Development and application of the Activity-BAsed Traveler Analyzer (ABATA) system

Abstract

While advanced technologies and big data are widely used in the transportation study, most transportation plans still rely on some variant of traditional four-step demand forecasting models. The most significant limitations of the four-step model are spatiotemporal aggregation of data and difficulty of considering individual travel behaviors. To address these drawbacks, activity-based modeling systems have increasingly been developed. In this paper, we present a new activity-based analytical system, called Activity-BAsed Traveler Analyzer (ABATA). The distinguishing feature of ABATA is the simulation of the present hourly service population that is determined from mobile phone data instead of a synthetic population. ABATA comprises multiple components, including an hourly total population estimator, activity profile constructor, hourly activity population estimator, spatial activity population estimator, and origin–destination estimator. To demonstrate the proposed method, a future aged society in Gangnam, Korea is evaluated as a case study. The results indicate that the hourly activity populations engaged in work, school, and private education decreased, while those engaged in home, shopping, recreation and other activities increased with the aging of the population. The associated changes in mobility were found to be rational and reasonable: older people tend to have a more flexible working time, make shorter-distance trips, undertake more trips for shopping, recreation, home, and other activities, and finish their trips earlier, before evening. The proposed ABATA system is expected to provide a valuable tool for simulating the impacts of future changes in population, activity schedules, and land use on activity populations and travel demands.