學生 : 黃群凱 Towards Mobility-based Clustering 1

Outline INTRODUCTION PRELIMINARIES SPOT CROWDEDNESS FUNDAMENTAL SPOT CROWDEDNESS IN PRACTICE HOT SPOTS AND HOT REGIONS FIELD STUDY EVALUATION RELATED WORK CONCLUSION 2

Introduction Identifying hot spots of moving vehicles in an urban area is essential to many smart city applications. The ultimate goal of this research is to have a better understanding of the city traffic via a quantitative research on hot spots. To define and quantify the vehicle crowdedness of an area. To picture the crowdedness distribution of the city and identify the hot spots. To investigate the evolution of hot spots. 3

Introduction The first major challenge is incomplete information. Existing algorithms (for static or mobile) are all density-based approaches that use inter-node distances as a critical measure. The sample object set is a specific type of vehicles. It has very limited to represent general vehicles. 4

Introduction 5

PRELIMINARIES Raw dataset characteristics The dataset is originated from the City Traffic Bureau. The instant speed The geographic location The status of occupied or unoccupied of the taxi. 6

PRELIMINARIES Road network grid The road topology and type will impact the vehicle, not only the speed, but also the drive pattern, hence we study the following problems based on road network grid. 7

PRELIMINARIES Observations and design principles. The first one is that vehicles prefer high mobility in a sparse region. To the opposite, for security concerns vehicles will drive slowly when the nearby area is crowded. Motivated by it, we employ vehicles as sensors using their instant speed to sense the vehicle crowdedness of vicinity. The second one is that the reported locations can be erroneous, while the reported speeds are usually quite accurate because they are directly obtained from the speedometers installed on taxis. In addition, for safety concerns sudden changes of speeds are rare. 8

SPOT CROWDEDNESS FUNDAMENTAL Assumptions and notations 9

SPOT CROWDEDNESS FUNDAMENTAL 10

SPOT CROWDEDNESS IN PRACTICE cumulated distribution function(CDF): 分布函數 11

HOT SPOTS AND HOT REGIONS 12

HOT SPOTS AND HOT REGIONS 13

FIELD STUDY EVALUATION Mean Absolute Difference (MAD): 絕對平均差 14

FIELD STUDY EVALUATION F-score is the weighted harmonic mean of precision and recall 15

Rwlated Work Object clustering is a well studied problem A great deal of research efforts being devoted in. One of the most promising approaches for spatial static clustering can be found in the research work of DBSCAN. Recently, clustering moving objects is becoming a hot research issue. Of related work focuses on the analysis of mobile traffic object data. They are mainly interested in the detecting areas of high traffic load. 16

CONCLUSION In this paper, we proposed mobility-based clustering, a novel approach to identify hot spots and hot regions in a highly mobile environment with extremely limited and biased samples. 17