Travel Demand Model - OKI

OKI/MVRPC Travel Demand Model – Version 6.0while people who use routes than run longer headways tend to pay attention to the schedule andarrive at the bus stop about 7-10 minutes before the bus is supposed to arrive. Typically oneexpects a higher coefficient on the initial wait when compared to the additional wait. Thisexpectation was fulfilled by the estimation result; however the initial wait variable had lowsignificance and a very high ratio with respect to the in-vehicle time.Run #70a considered a separate coefficient for transit drive access time. As shown in Table 4.2the estimated coefficient is too high and results in an unacceptably high ratio of drive access toin-vehicle time. The model also exhibits an unacceptably high ratio of walk access time to invehicletime.Run #67 was an attempt to estimate separate coefficients for parking cost and out-of-pocketcosts (auto operating cost and transit fare). In this run, as well as in several others where thisseparation was tried, the result was a positive coefficient for the out-of-pocket cost variable.Some of the other models explored considered the following:• Attempts were also made to estimate separate coefficients for centroid (i.e. access andegress) walk and transfer (i.e. sidewalk) walk, with no satisfactory estimation results.Typically this resulted in very high centroid walk access coefficients and insignificant transferwalk coefficients.• Alternative market segmentation schemes, including auto ownership, were also tried, but weconcluded that the chosen segmentation best explains modal usage given the availability (orlack thereof) of a private car for each household worker.• As mentioned above, the initial estimation runs considered local bus and express bus asseparate modes. In general this resulted in very low in-vehicle time coefficients, as shown inthe Appendix.• Terminal walk and park times were excluded from the model because they consistentlyshowed unacceptably high coefficients.• Due to the importance of the CBD in attracting work trips, a CBD indicator variable was usedto capture modal preferences for CBD trips after controlling for time and cost. While thevariable was significant, it had a very high correlation with the parking cost variable, andresulted in models with low, insignificant, and sometimes positive cost coefficients.• The effect of residential density on transit usage was also explored through an indicatorvariable. This variable was significant in models that considered local and express busmodes separately, but not in models that considered a single transit mode.Finally, several attempts were made to estimate a nested structure for the most promisingmultinomial models. Run #83, shown in Table 4.2, was one such attempt, in this case using atwo-level nest (auto vs. transit) for the model shown in Run #71h. As shown, the estimatedcoefficient for the logsum variable (Theta) is larger than 1.0, indicating that the nesting structureis not supported by the data. Similar results were obtained for various other nesting structures.Since the data do not support the estimation of a nesting structure, the logsum coefficients to beused in the application program were synthesized from other metropolitan area models.Proposed values for the OKI/MVRPC mode choice models are listed in Table 4.3, along with thevalues current used in Model 5.4. The logsum, or structural, model coefficients directly influencemodel elasticity at lower levels of the nest as well as the impact of lower level choices on upperlevel decisions. Based on a number of nested logit model estimation activities completed overthe last few years, it has become clear that logsum coefficient values less than 0.5 are unlikelyand may overstate the elasticity responses of lower level choices. Use of 0.2 and 0.3 values inthe existing model are, therefore, problematic and may distort overall model sensitivities. Therecommended set of values is more consistent with recent experience and applicable to thenesting structure proposed for the OKI/MVRPC mode choice model.Mode Choice - Home-Based Work Model Estimation 19