- Case study
- Open Access
GIS-based visualization of integrated highway maintenance and construction planning: a case study of Fort Worth, Texas
© The Author(s). 2017
- Received: 25 January 2017
- Accepted: 17 April 2017
- Published: 26 April 2017
This paper reports on a case study of the use of visualization of geospatial data that is distributed across data sets and requires integration over time and space to aid decision makers. Like many State Highway Agencies (SHAs) in the United States, the Texas Department of Transportation (TxDOT) is organized along the traditional functional lines of planning, design, construction, maintenance, and operations. It has historically relied on experience and longevity of its staff to efficiently and effectively plan its construction and maintenance projects. Although functional boundaries of maintenance and construction are fairly clearly defined, there tends to be some overlap in projects that can be executed by either of the functional groups. The department currently does not have a robust integrated information system for identifying potential planning conflicts between its construction and maintenance projects. This has led to suboptimal use of resources, including overlapping plans for maintenance and mobility enhancement projects.
With over 650 highway projects assigned across two functional groups within the district’s (TxDOT Fort Worth district) boundary, it is a challenging task to assemble a coherent plan for managing these projects over a multi-year planning period that is subject to external stakeholder input as well as shifting funding constraints. Temporal and spatial data needed for integrated planning resides in specialized information systems developed for the needs of individual functional groups. To address this challenge, this district decided to integrate and visualize data from individual information systems in a Geographic Information System (GIS). A GIS-based tool was developed to integrate, visualize, and analyze projects data from multiple information systems.
Discussion and evaluation
This paper documents the benefits associated with visualization and integration of projects data in a GIS to address planning challenges facing a typical highway agency. Among the lessons learned are the potential uses of GIS, which include detecting spatially and temporally overlapping projects, supporting integrated planning, and improving communication among functional groups within a state highway agency.
The study demonstrates that such spatial-temporal representations of project data can lead to early identification of potential overlaps during the planning phase. In a broader context, such geospatial visualization efforts can also form the basis for eliciting practitioners’ perspectives and knowledge input in the development of spatial decision support systems.
- Geographic information systems (GIS)
- Data integration
- Data visualization
- Maintenance and rehabilitation (M&R)
- Highway planning
Successful highway infrastructure planning and maintenance requires significant investments in terms of time, human resources, and money. Every year, billions of dollars are spent on maintaining highway infrastructure via new construction projects, road maintenance, and rehabilitation activities (Lee et al. 1996; Zhang et al. 2001). Increasing urbanization has led to a growing demand for highway infrastructure resulting in transportation systems becoming more complex in response to the demand (O’Brien et al. 2012; Podgorski and Kockelman 2006). Consequently, the need to optimally allocate limited resources to maintain and improve the state of transportation infrastructure cannot be overemphasized. These factors among others, significantly affect public funds expenditure on highway infrastructure development, thus drawing increased public scrutiny to budget planning and funds allocation for highway infrastructure (Sanchez 2006).
Today, the critical focus on and the need for efficient management practices in transportation planning and policy are underscored by key federal laws passed in the last three decades. These include the Surface Transportation and Uniform Relocation Assistance Act of 1987, the ‘Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA),’ and the recent ‘Moving Ahead for Progress in the 21st century Act (MAP-21).’ These laws have exhibited an increasing emphasis on integrated management practices and efficient use of federal funds. They also highlighted the need to invest in transportation systems by looking at the issue from the perspective of economic, socio-cultural, technological, and sustainable systems. Central to these considerations, is a push towards the use of data to drive highway agencies in making more informed decisions concerning highway planning and management (Thill 2000).
Such advancement notwithstanding, the transportation planning process is a continuous and arduous task involving data, models, and users. As part of the planning process, decision-makers need to use a vast collection of data and information to address a number of substantial issues like traffic management, construction scheduling, Right of Way (ROW) acquisition, public communication, and others (Nobrega and O’Hara 2011; Sankaran et al. 2016a; Woldesenbet et al. 2015). Highway agencies often collect a plethora of data and information about the nation’s network of highways. The sources of such data vary widely and their forms range from drawings, pictures, maps, tables, text descriptions, to accounts of personal experience (Flintsch et al. 2004). However, highway agencies usually have to deal with fragmented databases, multiple incompatible models, redundant data acquisition efforts, and sub-optimal coordination between the various agencies or departments operating on the same highway facilities (Chi et al. 2013; Ziliaskopoulos and Waller 2000). To compound this problem, multiple independent legacy information systems usually co-exist within the same agency (Chi et al. 2013; Thill 2000).
In spite of huge investments in data collection and archiving efforts, the amount of information and knowledge generated from data repositories are minimal and less supportive of informed decision making. Additionally, both practitioners and researchers have questioned the efficiency of data programs in meeting the needs of users for highway infrastructure planning purposes (Flintsch and Bryant 2006; Woldesenbet et al. 2015). Transportation professionals still face the onerous task of organizing highway data into suitable forms to support decisions concerning highway maintenance, rehabilitation, traffic control, highway monitoring, and projects prioritization. These issues have given rise to a surge in the demand for effective practices and tools that can integrate, manage, and analyze highway data (Parida and Aggarwal 2005).
