of Changing Tmvel Patterns : A Case Study

A n understanding of travel patterns is of key interest to transportation planners, researchers, engineers and policy analysts. The concern with the environment and traffic congestion and the focus on the balance between available supply and the resulting demand in view of the scarcity of fiscal resources has strengthened the need to better monitor travel behavior. This research uses traffic count data collected between 1976 and 1985 in Montgomery County, Maryland, a suburb of Washington, D. C., to shed fresh light on changes in patterns of traffic. To the authors’ knowledge, no attempt has been made to analyze traffic count data to corroborate changing travel patterns. A number of travel patterns have been identified in earlier analysis of personor household-based travel surveys. l-s These relate to changes in directionality, purpose, mode and time of trips. These surveys have the advantage of being able to track an person’s reported travel behavior and relate this behavior to demographic and geographic characteristics. However, surveys are limited in their dependence upon individual recall of travel behavior. A serious self-reporting bias exists in any survey and is evident to most analysts of travel data. Moreover, special effort and expense is required to conduct household travel surveys, thus they are conducted infrequently, often less than once a decade. Traffic counts, which directly measure volumes, are a more accurate measure of traffic patterns on specific facilities than could be obtained from any household travel survey. Measurement of roadway and intersection traffic volumes is an established field of traffic engineering utilized for several purposes, including signal design, highway engineering and measuring congestion. Traffic counts are routinely and frequently collected by most transportation departments. The relative usefulness of the data, however, is limited because of its inability to relate vehicles to specific trips. It is, however, possible to analyze direction of traffic, quantify traffic volumes during specific time periods and compare morning with afternoon traffic volumes. Based on survey findings that a predominant share of vehicular traffic during the morning peak period is for work trips as compared with the afternoon peak, which also accommodates a large number of nonwork trips, it is possible to comment on the nature of nonwork travel from traffic counts. This analysis concludes that there has been a faster increase in lateral (suburban-suburban) travel as compared to radial (suburb-city) and reverse radial (city-suburb) trips; that vehicular traffic volumes are greater in the afternoon peak than the morning; and that the peak is spreading. An interesting conclusion relates to a more pronounced directionality in radial as compared with lateral trips. This suggests that better use is made of existing roadway facilities by suburb-to-suburb travel than by the traditional suburbto-central city trips. Nonwork trips emerge as the more elastic trips, shifting out of the peak with an increase in congestion.