Passenger Counting Systems (2008) / Chapter Skim
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systems. At that time, manual passenger counting was the most prevalent technique in the transit industry.
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There is widespread involvement across departments in procurement of the APC system and use of the APC data. Downstream users typically access APC data electronically by means of standard reports, and 41% of agencies noted that downstream users could query the database directly.
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Successful agencies have developed • procedures (in-house or through an outside vendor) to match APC data to bus stops, clean and validate data, generate standard reports, simplify the process of creating ad hoc reports, and flag potential hardware and software problems for the maintenance and information technology departments.
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This is an important barrier to • success and is perhaps the major source of frustration to agencies that indicated dissatisfaction with their APC systems. Confidence in the accuracy of APC data is critical to their widespread use and acceptance within and outside the transit agency.
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An APC forum or workshop, webinars on APC implementation and use of APC data, and an electronic mailing list devoted to APC-related issues are all possibilities to extend the findings of this report and to provide a continuing means for agencies to share information and learn from each other's experiences.
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CHAPTER ONE inTrodUcTion proJecT BacKGroUnd and oBJecTiVes TCRP Synthesis 29: Passenger Counting Technologies and Procedures, published in 1998, summarized information from selected transit agencies regarding the benefits and pitfalls associated with various passenger counting technologies, as reported by users. The synthesis provided advice for agencies considering automatic passenger counter (APC)
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Appendix D summarizes APC implementation, including percentage of vehicles equipped with APCs, hardware supplier, software supplier, and procurement process, for each agency. Forty-five agencies responded to the broad-based invitation to participate in the survey, for an overall total of 86 transit agencies.
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Furth was the primary author of a comprehensive TCRP study that offered guidance on five subjects: Analyses that use automatic vehicle location (AVL) –• APC data to improve management and performance; AVL–APC system design to facilitate the capture of • data with the accuracy and detail needed for offline data analysis; Data structures and analysis software for facilitating • analysis of AVL and APC data; Screening, parsing, and balancing automatic passenger • counts; and Use of APC systems for estimating passenger-miles for • National Transit Database (NTD)
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aUToMaTic passenGer coUnTinG daTa and ModeLinG The reliability and sheer volume of APC data have encouraged researchers to use the data in modeling efforts. Several studies report on the use of APC data to develop a model to predict bus arrival times (19–21)
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(34) document the implementation of APCs in conjunction with the deployment of ITS technologies at the Metropolitan Atlanta Rapid Transit Authority for the 1996 Olympic Games.
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could skew the data. Use of automatic passenger counter (APC)
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13 combinations (28 of the 44 combinations reported in Table 5) and including the number of agencies using only one means, also noted in Table 5.
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TABLE 7 REASONS FOR NOT SWITCHING FROM MANUAL TO AUTOMATED DATA COLLECTION Agencies Responding Reason No. % Cost 10 71.4 Low priority at agency 6 42.9 Awaiting broader ITS purchase 4 28.6 Satisfied with manual data collection 4 28.6 Planning to change, but have not yet 4 28.6 Other 4 28.6 Total responding 14 100.0 Use oF aUToMaTic passenGer coUnTers aT TransiT aGencies Given the inroads that APCs have made in the transit industry among agencies of all sizes, the remaining questions in the survey focused on APC installation and use of APC data.
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How agencies process and manage the increased amount of ridership data is addressed in the next section. aUToMaTic passenGer coUnTinG daTa: processinG, VaLidaTinG, and reporTinG This section examines survey responses related to APC data processing, validation, and reports.
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Many validation techniques look for internal inconsistencies without reference to another data source to use as a comparison. TABLE 11 METHODS TO EDIT AND VALIDATE APC RIDERSHIP DATA Agencies Responding Method Purpose No % Compare with manual counts Accuracy 32 69.6 Look for unexplained variance across trips Validation 27 58.7 Compare ridership totals across days for reasonableness Validation 25 54.3 Rely on professional judgment of planners and schedulers Validation 24 52.2 Use an automated program to analyze APC data Validation 24 52.2 Compare ridership and revenue totals Accuracy 18 39.1 Compare on/off totals by trip and adjust as needed Validation 14 30.4 Other Varies 7 15.2 Total responding 46 100.0 Automated data validation programs can make life much simpler for data analysts.
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Some agencies use APC data for NTD purposes. FTA requires manual checks annually to validate APC data for NTD submittal.