Over the past two decades, many State Departments of Transportation (DOTs) have explored the use of digital information systems for highway management decision support (Kang et al. 2008; Lee et al. 1996). Accordingly, TxDOT relies on several information systems; these include but are not limited to the Pavement Management Information System (PMIS), a Maintenance Management Information System (MMIS) referred to as COMPASS, and the Design and Construction Information System (DCIS). The challenges associated with accessing and leveraging data from multiple information sources highlights the need for an integrated system that can fuse projects data and support applications to aid Maintenance and Rehabilitation (M&R) planning. While TxDOT has made progress to integrate some highway data, there is a lack of an automated process to visualize and integrate data from the individual information systems to better support highway project planning decisions.
The need for such a system has grown for metropolitan districts since they have significantly more lane-miles of on-system highways under their responsibility and consequently more projects in various phases of development and delivery at any given time. The funding mechanism for maintaining, rehabilitating and upgrading the existing system is complex. It has become further complicated since TxDOT’s funding is dependent on revenue from multiple sources with different permissible uses. Moreover, the planning process is fiscally-constrained at the category level; the amount of funding available determines the number of projects that can be planned within specific categories. Metropolitan planning organizations’ (MPO) policy boards have responsibility for certain funding categories requiring concurrence from TxDOT. In addition, at any given time, several projects are in various phases of construction. The actual cost of construction can vary from the budgeted costs that affect category funds available going forward. Construction costs can vary from budgeted costs at the time of bidding or throughout the construction phase owing to change orders, unexpected conflicts needing additional right-of-way, costs to relocated existing utilities, and many others. Furthermore, there are instances when existing roadways that were not expected to be rehabilitated within the planning horizon, have to be rehabilitated owing to faster deterioration in condition. This leads to reactive maintenance to maintain safety and pushing lesser priority projects down the list. The combined effect of these factors (and many more) creates a need for an integrated planning process leveraging modern visualization tools which will allow the integration of temporal and spatial data which can be viewed, reviewed, and updated in a dynamic setting.
The rest of the paper is organized as follows. The next section describes the general challenges in performing highway planning tasks and how GIS has been previously used to address some of the identified challenges. In the “Objective and Methodology” section, a research framework based on a case study is presented. Following this, the “Case Study” section points out district-specific barriers to planning tasks and how a GIS-based tool was developed to integrate data from multiple information systems used by the district. A formalized presentation of the benefits of GIS integration and visualization to M&R planning follows. Finally, the paper ends with conclusions on the findings of this study and the directions for future work in the “Conclusions” section.
Agency Goals: Multiple stakeholders including Metropolitan Planning Organization (MPOs), state and district agencies, and private institutions all have different mandates and pursue unique individual goals. Consequently, due to the competing interests of agencies and functional groups within highway agencies, the planning process often yields results that are sub-optimal for individual agencies or functional groups within the same agency.
Uncertainty: Uncertainties in ROW acquisition, environmental review, public acceptance, and political approval greatly affects an already complex highway planning process.
Knowledge Acquisition Approaches: Divergent epistemologies also create a major problem for integrated highway planning. More often than not, different approaches to knowledge extraction result in different conclusions about highway issues which affect the interventions that different agencies or functional groups would like to take.
Transparency: Highway agencies continue to struggle in employing multiple communication modes to make highway plans available while implementing decision models that have a satisfactory degree of transparency.
Limited Resources: SHAs usually do not have enough resources (human, cost, and time) to perform other relevant M&R functions. This includes effectively performing audits on integrated highway development plans and tracking highway project allocations on different pavement sections over the long term.
Data Availability and Quality: The increasing complexity of the application of information technology sometimes complicates the planning process. However, the crucial concern is that the tools developed to extract useful information for modeling and decision support are far from addressing the needs of users.
NCHRP studies (Neumann 1997; Systematics 2006) also reiterated the presence of these problems across many DOTs in the nation. According to NCHRP Report 798 (D’Ignazio et al. 2015), planners need to project long-term transportation needs, identify funding strategies and sources, and optimize resources available to attain the best value for highway investments. In conducting lifecycle planning for transportation assets, they also need to incorporate maintenance operations while planning for new capital construction projects. This process involves having functional groups within the same highway agency working across silos to comprehend and integrate agency-wide thinking; the key to this process being information flow.
Highway infrastructure problems in many cases involve spatial relationships between objects and events. Accordingly, visualization of highway data has been expedited by current advances in information technology and data collection technologies (Khattak and Shamayleh 2005). Given the spatial characteristics of road network data, the rational way to store and use such data should be via a consistent spatial referencing system such as a GIS (Medina et al. 1999). GIS is primarily used for storing, querying, analyzing, visualizing, and interpreting geographic data. It helps to reveal patterns and trends of objects that relate to one another in space and time (Ford et al. 2012).
With GIS, a user can integrate information from various sources and spatially connect that information to study aspects of an infrastructure system that was hitherto unapparent. By leveraging the capabilities of GIS, many highway agencies today are integrating highway data to improve highway operations and planning tasks (Flintsch et al. 2004). Transportation agencies are using GIS tools to collect data, communicate information about asset condition and needs, select and prioritize treatments, plan and manage work, and aid with disaster recovery (Rasdorf et al. 2003). In most cases, however, these applications are limited to collecting and displaying data and involves limited use of targeted spatial analysis functions (Hall 2015). Over the past two decades, several research efforts have demonstrated the applicability and value-added impact of GIS to highway pavement management and highway planning (Flintsch et al. 2004; Hall 2015; Sanchez 2006). Many other research efforts have explored this area, emphasizing the integration of decision-support tools and methods (Beiler and Treat 2014; Parida and Aggarwal 2005; Zhang et al. 1994; Zhou et al. 2009).
