Multiagency Electronic Fare Payment Systems (2017) / Chapter Skim
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Pages 58-67
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58 Because survey respondents were involved in procuring and deployment, the questionnaire focused on collecting and reporting maintenance and ridership topics rather than strategic and tactical planning. In many cases, the processes for analyzing data are developed after the system is accepted.
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59 More fare data are being made available to researchers and the public. For example, Washington State DOT is sponsoring research with the University of Washington to collect and publish electronic transaction data for "Transportation Planning and Travel Demand Management Uses" in support of its strategic goal "to optimize existing system capacity" (Results WSDOT, Moving Washington Forward, Research Portfolio)
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60 • Unique card use by agency (shows percentage of users who transfer) • Sales channels by sales type • Payment transactions vs.
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61 Respondents with mobile apps collect measures such as: • Total number of downloads per day (by operating system, cumulative downloads) • Average transaction price • Utilization by operating system (e.g., iOS, Android)
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62 CASE EXAMPLE: WMATA DATA MANAGEMENT, ANALYSIS, AND VISUALIZATION Considering the volume of fare data collected on a daily basis, access and analysis of the data are only achieved with managed datasets. As one of the first agencies to implement common electronic fare systems in the United States, WMATA has been collecting and managing fare data since the initial deployment (W.
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63 for complete data in mandatory fields, correct formats, meaningful data, and logical consistency between different data files. The procedures are driven by known problems and issues that are seen in the data.
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64 Unlike the Nextfare5 database, WMATA's data mart fare data are clean, validated, and already integrated, aggregated, and stored with other data sources. With this level of data treatment, application of analytical methods becomes repeatable, fast and reliable.
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65 the number of miles traveled on Metrorail; the number of trips made on Metrorail; and the number of stations visited. In developing the project specifications, specific data sources are identified as the system inputs, among them: automatic fare collection (AFC)
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66 of the few North American agencies that collects customer station entry and exit data, WMATA's information by location is exceptional. The planning group has made great use of the available data, having published studies based on its SmarTrip fare data on topics such as: • Metrorail ridership by day (see Figure 30)
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67 FIGURE 31 Metrorail transit delay Calculation (Source: PlanItmetro)
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