Methodology for nonwork travel analysis in suburban communities

Abstract

The increase in the number of nonwork trips during the past decade has contributed substantially to congestion and to environmental problems. Data collection methodologies, descriptive information, and reliable models of nonwork travel behaviour are needed to accurately forecast traffic volumes and to develop and assess policies aimed at alleviating congestion. This study investigated characteristics of the nonwork trip through the development and implementation of a household daily travel survey and through the analysis of the data collected. The accuracy of using self reporting as a method for collecting daily household travel behaviour was part of the evaluation and shown to be very effective within the limits of the survey. Results of the survey indicate that the most important factors in predicting household nonwork trip rates are geographic location, household size, household structure and the distribution of household members by gender. Individual nonwork trip rates were most influenced by gender, marital status and employment status. Trip chaining was shown to be a significant travel pattern with the majority of trip chains made on the work to home trip during the evening peak hour. Longer travel times appear to provide incentives to chain trips together, which suggests that increases in other travel costs might have a similar impact and motivate more efficient arrangement of trips. The findings are used to derive cross classification models for nonwork trip generation and are summarised as guidelines for designing travel demand management strategies that reduce nonwork travel.

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