Traditionally transit passengers have consulted schedule planners mainly for low frequency transit services where missing a departing vehicle could entail a long wait. With the increasing availability of GPS tracking to generate real time arrival and departure information, passengers can make more informed choices about their use of transit. The same messaging technology can also provide information on transportation options at the endpoint of a trip. All this may affect travel behavior and change how transit agencies schedule service and the emphasis placed on factors such as service frequency versus on-time performance.
Studies have shown that transit providers can improve customer satisfaction at relatively low cost by the use of countdown signage at station locations. This can result in passengers reducing their overestimates of wait time, reduce actual wait times or lead to changes in trip plans to minimize travel time, as well as increase passengers’ sense of security while waiting. On the other hand it also puts greater pressure on transit providers to put more emphasis on service regularity as passengers become more aware of and demand greater adherence to scheduling.
Effect on Wait Time
A study of the Massachusetts Bay Transportation Authority shows that passengers with access to countdown information incorporated it into their wait time estimates. For wait times around 5 minutes they were generally more satisfied with the transit experience. For longer wait times, close to ten minutes, passengers became more dissatisfied. Availability of accurate information led to an increase in ridership, however, inaccurate information reduced their trust in the system and led them to be more dissatisfied which may have dissuaded some from using public transit.
Impact on Trip Planning
Messaging can also affect riders’ choice of departure times and what routes they choose. Tracking changes in passenger travel behavior in response to messaging can inform providers’ decisions whether to invest in systems for a whole network or to target specific locations. Research finds passengers shifting from a “frequency-based” strategy to a “schedule-based” approach. In the frequency approach riders without specific scheduling information choose routes that are known to operate more frequently to maximize opportunities to reduce their time in transit, an assumption commonly used in many trip assignment models. With more scheduling information available, riders can choose between leaving later, arriving sooner, or minimizing wait times. Presumably this could lead to greater demand on faster lines since passengers can time their trips better, though it may also favor some infrequent lines where precise arrival and departure times are available, a behavior known as hyperpath stretching. 
A recent study modeled two types of schedule-based approaches compared with the frequency approach based on knowing only average headways. In this simulation model, “Busy” travelers leave at the last moment that allowed them to minimize total travel time and still reach their destination, resulting in more “saved time.” Those needing to reach a destination “as soon as you can” (ASAYC) picked stops and routes that got them there at the earliest possible time. Access to arrival/departure information reduced travel times by about 20% but also led to significant differences passenger loads depending on the available information and combination of passenger strategies.  Network managers should be aware of the potential implications of using these technologies. The fact that those using messaging greatly increased their travel suggests that agencies may be able to operate more efficiently by adhering to fixed headways rather than printed schedules.
Remotely Accessed Information
Transit information can also be made available to third party providers who can create smartphone apps for customers. As smartphone ownership continues to increase among the public, transit providers will be more able to supply to transit users regarding vehicle scheduling, fares, service disruptions, and future planning projects. Not all riders, of course, have smartphones with internet access. Still, research suggests a shift away from traditional methods of obtaining transit information and trip planning toward newer technology in ways that can enhance the riding experience. Bus riders in St. Louis who used smartphones were more satisfied with their ability to make transfer connections, and reported better perceptions of safety, and overall ridership satisfaction. The authors suggest that access to smartphones and texting can lead to more positive perceptions of safety and security, increase the likelihood riders with continue riding transit, and recommend it to others.
A web-based survey of transit riders in Seattle, Washington likewise found that riders using a mobile bus location app reported more satisfaction with transit service, were likely to take more trips, and felt safer using it. On the other hand, their expectations also increased, with most willing to tolerate margins of error of only 4 to 6 minutes with older users being considerably less tolerant. Those with greater tolerance for errors in predicted arrival times were more likely to take transit. Those with lower tolerance for errors rode transit less often, as did those who did not feel that reported errors were addressed, indicating that agencies may be able to improve ridership by improving the accuracy of real time arrival/departure predictions. Some blamed the app but others faulted the agency, suggesting that issue reporting features should be incorporated directly into the technology and careful attention paid to resolving customer complaints.
Agencies should take care in pairing with third party providers. They should also be aware that not all riders have the same access to smartphones; other options to consider include phones with interactive voice response (IVR) and computer-based websites. IVR would be especially useful to riders over 40 who are less likely to have smartphones. 
Graham Caywood and Shana Johnson, “Real-Time Transit Information Reaps Rewards,” TR News, No. 292, May-June 2014, p. 65.
- Graham Caywood and Shana Johnson, "Real-Time Transit Information Reaps Rewards," TR News, Number 292, May-June 2014, p. 65.
- Archille Fonzone and Jan-Dirk Schmocker, "Effects of Transit Real-Time Information Usage Strategies,"Transportation Research Record, No. 2417, 2014, pp. 121-129.
- William Chow, David Block-Schachter, and Samuel Hickey, "Impacts of Real-Time Passenger Information Signs in Rail Stations at the Massachusetts Bay Transportation Authority," Transportation Reserch Record, No. 2419, 2014, pp. 1-10.
- Sarah Windmiller, Todd Hennessy, and Kari Edison Watkins, "Accessibility of Communication Technology and the Rider Experience," Transportation Research Record No. 2415, Transportation Research Board of the National Academies, Washington, D.C., 2014, pp. 118-126.
- Aaron Gooze, Kari Edison Watkins, and Alan Borning, "Benefits of Real-Time Transit Information and Impacts of Data Accuracy on Rider Experience, Transportation Research Record, No. 2351, 2013, pp. 95-103.