Transit System Data Test Plan. The transit system data test plan will collect data to be used in many analyses, including transit, technology, cost benefit analyses, congestion, tolling, telecommuting, safety, environmental, and equity analyses. The local partners have not yet identified the specific plans, including locations, for the “enhanced stops/stations/terminals” or real-time information signs at stations. Data collection and analysis details related to these projects will be added once the specific plans are identified by the local partners. It is expected that, like the Redmond P&R/XXX, a focused analysis of stop-specific ridership data will be performed. Data Sources A wide range of transit data will be collected from a variety of sources as summarized below. Survey data—which indeed plays a very important role in the Transit Analysis—is not discussed here, but rather is included in the Surveys, Interviews and Workshops Test Plan.
Transit System Data Test Plan. Data Sources Muni APC System Logs. As part of their Transit Effectiveness Project (TEP), SFMTA installed automatic passenger counter (APC) devices on approximately 25 percent of their bus transit fleet. This system uses infrared sensor technologies to measure passenger boarding and alighting activity at each transit stop and to generate passenger loading information. Passenger loading and boarding information will be used by the national evaluation team to determine if changes in parking pricing caused a shift in travel demand to transit. Data by route, bus stop and time of day and week are needed. Muni AVL System Logs. Each APC-equipped bus also contains global positioning system (GPS) technologies as part of the automatic vehicle location (AVL) system. The GPS technologies can potentially be used to derive transit running times on corridors where the intelligent parking system has been deployed. GPS technology records the position of the transit vehicle when the doors are opened and closed for passenger boarding. Using this information, the evaluation team will compute the average running time between bus stops and the average corridor running time (the actual travel time minus the transit dwell time) as a surrogate for vehicle travel time. Unfortunately, the Muni AVL transit logs may not shed light on the source of delays between transit stops (i.e., whether transit delays were caused by parking maneuvers or signalized intersections, etc.). NextBus (or NextMuni) System Logs. Another potential source of transit travel time information is the NextBus system installed on SFMTA Muni transit vehicles, known as NextMuni. NextBus technology uses GPS positioning, coupled with computer modeling, to track transit vehicles on their route. The software uses the actual position of the vehicle, their intended stops, and typical traffic patterns to estimate (within a minute) the expected arrival time of buses at each stop. Anticipated bus arrival times at each stop location are then made available to transit riders via the NextBus website, electronic signs located at stops, and through mobile communication devices. It is unclear at this time whether or not this is a viable source of transit travel time information. To be a viable source of transit information, the evaluation team would need access to position information collected by vehicles as they traversed through the study corridors. It is unclear at this time what (if any) vehicle position information is retaine...
