National Academies Press: OpenBook

Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook (2008)

Chapter: Chapter 3 - ITS Data Applications in Market Research

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Suggested Citation:"Chapter 3 - ITS Data Applications in Market Research." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Chapter 3 - ITS Data Applications in Market Research." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Chapter 3 - ITS Data Applications in Market Research." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Chapter 3 - ITS Data Applications in Market Research." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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Suggested Citation:"Chapter 3 - ITS Data Applications in Market Research." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
×
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Suggested Citation:"Chapter 3 - ITS Data Applications in Market Research." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
×
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Suggested Citation:"Chapter 3 - ITS Data Applications in Market Research." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
×
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Suggested Citation:"Chapter 3 - ITS Data Applications in Market Research." National Academies of Sciences, Engineering, and Medicine. 2008. Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook. Washington, DC: The National Academies Press. doi: 10.17226/13917.
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20 To this point, transit market research methods and activities have been described, and the data recovered by ITS technolo- gies have been inventoried. In this chapter, market research and ITS data are combined to illustrate the role that the data can play in complementing, extending, and leveraging market research practices. The following presentation reflects the position of market research within the integrated marketing system, as was shown in Figure 1-1. In an integrated marketing system, ITS data applications support a market research program in two distinct ways. First, ITS data can be employed in monitoring and evaluat- ing program outcomes in a process where market research contributes to the definition of marketing action plans, and these plans, in turn, are implemented in the service delivery, promotional, and customer service functions of a transit agency. The knowledge gained through monitoring and evaluation then allows an integrated marketing system to be realized as a dynamic process by informing subsequent mar- ket research activities. Examples if ITS data applications in monitoring and evaluating the delivery of services to cus- tomers are presented in the next section. The second way that ITS data support the market research function is more direct. Here, ITS data can be applied to facili- tate the execution of traditional market research methods. The data can also be used to either maintain the currency of market research products or to provide a surrogate reference against which other market research products can be evaluated. In these capacities, applications of ITS data serve to leverage the market research process. Examples of such applications are presented in the final section of the chapter. The examples of ITS data applications in support of market research are drawn from the transit literature and from case studies of three transit properties. The literature offers a num- ber of examples of how ITS data can be used to monitor ser- vice delivery. This is not surprising given that service delivery monitoring was an important reason for acquiring several of the systems under study. In contrast, examples of using ITS data to leverage market research are drawn mainly from the case studies, suggesting that these applications are still emerg- ing and have not yet reached the mainstream of practice within the transit industry. The selection of case study properties occurred through several steps. First, an online survey of properties that had deployed or planned to deploy the technologies of interest— identified from the Volpe Center deployment surveys—was administered in July and August 2005. A total of 134 proper- ties were solicited to participate in the survey and 78 ultimately responded. The survey recovered information on the scale and scope of properties’ market research activities, the deployment status of the subject technologies, the ITS data archiving envi- ronment, applications and tools used in ITS data analysis and reporting, issues related to experiences in accessing and using ITS data, and lessons learned from those experiences. Evaluation of the survey responses led to the identification of 13 candidate case study properties. Telephone interviews were conducted with each property in April 2006. The final se- lection of properties—the CTA, TriMet, and City of Madison Metro Transit—reflected an interest in recovering informa- tion that collectively represented all fixed route modes, the technologies of interest, varying levels of experience with ITS data applications in market research, and a range of property sizes. ITS Data Applications: Monitoring Service Delivery Figure 3-1 presents an inventory of questions related to service delivery that can be addressed using ITS data. The questions are organized around the six subject areas com- monly covered in market research programs (as previously illustrated in Figures 1-1 and 2-6): area analysis, attitude studies, customer satisfaction, fare studies, market segmen- tation, and O-D studies. Although it is not directly within the realm of market research, a seventh subject area covering customer service and customer information is also included in Figure 3-1. C H A P T E R 3 ITS Data Applications in Market Research

