How to Develop Bus-Stop Time Models in Dense Urban Areas

Aug. 19, 2015

A new model for bus transit reliability can help operators improve planning and scheduling in urban areas. This study defines a new reliability variable, Total Bus Stop Time (TBST), which includes “dwell time” (DT) and the time it takes a bus to safely maneuver into a bus stop and then re-enter the main traffic stream. The newly released study, Development of Bus-Stop Time Models in Dense Urban Areas: A Case Study in Washington, D.C., is published by the Mineta National Transit Research Consortium (MNTRC). The authors are Stephen Arhin, PhD, and Errol Noel, PhD. The report is available for free download. 

“The report’s proposed regression models have a high explanatory power over the observed data,” said Arhin. “The models can therefore be used to adequately predict DTs and TBSTs at various bus stops and by time of the day with 95 percent confidence.”

The report recommends that:

  • For bus stops near intersections, buses should spend no more than 43, 47, and 67 seconds TBST (from exiting the stream of traffic to successfully reintegrating with it) during the morning, midday, and evening peak periods, respectively.
  • Similarly, buses at midblock bus stops should spend no more than 36, 33, and 31 seconds TBST for the morning, midday and evening periods, respectively.

Noel noted, “Thirty bus stops located at intersections and thirty midblock bus stops were used for this study. All were in heavily traveled routes within Washington DC. Due to potential changes in traffic patterns and land uses near bus stops, these models should be updated and validated on a 3- to 5-year cycle.”

The bus stop selection was based on the Stop Usage Report released by the Washington Metropolitan Area Transportation Authority (WMATA) in January 2014. This report ranked the bus stops based on the number of passengers boarding and alighting at each stop. The top-ranked bus stops in WMATA’s report were selected to ensure the occurrence of bus-stopping events during data collection. It should be noted that the resulting models are based on data collected at a specific transit jurisdiction and, as such, may not accurately predict TBST or DT for other jurisdictions.

The report includes 71 figures and 35 tables detailing the collected data.