CAR-RIDER SEGMENTATION ACCORDING TO RIDING STATUS AND INVESTMENT IN CAR MOBILITY

Document Type

Journal Article

Publication Date

2004

Subject Area

ridership - mode choice, ridership - drivers, economics - value of time, mode - car

Keywords

Waiting time, Value of time, Travel time, Transit riders, Transit penalties, Tel Aviv (Israel), Stated preferences, Revealed preferences, Multinomials, Mode choice, Modal choice, Mobility, Market segmented groups, Logits, Logit models, Journey time, Investments, Investment requirements, Choice of transportation, Choice models, Automobile passengers, Automobile drivers

Abstract

Population segmentations for mode choice models are investigated. Several researchers have shown that car and bus users differ substantially in their value of time, in both revealed-preference and stated-preference surveys. This line of research is followed, and a new methodology to segment the population is presented. The hypothesis that two further segmentation dimensions will produce meaningful results is verified. In the first segmentation, car drivers are distinguished from car passengers. In the second segmentation, the population is divided according to their investment in mobility by car. With data from a revealed-preference survey conducted in the Tel Aviv, Israel, metropolitan area, several multinomial logit models were estimated with respect to each population segment. The results show clearly that households with a high investment in car mobility have a much higher value of time than do those with a low investment in mobility. In addition, car drivers perceive higher transit penalties than do car passengers. An interesting result, which exceeded the expectations, was the strong increase of the waiting time penalty at high investment in car mobility. The cumulative effect of the value of time, bus transit penalties, and additional in-vehicle travel time by transit modes prevents most car drivers from potentially changing their travel mode to transit. The proposed procedure, if adopted in the estimation of transportation models, would reduce much of the evident gap between the expected and actual demand generated by changes in transit services.

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