Previous applications of GIS for project planning by DOTs
Today, many DOTs use GIS to visualize previous, active and planned construction projects in their respective states. With an increasing emphasis on transparency with the public, SHAs also tend to provide online versions of such visualization efforts via GIS server platforms. Most of the SHAs discussed below were recognized by the Federal Highway Administration (FHWA) for their data integration and geo-visualization efforts using GIS for highway project planning and communication.
Washington State Department of Transportation (WSDOT) created an integrated database that utilized pavement condition data, traffic data, pavement preservation planning data, and capital project planning data. This was used for projects prioritization of preservation and improvement projects. The maintenance data were also fused with pavement condition data to determine routine inspections and repairs on the state’s highway facilities (Rydholm et al. 2015).
Highway agencies, Colorado State DOT (CDOT), North Dakota DOT (NDDOT) and TxDOT have all created online interactive maps that visualize construction projects (usually capital construction projects) scheduled to take place in their respective states. Most of these maps give users the option to query the map for easier extraction of relevant information (Colorado Department of Transportation 2016; North Dakota Department of Transportation 2016; Texas Department of Transportation 2016a). In the case of TxDOT, however, projects are visualized by the different phases of project development and long term versus short term planned projects.
Michigan DOT has multiple online maps that document road and bridge projects for their 5-years transportation program. MDOT also created an online map for the different public agencies responsible for the maintenance of road segments throughout the state. Using their in-house developed tool (Transportation Management System), agency staff use GIS for spatial analysis of road condition data, asset inventory, and budget decisions for project planning (Federal Highway Administration FHWA 2012).
The Transportation Information Mapping System (TIMS) of Ohio DOT is one of the most robust and extensive GIS-based systems used by DOTs. It contains data on all their transportation infrastructure assets, safety, roadway information, environmental issues, and alternate modes of transportation in the state. It provides users with the ability to visualize capital highway projects scheduled for the next 4 fiscal years as well as projects completed in the previous 4 fiscal years on highway assets. Additionally, it has the capability to integrate external spatial data in multiple interoperable formats (including shapefiles, Google Earth files, latitude/longitude coordinates, among others).
However, in most of these endeavors, data integration, and visualization efforts have focused on using GIS to support either capital planning or highway maintenance operations—rarely integrating both. One of the few cases of such an endeavor is from Connecticut DOT (ConnDOT). ConnDOT were reported to have mapped candidate projects and planned work for maintenance and capital construction projects. It was estimated that this could lead to increased coordination between the construction and maintenance functional groups within the highway agency (Hall 2015).
This notwithstanding, the extant literature still lacks a formalized approach to integrating projects data from the “capital construction planning” and “maintenance” functional groups to support integrated decision making. To address this gap in practice and research, the aim of this study includes presenting a data integration framework for fusing projects data using GIS. Furthermore, this study provides a repeatable data integration process; providing highway agency staff with custom spatial functions to extract useful information from an integrated database, and links these functions to practical benefits that improve the highway planning process.
There are two components to the objective of this paper. The first involves documenting the existing M&R project planning procedures and identifying planning challenges that exist within districts. The second involves integrating projects data from disparate sources in a GIS environment to address information needs of highway planners and other functional groups within the district.
The initial part of the study involves an extensive literature review and input from highway agency staff on the planning challenges that they face. The agency staff who contributed to this phase included highway professionals who have worked in the construction and maintenance planning roles for at least a decade. After identifying the challenges faced, the research team focused on addressing a technological challenge that could aid in also addressing some of the organizational challenges. A data integration approach was proposed and a GIS-based tool was built to demonstrate the practicality of the framework. Finally, based on input from agency staff, researchers identified the practical benefits of this tool to M&R project planning.
The ‘on-system’ highway network excludes county roads, local city streets, and functionally classified city streets, which the district office is usually not responsible for. While there are instances where the district collaborates with city agencies to maintain city streets, the projects involved usually remain under the purview of the city’s planning agency. Also, the user interface mainly includes a “Quick Access” toolbar, “Custom Functions” toolbar, and feature layers displayed to the left of the screen (default in ArcMap application of ESRI’s ArcGIS Software).
Transportation plans (TxDOT)
Current state of practice (TxDOT)
Two of TxDOT’s stated goals and objectives consist of “delivering the right projects by implementing effective planning and forecasting processes to deliver the right projects on time, and on budget, and preserving its assets through preventive maintenance of the system and capital assets”(Texas Department of Transportation 2016b). In order to meet these and additional goals, TxDOT’s complex portfolio of projects consists of maintenance, rehabilitation, safety, bridge, widening, capacity-addition, and several other project types delivered by its district offices responsible for multi-county geographical regions of Texas. The simpler maintenance projects can either be done with in-house workforce or through contractors with very short turnaround times. Maintenance projects can cover both roadway and roadside maintenance tasks. Normally these projects are handled by the maintenance functional group of a district office. These projects are programmed in the aforementioned COMPASS system and can have very short planning time associated with them. Additional maintenance projects can be cyclical in nature. However, several M&R projects require planning and design effort with associated lead time and a formal process of letting to achieve cost-efficiencies. These projects are planned, programmed, and generally developed by the transportation planning and development (TPD) group of a district. They are managed within the DCIS information system which is the department’s primary system of record for design and construction projects. On the farther end, there are capacity addition and reconstruction projects managed by the TPD that take years to plan, design, and fund for construction.
