Journal of Transport and Land Use

Developing vehicular and non-vehicular trip generation models for mid-rise residential buildings in Kelowna, British Columbia: Assessing the impact of built environment, land use, and neighborhood characteristics

Muntahith Orvin

Daryus Ahmed

Mahmudur Fatmi

University of British Columbia

Gordon Lovegrove

DOI: https://doi.org/10.5198/jtlu.2021.1872

Keywords: Multi-modal trip generation model, ITE trip generation guideline, Latent segmentation-based negative binomial model, Unobserved heterogeneity, Built environment attributes

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Abstract

This study develops vehicular and non-vehicular trip generation models for mid-rise, multi-family residential developments. A comparative analysis of observed and Instiutue of Transportation Engineers (ITE) trip rates suggests that ITE rates consistently overestimate. A latent segmentation-based negative binomial (LSNB) model is developed to improve the methodology for estimating vehicular and non-vehicular trips. One of the key features of an LSNB model is to capture heterogeneity. Segment allocation results for the vehicular and non-vehicular models suggest that one segment includes suburban developments, whereas the other includes urban developments. Results reveal that a higher number of dwelling units is likely to be associated with increased vehicle trips. For non-vehicular trips, a higher number of dwelling units and increased recreational opportunities are more likely to increase trip generation. The LSNB model confirms the existence of significant heterogeneity. For instance, higher land-use mix has a higher probability to deter vehicular trips in urban areas, whereas trips in the suburban areas are likely to continue increasing. Higher density of bus routes and sidewalks are likely to be associated with increased non-vehicular trips in urban areas, yet such trips are likely to decrease in suburban areas. An interesting finding is that higher bikeability in suburban areas is more likely to increase non-vehicular trips. The findings of this study are expected to assist engineers and planners to predict vehicular and non-vehicular trips with higher accuracy.

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