01479132

Component

Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where elasticities are used in frequency allocation, a single across the board value is typically used for all routes and all times of the day. The most advanced applications might use two values, for peak and off-peak respectively. With growing availability of ridership data from many sources, estimation of spatially and temporally disaggregated elasticities of demand with respect to service frequency is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of elasticities corresponding to varying levels of spatial and temporal disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a Transit Network Frequency Setting framework: (1) temporal elasticities based on time of day, (2) spatial elasticities via grouping stops into demand, supply and land use classes and (3) spatio-temporal elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer to other modes. It is also important to capture longer term effects—over a year or more—in these models because seasonal activity patterns (e.g. school trips, vacation) may bias elasticity estimates over shorter time horizons. The experiments demonstrate that spatial detail in ridership elasticity estimation results in meaningful improvements in an objective function minimizing wait time and maximizing ridership, even when time periods are aggregated. Since much of this data is available at census tract level and collected by regional planning authorities, transit agencies could implement this frequency allocation formulation using rather coarse data.

This paper was sponsored by TRB committee AP025 Public Transportation Planning and Development.

01470560

13-5252

English

20p

2013

Transportation Research Board 92nd Annual Meeting

Digital/other

Figures; References; Tables

Bus routes; Bus transit; Headways; Level of service; Network analysis (Planning); Resource allocation; Ridership; Schedules and scheduling; Travel demand

Chicago (Illinois)

Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2013 Paper #13-5252

TRIS, TRB, ATRI

Feb 5 2013 12:59PM