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20 CHAPTER 3 ITS Data Applications in Market Research To this point, transit market research methods and activities case studies, suggesting that these applications are still emerg- have been described, and the data recovered by ITS technolo- ing and have not yet reached the mainstream of practice gies have been inventoried. In this chapter, market research within the transit industry. and ITS data are combined to illustrate the role that the data The selection of case study properties occurred through can play in complementing, extending, and leveraging market several steps. First, an online survey of properties that had research practices. The following presentation reflects the deployed or planned to deploy the technologies of interest-- position of market research within the integrated marketing identified from the Volpe Center deployment surveys--was system, as was shown in Figure 1-1. administered in July and August 2005. A total of 134 proper- In an integrated marketing system, ITS data applications ties were solicited to participate in the survey and 78 ultimately support a market research program in two distinct ways. responded. The survey recovered information on the scale and First, ITS data can be employed in monitoring and evaluat- scope of properties' market research activities, the deployment ing program outcomes in a process where market research status of the subject technologies, the ITS data archiving envi- contributes to the definition of marketing action plans, and ronment, applications and tools used in ITS data analysis and these plans, in turn, are implemented in the service delivery, reporting, issues related to experiences in accessing and using promotional, and customer service functions of a transit ITS data, and lessons learned from those experiences. agency. The knowledge gained through monitoring and Evaluation of the survey responses led to the identification evaluation then allows an integrated marketing system to be of 13 candidate case study properties. Telephone interviews realized as a dynamic process by informing subsequent mar- were conducted with each property in April 2006. The final se- ket research activities. Examples if ITS data applications in lection of properties--the CTA, TriMet, and City of Madison monitoring and evaluating the delivery of services to cus- Metro Transit--reflected an interest in recovering informa- tomers are presented in the next section. tion that collectively represented all fixed route modes, the The second way that ITS data support the market research technologies of interest, varying levels of experience with ITS function is more direct. Here, ITS data can be applied to facili- data applications in market research, and a range of property tate the execution of traditional market research methods. The sizes. data can also be used to either maintain the currency of market research products or to provide a surrogate reference against ITS Data Applications: Monitoring which other market research products can be evaluated. In these Service Delivery capacities, applications of ITS data serve to leverage the market research process. Examples of such applications are presented Figure 3-1 presents an inventory of questions related to in the final section of the chapter. service delivery that can be addressed using ITS data. The The examples of ITS data applications in support of market questions are organized around the six subject areas com- research are drawn from the transit literature and from case monly covered in market research programs (as previously studies of three transit properties. The literature offers a num- illustrated in Figures 1-1 and 2-6): area analysis, attitude ber of examples of how ITS data can be used to monitor ser- studies, customer satisfaction, fare studies, market segmen- vice delivery. This is not surprising given that service delivery tation, and O-D studies. Although it is not directly within the monitoring was an important reason for acquiring several of realm of market research, a seventh subject area covering the systems under study. In contrast, examples of using ITS customer service and customer information is also included data to leverage market research are drawn mainly from the in Figure 3-1.

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21 ITS Research Area Technology* ITS Data Application Reference Area Analysis What is the quality of service delivered to customers in defined service areas? APC (+GIS) Load Summaries by Defined Analyze loads/overloads for a defined area (segment, TriMet Case Study Area route, corridor, system) for a defined time period AVL Depart Times, Arrive Times Calculate run times and run time variation for segments TriMet Case Study and routes by service period CTA Case Study Levinson (1991) AVL Depart Times, Arrive Times Calculate schedule adherence and headway maintenance TriMet Case Study by defined area MDT Event Counts Compile security, mechanical, or accident events by FTA (2005) defined analysis area Where do customers access and use the system? APC, MAG, SC Boarding & Alighting Create a "passenger census" using station or stop-level TriMet Case Study Summaries by Stop or boardings and alightings for a defined time period CTA Case Study Station APC (+GIS) Boarding & Alighting Display passenger counts by defined area (e.g., CBD) FTA (2005) Summaries by Stop SC (+GIS) Card ID, Date & Time, Analyze smart card transactions by defined analysis area