Developing plans consisting of projects that undergo “starts” and “stops” during their varied development phases is a continual challenge faced by the district staff. The district’s maintenance and TPD functional groups have different challenges and processes for planning, design, funding, and delivery. This is primarily due to the type of projects within their purview and the associated expectations and funding constraints. In order to gain synergy between the plans of these functional groups, the district leadership depends on effective communication and collaboration among multiple stakeholders (Sankaran et al. 2016b). However, the above-described processes can benefit from emerging tools and techniques to save time and enhance effectiveness.
Prior to engaging in extensive visualization efforts, it is important to work with agency staff to understand the extant work practice, issues with the existing system, and identifying areas for improvement. As such, the use and potential benefits of integrating multiple data sources and technologies should be explored within this context. More importantly, this allows tacit and explicit knowledge gained to be better incorporated in the development of later versions of information systems (Tsai and Lai 2002). Thus, the planning challenges associated with the current highway planning process in this district are discussed below.
Planning challenges in fort worth district
In carrying planning tasks, TxDOT districts rely on individual functional groups within its organization. These groups collaborate throughout the planning process for highway infrastructure planning and development. However, due to disparities in information systems, unique functional group funding processes, and planning requirements; agency staff is continually challenged to attain a wholly integrated planning process (D’Ignazio et al. 2015).
Different information systems and data access
TxDOT’s functional groups currently use architecturally different information systems to support the project-planning process that each group undertakes. Furthermore, expertise needed for utilizing information in these systems is limited to specific functional group staff. Both systems do allow users to capture project information with multiple location attributes including linear references; nevertheless, each functional group has a preferred primary location attribute, which leads to an asymmetry of information about location attributes. Accordingly, planners for construction-let projects (in DCIS) tend to use, as their primary location attribute, qualitative descriptions like intersections (limits) that a project will expand to and from. On the other hand, staff in the Maintenance functional group often uses the Texas Reference Marker System (a type of a Linear Referencing System). This reduces the ease of information flow between these two departments and points to the need for a more integrated system that can visualize these projects obviating the need to be familiar with the other department’s preferred spatial referencing system.
Project scheduling periods
The project scheduling periods for the TPD and Maintenance functional groups are usually 10 years (UTP) and 4 years (PMP) respectively. As a result of this disparity in the planning horizons, it becomes difficult to track some of the DCIS projects that need to be programmed with corresponding maintenance activities in the COMPASS database. Staff from both functional groups needs to ensure that complementary projects are scheduled in a consistent manner. For example, “Level up” projects that usually complement “Seal Coat” projects need to be programmed together for every fiscal year. This process of tracking complementary projects and the dynamic changes that occur due to annual complexities in project priorities can benefit immensely from an integrated database of M&R projects.
Similarly, the funding sources often vary for projects residing in different information systems. Multiple stakeholders are involved in the selection of these projects, which call for stronger justification to the public, local agencies, and federal institutions. While, projects in COMPASS are simpler and consequently require simpler decision-making process as compared to the DCIS projects, the overlap in funding sources between the projects in the different systems further challenges the complexity in developing a funding strategy and consequently, planning for projects.
These issues make it difficult for such individual functional groups to develop maintenance plans while giving full consideration to upcoming construction projects by other divisions. Staff from different functional groups within the district would clearly benefit from data integration and visualization, as this would lead to improved information flow, better collaboration, and effective integrated planning.
Developing GIS-based tool for visualization
In view of the planning challenges identified above, the district decided to address the disparities in information systems and data access barriers. This was done via the development of an integrated GIS-based tool to fuse all the projects’ information from the TPD and Maintenance functional groups of the district. Thus, the team went through the following stages.
Stage 1: extraction of input data
Projects data source, relevant attributes and description
(Comma Separated Values - CSV)
• Project description
• Spatial (county, highway, reference markers, DFO)
• Temporal (fiscal year and letting dates)
• Cost (construction cost estimate)
This is a statewide automated information system which allows all district offices of TxDOT to manage projects data in a consistent common format. It mostly contains highway projects funded by all the 12 categories of funding by TxDOT.
(Hypertext markup language - Html)
• Activity description
• Spatial (county, highway)
• Temporal (fiscal year and letting dates)
• Cost (construction, equipment, labor, materials estimate)
The COMPASS system is the Maintenance Management Information System (MMIS) of TxDOT that allows districts to manage maintenance activities that are more routine and are usually of a smaller scope.
Highway Inventory Data - PMIS
(Excel spreadsheet - xls*)
• Spatial (county, highway, reference markers, DFO)
• Road condition data (distress, ride, skid, and condition scores)
• Pavement roadbed information (main lanes or frontage roads, single or divided roadbeds)
• Traffic data (AADT, etc.)
PMIS is a comprehensive database on the entire network of the state-maintained highway system. It contains extensive information for each pavement section in the entire network.
