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15 Ticket Vending Machines (TVM) 1. TVM ID 2. TVM Location (latitude/longitude) 3. Transaction Date and Time 4. Article Purchased (e.g., Adult All Zone pass, Youth/Student ticket, etc.) 5. Number of Article Units Purchased 6. Price per Article Unit 7. Type of Purchase (cash, credit, etc.) 8. Total Amount of Sale 9. Transaction Status (complete, cancelled, etc.) 10. Transaction Characteristics (credit card number, expiry date, service host identifier, etc.) Website Tracking Software 1. Date and Time of Visit 2. Duration of Visit 3. Pages Viewed 4. Entry Page 5. Exit Page 6. Path Through Site 7. Referrer (outside site referring visitor to host website) 8. Route Selected (for schedule or real time information queries) 9. Stop (for real time information requests) 10. Origin/Destination/Time/Path (for trip planning queries) 11. Communication Received (e.g., commendation/suggestion/complaint) 12. Files Downloaded Automated Telephone Systems 1. Call ID 2. Date and Time of Call 3. Duration of Call 4. Number Called 5. Caller ID 6. Call Abandoned 7. Call Routing (e.g. trip planning, real time info., complaints, lost and found) Traffic Counters (Loop Detectors) 1. Date and Time 2. Location (latitude/longitude, street/highway name; jurisdiction) 3. Direction 4. Vehicle Count 5. Vehicle Classification 6. Speed 7. Occupancy Figure 2-4. Inventory of ITS data for transit market research: other systems. Traffic loop detectors are the final technology given in of this information would otherwise be gathered only by using Figure 2-4. The data recorded by loop detectors include more costly and irregular manual collection techniques, if at location, direction of traffic, date, and time; and vehicle all. Some ITS data offer entirely new sources of information or classification, count, speed, and occupancy for a defined new data combinations. Furthermore, the layers of data time interval. around the core of traditional market research can improve understanding by linking customer perceptions and attitudes to extended data from ITS technologies. Finally, ITS data can Benefits and Limitations of serve an enabling role by assisting the collection and analysis of Combining Traditional and ITS Data data gathered using traditional techniques. While it may be tempting to contrast the "anonymous Figure 2-5 displays the range of ITS technologies and their customers" represented in ITS data with the "identified cus- respective functions, applications, and resolutions related to tomers" in traditional market research data, such a dichotomy the primary objectives of market research. As one moves masks the differentiation among ITS technologies in their abil- from the periphery toward the center of the figure, the "cus- ity to define customers. It is true that ITS data cannot capture tomer resolution" improves; that is, the characteristics and customers' stated attitudes, opinions, perceptions, and prefer- travel activities of specific customers and customer groups ences, the core information sought in traditional market become increasingly identifiable. At the center of the diagram research. At the same time, if individually stated preferences is the highest level of customer resolution, representing the occupy the core of the market research paradigm, then ITS data traditional market research goal of uncovering customers' form rings of highly useful information around the core. Much preferences and perceptions.

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16 Cu sto me rR es olu tio n APC SC AVL EFB MAG SC WEB Event MAG Surveyed attitudes and preferences EFB: electronic registering farebox Figure 2-5. ITS resolution in a market research context. In the outermost ring of Figure 2-5, ITS technologies, such stripe cards can potentially document the travel activity of an as AVL, provide information related to service delivery. At individual customer using the unique ID associated with each this level, customer identities and characteristics are com- card. This ring represents the highest level of customer pletely unresolved within the data. One sees only the general resolution obtainable with ITS technologies. Individual cus- service environment that a hypothetical customer would tomers can be tracked through time and space within the sys- encounter. This level of resolution is suited to assessing re- tem. For example, the transfer patterns of a specific customer search questions about the characteristics of service delivered could be tracked through the system as linked trips and com- to customers, such as on-time performance. Moving in to the pared over time. Deeper analysis of data at this level can even second ring, technologies such as APC, magnetic stripe and begin to reveal customer preferences. For example, given sub- smart cards, and Web and phone logs enumerate customers. stitutable nearby transit services, the choice of one service At this level, anonymous customers can be located and over another may reveal a customer's valuation of selected counted, and these counts can address questions pertaining service characteristics. Zhao (2004) uses analysis of this type to when, where, and how many customers are using the to determine how CTA rail customers trade off travel time for system. Or, in the case of Web and phone logs, questions can comfort in the form of available seats and ease of transfers. be answered about how many customers are requesting Thus, ITS technologies collect transit data over a wide range information about particular services or are communicating of customer resolution levels, from general service delivery information back to the agency. characteristics to individual customer actions and choices. Moving in to the third ring, technologies such as smart It is important to recognize the linked nature of ITS data. cards, magnetic stripe cards, and Mobile Data Recorders can Because most transit ITS data are time and location stamped, relate data to specific customer groups. For example, a smart data from different "rings" can often be joined. For this rea- card or magnetic stripe card can be linked to a specific pass son, it is better to think of the data layers moving toward the program participant, or records of lift deployments could center as being complementary and cumulative. For instance, link customers with disabilities to specific stops or trips. The using the unique ID of an electronic fare card, a customer can data in this ring are useful for answering research questions be tracked through the system. These data exist within the about which customer groups are using a specific service or customer behavior ring. Working toward the periphery, the system as a whole. additional ITS data such as fellow rider attributes (customer Finally, in the ring closest to the traditional market re- attributes), passenger loads (customer enumeration), and search core, technologies such as smart cards and magnetic schedule adherence (service delivery) could all be linked to

