Open Data: Challenges and Opportunities for Transit Agencies (2015)

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46 DOWNLOADS When survey recipients were asked to estimate how much data are being used or downloaded over a certain time frame, there was a wide variety of answers. Of the 31 responses to this question, just less than half (15) reported that they either do not know or cannot estimate how much data are being used or downloaded. Those who could estimate the volume reported the following: • Per day – 1,800,000 queries per day – 2,000,000 API calls per day – About 250,000 unique user accesses daily – Data download average is 4 gigabytes per day – 100,000 API requests per day – Number of daily transactions approximately between 20,000 and 35,000 per day – Approximately 85,000 requests per day • Per month – Less than 30 megabytes per month – 250 megabytes per month – Three terabytes per month • Per year – 7,213 downloads in the past year (about 40 gigabytes) – For the real-time feed, 18,045 gigabytes in the last 12 months – Approximately 100 megabytes per year, with a GTFS file slightly less than 1 megabyte • Per download: 100 megabytes per download. Obviously, the volume is based on several factors, includ- ing the amount of data being accessed, the size of the agency, and the number of applications accessing the data. The types of applications reported by survey respondents were as follows: • Thirty-seven respondents (88.1% respondents) who indicated that they have mobile applications counted a total of 764 applications; • Twenty-eight respondents (66.7%) who indicated that they have web-based applications counted 191 of them; and APPLICATIONS The survey contained several questions regarding how open data were being used in terms of customer applications, decision-support tools used by the agency itself, and non- transit applications. Further, there were two questions regard- ing how agencies monitor use of the open data. Customer applications are the most prevalent use of open transit data. As shown in Table 12, trip planning is the most common use of open data, followed by mobile applications and real-time transit information. The types of decision-support tools that use the open data are shown in Table 13. Data visualization is the most com- mon tool, followed by service planning and evaluation, and route layout and design. Survey respondents reported numerous applications that used their open data. The applications reported to be used most frequently are: • Google Maps • Google Transit • HopStop • OneBusAway • Open Trip Planner • Rome2Rio • RouteShout • TimeTable Publisher • WalkScore Although the other applications reported by respondents are too numerous to list and are, for the most part, locally developed, the survey responses indicate that even the small- est agencies have more than one application that uses open data. Almost two-thirds (33 or 63.5%) of respondents stated that they were not aware of other uses of their agency’s open data. Further, the same number of respondents do not track usage of their open data. Table 14 shows the methods used by the agencies that do track usage. chapter five SURVEY RESULTS: USES OF OPEN DATA

47 • Those indicating “other” (31%) types of applications counted 46 of them. The types of platforms running these applications are shown in Table 15. Of the respondents, 91.9% have Android and 91.9% have iOS applications. The next most commonly used platform is Windows Mobile. Approximately one-third (18 or 36%) of the respondents indicated that they have a web location where potential appli- cation customers can review available applications. Twenty-seven of the respondents estimate that 6,438 devel- opers are using their open data. Sixteen respondents indicate that they get 265,858,333 API calls per month, as of the begin- ning of 2014. VISUALIZATIONS Several visualizations were noted by survey respondents. Those mentioned by the MBTA, BART, and TfL are included as part of their case examples in chapter seven. Others were found in the literature and described in chapter two. One mentioned in response to a survey question is the distribution of rent and transportation cost burdens in the Denver area, which is shown in Figure 35 (http://www.denverregional equityatlas.org/, accessed on March 28, 2014). Customer Applications Number of Respondents Percent Trip planning 41 75.9 Mobile applications 38 70.4 Real-time transit information (arrival/departure times, delays, detours) 32 59.3 Maps 31 57.4 Data visualization 21 38.9 Timetable creation 17 31.5 Interactive voice response (IVR) 14 25.9 Accessibility 12 22.2 Other 8 14.8 Ridesharing 8 14.8 Crowdsourcing 5 9.3 From survey responses. TABLE 12 TYPES OF CUSTOMER APPLICATIONS USING OPEN TRANSIT DATA How Respondents Monitor Data Usage Number of Respondents Monitor data downloads 10 Keep track of applications developed 9 Other 5 Temporal analysis 3 Track number of downloads per application 2 Performance analysis 2 Service planning and evaluation 2 Travel time and capacity analysis 1 From survey responses. TABLE 14 WAYS TO MONITOR OPEN TRANSIT DATA USAGE Decision-support Tools Number of Respondents Percent Data visualization 17 51.5 Service planning and evaluation 13 39.4 Route layout and design 11 33.3 Performance analysis 11 33.3 Travel time and capacity analysis 10 30.3 Spatial analysis 7 21.2 Regional transit analysis 7 21.2 Other 7 21.2 Demand modeling 4 12.1 Temporal analysis 3 9.1 Financial analysis 3 9.1 Cost/benefit analysis 2 6.1 Energy consumption 2 6.1 Safety analysis 2 6.1 From survey responses. TABLE 13 TYPES OF DECISION-SUPPORT TOOLS USING OPEN TRANSIT DATA

48 Platforms for Mobile Applications Number of Respondents Percent Android 35 91.9 iOS (Apple) 35 91.9 Windows Mobile 11 29.7 Blackberry 7 18.9 Nokia 6 16.2 Mobile Linux 1 2.7 Other: Text messaging app (Dabnab) HTML5 Jolla Palm WebOS Pebble Short message service (SMS) Windows 7 OSX Mobile web apps Spotbros. From survey responses. TABLE 15 TYPES OF MOBILE PLATFORMS USING OPEN TRANSIT DATA FIGURE 35 Sample Denver regional transportation costs versus rents.