Electric Vehicles And Electric Bikes

Using Open Source Data to Populate, Calibrate and Validate a Simplified Integrated Transportation and Land Use Model





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Matthew Hardy, PhD.

Program Director for Planning and Policy


444 North Capitol, NW

Suite 249

Washington, DC 20001

(202) 624-3625

[email protected]


This research explores the process of populating, calibrating and validating a simpler transportation and land use modeling tool to support decision-making within metropolitan planning. Metropolitan planning is driven today, in part, by the need to develop and implement new policies such as smart growth, congestion pricing, and environmental regulation that affect transportation and land use. In addition, there are many different categories of metropolitan planning decision-making: policy development, visioning, strategic analysis, and tactical assessment, all of which need to be supported with data, analysis, and information. Thus, an important aspect of metropolitan planning is the ability to analyze policy scenarios in an integrated fashion using integrated transportation/land use modeling (ITLUM) tools. This research reviews the literature, documents the use of using open sources of data in developing a simpler ITLUM tool, and discusses the calibration and validation process. This research uses case study methodology with the Washington, DC region as the case study location.


Transportation and land use planning, collectively referred to as the metropolitan planning process, has a rich history in the United States (U.S.) that has evolved over the past century because of the involvement of decision makers and stakeholders in developing transportation and land use policies; federal requirements and funding availability; and advances in computing technologies and modeling theories. Any discussion of the metropolitan planning process inevitably includes a discussion of the models, tools, and methodologies used by decision makers to support various decisions ranging from transportation infrastructure placement to zoning for land use.

The increasing complexity of social, political and economic factors associated with metropolitan planning have driven researchers to develop a new regime of modeling tools called integrated transportation and land use models (ITLUM). These tools are being developed in part based upon the recognition that transportation and land use planning is a complex process; the need to analyze various inter-related policy initiatives to support decision-making; and the requirement to satisfyingly involve stakeholder groups in the metropolitan planning process. The ITLUM tools being created are often complex in nature requiring large amounts of detailed data, resources (time and money), functionality, and expertise. Unfortunately, many of the more complex tools are not easily accessible by many planning agencies in the U.S. And, researchers often criticize the use of simpler modeling tools given the complex nature of urban systems as being too simplistic [1].

However, there is some evidence to suggest that a complex modeling approach is not necessarily appropriate for all aspects of the decision-making process and that a simpler tool may be sufficient in order to capture the complexities associated with understanding the transportation and land use system dynamics [2]. In fact, if a goal of the planning process is to better engage stakeholders, then creating ITLUM tools that are better accessible to stakeholders and decision makers alike may be a useful exercise [2]. Thus, the motivation for this research is to further explore how a simplified modeling approach (one that requires less data, resources, functionality, and expertise to operate[footnoteRef:1]) can support decision making within the context of the metropolitan planning process in the U.S. The question of concern for this research is focused on what are the opportunities and limitations of using readily-available data sources to populate, calibrate, and validate a simplified ITLUM tool and what are the implications? [1: The distinction between simple and complex modeling approaches are based upon four requirement categories: data, resources (time and money), functionality, and expertise. Simple modeling approaches have lower requirements and complex modeling approaches have higher requirements.]

Literature Review

Computer modeling tools have a rich evolutionary history and researchers have described the evolution in different ways. Miller,et.al., and Wegener describe the evolution from simple to complex with the goal of developing tools that are, generally speaking, more complex in nature [3], [4]. Mile,et. al., however, describes it slightly differently where tool selection to support transportation planning is a trade-off between system complexity and spatial complexity [5]. Most recently, Hardy,et. al., in discussing the application and use of transportation models for work zone design and evacuation modeling, describe the evolution in terms of a spectrum of modeling tools where selection is based upon five aspects: functionality, results, time, training, and cost [6], [7]. What is absent from these frameworks of model evolution and selection is an inherent decision-making functionality that a computer modeling tool supports. Today, decision makers rely on a spectrum of modeling tools to support the necessary decision-making categories associated with the metropolitan planning process.

