Spatial Proximity and Dependency to Model Urban Travel Demand

Abstract

Link level annual average daily traffic (AADT) or travel demand is used in several urban planning, roadway design, operational, and safety analyses by transportation planners and engineers. Existing AADT estimation methods do not adequately account for spatial proximity, variations, and dependency to address modeling needs. The primary focus of this paper, therefore, is to incorporate these aspects and develop a method to estimate link level AADT by the urban road functional class. Geospatial analytical techniques were explored to capture spatial data within proximal areas of selected roadway links and develop statistical models to estimate link level AADT. Polygon-based network buffers were generated within the proximal roadway distance of each study link to account for actual connectivity and capture off-network data instead of Euclidean distance-based buffers. On-network characteristics of the study links and upstream, downstream, and cross-street network links were considered to account for the spatial dependency of on-network characteristics. The applicability of the method and predictive capability of the models to estimate link level AADT, considering all of the selected study links and by each road functional class, was researched. The working of the method and development of the models is illustrated using data for the city of Charlotte in the state of North Carolina. The generalized estimating equation (GEE) models developed indicate that a negative binomial distribution fits better than a Poisson distribution for the data considered in this research. The ideal proximal distance to capture spatial data and accurately estimate AADT was observed to vary when all study links and different road functional classes were modeled separately. Overall, the results obtained indicate that spatial proximity and dependency play a vital role in accurately estimating travel demand on various urban road functional classes.