论文标题
使用负二项式回归模型估算混合模式的城市步道流量
Estimating Mixed-Mode Urban Trail Traffic Using Negative Binomial Regression Models
论文作者
论文摘要
需要在多用途城市步道上进行非运动流量的数据和模型来改善城市运输系统的计划和管理。当因变量是具有过度分散的非负整数时，负二项式回归模型是适当且有用的。本文介绍了八种负二项式模型，用于使用1,898个每日混合模式交通数量估算城市步道的交通，从明尼苏达州明尼阿波利斯的六个地点进行活跃的红外显示器。我们的模型包括多达10个代表社会人口统计学，建筑环境，天气和时间特征的自变量。通用模型可用于估计未经监控流量的位置的流量。对于每个监视站点而不是邻域特定变量的六个地点模型，当不可用监视器计数时，可以使用特定于社区的变量来估计现有位置的流量。六个特定于步道的模型适用于估计天气和一周中的变化的流量变化。验证结果表明，负二项式模型优于普通最小二乘回归估计的模型。这些新模型平均在大约16.3％的误差范围内估计流量，这对于计划和管理目的是合理的。
Data and models of non-motorized traffic on multiuse urban trails are needed to improve planning and management of urban transportation systems. Negative binomial regression models are appropriate and useful when dependent variables are non-negative integers with over-dispersion like traffic counts. This paper presents eight negative binomial models for estimating urban trail traffic using 1,898 daily mixed-mode traffic counts from active infrared monitors at six locations in Minneapolis, MN. Our models include up to 10 independent variables that represent socio-demographic, built environment, weather, and temporal characteristics. A general model can be used to estimate traffic at locations where traffic has not been monitored. A six-location model with dummy variables for each monitoring site rather than neighborhood specific variables can be used to estimate traffic at existing locations when counts from monitors are not available. Six trail-specific models are appropriate for estimating variation in traffic in response to variations in weather and day of week. Validation results indicate negative binomial models outperform models estimated by ordinary least squares regression. These new models estimate traffic within approximately 16.3% error, on average, which is reasonable for planning and management purposes.