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

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

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

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

In some applications, data from more than one ITS tech- nology can be used to address a service delivery question, although different levels of customer or system resolution may call for different data or analysis techniques. For exam- ple, the market segmentation question “What is the ridership to special events?” can be addressed using APC, EFB, mag- netic stripe, or smart card data, but the details captured will differ by technology. For this particular question, APCs pro- vide only passenger counts; EFBs further segment boardings by fare type; magnetic stripe or unregistered smart cards pro- vide additional detail on linked trip paths; and registered smart cards provide customers’ identities and place of resi- dence. All of the listed technologies provide data suitable for addressing the general research question; however, the depth of analysis is limited by the level of customer resolution that each ITS technology is capable of achieving. Thus, even if an agency currently has only a partial complement of the tech- nologies just listed, the data still may be suitable for analyzing a considerable range of market research questions. Particularly when the object of analysis is a specific area or set of areas, integration of ITS data with a GIS is a convenient way to incorporate spatial boundaries. Because ITS data are usually available for an entire transit system, the potential for spatial analysis of user-defined subareas is great. Unlike a traditional survey in which geographic sampling boundaries must be set in advance, analysis area boundaries can be drawn and redrawn as necessary with locationally referenced ITS data. GIS applications using ITS data also allow the incor- poration of census demographic data or tax-lot data for a defined service area. Finally, the mapping capability of a GIS provides an effective means of communicating analysis and information to decisionmakers and stakeholders. ITS Data Applications: Leveraging Traditional Market Research In addition to applications of ITS data in monitoring service delivery, the case studies identified examples in which ITS data are combined with traditional methods and data. Figure 3-2 presents a range of applications in which ITS data combines with traditional market research methods and data. The appli- cations are organized by traditional market research data collection methods: on-board surveys, telephone surveys, on- street surveys, mail surveys, and focus groups. Following the organization of applications in monitoring service delivery, the presentation in Figure 3-2 identifies the specific technolo- gies and data elements or reports necessary to perform the analysis, and refers to the case study where such analysis has been performed. On-board surveys benefit from ITS data in the research de- sign, survey administration, and response evaluation phases. By providing documentation of actual customer flows through the system, APC and/or fare card data prevent researchers from entering the field “blind,” or with incomplete or outdated in- formation. For example, passenger counts by time and location provide a contemporary census of the riding population, from which sampling plans can be designed and personnel and other resources can be assigned (TriMet Case Study). After survey data are collected, ITS data on actual service attributes can be compared with surveyed perceptions and attitudes to obtain a more complete picture of customer preferences (CTA Case Study; TriMet Case Study). Finally, completed surveys may provide inputs into subsequent ITS data analysis by validating, for example, O-D models estimated from fare transactions data (CTA Case Study). ITS data can thus enter at any point in the cycle of an on-board survey project: from design through administration, analysis, and evaluation. Telephone surveys are commonly used to gather attitudi- nal data from both riders and non-riders. Surveyed percep- tions of specific service attributes can be compared with actual system performance, as measured by ITS data. Joint analysis of survey responses and service delivery data can then be used to target service improvements with the greatest potential for improving satisfaction and increasing ridership (TriMet Case Study). Although they do not constitute a scientific sample, cus- tomer complaints provide important snapshots of customer perceptions of service quality. TriMet and Madison Metro compare customer complaints with ITS data to “validate” the complaint (e.g., was a given bus actually speeding, or did it actually leave the stop early?), to explain to a customer why the event associated with their complaint occurred (e.g., does event data indicate that a pass-up occurred because the vehi- cle was overloaded or directed by dispatch to skip stops?), or to better understand how actual operating conditions relate to rider perceptions (e.g., are routes with more complaints per thousand boardings also less reliable or subject to more crowding?). On-street intercept surveys benefit from ITS data in many of the same ways as on-board surveys. Data on passenger movements from APCs and electronic fare cards aid in iden- tifying intercept locations (TriMet Case Study). Other ITS data contribute to the analysis of intercept data. ITS data on reliability and passenger volumes can be used to identify sim- ilar locations to use as a “control” when surveying a “treat- ment” group’s perceptions of service improvements (CTA Case Study). Intercept surveys can also inform or validate models estimated from ITS data. For instance, CTA intends to use intercept surveys to validate its electronic fare card- based O-D model. By providing service delivery data cover- ing an entire transit system, ITS data provide useful inputs for intercept surveys; at the same time, intercept surveys also provide key data inputs to support models estimated from ITS data. 25

