预测电动滑板车替代直接和访问旅行的方式和距离
Forecasting e-scooter substitution of direct and access trips by mode and distance
摘要：
一个电动滑板车旅行模型是从美国四个城市估计的：波特兰，奥斯汀，芝加哥和纽约市。基于用户年龄、人口、土地面积和踏板车数量，估计了电动踏板车出行的对数-对数回归模型。该模型预测，在曼哈顿部署2000辆电动滑板车，每天将有7.5万次电动滑板车出行，年收入将达到7700万美元。我们提出了一种新的非线性，多因素模型，打破了日常出行的数量的替代交通方式，他们可能会取代基于统计相似性。模型参数揭示了曼哈顿公共交通与自行车、步行、拼车、汽车和出租车的直接出行以及进出出行的关系。我们的模型估计，电动滑板车可以取代32%的拼车，13%的自行车和7.2%的出租车出行。从访问/出口行程的收入的距离结构被发现不同于其他替代行程。
英文摘要：
An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New York City. A log-log regression model is estimated for e-scooter trips based on user age, population, land area, and the number of scooters. The model predicts 75K daily e-scooter trips in Manhattan for a deployment of 2000 scooters, which translates to 77 million USD in annual revenue. We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternative modes of transportation that they would likely substitute based on statistical similarity. The model parameters reveal a relationship with direct trips of bike, walk, carpool, automobile and taxi as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters could replace 32% of carpool; 13% of bike; and 7.2% of taxi trips. The distance structure of revenue from access/egress trips is found to differ from that of other substituted trips.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
