Household Activity Pattern Problem with Autonomous Vehicles

The pace of changes in automating cars has sped up in the last few decades. Autonomous Vehicles (AVs) will dramatically change the future of transportation, and household-level decisions will play a large role in the AV market. However, no data is readily available on household travel behavior using AVs. This study introduces a framework to assess households’ adaptation to AV operations. We developed a mixed integer program, Household Activity Pattern Problem with AV (HAPPAV), to model traveler behavior under realistic conditions while using AVs. The model generates feasible activity patterns for household members under spatial and temporal constraints. The model is able to consider complete driverless operations, such as AV pick-up and drop-off, parking availability, empty trips, and carpooling. A decomposition method is developed to solve the NP-hard problem HAPPAV. The method includes two major stages; the first stage is to generate all feasible travel patterns for household members and the second stage finds the best AV route along with detailed travel patterns. We also use novel pruning rules to enhance the performance of the decomposition method. The model is applied on the California Statewide Travel Survey. The results indicate that 62% of households can perform their daily activities with only one AV in place of two or three regular vehicles. However, AV empty trips increase total VMT by 15%. The new method improves the average runtime and solution quality by 86% and 23%, respectively.