Detroit Region Travel Behavior Changes: 2005 - 2015
Corresponding Author: Liyang Feng, SEMCOG
Presented By: Liyang Feng, SEMCOG
Southeast Michigan Council of Governments (SEMCOG), with consultant support from Westat, just completed a year 2015 regional household travel survey. The survey collected about 12,000 households. Prior to this survey, SEMCOG collected about 6,500 households travel data in year 2005. Both surveys were activity based. The survey sample expansion process adopted a person-household two dimensional weighting procedure. This new approach improved both household and person trip rate estimations and provided a solid background for this paper.
In the past ten years, SEMCOG area economy experienced a dramatic downturn and it is now slowly recovering. For example, the regional population decreased about 5% during the past 10 year period, while total employment reduced about 6%. For a region with negative growth rate, how the travel patterns (trips, tours, and spatial distribution) have changed and the magnitude of these changes become important topics for travel modelers. The paper is trying to explore how these changes might influence the model development and calibration strategies. Meanwhile, the impact of new technologies and generation gap (such as baby boomers vs. x generation) were also discussed.
In general, the regional total trips reduced about 4% from 2005 observation and home based work trips reduced 14%, while work related trips kept the same. This probably was due to permanent job decrease and growth of self-employment status in the region. The work at home trips represented a small fraction of the total work trips, but had a significant increase (more than 50%) over the last decade. New technology might be a major contribution factor.
However, at regional level, the survey revealed that the regional trip distribution pattern over the past ten years was very stable. Trip distribution tables were used in the comparison among eight SEMCOG counties.
For trip and tour productions, the paper used life cycle parameters at both person and household level to identify travel trends. This is especially useful for both trip based and activity based modeling processes.