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TABLE 16 DEPARTMENTS INVOLVED IN AGENCY PURCHASE, MANAGEMENT, AND USE OF APC SYSTEMS Department % Involved in APC Purchasing Decision % Primary Ownership of APC Management and Operation % User of APC Data Planning 90 42 91 Operations 62 20 73 Information services/computer services 67 16 14 Scheduling 57 7 82 Budget/finance 52 2 34 Maintenance 43 2 14 Marketing/market research 24 0 59 Senior management N/A N/A 52 Other 7 9 9 Total responding/ percentage 42/100 44/100 44/100 N/A = not available. TABLE 14 DEVELOPMENT OF DATA PROCESSING AND REPORTING SOFTWARE Agencies Responding Source No.
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19 The most frequently mentioned challenges to successful implementation and operation included problems ensuring that bus assignments were completed, new demands for reports, priority for APC equipment in the maintenance department, and unrealistic expectations regarding turnaround time and data quality. One respondent characterized maintenance personnel's attitude as very negative: APCs drain power from the bus batteries and take technicians' time away from important work.
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Median Capital Cost per APC Unit Installed ($)
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The percentage of raw APC data that is converted into useful information for service planners, schedulers, and others averages 74%, comparable to findings from 10 years ago, with a median value of 80%. A majority of agencies rely on the hardware vendor for data processing and report generation software, but several indicated in-house software development or use of an outside vendor other than the hardware vendor.
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. An attempt was made to standardize cost data by asking the cost per APC unit installed.
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Specific topics include agency satisfaction with current methods, potential improvements, and lessons learned. saTisFacTion WiTh aUToMaTic passenGer coUnTer sYsTeMs Table 21 shows transit agency satisfaction with the performance of its APC system in terms of counting passengers.
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Ensure that staff is capable and comfortable analyzing • and using APC data. Find out how valuable the canned reporting software • is, and be prepared to buy a good program and hire or use people in your agency to develop a program.
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25 Understand uses and limitations of the data, depending • partly on how many APC units are deployed. Realize that software issues are part of APC growing • pains.
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Get prior FTA approval to use APC data for National • Transit Database (NTD) reporting.
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, Ottawa, Ontario, Canada RTD (Regional Transportation District) , Denver, • Colorado NFTA (Niagara Frontier Transportation Authority)
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Critical factors analyzed in the validation of APC data include the balance between boardings and alightings at the trip level, total trip distance, stop recognition, and start–end times. These same parameters will be used after the software change, with an expectation of improved stoplevel accuracy through the use of GPS.
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APC data are more important than ever for OC Transpo's planners and managers, who rely on them as the primary input to all planning decisions covering, for example, route planning, bus stop usage, shelter justification, long-range plans, and transit priority strategies. Like all transit agencies, OC Transpo is under added scrutiny to ensure that it spends public funds wisely.
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Defective units are removed and sent to the vendor for repairs on a unit cost basis. The end result is that hardware maintenance is carried out by expert technicians; specialists oversee APC data, software applications, and all data processing; and all service development staff understands the use of APC data.
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In 2005, NFTA began investigating the ability of using APC data for NTD reporting. There was very little information on this at the time.
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NFTA encountered several problems in implementation of the APC system, and describes a painful growing process until it got the system tailored to its service and needs. Passenger counting is very accurate, but the unreliability of passenger-mile calculations, which precludes use of APC data for NTD reporting, is an unresolved issue.
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Route and stop information are housed within the computerized scheduling software, which is accessed by the ITS/APC database. Introduction of APCs results in training needs.
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It was not possible to anticipate all the types of desired reports, especially because the availability of so many data through APCs leads to ideas and requests for different reports. Staff versed in SqL programming can create needed reports and thus make maximum use of APC data.
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The resulting data are used primarily within the agency to provide an order of magnitude type of comparison of passenger activity by stop. Overcoming the various issues with APC data is a challenge.
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Use of APC data for NTD reporting is a topic of great interest. TriMet keeps a separate database to calculate loads as an input to the passenger mile calculations required for NTD.
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TriMet has such a long history (25 years) with APC data that it has been able to address all the problems encountered.
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• Implementation APC data: Processing, Reporting, and Validating• Agency assessments of APCs• Lessons learned • Conclusions and areas of future study• The further research needs offered here attempt to place the study findings in a larger context of how APC use might evolve at transit agencies. aUToMaTic passenGer coUnTer iMpLeMenTaTion The most common reason to collect ridership and travel • time data is to compile ridership by route, although the majority of respondents also collect ridership and travel time data for more specific microlevel uses at the route segment or stop level.
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The percentage of raw APC data that is converted into • useful information averages 74%, comparable to findings from 10 years ago, with a median value of 80%. A majority of agencies rely on the hardware vendor • for data processing and report generation software, but several indicated in-house software development or use of an outside vendor other than the hardware vendor.