26 Research Method ITS Technology* ITS Data Application Reference On-board Surveys APC Load Summaries by Stop, Route From load summaries, determine sampling rates, how many surveys to print, and weighting factors for expanding sample survey responses to population totals [TriMet Case Study] APC Load Summaries by Route, Trip Survey responses on satisfaction with “overcrowding” are compared with passenger load data to identify specific circumstances where high passenger loads are affecting customer satisfaction [TriMet Case Study] APC Load Summaries by Route, Trip, Stop Survey vendors draw on stop and route passenger data to gain a better understand of the dynamics of the system’s operating environment [TriMet Case Study] APC Load summaries by Stop Compare downtown trip estimates from O-D survey with the number of APC boardings/alightings downtown [TriMet Case Study] AVL Performance Reports by Route Compare route-level reliability indicators (on-time performance, headway maintenance, excess waiting time) with surveyed satisfaction with "reliability" to assess correspondence [TriMet Case Study] APC, MAG, SC Boarding and Fare Card Use Summaries by Trip, Stop Inform sampling plan and provide expansion factors for O-D survey [CTA Case Study] MAG, SC Transaction Summaries Calibrate stated preference-based Fare Change Model using actual card usage data [CTA Case Study] GPS/GIS Geocoded Survey Locations Use geocoding digital pens to record where surveys were administered [CTA Case Study] MAG, SC Transaction Summaries by Pass Group University student (Upass Card) ridership related to surveyed perceptions of safety and security [CTA Case Study (considering)] SC, MAG Card ID, Date & Time, Vehicle/Station ID Use fare card data to continuously update survey-based O-D tables (after initial validation using on-board O-D survey totals) [CTA Case Study] Telephone Surveys APC, AVL Load Summaries, Performance Reports Compare surveyed customer satisfaction on specific service attributes with actual system performance on specific attributes to identify improvement areas with greatest potential to improve satisfaction [TriMet Case Study] APC, AVL Load Summaries, Performance Reports Compare changes in customer perceptions of service attributes over time with actual performance trends to determine whether actual performance trends correspond to changes in customer satisfaction [TriMet Case Study] APC, AVL (+GIS) Load Summaries by Route, Trip, Stop; Service Frequency & Coverage Given attitudinal market segments determined by surveys, evaluate effectiveness of targeted marketing/service improvement programs by comparing ridership response in areas where targeted segments are prevalent versus other areas [TriMet Case Study] MAG Card Usage Summaries by Stop, Route Employ card usage data to inform sampling plan for perceptions survey [Madison Case Study (planned)] SC Card ID Compare surveyed demographics and perceptions of registered and non-registered smart card users with those of other riders/non-riders [CTA Case Study] On-street Surveys APC Boarding & Alighting Summaries by Stop Identify stops with similar passenger volumes to use as treatment and control groups for survey of perceived reliability before and after installation of real time arrival displays [CTA Case Study] AVL Performance Reports on Stop- level Reliability Indicators Compare surveyed customer perceptions of waiting time and reliability with changes in actual reliability indicators before and after a change (e.g., installation of real time arrival display) [CTA Case Study] Schweiger (2003) APC Boarding & Alighting Summaries by Stop by Period Determine the best times to survey and when or where more than one surveyor is needed [TriMet Case Study] MAG, SC Card ID, Date & Time, Station ID Use surveys to validate cross-platform transfer rates estimated from card data [CTA Case Study] Mail Surveys APC Load Summaries by Route, Trip, Stop Use ridership data for household travel survey sampling and expansion factors [TriMet Case Study (planned)] Figure 3-2. Applications of ITS data: leveraging traditional market research methods.

27 MAG Card Transaction Summaries by Pass Group Replace pass program user surveys with card transaction summaries to document pass program ridership in negotiating pass program contracts [Madison Case Study] SC Card ID, Transaction Date & Time, Vehicle/Station ID Validate household travel survey responses with actual recorded transit trips from smart cards issued to survey respondents [CTA Case Study (planned)] Focus Groups MAG, SC Card ID and Card Usage History; Customer Contact Information Structure fare policy focus groups by card type and use to represent different perspectives [CTA Case Study] APC, AVL Load Summaries and Performance Reports Recruit "rider experience" focus group based on actual service delivery data [TriMet Case Study] 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 counter AVL = Automatic vehicle location GIS = Geographic information system GPS = Global positioning system MAG = Magnetic stripe card SC = Smart card Research Method ITS Technology* ITS Data Application Reference Figure 3-2. (Continued). The three case study properties had not yet directly com- bined ITS data with mail surveys. However, both CTA and TriMet plan to incorporate ITS data in the future. ITS data could potentially provide sampling and expansion factors for travel diary surveys. In addition, if survey participants are given registered smart cards, trips recorded by the cards could serve as a check on transit trips recorded in travel diaries. The correspondence between participants’ “smart card trips” and “diary trips” could be assessed to determine underreporting rates and provide prompting information to correct diaries for missing or misreported trips. For transit properties with websites, web logs of site visits can provide information on how well information is reach- ing customers. TriMet tracks how many times important service announcements are viewed. If views are less frequent than expected, additional communication may be needed. ITS data is useful for focus group research, particularly for selecting participants. Both CTA and TriMet have used fare card and APC data to target specific rider groups in efforts to capture a desired range of customer perspectives. For exam- ple, fare policy focus groups may be structured by fare card type and use data. As in the case of service monitoring applications presented in Figure 3-1, the leveraged applications presented in Figure 3-2 do not exhaust the potential of ITS data to support tran- sit market research. As ITS data become more familiar and accessible to market researchers, additional applications will be developed. Even so, the applications reported by the case study properties provide a framework for pursuing leverag- ing opportunities. First, ITS data provide inputs about system activity that facilitate the use of traditional market research methods. Second, since ITS technologies gather data continuously and comprehensively, they allow surveyed perceptions and preferences to be compared with actual service performance. Third, leveraging may also work in the reverse direction, with traditional survey techniques provid- ing benchmark information for validating models based on ITS data.

Next: Chapter 4 - Data Management, Reporting, and Staffing Considerations »
Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook Get This Book
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TRB's Transit Cooperative Research Program (TCRP) Report 126: Leveraging ITS Data for Transit Market Research: A Practitioner's Guidebook examines intelligent transportation systems (ITS) and Transit ITS technologies currently in use, explores their potential to provide market research data, and presents methods for collecting and analyzing these data. The guidebook also highlights three case studies that illustrate how ITS data have been used to improve market research practices.

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