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

IF 1.6 4区 工程技术 Q4 TRANSPORTATION Journal of Transport and Land Use Pub Date : 2021-11-21 DOI:10.5198/jtlu.2021.1872
M. Orvin, Daryus Ahmed, M. Fatmi, G. Lovegrove
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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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为不列颠哥伦比亚省基洛纳市的中层住宅开发车辆和非车辆出行生成模型:评估建筑环境、土地利用和社区特征的影响
本研究开发了用于中高层多户住宅开发的车辆和非车辆出行生成模型。对观察到的和运输工程师协会(ITE)出行率的比较分析表明,ITE的出行率一直被高估。开发了一种基于潜在分割的负二项(LSNB)模型,以改进估计车辆和非车辆出行的方法。LSNB模型的关键特征之一是捕获异构性。车辆和非车辆模型的分段分配结果表明,一个分段包括郊区开发,而另一个分段则包括城市开发。结果表明,住宅单元数量越多可能与车辆出行次数增加有关。对于非车辆出行,更多的住宅单元和更多的娱乐机会更有可能增加出行量。LSNB模型证实了显著异质性的存在。例如,更高的土地利用组合更有可能阻止城市地区的车辆出行,而郊区的出行可能会继续增加。公交线路和人行道密度的增加可能与城市地区非车辆出行的增加有关,但郊区的非车辆出行可能会减少。一个有趣的发现是,郊区较高的可骑行性更有可能增加非车辆出行。这项研究的结果有望帮助工程师和规划者更准确地预测车辆和非车辆出行。
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来源期刊
CiteScore
3.40
自引率
5.30%
发文量
34
审稿时长
30 weeks
期刊介绍: The Journal of Transport and Land Usepublishes original interdisciplinary papers on the interaction of transport and land use. Domains include: engineering, planning, modeling, behavior, economics, geography, regional science, sociology, architecture and design, network science, and complex systems. Papers reporting innovative methodologies, original data, and new empirical findings are especially encouraged.
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