On-system Highway Network (Shapefile - shp)
• Spatial (county, highway, reference markers, DFO)
• Pavement roadbed information (main lanes or frontage roads, single or divided roadbeds)
This is a “polyline” shapefile which was used as the “route event” feature dataset on which events/projects were visualized. It contains all the relevant spatial attributes and features to visualize projects in GIS.
County Boundaries and Reference Markers
(Shapefile - shp)
• Spatial (county, reference markers, DFO)
• County polygons
These are “polygon” and “point” shapefiles that contain the county delineations and linear reference markers respectively.
Projects breakdown by information system source in TxDOT (Fort Worth)
Number of Projects
Number of Projects
Others (Miscellaneous, etc.)
Grand Total Number of Projects
Stage 2: data/geo-processing
The processing took place in two steps—data processing and geoprocessing using GIS. The first step involved data processing in a spreadsheet environment for the extracted projects data. This comprised data cleaning, data validation, and sorting. Data cleaning is concerned with identifying and removing errors and inconsistencies from data in order to enhance the quality of data (Rahm and Do 2000). Most of the data-processing tasks involved data cleaning. A new field was also created to query the highway inventory database for GIS-compatible linear reference values for project records. This was done by cross referencing the other location referencing systems to a central and comprehensive linear referencing system known as the “Distance from Origin (DFO) system. The PMIS of TxDOT contains three different linear referencing systems for each discretized pavement section for the entire highway network. Accordingly, after the DFO system was selected as the main LRS for integration purposes, all the other referencing systems were converted to the DFO system. Given that all the referencing systems are linear, intermediate values were interpolated. In a few instances (less than 5%) where only qualitative location descriptions were given, the authors used the “Statewide Planning Map” (Texas Department of Transportation 2016a) developed by TxDOT to extract the DFO values of project limits.
Validation rules implemented on spatial data quality fields (based on PMIS data)
Spatial Data Attribute
Valid Value Range
Texas Reference Marker System
Beginning Reference Marker Number
Ending Reference Marker Number
Beginning Reference Marker Displacement
0.00 – 0.99
Ending Reference Marker Displacement
0.00 – 0.99
Mile Point Reference System
Beginning Mile Point
0.00 – 50.00
Ending Mile Point
0.00 – 50.00
Distance From Origin (DFO)
0.00 – 454.50
0.00 – 454.50
For the complementary attribute field, the highway number had to be exactly the same format across the different project data sources. As such, the original fields were formatted to be consistent with the valid value range and data format. For example, first, the entry “IH 20” had to be formatted to “IH0020.” Secondly, to indicate whether the road was a left or right main lane, left or right frontage road, or one undivided roadbed, a 2-letter appendix (“-?G”) was added. This naming convention is consistent with TxDOT’s official highway name designations for main lanes and frontage roads.
Stage 3: visualization output (layers)
Stage 4: custom functions
“What are the construction or maintenance (DCIS or COMPASS) projects in County A, of project type B that cost C dollars, which are scheduled to take place in D fiscal year?”
The left image in Fig. 9 shows the projects on pavement sections that satisfy the query that was constructed for the system. Similarly, the right image shows a snapshot of the attribute table of the resulting layer created to demonstrate the accuracy of the query results and the information that a typical user(s) can access. The highway names are in the standardized format used by TxDOT and the project classification “SC” represents “Seal Coat.” Also, the roadbed type designation “ML” represents “Main Lanes.”
“Are there any conflicting projects on Highway A in County B, of project type(s) C scheduled for the fiscal year, D?”
These custom functions provided users with advanced capabilities of extracting useful information about projects across databases (DCIS and COMPASS) to support highway-planning decisions. The next section will throw more light on the practical ways in which this GIS-based tool can be used to enhance the project planning process for maintenance and construction projects.
This case study highlights how GIS technology can improve, through visualization, the decision-making process for planning highway projects. The primary functions of GIS in this regard include among others, the ability to integrate data from heterogeneous highway data sources and visually displaying projects in a way which is more useful for agency staff. GIS also affords highway agencies the opportunity to execute both spatial queries and conflict analyses that would, hitherto, have been difficult to achieve in a solely database environment. The benefits enumerated below present ways in which GIS was, and can be used to support highway project planning.
Detecting redundant projects
Spatial Overlaps and Conflicts identified in DCIS and COMPASS
For Fiscal Years (2017–2019)
Intra-database Overlaps* (mileage)
Inter-database Conflicts (mileage)
Annual (3 years continuous)
Annual (3 years continuous)
The numbers (outside the brackets) in Table 4 represent the number of instances where conflicting or overlapping projects were identified on the same pavement section. It is evident from Table 4 that there is a significantly lower number of intra-database overlaps for pavement sections which can be attributed to the fact that construction projects in DCIS are more intermittent than maintenance operations projects. Furthermore, the maintenance projects database also includes more regular routine maintenance projects. This makes it more likely for the same pavement section to receive multiple instances of routine maintenance activities across different fiscal years.
