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