Direct-demand models of pedestrian volumes (identifying relationships with built environment characteristics) require pedestrian data, typically from short-duration manual counts at a limited number of locations. We overcome these limitations using a novel source of pedestrian data: estimated pedestrian crossing volumes based on push-button event data recorded in traffic signal controller logs. These continuous data allow us to study more sites (1494 signalized intersections throughout Utah, US) over a much longer time period (one year) than in previous models, including the ability to detect variations across days-of-week and times-of-day. Specifically, we develop direct demand (log-linear regression) models that represent relationships between built environment variables (calculated at ¼- and ½-mile network buffers) and annual average daily and hourly pedestrian metrics. We control spatial autocorrelation through the use of spatial error models. All results confirm theorized relationships: There is more pedestrian activity at intersections with greater population and employment densities, a larger proportion of commercial and residential land uses, more connected street networks, more nearby services and amenities, and in lower-income neighborhoods with larger households. Notably, we also find relevant day-of-week and time-of-day differences. For example, schools attract pedestrian activity, but only on weekdays during daytime hours, and the coefficient for places of worship is higher in the weekend model. K-fold cross-validation results show the predictive power of our models. Results demonstrate the value of these novel pedestrian signal data for planning purposes and offer support for built environment interventions and land use policies to encourage walkable communities.

行人流量的直接需求模型（标识与已建环境特征的关系）需要行人数据，通常是在有限数量的位置上的短时人工计数。我们使用行人数据的新颖来源克服了这些局限性：根据交通信号控制器日志中记录的按钮事件数据估算行人过路量。与以前的模型相比，这些连续的数据使我们能够在更长的时间段（一年）内研究更多的站点（在美国犹他州的1494个信号交叉口），包括能够检测周几和时间间隔内的变化。日。具体来说，我们开发了直接需求（对数线性回归）模型，该模型表示已构建的环境变量（在1/4和½英里网络缓冲区中计算）与年度平均每日和每小时行人指标之间的关系。我们通过使用空间误差模型来控制空间自相关。所有结果都证实了理论上的关系：在人口和就业密度更高的交叉路口，行人活动增多，商业和住宅用地的比例更大，街道网络连接更多，附近的服务和设施也更多，而家庭较多的低收入社区。值得注意的是，我们还发现了相关的星期几和一天中的时间差异。例如，学校吸引行人活动，但仅在白天的工作日内，在周末模式中，礼拜场所的系数较高。K折交叉验证结果显示了我们模型的预测能力。结果证明了这些新颖的行人信号数据对于规划目的的价值，并为建筑环境干预措施和土地使用政策提供了支持，以鼓励步行社区。