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1 SUMMARY Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook The transit industry is increasingly drawing on data recovered by intelligent transporta- tion systems (ITS) technology in its commitment to delivering high quality service to its cus- tomers. As it has gained experience with advanced technologies, the industry has discovered new opportunities for using ITS data to better understand customers and markets. A large volume of customer-relevant ITS data is beginning to complement and reinforce customer information obtained from traditional research methods to provide a more comprehensive and contemporary understanding of transit markets. The technologies offering the greatest potential to support customer and market research include automatic vehicle location (AVL), automatic passenger counters (APC), mobile data terminals (MDT), electronic reg- istering fareboxes, magnetic stripe cards, and smart cards. In addition, automated phone systems and the Web are not only providing information to customers, they are also obtaining information from customers. Nearly half of the transit properties reporting to the Federal Transit Administration's National Transit Database have now deployed these technologies. This Guidebook has been prepared to assist market research practitioners in their efforts to use ITS data in analyzing transit customers and markets. It envisions an integrated marketing system within which research informs service development and delivery. The contributions of ITS data in the integrated marketing system are divided between two main support channels. The first channel is dedicated to supporting ongoing practices dedicated to monitoring and evaluating services delivered to or consumed by customers. Monitoring and evaluation practices focus on such questions as What is the quality of service delivered to customers? How many customers are using the system, when are they using it, where are their access and egress locations, and what transfers are they making? How are customers' route or path choices related to service attributes? How are customers responding to changes in fares, level of service, route design, or marketing and promotion? Traditionally, analysis of these questions required manual data collection, which was a time consuming, costly, and, at best, periodic process. ITS technologies now automatically recover data to analyze these questions in a more comprehensive and timely way, at very low cost. The second channel through which ITS data support the research process is by facilitat- ing and leveraging traditional market research methods, such as customer surveys, market surveys, and focus groups. Passenger data recovered from ITS technologies can be used to define sampling plans, establish sampling weights or expansion factors, and determine the
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2 best times to survey. ITS service performance data can be used to help identify times and locations for recruiting focus group participants. Leveraging opportunities also exist in relating ITS data to survey information. For exam- ple, origin-destination (O-D) survey information can be used to validate passenger flow models estimated from farebox, fare card, and APC data. In the interim period between O-D surveys, which is often 10 years or longer, ITS data can be used to maintain contemporary estimates of passenger flows to support marketing and planning activity. In another exam- ple, information from rider satisfaction surveys can be related to operations data recovered by AVL, APCs and MDTs to assess the correspondence between riders' perceptions of the quality of their experience and the transit agency's metrics of the quality of service that was delivered to them. The Guidebook presents case studies describing how three transit properties of varying size have capitalized on ITS data. For example, the Chicago Transit Authority (CTA) is using its smart card and APC data to develop passenger flow models for rail and bus service. It is also developing tools using other ITS data that can be used by staff throughout the agency on its intranet. TriMet has drawn on its extensive market research experience in developing customer-oriented service performance measures based on AVL and APC data. Madison Metro Transit, despite limited experience with ITS technologies, is drawing on magnetic stripe card data in developing its employer and student pass programs, which now account for half of their system's annual ridership. Most of the ITS technologies now in operation among transit properties have been deployed over the past 10 years. The pace of deployment represents a fairly rapid and dra- matic transformation for the industry. There are a number of lessons that can be learned from deployment experiences that collectively offer the potential to achieve greater benefits in leveraging ITS data for market research. An important lesson learned early is the need to plan for systems compatibility, especially among AVL, APC, MDT, and fare cards. For some properties, inadequate systems planning has meant procuring and maintaining multiple AVL systems to support other on-board technologies. The second lesson is to plan for data validation and management. An enterprise data sys- tem must be designed to coordinate and manage the enormous volume of ITS data that are generated. There must be an assurance that the data produced by each new technology con- forms to an established data model. A screening process must be developed to verify the validity of ITS data. The third lesson is to involve data users early in the technology planning process. Other- wise, market research practitioners will be less productive using ITS data defined by others. The fourth lesson is to recognize that new skills will be needed among market research and other staff, requiring a strategy or plan for staff recruiting, retention, and career development. The final lesson is for senior management to recognize the need to develop a technology plan and budget for the agency that coordinates the hardware, information, and human resource infrastructures associated with the ITS technology life cycle.