Understanding Origin-Destination Data Series

Cynthia Chen, Xuegang (Jeff) Ban, Feilong Wang, Jingxing Wang, Choudhury Siddique, Rong Fan, Jaehun Lee
Tuesday, January 1, 2019
Traditional and Emerging Data

Despite many years of effort improving the practice of travel forecast modeling, many forecasting models still struggle to accurately represent current year travel patterns. The most critical deficiency occurs with the representation of trip origin-destination (OD) patterns. This critical difficulty in replicating the spatial distribution of trips is widely acknowledged in both practice and research as the largest source of error in travel forecasting. Moreover, an accurate understanding and representation of OD patterns is critical to many important analyses such as whether travelers might change modes, pay a toll to use an express lane, or send their autonomous vehicle home rather than pay to park it. Over the course of several volumes, this series reviews and explores both traditional and new emerging sources of data on OD patterns. An emphasis is placed on understanding the limitations of the various types of data to help encourage thoughtful and appropriate use of these data. However, while acknowledging the various issues and limitations of each type of data, the series also demonstrates how various sources of data can already be used in combination to produce more data-driven forecasts and highlight promising new methods that ultimately yield more accurate OD patterns, especially with the understanding of where the biases are.