Exploration and Prediction of the Elderly Travel Behavior Based on a Novel GR-GA-BP Hybrid Model

Abstract

With the aging trend in megacities, the travel behaviors of the elderly have attracted much attention. Accurate prediction of the travel behaviors of the elderly is a key link to meet the traffic demand and public facilities’ optimization. The aim of this paper was to explore the link between the travel characteristics and variables of the daily activities of the elderly. Based on a stratified sampling survey, the internal relationship between the characteristics of the elderly and their travel behavior was studied and discussed in this work. A novel grey correlation degree–genetic algorithm–back propagation (GR-GA-BP) hybrid model was proposed to predict the travel behavior of the elderly. Then, a grey correlation degree module was established and used to analyze the correlation between the individual elderly characteristics and their travel behavior. The results showed the following: (1) Both the times of weekly trips (y1) and average round-trip travel time (y2) were highly sensitive to the external environment, especially buses, subway stations, and recreational facilities. The size of the family was less sensitive to the travel behavior. (2) Referring to prediction of the times of weekly trips, the MRE of the proposed model was 23.12%, which was 15.22% less than the baseline models. (3) In terms of the prediction of round-trip travel time, the MRE of the proposed model was 7.13%, which was 14.00–69.41% lower than the baseline models. (4) The times of trips per week were 3.5. In summary, this paper provides technical support for formulating traffic demand policies and facilitates the configuration of cities for an aging society.