交通需求模型规范中最重要的是什么:中等规模网络的输出比较

T. D. Chen, K. Kockelman, Yong Zhao
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引用次数: 2

摘要

本文研究了旅游需求模型分解技术在中等规模社区环境下的影响。通过基于德克萨斯州Tyler网络的案例研究,评估了具体的TDM改进策略的预测能力和灵活性。结果表明,在基本TDM框架中添加每日时间分解,特别是与多类分配相结合,对产出有最显著的影响。显示影响输出的其他策略包括添加logit模式选择模型和合并拥塞反馈回路。对于资源受限的社区,这些结果显示了模型输出和灵活性在不同的设置和场景下是如何变化的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What Matters Most in Transportation Demand Model Specifications: A Comparison of Outputs in a Mid-size Network
This paper examines the impact of travel demand modeling (TDM) disaggregation techniques in the context of medium-sized communities. Specific TDM improvement strategies are evaluated for predictive power and flexibility with case studies based on the Tyler, Texas, network. Results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on outputs. Other strategies shown to impact outputs include adding a logit mode choice model and incorporating a congestion feedback loop. For resource-constrained communities, these results show how model output and flexibility vary for different settings and scenarios.
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