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17 the individual customer's travel, providing useful context within the exclusive purview of traditional market research data for analysis of travel choices. Relevant ITS data from any methods. level can also be linked to the traditional market research Figure 2-6 presents a graphical representation of the po- core. For example, APC load data and AVL reliability data tential contribution of ITS data in six traditional market could be linked to surveyed customer perceptions of crowd- research applications: attitude studies, market segmentation ing and reliability, respectively, providing an opportunity for analysis, customer satisfaction surveys, O-D studies, fare comparing perceived and actual conditions. studies, and area analysis. Each research application's ITS data cannot be easily generalized. Different technolo- diagram in the figure provides a rough portrayal of how ITS gies record data at different levels of resolution. The resulting data relate to traditional market research data sources, and data have widely varying applications in market research. also notes the primary data available relevant to the specific While ITS data cannot replace the core of traditional market application. research--individually stated preferences--in many cases, First, the area uniquely within each rectangle in the figure ITS technologies provide lower cost and expanded data that represents the data requirements for each traditional mar- support and extend the core. The ability to use and link data ket research application in the absence of ITS data. Next, the from different resolution levels allows a more complete area uniquely within the ovals represents the range of data understanding of customer preferences and behavior. When that ITS technologies make available. Finally, the area of combined, ITS and traditional data are highly complemen- overlap represents the opportunity for ITS data to combine tary in addressing market research questions. with traditional market research data in each application. In ITS technologies gather data on many aspects of service de- this role, ITS data can facilitate or enrich traditional data livery and customer activity continuously, systemwide, and at collection and analysis. For instance, a customer satisfaction low cost when compared to manual data collection. Tradi- study on crowding could use APC-generated passenger load tionally, market researchers have relied on manual collection data to determine sampling times and locations. In addi- for such service delivery and consumption data. In an inte- tion, surveyed satisfaction data on crowding could be com- grated market research system, different ITS data elements may pared with actual passenger loads to relate perceptions of replace, extend, or complement traditional market research overcrowding to its actual incidence. Ideally, the APC data data. However, certain information and data, especially those would be drawn from the actual trip where the survey was related to attitudes and non-rider characteristics, remain administered. Attitude Studies Rider & non-rider attitudes covering all aspects of the transit service & traveling experience Market Segmentation Area Analysis Riders & non-riders Rider & non-rider surveyed usage, attitudes Actual behavior demographics, and behavior related to attitudes; and attitudes inferred attitudes Continuous data By time, day, on usage; service season, area; delivery by user-defined limited analysis areas demographic data Fare Studies Customer Satisfaction Surveyed fare usage and Surveyed satisfaction preferences O-D Studies O-D surveys or person trip Surveyed satisfaction linked Continuous, comprehensive diaries to continuous data on service fare usage data and immediate quality indicators ridership data pre & post fare Continuous O-D changes estimation within system; online (unidentified) trip requests Figure 2-6. Traditional and ITS data in market research applications.

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18 Traditional market segmentation studies survey riders and tables. ITS data can both support traditional survey tech- non-riders to delineate distinct groups based on demograph- niques and provide independent O-D estimates, with or ics, reported travel characteristics, and attitudes. ITS data without survey-based validation. Survey-based O-D trip collected from APCs and electronic fare cards can extend tables can be checked for correspondence with APC or smart traditional data by providing continuous records on actual card data. APC boarding/alighting totals by stop can be com- system use by time and location. For instance, using APC pared with survey-based estimates, or individual household data, market researchers can segment different temporal user travel surveys can be compared with actual transit trips groups (e.g. time of day, day of week, or season) and analyze recorded by household smart cards over the survey period. each group by location, ridership share, and trends over time. CTA plans to use both of these approaches in future O-D For example, using smart card data, Utsunomiya et al. (2006) survey efforts (see Appendix A). Furthermore, ITS technolo- segment CTA customers by residential location, transfer pat- gies can provide either direct O-D data or inputs suitable for tern, route/stop consistency of use, general frequency of use, O-D estimation. Rahbee and Czerwinski (2002) report accu- and system access distance. Finally, ITS data can be combined rate O-D estimates for rail passengers using fare card data with traditional data to enable or enrich market segmentation on boardings only. As electronic fare card use becomes studies. A study seeking to segment off-peak riders, for in- more widespread, ITS data also promise low-cost updating stance, could use off-peak counts from APCs to target surveys of trip tables between the times when traditional surveys are or to provide sampling weights for expanding the survey re- undertaken. sults. APC data could also enrich traditional data by linking Fare studies are traditionally undertaken using a combina- known market segments to actual system use. For example, tion of surveyed fare preferences and manually collected fare areas with a preponderance of certain attitudinal groups (e.g., usage data. ITS data from electronic fare cards and electronic "transit lifestyle" or "necessity