The use of computer modeling tools has been an integral part of metropolitan planning and supporting the decision-making categories, though very few tools exist to look at the transportation and land use system in an integrated manner [8]. Within the transportation planning process an entire set of transportation forecasting modeling tools has been developed that date back to the 1950s and are primarily based upon the four-step planning process [9][footnoteRef:2]. Today, every federally designated metropolitan planning organization employs some type of transportation forecasting model. The land use planning process, however, is not as well-established in terms of tool development or application. Of the 35 largest metropolitan areas, only 12 were using commercially available land-use models [10]. [2: The four-step planning process consists of trip generation, trip distribution, mode split, and route assignment.]

Iacono, et. al, refer to the early development of computer modeling tools as being focused on the available expertise associated with developing a modeling approach and the availability of data to test the approach [9]. Over time, expertise began to grow and the ability to reasonably collect data increased such that the focus of model development shifted to incorporating more functionality into the model and better representation of real-world systems. For example, the development of microsimulation modeling approaches occurred in parallel with increasing computing power and data storage.

While many resources have been devoted to the development of more complex computer modeling tools (e.g., UrbanSim, TRANSIMS, SHRP II) less attention has focused on simpler modeling approaches. Just as one cannot use only a hammer to construct a house, one modeling tool cannot support the entire metropolitan planning decision-making process. Recently, the use of system dynamics[footnoteRef:3] as a simpler approach has been discussed as a means to support the metropolitan planning decision-making process. Abbas and Bell articulate twelve reasons why system dynamics might positively contribute to the metropolitan planning process [1]. Many of these reasons have been supported by later research most notably that of Sussman, et. al, who articulate the need for system dynamics as part of a regional strategic planning process to better engage stakeholders [2], [11]. [3: System dynamics is a modeling approach designed to incorporating complex feedback relationships to better assess system analysis. A detailed discussion is presented in Section Error! Reference source not found..]

To this day, researchers and policy makers continue to debate the role and purpose of computer modeling tools to support metropolitan planning. On one hand, researchers cite the need for complex, large-scale modeling tools that are able to include more functionality with a higher degree of accuracy in the results [12], [13]. These researchers suggest the need to further develop the complex and comprehensive tools that were created as a result of ISTEA and the 1990 Clean Air Act Amendments (CAAA) such as UrbanSim or TRANSIMS [13]. On the other hand, some researchers and decision makers indicate the high cost, difficulty of use, and large data requirements required to run large-scale models and identify the need for simpler models [14]. This group suggests there is a need to incorporate the complexities of the dynamic urban process by way of simpler computer modeling tools that are more accessible to decision makers [11]. The research presented here builds upon this debate and provides evidence to suggest that the use of a simpler modeling approach can be a cost-effective approach to supporting certain aspects of the metropolitan planning decision-making process.




Case Study

This research uses a descriptive case study to illustrate the role that a simpler ITLUM can play in the metropolitan planning process. While no two regions are exactly the same, the results of this case study (examining the Washington, DC region) can be used to generalize how to deploy a simpler ITLUM tool since detailed data are identified regarding model requirements, calibration, and testing[footnoteRef:4]. The data available for the Washington, DC region is similar to other regions in the U.S. and many regions are facing similar challenges regarding transportation and land use policies. [4: See Hardy (2011): Simplified Integrated Transportation and Land Use Modeling to Support Metropolitan Planning Decisions: An Application and Assessment for a complete documentation of data sources and calaculation.]

The case study method has been described by some researchers as a weak research method within the realm of social science. Critics point to the insufficient precision of the results, lack of objectivity by the researcher, lack of generalizability, and the limited academic rigor. Nonetheless, proponents point to recent evidence suggesting that it is a commonly used method that, if conducted properly, can mitigate concerns raised by critics. There are three key criteria that can be used to judge the quality of descriptive case studies [15]. First is construct validity or the establishment of the correct operational measures for the concepts being studied. In this case study, the operational measures focus on how the Washington Region model is constructed and then how it could be used. Second is external validity or the ability to make generalizations from the case studys findings. While every region in the U.S. is unique in some way, including the Washington, DC region, this region is typical of many regions in the U.S. in terms of data availability and transportation/land use policies being debated. Thus, the findings are applicable to other regions in the U.S. (though not necessarily other countries). Third is reliability or the ability to repeat the operations of the study with the same results. An important component of developing this case study is documenting exactly where the data comes from and how the simpler modeling approach is created.

