A description is given of a methodology for estimating transit walk accessibility at the home end of transit trips and for forecasting transit walk accessibility at the home end for a future year, given forecast population and employment data, transit route information, and type of street configuration. The methodology for estimating transit walk accessibility overcomes the problems associated with natural and man-made barriers such as water bodies and community walls and the problem of uneven distribution of population. A comparison of the results with those from the traditional buffer method, as well as with network ratio methods that consider actual walk distance along streets, showed that both the buffer method and network ratio methods tended to overestimate transit walk accessibility. Regression analysis also showed that the new transit walk accessibility measure was a stronger predictor of transit use than that produced using the buffer method. The methodologies may be applied to transit planning, urban design for sustainable development, and long-range transit demand modeling.

This paper describes the development of a geographically weighted regression (GWR) model to explore the spatial variability in the strength of the relationship between public transit use for home-based work (HBW) trip purposes and an array of potential transit use predictors. The transit use predictors considered include demographics and socioeconomics, land use, transit supply and quality, and pedestrian environment. The best predictors identified through model estimation include two global variables (regional accessibility of employment and percentage of households with no car) and three local variables (employment density, average number of cars in households with children, and percentage of the population who are black). The models were estimated on the basis of the 2000 Census Transportation Planning Package data for Broward County, Florida. Model testing indicates the GWR model has improved accuracy in predicting transit use for HBW purposes over linear regression models. The GWR model also indicates that the effects of the independent variables on transit use vary across space. The research points to future research to explore different model structures within a geographic area.

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Oversaturated traffic networks with spill-back conditions are becoming more common, and their signal timing is difficult to optimize. Up through release 7, TRANSYT-7F, a signal timing tool for traffic network simulation and optimization, was designed for modeling only undersaturated networks. Release 8, which was recently completed, has the added capability to model oversaturated networks. Because signal timing optimization for oversaturated networks requires emphasis on traffic throughput and queue management rather than on traditional delay and progression, four new optimization objective functions were introduced in release 8. This paper introduces these new objective functions and compares their ability to produce optimal timing plans with the abilities of the four traditional objective functions. The types of spill-back effects explicitly modeled by release 8 are also introduced. For performance evaluation, a data set involving two closely spaced ramp terminals at an urban diamond interchange was modeled. The CORSIM microscopic simulation program was used as a tool to evaluate optimal timing designs produced by different objective functions. The results indicate that the new objective functions were able to produce superior timing plans that yield lower average vehicle delay and reduce spill-back conditions.

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A key feature in estimating and applying destination choice models with aggregate alternatives is to sample a set of nonchosen traffic analysis zones (TAZs), plus the one a trip maker chose, to construct a destination choice set. Computational complexity is reduced because the choice set would be too large if all study area TAZs were included in the calibration. Commonly, two types of sampling strategies are applied to draw subsets of alternatives from the universal choice set. The first, and simplest, approach is to select randomly a subset of nonchosen alternatives with uniform selection probabilities and then add the chosen alternative if it is not otherwise included. The approach, however, is not an efficient sampling scheme because most alternatives for a given trip maker may have small choice probabilities. The second approach, stratified importance sampling, draws samples with unequal selection probabilities determined on the basis of preliminary estimates of choice probabilities for every alternative in the universal choice set. The stratified sampling method assigns different selection probabilities to alternatives in different strata. Simple random sampling is applied to draw alternatives in each stratum. However, it is unclear how to divide the study area so that destination TAZs may be sampled effectively. The process of and findings from implementing a stratified sampling strategy in selecting alternative TAZs for calibrating aggregate destination choice models in a geographic information system (GIS) environment are described. In this stratified sampling analysis, stratum regions varied by spatial location and employment size in the adjacent area were defined for each study area TAZ. The sampling strategy is more effective than simple random sampling in regard to maximum log likelihood and goodness-of-fit values.

Dwell time at a bus stop is one of the major components of bus travel time, and it is highly correlated with numbers of boarding and alighting passengers. With more resources from federal, state, and local governments currently being devoted to improving public transit services, transit ridership rates are expected to increase. Consequently, dwell time needs to be modeled in terms of ridership to allow accurate estimations of bus travel time. Typically, passengers can board a bus only through the front door but can choose to exit the vehicle through either the front or rear door. To estimate dwell time accurately, this study used data collected from Florida's Broward County Transit system to develop a binary door choice model predicting the proportion of alighting passengers who will use the front or rear door to disembark from the bus. Because the model explicitly considered passengers disembarking through the front and rear doors as well as passengers boarding through the front door, it was more effective in quantifying transit dwell time than the existing simulation algorithms available in CORSIM and VISSIM.