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86 I N N O VAT I O N S I N T R AV E L D E M A N D M O D E L I N G , V O L U M E 2 of the additional variables described in the following sec- 8. Commercial and information services at tran- tion, to allow for more advanced model improvements sit stations and at park-and-ride lots in the future. 9. Frequency and location of stops on the way to and from the primary destination 10. Individual car availability for person and EXPLANATORY VARIABLES given travel tour, taking into account cars in dis- repair, which reduces the household's typical car Conventional travel demand modeling has developed in availability measure, and renting cars, which an environment that stressed economizing on explana- supplements it tory variables as much as possible to avoid extensive 11. Joint travel arrangements with the other model segmentation. This emphasis led to a standard household members approach that was expressed in a limited set of variables 12. Individual geographical information sys- like household size, number of workers, car ownership, tembased walk time and pedestrian conditions income group, and the like that are indeed important for for transit and nonmotorized modes travel behavior but are not nearly exhaustive. 13. Road and personal safety, crime rate and Zonal "attractiveness" was measured by a limited public image associated with area of transit sta- number of employment variables stratified into three or tionline four major categories like industrial employment, office 14. Person type-, gender-, age-, and income- employment, and commercial employment. In a similar specific time and cost perceptions [value over way, level-of-service variables by different travel modes time (VOT)] were limited to average time and cost components that 15. Nonlinear effects corresponding to marginal could be skimmed by existing network simulation impacts of time, cost, and other variables as procedures. functions of trip length New modeling frameworks open a constructive way 16. Comfort and convenience in transit cars: pos- to add variables and explanatory power to travel mod- sibility of reading or using laptop, air conditioning els. The authors believe that considerable improvements Destination choice can be made within a conventional decision-making Traditional variables framework by adding explanatory variables. Of course, 1. Mode-choice log sum or particular time, cost, for these variables to be available to the modeling and distance variables process, they must be present in the surveys. 2. Zone attraction variable based on the Here is a list of traditionally used variables and new employmentenrollment mix variables that could add significant explanatory power New variables to such important travel models as mode and destination 1. Bottleneck facilities (river crossings, bridges, choice (trip distribution) taken as examples: tunnels) 2. Statutory borders (states, counties, munici- Mode choice palities, school districts) Traditional variables 3. Social frictions (income incompatibility, 1. Average travel time and cost social and ethnic clusters) 2. Number of transfers 4. Special sensitivity to transit accessible desti- 3. Household car ownershipsufficiency nations of nondriving population (children 4. Household income under 16, zero-car households) 5. Person age and driver's license possession 5. Household composition and activity patterns 6. Area-type constants that limit spatial domain of activity (presence of New variables child at home) 1. Travel time uncertainty (probability of 6. Individual attraction characteristics and spe- delays) cial trip generators that take into account size 2. Reliability in relation to transit schedule and profile of the individual attraction (going adherence into more and more detail on the house- 3. Parking constraints, search, and conditions holdperson side but still having terrible aggre- 4. Individual parking cost, including free park- gate zonal-attraction variables that are based on ing and discounted parking eligibility three or four crude employment variables) 5. Driving conditionsroad type 7. Cognitive maps based on the spatial domain 6. Probability of having a seat for transit of the household and person with the pivot 7. Probability of having a parking place for auto points corresponding to most frequently visited and of park and ride usual locations (residential, work, school).