Scalable Activity-Travel Pattern Monitoring Framework for Large-Scale City Environment

In this paper, we introduce Activity Travel Pattern (ATP) monitoring in a large-scale city environment. ATP represents where city residents and vehicles stay and how they travel around in a complex megacity. Monitoring ATP will incubate new types of value-added services such as predictive mobile advertisement, demand forecasting for urban stores, and adaptive transportation scheduling. To enable ATP monitoring, we develop ActraMon, a high-performanceATP monitoring framework. As a first step, ActraMon provides a simple but effective computational model of ATP and a declarative query language facilitating effective specification of various ATP monitoring queries. More important, ActraMon employs the shared staging architecture and highly efficient processing techniques, which address the scalability challenges caused by massive location updates, a number of ATP monitoring queries and processing complexity of ATP monitoring. Finally, we demonstrate the extensive performance study of ActraMon using realistic city-wide ATP workloads.

1 Introduction

Understanding diverse aspects of complicated modern cities has long been a pivotal issue. Activity-Travel Pattern (ATP) has been used as one of the most useful tools to define and understand city residents' everyday lives in domains such as urban planning, geography, and transportation [1], [2], [4], [32]. ATP represents where people and vehicles are located, how they move in urban areas, and which activities they do at the places in certain patterns. As a city encompasses more people, vehicles, spaces and services, understanding ATP becomes more useful yet challenging. Because of its practical importance, governmental organizations have periodically observed and studied ATPs in terms of space-time use of citizens over major cities [22], [24], [25]. These observations have been performed through manual questionnaires and annotations, requiring huge amounts of time, labor, and expense.