Integrated planning and visualization
Within DOTs, there is a perennial challenge of integrating plans from different functional groups with individual goals and priorities, which is exacerbated by using different information systems. By leveraging GIS-based visualization of projects data, staff from different departments who are trying to make project planning decisions, can have access to and utilize useful information from other departments. Taking advantage of GIS’ spatial contextualization of data would lead to access and consideration of scheduled M&R projects during capital project planning and vice versa. Consequently, agency staff in the Maintenance group can better plan for maintenance projects because they will be aware of current and future capital construction projects scheduled for the different pavement sections in the district.
Audit planned projects
Due to the complexities and constraints of financial sources that are used in funding projects, sometimes highway projects are split up into 2 smaller scope projects and put in the capital construction projects database and the maintenance projects database. Consequently, transportation planners occasionally schedule projects that are complementary but exist in different information systems or databases. Functions like spatial queries and conflict analyses in an integrated GIS environment allow highway engineers to audit and confirm the logic of spatial and temporal relationships between construction-let and maintenance projects scheduled for the same highway section. In this case study, spatial queries were used by highway planning engineers to detect the absence or otherwise of complementary M&R projects that were supposed to be in the ‘other’ highway projects database. This led to a review of that procedure and corrective action was taken to update the information system.
Re-design and improve workflows
In conversations about relationships between technology and work practices, there are usually two main schools of thought. One is that technology should be used to support and make more efficient, the existing work practices of an entity. The other is that technological advancement needs to disrupt or redefine how workflows take place. For many highway agencies, the former is more likely to hold sway among decision makers. Accordingly, in the context of this case study, GIS-based visualization and data integration can be used to better support the project-planning process in the latter stages of project programming or be used to re-design some of the workflow processes for highway projects.
In this case study, conflict analyses were used to identify highway road sections that have been receiving a high frequency of rehabilitation projects over the planning horizon. As seen in Table 4, performing intra-database analysis affords highway planners the opportunity to review pavement sections that are receiving repetitive rehabilitation projects over the planning period. This can lead to the selection of a more elaborate long-term pavement treatment option. In this example, the proposed workflow incorporates lessons from the GIS-based system during the project-planning and -programming stage in lieu of using the system only to manage already scheduled projects. For instance, 14 out of the total 67 intra-database overlaps in the COMPASS database were sections of pavement with at least one incident of a light or medium rehabilitation project for 3 consecutive fiscal years. These pavement sections can, therefore, be reviewed for potential consideration of a major capital construction project or a heavy rehabilitation project that can have a more effective long-term impact on pavement performance.
Improved communication among decision-makers
Visualization of projects provides transportation agencies with a more “natural” way of presenting and viewing highway projects information. This leads to an ideal approach to support more intuitive and effective communication of scheduled projects with other decision makers and stakeholders in general. As pointed out by Jha et al. (2001), such geospatial efforts can lead to a wider participation of decision-makers at early stages of project planning while aiding in the anticipation of planning errors.
In the context of this case study, the GIS tool is currently available to agency staff in the Fort Worth district including all the area engineers in the district. Other stakeholders who also need access to the tool are granted access to support a more collaborative effort during project planning. The tool allows the district staff to better communicate with area offices about other projects “in the pipeline” which in turn, allows area supervisors to be more effective with the proposed list of candidate highway projects.
The maintenance and rehabilitation (M&R) planning process is complex; multiple sources of funding with restrictions, different planning periods, and multiple stakeholders within and outside the DOT. For many DOTs — particularly with rapidly growing urban and metro areas — the process is challenging to manage with existing toolsets and databases. This study reports on the use of a GIS tool to integrate disparate data sources into a single, visual interface. The visualization tool makes identification of problems and opportunities more straightforward by; shortening the planning time and increasing the agency’s abilities to make better use of limited funding.
The merits of GIS include valuable visualization, contextualization of information, and integrated database management. This study has evaluated the benefits associated with integrating highway projects data with GIS. It indicates that such integration efforts could be used to better support construction and maintenance operations; improve the planning process; save time and resources, and ensure that accurate information is available to different functional groups within the department.
In this case study, the automation of data processing and geoprocessing of projects data was complicated by a myriad of data quality issues; including missing data, inaccurate data, asymmetric data granularity, and semantic interoperability issues. More work is needed to delineate practical data integration challenges with GIS and to propose recommendations for improved data management practices for the district. Additionally, future work may also include the integration of highway conditions data, accidents data, and traffic information to provide a central source of information for the district to aid in project planning.
The authors recognize that every DOT is different and the highway planning practices may differ from one agency to another. However, this paper presented practical issues that are familiar to most DOTs and highway agencies in the domain of highway project planning. The findings of this study highlight an opportunity for highway agency staff to take advantage of GIS to improve integrated planning of highway infrastructure. The key to this process is the need for DOTs to, first, identify existing challenges with their current planning process and, second, employ GIS where necessary to address data-related issues. As highlighted by this case study, the opportunities for improvement are substantial and warrant investment in new tools and supporting processes.
The case study project for this paper was funded by the Fort Worth District of TxDOT. The authors would also like to thank the Directors, Area Engineers, and District Engineer for their contributions and feedback during the development of the GIS-based tool. Any opinions, findings, conclusions, or the recommendations presented in this paper are those of the authors and do not necessarily reflect the views of TxDOT.
This research project was sponsored by an Inter-Agency Contract (IAC) with the Texas Department of Transportation (TxDOT) with notable mention of the Fort Worth District. The principal investigators were Dr. William O’Brien and Nabeel Khwaja. Most of the projects data were also obtained from TxDOT.