riders") could be linked with fareboxes can largely replace manually collected usage data the areas' actual ridership data and further analyzed along and provide continuous, accurate fare usage data. For exam- temporal dimensions (e.g., weekday/weekend, peak/off-peak, ple, City of Madison Metro Transit (Appendix B) has used seasonal). magnetic stripe card data on pass program users in negotiat- Customer satisfaction is usually gauged by surveys. ITS ing pass program contracts with employers and institutions technologies allow stated satisfaction and customer feedback in their district. ITS data can also support ongoing monitor- to be linked with actual service delivery data. For example, ing of customer responses to fare changes and validation of TriMet (Appendix C) has compared surveyed perceptions of survey-based fare models. The CTA (Appendix A) analyzed "overcrowding" with actual passenger loads on specific trips actual fare card usage data in updating its survey-based fare recorded by APCs. The comparison provides a more nuanced change model. APC data might be used to monitor ridership understanding of how actual conditions relate to customer changes following a fare change. ITS data on fare use can also perceptions and satisfaction. In addition, important factors combine with traditional surveys to better understand influencing customer satisfaction that emerge from satisfac- changes among different market segments. CTA compared tion surveys may be monitored continuously to better under- demographics and perceptions of smart card users with those stand trends between surveys. This monitoring function may of other riders and non-riders to better understand why cer- permit earlier market research findings to be used preemp- tain customer groups prefer specific fare options. Thus, ITS tively to forestall decreases in customer satisfaction. For ex- data on fare choice and response to fare changes improves the ample, periodic satisfaction surveys at TriMet (Appendix C) currency of fare data and extends analysis possibilities. revealed that both reliability and overcrowding were increas- Area analysis is often performed by surveying riders and ingly affecting overall rider satisfaction. Analysis of AVL and non-riders in a defined geographic area, and recovering their APC data suggested that deteriorating headway maintenance attitudes and preferences regarding existing or planned tran- (i.e., bus bunching) was related to the downward satisfaction sit service. Because most ITS data are location stamped, or- trend. Thus, the combination of traditional and ITS data ganizing them around specific analysis areas is relatively easy. within market research can provide managers with both satis- Furthermore, the ability to define the analysis area after data faction metrics and related service delivery measures to are collected provides a considerable advantage over tradi- address conditions related to customers' concerns. In this tional surveys. ITS data provide continuously updated infor- example, operations managers at TriMet became aware of the mation on both service delivery and usage within any defined importance of maintaining headways in order to improve analysis area. For example, Utsunomiya et al. (2006) examine customer satisfaction rather than simply promoting headway consistency of station and route choices in different areas adherence as "good operations practice." using smart card data, finding that some areas' usage patterns O-D studies are conducted by surveying riders or a sample vary considerably more than others. Such analysis provides of households and extrapolating the data to construct trip the market researcher with a better understanding of the

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19 customer types served in specific areas. There is also potential rider and non-rider attitudes, ITS data can in many cases for leveraging ITS data for specific areas with survey-based facilitate data collection or enrich the interpretation of results. area analysis. Perceptions about service quality could be com- It should be mentioned that Figure 2-6 simplifies poten- pared with actual area service performance to determine tial research applications somewhat. In fact, data typically whether, for instance, an area with below average perceptions associated with one application may be useful for answering of reliability actually experiences lower than average reliabil- questions in other applications as well. This is especially true ity, as measured by AVL. with combined traditional and ITS data. For instance, the Attitude studies constitute much of the core activities of CTA has considered combining fare card data on college stu- traditional market research practice and are typically admin- dent pass riders (fare study) with surveyed perceptions of istered by intercept, phone, or mail methods. While ITS tech- safety (customer satisfaction) to assess whether the presence nologies cannot recover customer attitudes directly, there are of college students on certain routes improves riders' per- opportunities for ITS data to support and extend traditional ceptions of safety. Thus, the diagram probably understates attitude studies. In the simplest case, ITS data on stop-level the total contribution of ITS data to each market research ridership can be useful for planning and expanding user atti- application. tude surveys. Surveyed attitudes and attitude trends can also ITS data provide useful inputs for various market research be compared with corresponding service delivery trends to applications. In many cases, ITS technologies provide data that discern whether customers are becoming more or less sensi- are useful for addressing market research questions directly. tive to specific service quality conditions or whether the Some of the data are entirely new and some replace traditional service conditions themselves are changing. In addition, in data gathering techniques at lower cost and with improved cur- certain cases ITS data provide a sufficient basis to directly infer rency. In other cases, ITS data enable or enrich traditional data customer preferences. For example, Zhao (2004) estimates the gathering and analysis. Finally, some data remain within the implied monetary value of service amenities like seat exclusive purview of traditional market research methods. The availability and ease of transfer using smart card data on pas- size and scope of the contribution of ITS data to market sengers' choices among substitute rail services. Thus, while research vary by application and by the data analysis capabili- traditional techniques remain the primary tool for gathering ties of a transit agency's market research staff.