufactures, customers, and individual shippers, which form the general cargo transportation demand. The other types are the supply side of cargo transportation, and are divided by the agents’ core business concern.

The population of agents means the whole collection of different types of agents. Being a part of the population, an agent may increase their possibilities by learning new successful strategies within a population. For example, an airport manager can learn from the population of managers who face similar issues.

The MLS is a CAS, and includes several agents and all of their strategies. Each airport’s logistics development actions within the MLS are tied very closely to the actions of other agents within the system. Moreover, airports in the system are actively striving to improve their share of the market. The first type of agents uses the same strategy criteria in cargo transportation, selections of airports and air carriers. Shippers select and de-select air forwarders. The forwarders choose the airports of origin and destination. The air carriers adopt the air service strategies at the airports that attract their service. Airports operators study, develop and implement strategies to attract carriers, and improve their airport facilities for forwarders. The organizations within these populations may, therefore, respond to market actions in a purposeful way.

Agents gather according to the tagged characteristics. In the MLS, there are two types of aggregation, i.e., spatial concentration and organizational groupings. Spatial concentration strengthens the exchange between agents and generates economies of scale. Organization grouping creates significant, powerful agents from small, low-level agents. The adaptive aggregation behaviors of agents optimize the structure of the MLS.

Spatial concentration in the MLS often forms distinct types of districts. According to the business category, agents tag each other and gather in express centers, air logistics parks, cargo villages, high-tech industrial parks, business parks and the FTZs.

In the Northeast MLS, JFK is the largest and busiest air cargo airport. JFK is seven miles from LGA and 20 miles from EWR. The on-airport cargo village at JFK airport is a distinct asset with the potential for the development of new forwarder complexes, while the industrial development component of the Village contains the existing off-airport businesses, potential new shipping complexes, and air cargo support operations (see Fig. 11). The JFK cargo village has more than 1,000 cargo companies, which covers the entire supply chain of the air logistics, such as forwarding, packing, insurance, truck rental, shipping, customs brokers,airfreight services, and supply chain solutions, etc.

Moreover, there are FTZs, on-airport and off-airport cargo facilities at each airport listed in Table 3. The entire air cargo area of JFK is designated as an FTZ.

Organization grouping in the MLS often includes entity aggregation and virtual aggregation.

Entity aggregation results in the formation of a significant management group of major airports. Table 4 shows the operators of the airports in the Northeast MLS. Some of the airports are under the control of a central administration, but belong to different states, such as JFK and EWR. Other airports are just the opposite, such as SWF and ALB, MDT and ABE, RIC and ORF. The Port Authority of NY and NJ (Port Authority) builds, operates, and maintains critical transportation and trade assets, which include four separate airports, JFK, EWR, SWF and LGA, interconnected by a vast work of roads and rail transportation. The Port Authority has developed a strategic plan for all their airports to enhance connections between the airports.

In addition, there is entity aggregation in the logistics parks within the MLS. For example, Jones Lang LaSalle Incorporated (JLL) provides commercial real estate strategy, services, and support to organizations around the globe. JLL operates numerous logistics centers within a close proximity of airports in the U.S., such as Prologis LAX Logistics, Goodman Logistics Center (OAK), JFK Logistics Center, Aerotropolis Logistics Park (MEM), 161 Distribution Center (DFW), etc. In addition, AMB is the world’s leading provider of air cargo facilities, targeting key international hub and gateway airports. AMB operates numerous logistics centers, such as AMB DFW Cargo Centers, AMB Liberty Logistics Center (EWR), and AMB JFK logistics center, etc.

The combination of or diverse agents, creates an environment where a common goal, at a single time, provides for a joint decision process in the virtual aggregation of authorities. Such as, several related governmental agencies have combined for the purpose of air cargo industry development in the New York region. These agencies include the Port Authority of NY and NJ, the New York Metropolitan Transportation Council, the New York City Department of Transportation, the New York State Department of Transportation, the New York City Economic Development Corporation, and various stakeholder groups.

4.4 Self-education and building blocks

In the MLS, agents gain experiences from the interaction with other agents and the environment. They store, refine and pick up the experience in functional blocks, this is a self-education process. They use these blocks to build an internal model. As a result, the MLS can predict the future markets and adopt strategies for development.

FTZ development strategy in Indianapolis is one of these building blocks. As a result of the FTZ, airports authorities have learned assist businesses in staying competitive. FTZ development is an important strategy for air cargo development. The Indianapolis Airport Authority administers FTZ #72 (INzone). Because of the mixing of FTZ, FedEx hub, and the USPS Eagle Network Hub, each year millions of dollars of commodities travel through the Indianapolis area. As a result, airport development cargo development in the Northeast MLS use FTZ #72 as a study case in forming development strategies. Once airports reliable ground access via major highways was a valuable asset, they used this mode of transportation in their internal model. To provide efficient transport for air cargo, both BWI and IAD have planned and constructed facilities in a comprehensive way. They all emphasize climate-controlled warehousing services for sensitive items such as foods and perishables, and in turn initiated improvements in their FTZs, which enabled them to compete with other airports within a six to eight-hour truck driving radius.

Airports have found it an effective strategy to form development departments to clear obstacles to cargo development, which enables them to apply this strategy in the development of their internal model. In an air cargo development strategic planning process for New York region, several related governmental agencies formed a joint department. They first compared the JFK to major competing airports, such as Miami, Los Angeles, Chicago, and Atlanta, so as to develop a workable air cargo action agenda. Then they discussed the advantages and disadvantages of the JFK, EWR, and PHL, etc., so as to coordinate their development strategies.

5 Concluding remarks and suggestions

There is a broad discussion on air cargo development in the MAR. However, a self-organizing and coordinated study of MLS has never been conducted. The primary difference between the MAS and the MLS is the airport choice behavior mechanisms, which include air passengers and cargo shippers. Within the 177 ACI North American Airports, there are six main airport clusters in the U.S., i.e., Seattle Area, Great Lakes and Ohio River Region, Northeast Region, California, Texas, and Florida; and the Northeast MLS which is the most complex. According to the multiple cases study, the future studies on the MLS should be carried out within the CAS framework, especially on the features and mechanisms of the MLS.

This paper also introduces additional three aspects for future research. First, the changing role of airports’ in the MAS should be carried out sequentially. Some findings in the first phase of cases study are not our concern in this paper, but it may also be an attractive topic for further studies on the MAS. The passenger traffic share within some of the MASs changed from 2008 to 2014 (compared to the results of Dr. Bonnefoy [3]). For example, OAK and PHF changed from a primary one to a secondary airport, while MDW upgraded to a primary airport from a secondary one. Second, the diversity of the MLS types should be clarified. To solve the limitation of the system,different types of resources and capacities complementary to the MLSs make up the global airport logistics network should be analyzed. The types of the MLS in the U.S. can be divided by several factors, such as spatial features, degree of cooperation, competition style, etc. Clarifying these types within the MLS can assist others in learning from their experience more effectively. Moreover, the evolution of the MLS Model should be conducted by simulation studies, to clearly understand the logistics within the MLS.

Footnotes

Acknowledgments

We thank the support of the National Social Science Foundation of China (No. 15CJY043), Natural Science Foundation of Jiangsu Province (No. BK20151479), and the Fundamental Research Funds for the Central Universities (No. 3122013D024). We also very appreciate the funding provided by the China Scholarship Council (CSC) for corresponding author’s visiting scholarship at Florida Institute of Technology (FIT). Moreover, thank the faculties from College of Aeronautic (CoA). We also thank ACI North America for providing us the full version of their ACI-NA 2014 Traffic Report.

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