A New Approach of Generating Travel Demands for Smart Transportation Systems Modeling 2020-01-1047
The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographic information and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs. To address this data gap, we propose a new approach of travel demand generation based on households. Census data provide the demographic information for each household (e.g., the number of adults and kids, income and education level, vehicle ownership etc.). The travel demands of each individual in the household are modeled as chains of trips with spatial and temporal details that match the travel patterns of the individual’s demographic profile. The trip chains also consider multi-person trips, accounting for group traveling of the individual with others within and outside of the households. Using Miami as a case study city, we demonstrate the feasibility and validity of the proposed approach. The proposed approach can be applied to any city using publically available data as inputs.
Ruoxi Wen, Zhen Jiang, Chen Liang, Cassandra Telenko, Bo Wang, Yan Fu, Hua Cai