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case study • is typical of the stage in many APC implementations where there is no confidence in the APC data at any level other than stop-level boardings and alightings. Potential solutions involving revisions of the bus stop inventory and route itineraries are known but are difficult to implement with limited staff resources.
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Which elements of reporting software are most useful, and what is the best way to create ad hoc query ability? How can APC data be integrated most usefully with existing agency databases?
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Strathman, "Using Archived AVL–APC Data to Improve Transit Performance and Management," Transit Cooperative Research Program Report 113, Transportation Research Board, National Research Council, Washington, D.C., 2006.
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and E Bruun, "OmniLink: Case Study of Successful Flex Route–Capable Intelligent Transportation System Implementation," Transportation Research Record 1971, Transportation Research Board, National Research Council, Washington, D.C., 2006, pp.
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SAS Statistical Analysis System SPSS Statistical Package for the Social Sciences SqL Structured query Language SSR spread spectrum radio TCRP Transit Cooperative Research Program TRIS Transportation Research Information Services TriMet Tri-County Metropolitan Transportation District of Oregon (Portland, Oregon)
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45 appendiX a Tcrp sYnThesis sUrVeY: passenGer coUnTinG TechnoLoGies
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57 appendiX B Tcrp sYnThesis sUrVeY resULTs passenGer coUnTinG sYsTeMs respondenT inForMaTion 1.
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How does your agency collect ridership data? Automatic passenger counters
____________12 14.0% Other automated methods such as handheld
data collection units or registering fareboxes ____________________12 14.0% Manually (paper and pencil)
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59 Farebox plus farecard plus manual
__________1 2.4% Farebox plus handheld
____________________1 2.4% Farebox plus manual plus on-board survey
____1 2.4% Farebox plus on-board survey
______________1 2.4% 13. If other automated methods are used, please describe these.
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Direct downlink (probe) of APCs with a
physical connection ______________________4 8.5% Retrieval of APC data at garage without a
physical connection _____________________23 48.9% Real-time dynamic or periodic remote
retrieval of APC data ____________________ 13 27.7% Other (please specify)
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(check all that apply) Compare ridership and revenue totals
_______ 18 39.1% Look for unexplained variations across trips
__27 58.7% Compare ridership totals across days
for reasonableness ______________________25 54.3% Rely on the professional judgment of
planners/schedulers _____________________24 52.2% Use an automated program to analyze
APC data _____________________________24 52.2% Compare on/off totals by trip and adjust
as needed _____________________________ 14 30.4% Compare with manual counts
_____________ 32 69.6% Other (please specify)
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Yes (please specify)
_____________________ 19 46.3% No changes required
____________________22 53.7% Changes include: data storage changes; switch to Oracle database and in-house software to clean and schedule-match APC data; separate server; additional server; scrap vendor-supplied matching software and write our own; hardware and network connections to host APC software and data servers; installation of hardware and related software; new servers; developing reports and GIS access; in-house application to correlate APC data with bus stop database; purchased Ridecheck Plus, vendor software incapable of generating needed reports; vendor software unable to analyze data or perform diagnostic tests to validate data and make needed adjustments; new servers for data storage; third-party solution being considered to address this issue; new servers and vendor-supplied software as part of larger ITS program; network upgrade; installation of SPSS; integrated database; whole new large software package as part of AVL purchase; separate server; creation of Crystal Reports queries/reports for additional analysis.
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In-house, by information systems or
computer services department _____________ 13 27.7% In-house, by end users of data
_____________ 16 34.0% Through the hardware vendor
_____________26 55.3% Through another outside vendor
___________12 25.5% Other (please specify)
___________________ 3 6.3% Other includes: plan to use another outside vendor; occasional help from IT department; in-house by a technical person hired to support end users of the data; hardware vendor or end users; part of hardware package; two outside programs; user developed for farebox, computer services department test-processing for APC data.
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____________________3 6.8% Other includes: transit priority group within city government; local businesses, Federal and local governments, Board of Directors, local government planners 43. How do downstream units access APC data?
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44. If there are multiple users of APC data, please describe the interaction among the different groups and any synergy or tensions/conflicts that have arisen.
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Vehicles with APCs Median Capital Cost ($) Median Capital Cost per APC Unit Installed ($)
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67 assessMenT 52. How satisfied has your agency been with the performance of its APC system in terms of counting passengers?
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68 Other: procedures
_______________________6 17.6% Other: staff/management
__________________3 20.6% Other: APC system inputs
_________________2 17.6% 57. Is there another transit system that you suggest we contact for this synthesis project?
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Mountain Metropolitan Transit23. Columbus, Ohio Central Ohio Transit Authority24.
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Nashville, Tenn. Nashville Metropolitan Transit Authority51.
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