Lehtonen et al. (2002) Vehicle ID for a defined time period CTA Case Study When do customers use the system? APC Boarding & Alighting Analyze boardings by time of day (peak/off-peak), day Summaries by Time of Day, of week (week/weekend), and season by defined -- Day of Week or Season analysis area Which customer groups access the system in defined service areas? SC (+GIS) Card ID, Date & Time, Calculate frequency of use by residence location Utsunomiya et al. (2006) Transaction ID (+geocoded (inferred from billing address) registered address) How do different customer groups access and use the system? SC (+GIS) Card ID, Date & Time, Graphically analyze mode choices within a defined CTA Case Study Vehicle/Station ID, boarding analysis area location (+geocoded registered address) MAG or SC Card ID, Date & Time, Analyze day-to-day consistency of system access point Utsunomiya et al. (2006) (+GIS) Vehicle/Station ID, and/or route path by defined analysis area boarding location (+geocode analysis zones) How is transit use changing or likely to change over time in specific service areas? APC (+GIS) Boarding & Alighting Compare current and past ridership by defined analysis FTA (2005) Summaries by Stop area APC (+GIS) Boarding Summaries by Estimate stop-level boardings using combined APC stop Kimpel et al. (2007) Stop and area tax-lot data for defined time periods How are customers responding to new service, changes in service, service delivery, or marketing? APC, AVL Boarding & Alighting Monitor ridership, on-time performance, and schedule Gehrs et al. (2007) Summaries by Stop, Route; efficiency for service extended to new markets. Running Time Summaries by Route; Actual v. Scheduled Leave Times by Time Point APC Boarding & Alighting Monitor ridership on affected service before and after FTA (2005); Summaries by Stop, Route service changes TriMet Case Study AVL (+APC) Performance Summaries by Monitor ridership before and after measured FTA (2005) Trip, Route (+Boarding performance changes Summaries by Trip, Route) MDT Date & Time, Operator- Evaluate ridership changes during promotional period Schneider (2005) keyed bicycle loading event (e.g., bicycle on bus customers ride free) SC Card ID, Date & Time, Monitor individual customer behavior on targeted CTA Case Study Vehicle/Station ID service before and after changes How many customers and travelers are shelter or on-board advertisements likely to reach? APC, Traffic Boarding Summaries by Use boarding totals to estimate number of on-board ad Madison Case Study Counters Stop, Route views (use boarding & alighting totals with traffic counts for shelter ads) Attitude Studies How do customers make trade offs among different service variables? APC (+GIS) Boarding Summaries by Estimate the effects of access distance on stop-level Kimpel et al. (2007) Stop ridership MAG or SC Card ID, Station ID, Estimate implicit monetary value of comfort and Zhao (2004) Alighting Location convenience by analyzing customer trade offs between (recorded or imputed) travel time and other trip characteristics (e.g., seat availability, transfer impediments) when substitute service is available Figure 3-1. Applications of ITS data: monitoring service delivery. (continued on next page)

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22 ITS Research Area Technology* ITS Data Application Reference SC (+GIS) Card ID,Station ID, Analyze trade offs across different customer groups CTA Case Study Alighting Location (either collected directly or inferred from census data (recorded or imputed) based on billing address) between travel time and comfort when substitute service is available. When both bus and rail service is available, which is chosen? Is one chosen over another despite a longer access or travel time? Customer Satisfaction How crowded is the service? APC Load Summaries by Route, Analyze loads/overloads by stop, trip, route, or service FTA (2005) Stop type APC Load Summaries by Trip Calculate volume/capacity ratios for service over TriMet Case Study Segment and Peak Hour defined times and corridors How reliable is the service? APC, AVL Stop-Level Boardings, Calculate excess wait times based on headway TriMet Case Study Headways adherence and boardings AVL Date & Time, Vehicle ID Compare customer reliability complaints with actual Madison Case Study performance AVL Arrive Times, Depart Times Compare schedule adherence and headway maintenance FTA (2005) by trip or route MAG or SC; Card ID, Date & Time; Calculate percentage of customers experiencing CTA Case Study AVL, APC Boardings; Arrive Times; "acceptable" wait times Depart Times How safe is the service? MDT Event Counts Compare security, mechanical, accident, or shelter FTA (2005) vandalism events by stop, trip, route, service type, or area How will print information best reach customers? APC Boarding & Alighting Target stops for information displays CTA Case Study Summaries GIS Census Block Group Data Identify languages spoken at home (inferred from CTA Case Study census data) near identified stops & stations to provide customer information in appropriate languages SC (+GIS) Card ID, Boarding History Identify customers likely to be affected by service CTA Case Study (+ geocoded registered interruptions/changes address) Where should stop facilities be upgraded or deployed? APC Boarding & Alighting Use stop-level boardings & alightings to prioritize TriMet Case Study; Summaries by Stop facility improvements based on passenger volumes Madison Case Study APC (+GIS) Boarding & Alighting Present stop-level activity on a map to communicate Madison Case Study Summaries by Stop stop relocation rationale to public AVM Lift Event Summaries Use stop-level lift deployments to prioritize facility TriMet Case Study improvements for customers with disabilities Fares What is the frequency of use of different fare types? MAG or SC Card ID Analyze ridership for specific pass program clients Madison Case Study EFB, MAG, SC Farebox & Card Calculate shares of smart card, magstripe, & cash Utsunomiya et al. (2006) Transaction Summaries transactions SC Card ID, Date & Time Calculate turnover rate among smart card users (i.e., Bagchi and White what portion of smart cards become inactive over a (2005) given period). On systems with high rates of smart card use, this may proxy ridership turnover rates. How do customers respond to fare changes? APC Boarding Summaries by Compare ridership on affected service before and after FTA (2005) Stop, Trip, Route changes TriMet Case Study SC Card ID, Date & Time, Compare individual customer behavior on affected CTA Case Study Vehicle/Station ID service before and after changes Which customers prefer smart cards? SC (+GIS) Card Registration Address, Compare smart card users as a group by customer CTA Case Study Census Block Group Data demographics (either collected from cardholders or inferred from census data based on registration) How much fare revenue are we collecting per route? EFB, MAG, SC Transaction Summaries Calculate fare revenue by route CTA Case Study How do customers use stored value cards? MAG or SC Card ID, transaction records Analyze frequency and amount of typical card recharges CTA Case Study Figure 3-1. (Continued).

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23 ITS Research Area Technology* ITS Data Application Reference Market Segmentation & Marketing When do customers use the system? APC Boarding Summaries by Analyze boardings by time of day (peak/off-peak), day TriMet Case Study Time of Day, Day of Week of week (week/weekend), and season by stop, trip, or Season route, or service type AVM Date & Time, Lift Event Analyze lift use by time of day or day of week FTA (2005) MDT Date & Time, Operator- Analyze bicycles on bus trips by time of day, day of Schneider (2005) keyed bicycle loading event week, or season SC Card ID , Date & Time Analyze temporal dimensions of smart card use Lehtonen et al. (2002) SC (+GIS) Card ID, Date & Time Compare day of week, time of day, or seasonal usage by CTA Case Study (+geocoded registered customer demographics (either collected from address) cardholders or inferred from census data based on billing address) Where do customers access and use the system? APC Boarding Summaries by Analyze aggregate use by stop or route TriMet Case Study Stop, Route MDT Date & Time, Operator- Analyze bicycle loadings on bus trips by boarding Schneider (2005) keyed bicycle loading event, location Vehicle ID MDT, AVM Event Records Analyze boarding locations of special customer groups TriMet Case Study (disabled customers, fare evaders, young riders) MAG Card ID, Transaction ID Combine census data with fare card data to develop Madison Case Study route user profiles (planned) SC (+GIS) Card ID, Vehicle/Station ID Analyze stop or station use by customer demographics CTA Case Study (+geocoded registered address), Census Block Group Data How do different customer groups access and use the system? MAG or SC Card ID, Date & Time, Segment individual customers by travel patterns over CTA Case Study Vehicle/Station ID day, week, month, year MAG or SC Card ID, Date & Time, Segment customers by consistency of access point, CTA Case Study Vehicle/Station ID route, or service type MAG or SC Card ID, Date & Time, Segment customers by modal transfer pattern (e.g., rail- Utsunomiya et al. (2006) Vehicle/Station ID bus-rail) and analyze frequency of each pattern SC Card ID, Date & Time Segment individual customers by frequency of use -- SC Card ID, Vehicle/Station ID Compare customer demographics by service type (e.g., -- bus/train, local/express) SC Card ID, Vehicle/Station Compare transfer patterns by customer demographics -- ID, Time SC Card ID Analyze ridership patterns of specific pass program Bagchi and White groups (e.g. elderly, students, employees) over time (2005) SC (+GIS) Card ID, Date & Time, Segment customers by system access distance over Utsunomiya et al. (2006) Vehicle/Station ID, Date actual travel network and Time (+geocoded customer address, geocoded transit stops) How are the shares of different segments changing over time? APC Boarding Summaries by Compare current and past ridership by time of day TriMet Case Study Time of Day, Day of Week (peak/off-peak), day of week (week/weekend), and or Season season by stop, trip, route, or service type EFB, MAG, SC Card ID Analyze changes in shares of different fare media or CTA Case Study pass programs SC Card ID Analyze changes in frequency of use by customer -- demographics What is the ridership to special events? APC or EFB Boarding Summaries or Analyze ridership to special events CTA Case Study Farebox Transaction Data for Trips Serving Event MAG or SC Card ID, Date & Time, Analyze individual linked trips to special events CTA Case Study Vehicle/Station ID SC Card ID, Date & Time, Analyze demographic information (either collected from Vehicle/Station ID, cardholders or inferred from census data based on -- Customer Information billing address) for special event customers Figure 3-1. (Continued).

