Using automated fare collection data, GIS, and dynamic schedule queries to improve transit data and transit assignment model.
Using automated fare collection data, GIS, and dynamic schedule queries to improve transit data and transit assignment model.
复制标题
使用自动收费数据、GIS 和动态时间表查询来改进公交数据和公交分配模型。
DOI：
10.1007/978-0-387-84812-9_6
复制
发表时间：
2009
影响因子：
2.3
通讯作者：
Robert Freimer
中科院分区：
文献类型：
--
作者：
H. Slavin;A. Rabinowicz;J. Brandon;G. Flammia;Robert Freimer
本文提供了一份关于一项新研究工作的临时报告，旨在开发用于超大型交通系统需求预测的改进数据和模型。我们探讨的性质和使用的自动售检票（AFC）系统的数据，具有很大的潜力，用于表征和预测过境使用，但它需要相当大的努力，使其有用。本文所讨论的内容是基于我们在纽约市的工作，但研究应该可以转移到许多其他的大型系统，虽然有各种理论和数学模型的公交路线选择，很少有人知道在大型公交系统的特点是许多替代品为同一行程的行为。一个原因是，对于过境使用者来说，更强调的是方式选择而不是路线选择。另一个原因是，可获得的经验信息很少。机载调查在大型系统中可能是困难的，并且通常可能受限于所获得的数据的范围，
This paper provides an interim report on a novel research effort aimed at developing improved data and models for demand prediction for very large transit systems. We explore the nature and use of automated fare collection (AFC) system data which has great potential for characterizing and forecasting transit use, but it requires a considerable effort to make it useful. The material discussed is motivated by work that we are performing in New York City, but the research should be transferable to many other large systems.Although there are a variety of theories and mathematical models for transit route choice, little is known about traveler behavior in large transit systems that are characterized by many alternatives for the same trip. One reason is that there has been greater emphasis on mode choice than on route choice for transit users. Another reason is that there is little empirical information available. Onboard surveys can be difficult in large systems and often may be limited in the scope of data obtained making inferences about