By addressing the issues raised by critics, the case study method can be used as an effective investigative tool. The case study approach is useful when addressing research questions associated with how something can be used; where the researcher has little control over events; and the focus is on contemporary phenomena within a real-world context [15]. In other words, the case study approach is valuable when the context of the problem being explored is an important component of the analysis and comparative data is not available due to the contemporary nature of the problem at hand.

MARS Model

This research employs the Metropolitan Activity Relocation Simulator (MARS) model as a simplified ITLUM tool[footnoteRef:5]. The MARS model is a system dynamics model originally developed in Vienna, Austria by Pfaffenbichler [16]. Subsequently, it has been applied in sixteen European and Asian cities and one South American city (Porto Alegre, Brazil)[footnoteRef:6]. The MARS model is deterministic in nature, meaning there are no stochastic elements built into the model. While a stochastic model would yield different results each time it is run, the MARS model will yield the same results each time it is run unless an exogenous variable is changed prior to the start of the model run. The deterministic nature of MARS is one characteristic of it being a simpler tool. This research is the first application of the MARS model in the U.S. and is based upon the application of the MARS model to the city of Leeds, England. [5: A complete description of the MARS model is available in Pfaffenbichler (2008). What is included in this section is a qualitative description of the MARS model structure and functionality.] [6: For a detailed description of where the MARS model has been applied, see Pfaffenbichler (2008).]

The MARS model is an ITLUM tool consisting of two basic sub-models: the transportation model and the land use model. These two sub-models represent both the demand (land use) and supply (transportation) of a metropolitan region. Changes in the transport system cause time- lagged changes in the land use system and changes in the land use system cause immediate reactions in the transport system. The land use sub-model can be further subdivided into a residential and a workplace location sub-model. The links between the sub-models are shown in Figure 1.


Figure 1 MARS Sub-model Relationships


Washington, DC REgion Case Study

Network Development

The purpose of the description of the network development is to indicate the tractability of obtaining the necessary data for the Washington DC MARS Model. Obtaining the required data to support a transportation and land use model is often seen as one of the largest barriers to their use [8]. Thus, developing a transportation and land use model that does not require the collection of unique and specific data for a specific region, but one that can use readily-available data could be seen as an important aspect of using the model. To that end, open sources of data were used to populate the Washington DC MARS Model and the following data sources were used:


GMU Center for Regional Analysis

Google Maps

Google Transit Feed Specification (GTFS)

National Household Travel Survey (NHTS)

WashCOG Round 7.1 Cooperative Forecast

U.S. Census Bureau

U.S. Geological Survey (USGS) Land Cover Data Set


The first step in developing a MARS model is to identify individual MARS analysis zones that are consistent in terms of land use type and density which is the similar process used to develop traffic analysis zones in four step models (Pfaffenbichler, Gnter Emberger, and Shepherd 2008). The Washington DC MARS Model network was developed based upon existing research conducted by the Washington Council of Governments (WashCOG). The 2,191 traffic analysis zones and 59 regional activity centers and clusters were used to identify 97 individual MARS analysis zones.The second step in developing a MARS model is to collect the necessary data describing the individual MARS analysis zones as well as the travel characteristics, in aggregate, among the analysis zones. Four categories of data are required: regional data, zonal data, passenger car data, and public transportation data. A detailed description of developing the required input data is available in Hardy 2011.

A major hurdle in using any transportation-based simulation model is the development of the transportation network. When first examining the use of the MARS model for this research, developing the transportation network (both roadways and transit) appeared to be the most daunting task even with the simplification of the transportation network to a single link between each origin/destination pair. However, the use of web-based tools such as Google Maps and the GTFS significantly improved this aspect of building the model. In fact, since Google Maps covers all of the U.S., using the procedures documented in this research could be easily replicated in other regions. Also, GTFS continues to evolve with many transit systems publishing schedule data in the GTFS format.

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