Use of Decisionmaking and Information Management Systems in Mainstreaming TSMO

3. Information Management Systems and Mainstreaming TSMO

IMSs allow organizations to collect, organize, store, analyze, and report data. They are used with DSSs or alone to support TSMO. DSSs often have a processing module that makes recommendations to the user. The effective use of IMSs is key to planning operational improvements and assessing their potential or actual effects. This chapter describes general use of IMSs in business and industry, typical data sources, the use of data, and current transportation agency uses of IMS. It also provides ideas for agencies related to mainstreaming TSMO, including the role of integrating an IMS with other agency systems, agency infrastructure, and the overall context important to successfully mainstreaming TSMO with the use of IT related tools.

General Approaches

There are many types of IMSs that play key roles in business and industry. Information systems in business organizations can often be grouped under one of two broader categories—operations support systems (support of business operations) or management support systems (support of managerial decisionmaking). (Al-Mamary, Shamsuddin, and Aiati 2014) Figure 1 provides a conceptual diagram of the distinct type of operations and management support systems.

Tree starts with Information Management Systems at the top. Below is Management Support Systems and Operations Support Systems.

Source: Adapted from O’Brien and Marakas 2007

Figure 1. Diagram. Operations and management classifications of information systems.

Management decisions tend to be longer term and strategic. IMSs will support these functions in a variety of ways, including reporting that presents tailored information for managers to make appropriate decisions and inputs to DSSs, executive-level information systems, and specialized processes. Similarly, there are business operations that need support, such as transaction processing, process control systems, enterprise collaboration tools, and specialized processing systems. These two pathways combine to provide expert advice to decisionmakers, to manage organizational knowledge (including transmitting knowledge from retiring staff to new staff and training), to highlight strategies to be used for competitive advantages, and to support basic business (usually short-term or daily) functions.

In addition, there are four types of analytics solutions in the information management area, which all build on one another and can lead to improved performance: (IBM Software 2013, James 2017)

Descriptive. Data and business intelligence are used to ask questions about things that have happened.
Diagnostic. Data are compared to assess what might be wrong (i.e., questions related to what might be happening that is not correct).
Predictive. Statistical models are used to focus on questions related to what could happen given possible scenarios (including if nothing changes).
Prescriptive. Optimization and simulation are used to derive answers to questions related to what should be done.

There are other ways to conceptualize or categorize IMSs, but this structure provides a useful framework for the current discussion about TSMO efforts.

Transportation Agency Uses of Information Management Systems and Big Data

Transportation agencies have developed a variety of uses of IMS, including manipulating and mining ever-increasing large data sources. The use of large data sets creates new possibilities for agencies to enhance TSMO. Transportation organizations have a vast amount of data available to them, but it is not always clear how the data are used and how different data sets relate to one another. Within the private transportation sector, data are used to analyze traveler preferences and habits on a macro and micro scale, optimize capacity and pricing, and predict maintenance needs. These benefits can be translated to public sector transportation agencies as well. (IBM Big Data and Analytics 2014) These data can be used to show the potential or actual benefits of TSMO strategies, leading to greater consideration in planning alternatives analysis and helping TSMO projects compete for funding.

Transportation agencies use traveler data to respond to needs in real-time. Floating travel data are collected by Bluetooth®-enabled mobile phones, global positioning system devices, and other technology used at the customer level. Using this information in addition to trip information, transportation agencies can immediately map areas of traffic congestion, incidents and lane closures, and modes experiencing substantial delays. This knowledge elicits immediate response as well, such as rerouting given by dynamic signage, changes in pricing of toll lanes, signal timing adjustments, and alerts to media and traveler information systems.

Eventually, real-time responses become historical data that, when analyzed, provide means to evaluate how the operational changes mitigated issues. This continuous analysis of solutions strengthens traffic system models and heightens reliability. The wealth of historical travel data also enables system analysts to make predictions. Using multiple historical variables, such as weather, traffic, time of day, and destinations, transportation system analysts can make adjustments prior to real-time data collection. (Buckley and Lightman 2014)

Transportation studies using big data can describe the larger features of transportation systems and also unique user experiences because outputs (e.g., destinations, speeds, and times) are not inferred. These measures can inform operational and physical changes, including those of road design and maintenance needs. (Sweet, Harrison, Buckley, and Kanaroglou 2016)

In addition to big data, transportation agencies use IMSs for a variety of purposes, most notably to advance TSMO in transportation planning and identify operational needs (table 4). DOTs have integrated their systems with other preexisting systems, established interoperability with other agencies, used software and researchers to build data frames, and established IT policies. Artificial intelligence capabilities of next generation IMSs are also a natural evolution of managing information and big data in service of planning and operational needs.

