2008 TCRP report, "Guidebook for Measuring, Assessing, and
Improving Performance of Demand-Response Transportation"- http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_124.pdf
Summary:
| Project Data | |
| Funds: | $250,000 |
| Research Agency: | KFH Group |
| Principal Investigator: | Elizabeth Ellis |
| Effective Date: | 9/8/2005 |
| Completion Date: | 3/8/2007 |
Background. Demand-response transportation (DRT) systems are under increasing pressure to improve performance because of increased demand for service and financial constraints. Improving DRT performance requires understanding the characteristics of DRT services and the factors that affect performance. To identify opportunities for improvement, DRT systems need better data and methods to measure and assess performance consistently and systematically.
Assessing and improving performance of DRT systems is complicated because there are many types of DRT systems, and the performance of DRT systems is influenced by many factors--both controllable and uncontrollable. Controllable factors are those within the DRT operator's domain, such as service policies (e.g., pickup time windows, maximum allowed onboard time, and curb-to-curb versus door-to-door service); fleet mix (e.g., vehicle capacity, vehicle design, and fleet size); trip-scheduling method (i.e., the extent to which it produces viable and efficient vehicle routes and schedules); dispatch control method (e.g., re-scheduling late trips and making use of capacity in the event of late cancellations and passenger no-shows); and driver and dispatcher training. Uncontrollable factors include physical and geographical factors (e.g., size of service area and geographic barriers, such as bridges); service type (e.g., ADA complementary paratransit service versus other demand-response services); and passenger demand.
The existence of diverse types of DRT systems affected by different controllable and uncontrollable factors makes it difficult to compare the performance of different DRT systems and identify opportunities for improvement. For example, a DRT system with low ridership could be operating in an area where few passengers are eligible to use a service. Conversely, low ridership could be caused by poor service scheduling that does not maximize vehicle utilization and ride-sharing.
DRT systems need reliable data and useful measures that allow for meaningful assessments of performance over time and across DRT systems. Historically, data collection and reporting have not been rigorous among DRT systems. Data on performance have not been consistently defined and methods for collecting data have not been consistently or rigorously applied. Similarly, performance measures have not been widely or consistently used as an element of performance assessments. For example, service effectiveness for DRT, a measure of utilization or productivity, can be defined as the number of passenger-trips per vehicle-hour of operation. However, the exact definitions of the terms "passenger-trips" and "vehicle-hours" vary across
Assessing and improving performance of DRT systems is complicated because there are many types of DRT systems, and the performance of DRT systems is influenced by many factors--both controllable and uncontrollable. Controllable factors are those within the DRT operator's domain, such as service policies (e.g., pickup time windows, maximum allowed onboard time, and curb-to-curb versus door-to-door service); fleet mix (e.g., vehicle capacity, vehicle design, and fleet size); trip-scheduling method (i.e., the extent to which it produces viable and efficient vehicle routes and schedules); dispatch control method (e.g., re-scheduling late trips and making use of capacity in the event of late cancellations and passenger no-shows); and driver and dispatcher training. Uncontrollable factors include physical and geographical factors (e.g., size of service area and geographic barriers, such as bridges); service type (e.g., ADA complementary paratransit service versus other demand-response services); and passenger demand.
The existence of diverse types of DRT systems affected by different controllable and uncontrollable factors makes it difficult to compare the performance of different DRT systems and identify opportunities for improvement. For example, a DRT system with low ridership could be operating in an area where few passengers are eligible to use a service. Conversely, low ridership could be caused by poor service scheduling that does not maximize vehicle utilization and ride-sharing.
DRT systems need reliable data and useful measures that allow for meaningful assessments of performance over time and across DRT systems. Historically, data collection and reporting have not been rigorous among DRT systems. Data on performance have not been consistently defined and methods for collecting data have not been consistently or rigorously applied. Similarly, performance measures have not been widely or consistently used as an element of performance assessments. For example, service effectiveness for DRT, a measure of utilization or productivity, can be defined as the number of passenger-trips per vehicle-hour of operation. However, the exact definitions of the terms "passenger-trips" and "vehicle-hours" vary across