A Two-Phase Approach for Predicting Highway Passenger Volume

Abstract

With the continuous process of urbanization, regional integration has become an inevitable trend of future social development. Accurate prediction of passenger volume is an essential prerequisite for understanding the extent of regional integration, which is one of the most fundamental elements for the enhancement of intercity transportation systems. This study proposes a two-phase approach in an effort to predict highway passenger volume. The datasets subsume highway passenger volume and impact factors of urban attributes. In Phase I, correlation analysis is conducted to remove highly correlated impact factors, and a random forest algorithm is employed to extract significant impact factors based on the degree of impact on highway passenger volume. In Phase II, a deep feedforward neural network is developed to predict highway passenger volume, which proved to be more accurate than both the support vector machine and multiple regression methods. The findings can provide useful information for guiding highway planning and optimizing the allocation of transportation resources.

Artificial Intelligence (AI) Applied in Civil Engineering

“…In transportation engineering, Xiang et al [30] propose a two-phase approach in an effort to predict highway passenger volume. The datasets subsume highway passenger volume and impact factors of urban attributes.…”

How Intercity Expressway Passenger Flow Varies? A View from Month, City Characteristics, and Weather at the Origin and Destination

“…The characteristics of origin and destination cities, such as GDP (Li et al, 2016;Feng et al, 2019;Xiang et al, 2021), population (Feng et al, 2019;Xiang et al, 2021), city tier (Caceres et al, 2018), and industrial structure (Feng et al, 2019;Xiang et al, 2021), have also been found to impact expressway passenger ow. However, there is currently no consensus regarding the speci c in uence of city characteristics on intercity expressway passenger ow.…”

Spatial Heterogeneity of the Recovery of Road Traffic Volume from the Impact of COVID-19: Evidence from China

“…Since the 1980s, the analysis of factors influencing road traffic volume has gradually attracted more and more attention. For example, Gu et al used the Bayesian combination model [30], the neural network model [31], and other algorithms to test the possible effect of factors such as economic development on road traffic volume. In general, areas with higher income and economic development have higher road traffic volume, a phenomenon supported by studies from the United States [32] and Greece [33].…”