Urban Computing with Taxicabs TMSG Cappuccino Oct. 12th, 2011

About the Authors.. Yu Zheng, Yanchi Liu, Jing Yuan, Xing Xie from M$RA Yu Zheng Ubicomp - Ubiquitious computing group WSM - Web Search & Mining Group Yu Zheng’s weibo : http://www.weibo.com/msyuzheng

About the Authors.. Yu Zheng, Yanchi Liu, Jing Yuan, Xing Xie from M$RA Yanchi Liu, also from University of Science and Technology Beijing Paper published by ACM on Sept. 2011 ( ? )

Agenda About the authors(finished) Introduction Overview Evaluation Settings Evaluation Results Related Works Conclusion Comments

Introduction GPS equips are generally used nowadays. GPS-equipped taxicabs can be viewed as ubiquitious mobile sensiors, gathering datas of traffic flows.

Introduction This paper’s goal is to detect the flawed and less effective urban planning in a city according to the GPS trojectories of taxicabs. Saying Beijing has 35 million personal trips per day created by various kinds of vehicles and that 1.44 million personal trips are generated by taxi. Thus, the percentage of taxi trips from total trips are 4.2%. The author believed 4.2% is a significant sample reflecting to urban traffic flow.

Overview Let’s introduce the variables one at a time..

Overview

Overview

Overview Consisted of two major components: Modeling citywide traffic Detecting flawed planning

Overview Disjoint regions using major roads.

Overview Building region matrix: This process can be break down into 3 steps... 1. Temporal partition Weekday/weekend Rush hours/un-rush hours

Overview 1. Temporal partition

Overview 2. Transition construction Pick out effective(occupied) taxi trajectories Construct transitions between two regions according to Def. 3 Transitions Def. 3

Overview 2. Transition construction Note that a trajectory discontinuously traversing two regions still formulate a transition between two regions.

Overview 2. Transition construction

Overview 2. Transition construction Black point represents a region pair, while blue and red points are the projections of these re- gion pairs on XZ and YZ spaces.

Overview 2. Transition construction Most taxis intend to travel through a shortcut instead of the roundabout route if the shortcut is effective. On the contrary, if most taxis pass additional regions, that means the route directly connecting two places is not very effective.

Overview 3. Build region matrix

Overview Detecting flawed planning Skyline detection

Overview In this paper, which means there’s no region pair a(p, q) having a lower speed and bigger theta than those belongs to skyline.

Overview The detected skyline is comprised of 3 kinds of region pairs. 1. Very small E(V) and theta, which means the two regions are connected with some direct routes while the capacity of these routes are not sufficient.

Overview 2. A region pair with a small E(V) and big theta, which means people have to take detours and also suffer from a very slow speed. Worse case. 3. A region pair with a big E(V) and big theta. Meaning that the travel speed is fast but far, still has flaws.

Overview Examples of 1, 2, and 3.

Overview Pattern mining from skylines(2 steps) Formulating skyline graphs 2. Mining frequent sub-graph patterns 先點一下！

Overview Formulating skyline graphs connect two consecutive slots if they are spatially close to each other. There could be multiple isolated graphs pertaining to a day.

Overview Formulating skyline graphs

Overview 2. Mining frequent sub-graph patterns Mining the association rules. The mined association rules can consist of over 2 patterns. E.g. g1, g2 => g3. Also, these association rules may NOT be geospatially close to each other.

Evaluation Settings Taxi trajectories

Evaluation Settings Map data Road network of Beijing, consist of 106,579 road vertices and 141,380 road segments. Picked out 25,262 road segments w/ leveling from 0 to 6. Use only the 0 to 2 level. (0 is highest representing highways) Create 444 regions in result. 106,579 路口 ; 141,380路段

Evaluation Settings Verify the detected flaws in the following 2 ways: Verify the urban plannings that had been implemented between the times of the two datasets. Check if some flaws that have been detected in both datasets by our methods embodied in the future urban planning of Beijing.

Evaluation Results

Evaluation Results Sum up that the traffic conditions in Beijing become worse in 2010 than 2009.

Evaluation Results Taxi drivers took fewer passengers than b4. The average speed dropped between the two years. In short, the traffic condition became worse.

Evaluation Results Most regions becomes shallower in 2010 than 2009, especially in hot areas. The travel speed of taxis in these regions decreased.

Evaluation Results By looking at the results, we observe two aspects: 1. Some flawed planning occurring in 2009 disappeared in 2010. 2. The number of regions having defects increased in 2010 beyond 2009 and some flaws occuring in 2009 still exist.

Evaluation Results Some flawed planning occurring in 2009 disappeared in 2010.

Evaluation Results Some flawed planning that still exists in 2010. The planning of these two subway lines denotes that the urban planner has recognized the problem existing in the regions, justifying the validity of the results generated using our method.

Evaluation Results Can also found association rules between the detected patterns. Support = 0.05 Confidence = 0.7

Evaluation Results We will show more interesting results and a live demo during the presentation at the conference.

Related works Mining taxi trajectories Urban computing Effectiveness, like what I’ve presented last time. Destination prediction, like what Danny presented last time. Urban computing Most papers do their researchs on social computing and some user interaction stuff while this paper explore the urban computing from the perspective of urban planning.

Conclusion This paper detect the flaws in the existing urban planning of a city using the GPS trajactories of taxis traveling in the urban areas.

Conclusion Future plans: 1.Studying the geographic features of a region, such as the road segments and POI. 2. The purpose of people’s travel. Point of interest

Comments Easy reading Interesting and well-applied on a useful case Chart arrangement thing again. Like reading a novel or comic, no highlights.

Urban Computing with Taxicabs The End 