Identifying the Underlying Causes of Transit Ridership Decline

Before the start of the COVID-19 pandemic, bus ridership in the United States was already at its lowest point since 1973. With colleagues from Georgia Tech, I am analyzing automatically-collected data at over 50,000 bus stops in four cities. To explain changes in ridership, I use population and demographic data from the census, amount of service data from GTFS, and ridehailing data from a major provider. This research is revealing the underlying causes of ridership decline on a highly specific spatial and temporal scale. In collaboration with transit agencies, we are identifying strategies to reverse the trend.

• Berrebi, S., & Watkins, K., (2020) Who’s ditching the bus?, Transportation Research Part A: Policy and Practice.

• Berrebi, S., Gibbs, T., Joshi, S., & Watkins, K., (2020) On ridership and frequency, Preprint submitted to ArXiv.

• Ederer, D., Berrebi, S., Diffee, C., Gibbs, T., & Watkins, K., (2019) Comparing transit agency peer groups using cluster analysis. Transportation Research Record.

• Watkins, K., Berrebi, S., Diffee, C., Kiriazes, R, & Ederer, D. (2019) TCRP Report 209: Analysis of recent transit ridership trends. Transportation Research Board, Washington, DC.

A Dynamic Holding Method to Avoid Bus Bunching on High-Frequency Transit Routes: From Theory to Practice

I have developed a new dispatching method to stabilize headways and stop bus bunching. The method uses real-time vehicle information to replace schedules on high-frequency transit routes. In a series of simulation experiments, I found that the proposed holding method dispatched transit vehicles with more stable headways and used less holding time than other methods used in practice and recommended in the literature. I established research partnerships with transit agencies and successfully implemented the holding method on the Atlanta Streetcar, the VIA Bus Rapid Transit Primo Route in San Antonio, TX, and the Georgia Tech Red Stinger Route. I developed an open-source platform to compute holding times and trained operators to receive holding instructions from the software instead of their regular schedule. The method helped reduce bus bunching and passenger waiting time; it is currently being considered for permanent implementation.

• Berrebi, S. J., Crudden, O., S., & Watkins, K. E. (2018). Translating research to practice: Implementing real-time control on high-frequency transit routes. Transportation Research Part A: Policy and Practice.

• Berrebi, S. J., Hans, E., Chiabaut, N., Laval, J. A., Leclercq, L., & Watkins, K. E. (2017). Comparing bus holding methods with and without real-time predictions. Transportation Research Part C: Emerging Technologies.

• Berrebi, S. J., Watkins, K. E., & Laval, J. A. (2015). A real-time bus dispatching policy to minimize passenger wait on a high frequency route. Transportation Research Part B: Methodological. Special Issue: Optimization of Urban Transportation Service Networks.

Minutes Matter: A Guide to Bus Transit Service Reliability, Transit Cooperative Research Program Report A-42

The TCRP Report A-42 is a guide for transit agencies to diagnose and manage bus transit service reliability. Based on the literature and on a survey of 150 transit agencies, I have drafted a report on factors of unreliability and possible treatments. I categorized factors of reliability by type and discussed their effect on customer experience and operations. I reported on cost and effectiveness of available treatments to address causes of reliability. The report will be published in early 2019.

• Danaher, A., Wensley, J., Watkins, K., Dunham, A., Berrebi, S., Connor, M., Queen, C., Orosz, T. (2019) TCRP report A-42: Minutes Matter: A Guide To Bus Transit Service Reliability. Transportation Research Board, Washington, DC.

Tara Wright, Atlanta Streetcar dispatcher, using the DynamicTime software to dispatch vehicles