JFM made significant contributions to the concept and design of this research study. This author also drafted a substantial portion of the manuscript and made revisions to reflect pertinent intellectual content and comments from other authors during the internal review process. Furthermore, this author performed a significant proportion of the data processing and data analysis tasks for this study. This author also agrees to be accountable for all aspects of this manuscript with respect to the accuracy and integrity of the study. WOB drafted significant portions of the manuscript and provided insightful comments on the design and structure of the research approach. Moreover, this author was involved in conducting critical revision of the manuscript to reflect important intellectual content and the communication of the contributions of this study to the existing body of knowledge. This author also gave final approval of the version to be published and agrees to be accountable for all aspects of this manuscript with respect to the accuracy and integrity of the study. NK wrote a substantial part of the manuscript and was active in the design and implementation of the research approach. This author was also engaged in the critical revision of the manuscript. Additionally, this author performed some of the data processing and data analysis tasks for this study. This author also gave final approval of the version to be published and agrees to be accountable for all aspects of this manuscript with respect to the accuracy and integrity of the study. LCB was responsible for critical revision of the manuscript to reflect key intellectual content and lead the application of theoretical concepts in the study within a pragmatic construct. This author also facilitated access to pertinent data and made key contributions in the interpretation of the results from data analysis. This author also gave final approval of the version to be published and agrees to be accountable for all aspects of this manuscript with respect to the accuracy and integrity of the study. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Beiler, M. R. O., & Treat, C. (2014). Integrating GIS and AHP to prioritize transportation infrastructure using sustainability metrics. Journal of Infrastructure Systems, 21(3), 04014053.View ArticleGoogle Scholar
- Caldas, C. H., Zhang, Z., Kockelman, K. M., Persad, K. R., Al, R. (2011). Development of best practices for right-of-way valuation and negotiations in transportation projects. Journal of the Transportation Research Forum, 50(3), 23–41.Google Scholar
- Chi, S., Hwang, J., Arellano, M., Zhang, Z., & Murphy, M. (2013). Development of network-level project screening methods supporting the 4-year pavement management plan in Texas. Journal of Management in Engineering, 29(4), 482–494.View ArticleGoogle Scholar
- Colorado Department of Transportation. (2016). Where Colorado DOT Spends Transportation Dollars - GIS Map. <http://dtdapps.coloradodot.info/otis/YCD/Where>. Accessed 24 Oct 2016.
- D’Ignazio, J., Rhodes, S., & Secrest, C. (2015). NCHRP 798 -the role of planning in a 21st century state department of transportation—supporting strategic decision making.View ArticleGoogle Scholar
- Dragićević, S., & Balram, S. (2004). A Web GIS collaborative framework to structure and manage distributed planning processes. Journal of Geographical Systems, 6(2), 133–153.View ArticleGoogle Scholar
- Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.Google Scholar
- Federal Highway Administration (FHWA). (2012). Best practices in geographic information systems-based transportation asset management.Google Scholar
- Flintsch, G. W., & Bryant, J. W. (2006). Asset management data collection for supporting decision processes. Washington, DC: US Department of Transport, Federal Highway Administration.Google Scholar
- Flintsch, G., Dymond, R., and Collura, J. (2004). NCHRP Synthesis 335 – Pavement Management Applications Using Geographic Information Systems. Transportation Research Board. Washington, DC (335), 1–73.Google Scholar
- Ford, M., Kirkman, R., Cox, J., & Ringeisen, D. (2012). Benefits of geographic information systems in managing a major transportation program: evaluation and lessons learned. Transportation Research Record: Journal of the Transportation Research Board, 2291, 1–7.View ArticleGoogle Scholar
- Hall, J. P. (2015). NCHRP Report 800 - Successful Practices in GIS-based Asset Management (No. Project 08–87). Transportation Research Board. Washington, DC (306), 22–38.Google Scholar
- Jha, M. K., McCall, C., & Schonfeld, P. (2001). Using GIS, genetic algorithms, and visualization in highway development. Computer-Aided Civil and Infrastructure Engineering, 16(6), 399–414.View ArticleGoogle Scholar
- Kang, Y., O’Brien, W. J., Thomas, S., & Chapman, R. E. (2008). Impact of information technologies on performance: cross study comparison. Journal of Construction Engineering and Management, 134(11), 852–863.View ArticleGoogle Scholar
- Khattak, A. J., & Shamayleh, H. (2005). Highway safety assessment through geographic information system-based data visualization. Journal of Computing in Civil Engineering, 19(4), 407–411.View ArticleGoogle Scholar
- Lee, H., Jitprasithsiri, S., Lee, H., & Sorcic, R. (1996). Development of geographic information system-based pavement management system for Salt Lake city. Transportation Research Record: Journal of the Transportation Research Board, 1524, 16–24.View ArticleGoogle Scholar
- Liu, W., Jaipuria, S., Murphy, M. R., & Zhang, Z. (2012). A four-year pavement management plan (FY 2011–FY 2014). In Pilot implementation of a Web-based GIS system to provide information for pavement maintenance decision-making.Google Scholar
- Medina, A., Flintsch, G., & Zaniewski, J. (1999). Geographic information systems-based pavement management system: a case study. Transportation Research Record: Journal of the Transportation Research Board, 1652, 151–157.View ArticleGoogle Scholar
- Neumann, L. A. (1997). Methods for Capital Programming and Project Selection. NCHRP Synthesis of Highway Practice, (243), 1–91.Google Scholar
- Nobrega, R. A., & O’Hara, C. (2011). Evaluating the design of the streamlined GIS-based transportation corridors. In Proc., TRB 90th annual meeting, transportation research board, Washington, DC.Google Scholar
- North Dakota Department of Transportation. (2016). NDDOT- GIS. Accessed on <https://www.dot.nd.gov/business/gis-mapping.htm>. (Oct. 24, 2016).