Table 4. Examples of Agency Use of Information Management Systems and Big Data.
Agency and Decisionmaking Area	Example
Washington State DOT Supports TSMO-related decisions in planning and performance management.	Washington State DOT has a Corridor Sketch database that it uses to identify locations within the 300 corridors on the transportation system having the greatest likelihood of congestion issues. Washington State DOT Regions use this database to enter their needs. Database development was led by the Planning Division within Washington State DOT and has helped the agency focus on TSMO strategies as the first line of investment. This database is used for Washington State DOT’s integrated scoping process, which provides a more comprehensive approach for project scoping and is an example of mainstreaming TSMO efforts. In partnership with the STAR Laboratory at the University of Washington, Washington State DOT helped develop and use the Digital Roadway Interactive Visualization and Evaluation Network (DRIVENet), an online platform for data sharing, integration, visualization, and analysis. This was developed to integrate the data silos within Washington State DOT and support effective decisionmaking. Processed data from DRIVENet are used to develop Washington State DOT’s Gray Notebook. (Washington State DOT 2018)
Maryland DOT SHA Supports TSMO-related decisions in performance management, planning, and operations.	Maryland DOT SHA, like many other DOTs, rely on partnerships with academic, research, and private institutions for big data analysis and data warehouse development; these partners play a role in data management and determination of measures and targets. For example, Maryland DOT SHA has a partnership with the University of Maryland and actively collaborates on platforms like the Regional Integrated Transportation Information System, a situational awareness, data archiving, and analytics platform.
Texas DOT Supports TSMO-related decisions in planning and performance management.	Texas DOT, as well as other DOTs, use commercial, off-the-shelf technology for analysis and reporting, and in-house staff to do the data science work to tailor it to their needs. Texas DOT has branded its system (Statewide Traffic Analysis and Reporting System (STARS)) but uses commercially available software to forecast, map, and visualize data, including TSMO- related data. (Knowles and Carrizales 2014) Texas DOT’s IMS has moved beyond TSMO into business management (which may be important in mainstreaming TSMO efforts, where TSMO can potentially be used to support various business and reporting functions throughout the agency). (Texas DOT 2018)
Wisconsin DOT Supports TSMO-related decisions in planning.	Advances in software systems and data integration provide opportunity to streamline the TSMO planning process for many entities, including the Wisconsin DOT. Wisconsin DOT has made advances in software systems and data integration that enable streamlining of the TSMO planning process. Chief among these is the spatial database now in place for all ITS and ITSNet inventory and GIS tools, applications, and mapping. (Wisconsin DOT 2014)
Florida DOT Supports TSMO-related decisions in project development.	Florida DOT is focusing on governance and standards as a part of its IT strategy. Within several district offices, two transportation management centers, a central office, a private highway enterprise, and non-IT central office groups, standardization is encouraged so that projects can draw from and be applicable to all users.
Iowa DOT Supports TSMO-related decisions in planning.	An example of IMS informing planning and project prioritization is the ICE-OPS tool at Iowa DOT, which establishes specific criteria to assess sections of the transportation system with operational and safety concerns. This information is then combined with a specific, data-driven process to identify and prioritize transportation improvement projects as a part of its 5-year plan. The tools for this process are in development. (Iowa DOT 2018)
Ohio DOT Supports TSMO-related decisions in planning.	Ohio DOT shares transportation and systems data via a web-based tool, TOAST. It plans to expand this to include more data and assist other program areas with planning and prioritization of projects.
Pennsylvania DOT Supports TSMO-related decisions in planning and operations.	Pennsylvania DOT created One Map, which is software that overlays transportation data onto a map. (Pennsylvania DOT 2018) It supports TSMO planning decisions about the types of operations tactics to use and locations (e.g., placement of ramp meters and other ITS assets). It includes crash data and identifies where to best spend a limited budget.

Asset management systems are a core part of a transportation agency’s functions and provide opportunities to mainstream TSMO through the integration of ITS and traffic signals in asset management systems. Table 5 includes examples of asset management systems in transportation, noting the role of IMS and potential for use in mainstreaming TSMO.

Table 5. Examples of TSMO and Asset Management Systems
Agency	Example
Arizona DOT	Arizona DOT undertook several initiatives to improve its business practices related to asset management (and in general). Core to these initiatives was an integrated information system that would facilitate the implementation of improvements. It would allow agency staff to assemble and analyze data from multiple sources, including from asset management and TSMO. (Arizona DOT 2017)
Nevada DOT	Nevada DOT developed a data warehouse linked to interactive dashboards with maps and advanced analytics for data within the pavement management system. TSMO-related measures were incorporated within a single asset management platform and used some of the DSS principles. This demonstrates the cross-sector usage of data that can be an advantage to mainstreaming TSMO.

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