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24 ITS Research Area Technology* ITS Data Application Reference Origin- Destination Which system origin-destination pairs are accessed by customers? EFB Boardings Estimate origin-destination patterns Navick and Furth (2002); Barry et al. (2002); Richardson (2000) SC, MAG Card ID, Vehicle/Station Analyze inferred or actual destinations by customer Rahbee & Czerwinski ID, Boarding Location information for rail trips (2002); CTA Case Study Customer Service Information & Human Resources What origin-destination pairs are customers requesting trip planning information about? Phone/Web Trip Requested Origins and Analyze requested origins and destinations of phone and Trepanier et al. (2005) Planning Logs Destinations; Path Chosen website users by desired departure time What are the travel preferences of customers requesting trip planning information? Phone/Web Trip Requested Origins and Analyze trade offs by Web users between travel time Trepanier et al. (2005) Planning Logs Destinations; Requested and other trip characteristics (e.g., walk distance, Travel Options transfers) What stops are queried for real time arrival information? Phone/Web Stop ID, Date, Time, Analyze the pattern of requests for arrival times in Arrive Time Boardings & Alightings relation to actual stop-level boardings and alightings to -- Logs, APC assess real time information needs Are customers well informed about service changes and special circumstances? Phone/Web Logs, Page Views, Call Bin Code Analyze the share of affected customers requesting TriMet Case Study APC Counts, Boardings & information about construction, re-routes, schedule, fare, CTA Case Study Alightings & service changes, and inclement weather service. If the share is low, consider redesign of phone/Web information or supplement with information dissemination through other media How can customer complaints about service delivery be assessed? Phone/Web Logs, Route/Trip/Operator ID, Check "leave early" complaints against actual depart Madison Case Study AVL, MDT Depart Times, Speed, Event times; check passup complaints against overload events; [TriMet Case Study] Code check speeding complaints against actual speeds Are commendations or complaints and service delivery performance related? Phone/Web Logs, Route/Trip/Operator ID, Analyze the relationship between complaint or Strathman et. al (2002) AVL, APC, MDT Arrive & Depart Times, commendation counts and on-time performance, Passenger Loads, Event passenger loads, and relevant event counts from the Counts operator-trip level to the route-time period level Note: Italicized entries in the ITS data column indicate a need for the ITS data to be processed or analyzed to address a given market research question. APC = Automatic Passenger Counters AVL = Automatic vehicle location AVM = Automatic vehicle monitoring EFB = Electronic registering farebox GIS = Geographic information system MAG = Magnetic stripe card MDT = Mobile data terminal SC = Smart card Figure 3-1. (Continued). Within each subject area, a series of service delivery-related Similarly, the technologies themselves are more effective ad- questions is listed. Following each question, the ITS tech- dressing questions in some areas than in others. For example, nologies and relevant data elements supporting analysis are the continuous reliability data collected by AVL is essential in identified. A brief description of the analysis is then given, analyzing many service delivery questions that relate to cus- along with a reference to the literature or a case study where tomer satisfaction, while it has limited usefulness in addressing further elaboration can be found. fare-related questions. In general, ITS data are most useful for As Figure 3-1 reveals, ITS data are more capable of analyzing answering questions about when, where, how, and how many re- service delivery questions in some subject areas than in others. garding service delivery or customer activity. Electronic fare For instance, ITS technologies are very useful in providing con- cards with unique IDs can also answer some questions about tinuous data on service performance and utilization in specific which customers are making specific trips. Generally, ITS data analysis areas, while automated data collection is less useful in are less capable of addressing service delivery questions about service delivery analysis corresponding to attitude studies. who customers are and why they are behaving as they do.