Abstract

Street attributes are thought to play an important role in influencing pedestrian route choices. Faced with alternatives, pedestrians have been observed to choose faster, safer, more comfortable, more interesting, or more beautiful routes. Literature on pedestrian route choice has provided methods for assessing the likelihood of such options using discrete choice models. However, route choice estimation, which is data intensive and computationally challenging, remains infrequently deployed in planning mobility analysis practice. Even when coefficients from previous studies are available, operationalizing them in foot-traffic predictions has been rare due to uncertainty involved in the transferability of behavioral effects from one context to another, as well as computational challenges of predicting route choice with custom attributes. This paper explores a simpler method of route choice prediction, implemented in the Urban Network Analysis toolbox, which assigns probabilities to available route options based on distance alone. We compare the accuracy of distance-weighted approaches to the more detailed utility-weighted approach using a large dataset of observed GPS pedestrian traces that include numerous trips between same intersections pairs in downtown San Francisco as a benchmark. Even though a utility-weighted model matches observed pedestrian flows most accurately, a distance-weighted model is only marginally inferior, on average. However, shortest-distance and highest-utility route predictions are both significantly inferior to the utility-weighted and distance-weighted sample-enumeration methods. Our findings suggest that simplified assumptions can be used to predict pedestrian flow in practice with existing software, opening pedestrian flow predictions to a wider range of planning and transportation applications.

摘要

街道属性被认为在影响行人路线选择方面起着重要作用。面对替代方案，观察到行人会选择更快、更安全、更舒适、更有趣或更漂亮的路线。关于行人路线选择的文献提供了使用离散选择模型评估此类选项可能性的方法。然而，路线选择估计是数据密集型和计算上的挑战，在规划移动性分析实践中仍然很少部署。即使先前研究的系数可用，由于行为影响从一种环境到另一种环境的可转移性涉及不确定性，以及使用自定义属性预测路线选择的计算挑战，在人流量预测中操作它们也很少见。本文探讨了一种在城市网络分析工具箱中实施的更简单的路线选择预测方法，该方法仅根据距离为可用路线选项分配概率。我们使用观察到的 GPS 行人轨迹的大型数据集，将距离加权方法的准确性与更详细的效用加权方法的准确性进行比较，其中包括旧金山市中心相同交叉路口对之间的多次行程作为基准。尽管效用加权模型最准确地匹配观察到的行人流量，但距离加权模型平均而言仅略逊一筹。然而，最短距离和最高效用路线预测都明显不如效用加权和距离加权样本枚举方法。