- O’Brien, W. J., Gau, P., Schmeits, C., Goyat, J., & Khwaja, N. (2012). Benefits of three- and four-dimensional computer-aided design model applications for review of constructability. Transportation Research Record: Journal of the Transportation Research Board, 2268(−1), 18–25.View ArticleGoogle Scholar
- Parida, M., & Aggarwal, S. (2005). Enhancing pavement management systems using GIS. Proceedings of The Institution of Civil Engineers-transport - PROC INST CIVIL ENG-TRANSPORT, 158(2), 107–113.View ArticleGoogle Scholar
- Podgorski, K. V., & Kockelman, K. M. (2006). Public perceptions of toll roads: a survey of the Texas perspective. Transportation Research Part A: Policy and Practice, 40(10), 888–902.Google Scholar
- Rahm, E., & Do, H. H. (2000). Data cleaning: problems and current approaches. IEEE Data Engineering. Bulletin, 23(4), 3–13.Google Scholar
- Rasdorf, W., Cai, H., Tilley, C., Brun, S., Karimi, H., & Robson, F. (2003). Transportation distance measurement data quality. Journal of Computing in Civil Engineering, 17(2), 75–87.View ArticleGoogle Scholar
- Rydholm, T. C., Luhr, D. R., et al. (2015). Visualizing pavement management data at the project level. In 9th international conference on managing pavement assets.Google Scholar
- Sanchez, G. (2006). 3‐year analysis of Texas department of transportation (TxDOT) :roadway improvement projects in the San Antonio district. San Antonio: University of Texas.Google Scholar
- Sankaran, B., France-Mensah, J., O’Brien, W. J. (2016a). Data Integration Challenges for CIM in Large Infrastructure: A Case Study of Dallas Horseshoe Project. 16th International Conference on Computing in Civil and Building Engineering. Osaka University; JapanGoogle Scholar
- Sankaran, B., O’Brien, W. J., Goodrum, P. M., Khwaja, N., Leite, F. L., Johnson, J. (2016b). Civil Integrated Management for Highway Infrastructure. Transportation Research Record: Journal of the Transportation Research Board. 2573, 10–17.Google Scholar
- Systematics, C. (2006). Performance Measures and Targets for Transportation Asset Management, NCHRP Report 551. Transportation Research Board. Washington, DC. Project Number 20-60, 1–181.Google Scholar
- Texas Department of Transportation. (2016a). TxDOT - Statewide Planning Map. <http://www.txdot.gov/apps/statewide_mapping/StatewidePlanningMap.html>. Accessed 24 Oct 2016.
- Texas Department of Transportation. (2016b). Fort Worth District Profile. <http://ftp.dot.state.tx.us/pub/txdot-info/ftw/district-profile.pdf>. Accessed 9 May 2016.
- Thill, J.-C. (2000). Geographic information systems for transportation in perspective. Transportation Research Part C: Emerging Technologies, 8(1), 3–12.View ArticleGoogle Scholar
- Tsai, Y. J., & Lai, J. (2002). Framework and strategy for implementing an information technology-based pavement management system. Transportation Research Record: Journal of the Transportation Research Board, 1816, 56–64.View ArticleGoogle Scholar
- Waddell, P. (2011). Integrated land use and transportation planning and modelling: addressing challenges in research and practice. Transport Reviews, 31(2), 209–229.View ArticleGoogle Scholar
- Woldesenbet, A., Jeong, H. D., & Park, H. (2015). Framework for integrating and assessing highway infrastructure data. Journal of Management in Engineering, 32(1), 04015028.View ArticleGoogle Scholar
- Yin, R. K. (2013). Case Study Research: Design and Methods (5th edition) Thousand Oaks: SAGE Publications.Google Scholar
- Zhang, Z., Dossey, T., Weissmann, J., Hudson, W. R. (1994). GIS integrated pavement and infrastructure management in urban areas. Transportation research record, (1429), 84–89.Google Scholar
- Zhang, Z., Smith, S., & Hudson, W. (2001). Geographic information system implementation plan for pavement management information system: Texas department of transportation. Transportation Research Record: Journal of the Transportation Research Board, 1769, 46–50.View ArticleGoogle Scholar
- Zhou, G., Wang, L., Wang, D., & Reichle, S. (2009). Integration of GIS and data mining technology to enhance the pavement management decision making. Journal of Transportation Engineering, 136(4), 332–341.View ArticleGoogle Scholar
- Ziliaskopoulos, A. K., & Waller, S. T. (2000). An Internet-based geographic information system that integrates data, models and users for transportation applications. Transportation Research Part C: Emerging Technologies, 8(1), 427–444.View ArticleGoogle Scholar