1. DISSECTING REGIONAL WEATHER-TRAFFIC SENSITIVITY THROUGHOUT A CITY Ye Ding 1, Yanhua Li 2, Ke Deng 3, Haoyu Tan 1, Mingxuan Yuan 4, Lionel M. Ni 5 1 Guangzhou HKUST Fok Ying Tung Research Institute, The Hong Kong University of Science and Technology 2 Computer Science Department, Worcester Polytechnic Institute 3 School of Computer Science and Information Technology, RMIT University 4 Noah’s Ark Lab, Huawei Technologies Co., Ltd. 5 University of Macau yeding@ust.hk, yli15@wpi.edu, ke.deng@rmit.edu.au, haoyutan@ust.hk, yuan.mingxuan@huawei.com, ni@umac.mo

2. INTRODUCTION 2 Overview Dissecting Regional Weather-Traffic Sensitivity throughout a City Heavy RainTraffic Jam Why? Inappropriate Urban Planning or Infrastructure Tunnels with Bad Sewer Systems Highways with Bad Entrance Structures How to Detect Regional Weather-Traffic Sensitivity? Low High Tour Attraction Bad Weather + Crowd = Traffic Jam Prove Our Method Makes Sense Regular Community No Conspicuous Reasons Alert City Planners to Examine

3. THE WEATHER-TRAFFIC INDEX SYSTEM 3 System Architecture Dissecting Regional Weather-Traffic Sensitivity throughout a City

4. 4 THE WEATHER-TRAFFIC INDEX SYSTEM Data Preparation TAXI TRAJECTORIES WEATHER REPORT DATA REGIONAL INFORMATION ROAD NETWORK  Provided by the government  ~33,000 road segments

5. Dissecting Regional Weather-Traffic Sensitivity throughout a City 5 THE WEATHER-TRAFFIC INDEX SYSTEM Data Preparation TAXI TRAJECTORIES WEATHER REPORT DATA REGIONAL INFORMATION ROAD NETWORK  Taxis = traffic sensors  Provided by the government  4,529 taxis, ~115.2 GB  Jan. 2006 – Nov. 2007  Sampling rate: ~20 seconds  Traffic measure: average driving speed Trajectory Sample Point Taxi ID: 10001 Location: 121.3926, 31.1655 Time: 2006-01-06 10:03:01 Driving Speed: 40

6. Dissecting Regional Weather-Traffic Sensitivity throughout a City 6 THE WEATHER-TRAFFIC INDEX SYSTEM Data Preparation TAXI TRAJECTORIES WEATHER REPORT DATA REGIONAL INFORMATION ROAD NETWORK  Crawled from wunderground.comwunderground.com  Reported on hourly basis  14 weather features

7. Dissecting Regional Weather-Traffic Sensitivity throughout a City 7 THE WEATHER-TRAFFIC INDEX SYSTEM Data Preparation TAXI TRAJECTORIES WEATHER REPORT DATA REGIONAL INFORMATION ROAD NETWORK  Categories:  # of POIs (place-of-interests)  Area structure  Density of POIs and roads  Community information  Crawled from:  dianping.com (like Foursquare) dianping.com  fang.com (like Zillow) fang.com

8. Dissecting Regional Weather-Traffic Sensitivity throughout a City 8 THE WEATHER-TRAFFIC INDEX SYSTEM Region Partitioning TAXI TRAJECTORIE S WEATHER REPORT DATA REGIONAL INFORMATION ROAD NETWORK  Road-intersection-oriented partitioning  Voronoi diagram  Only major road segments are used VORONOI CELL / REGION TRAFFIC PARAMETERS WEATHER INFORMATION REGIONAL FEATURES

9. Dissecting Regional Weather-Traffic Sensitivity throughout a City 9 THE WEATHER-TRAFFIC INDEX SYSTEM Weather-Traffic Correlation Detection Heavy Rain Traffic Parameters Weather Information Infe r Traffic Jam  High accuracy = area traffic is more sensitive to weather  Low accuracy = area traffic is less sensitive to weather  There are many other reasons which impact traffic:  The traffic in peak-hour differs from that in non-peak hours  The traffic accident in one road segment will influence the traffic in nearby road networks  The road works slow down the average speed  …  These reasons are dominant in most cases

10. Dissecting Regional Weather-Traffic Sensitivity throughout a City 10 THE WEATHER-TRAFFIC INDEX SYSTEM Weather-Traffic Correlation Detection Predict t 0, …, t n * Algorithm details are shown in the paper

11. Dissecting Regional Weather-Traffic Sensitivity throughout a City 11 THE WEATHER-TRAFFIC INDEX SYSTEM Factor Analysis  Which regional features affect weather-traffic index the most?  Use weather-traffic indices of adjacent cells to predict the weather-traffic index of each cell  Use regional features to construct the similarity function  Feature selection on all regional features via the above inference method Similarity

12. Dissecting Regional Weather-Traffic Sensitivity throughout a City 12 EMPIRICAL STUDY Weather-Traffic Index  1 Yu Garden, a tourism attraction  2 Shanghai Confucian Temple, a tourism attraction  3 Shanghai Town God Temple, a tourism attraction  4 / 5 No conspicuous reasons, maybe construction areas

13. Dissecting Regional Weather-Traffic Sensitivity throughout a City 13 EMPIRICAL STUDY Factor Analysis  Community features have the most influence on weather-traffic index  # of POIs have the least influence on weather-traffic index # OF NEIGHBOURING CELLS TOTAL ROAD LENGTH PER SQUARE METER RATIO OF MAJOR / MINOR ROADS AVERAGE HOUSE AGE AVERAGE HOUSE UNIT PRICE # OF LEISURE SPOTS PER SQUARE METER MINOR ROAD LENGTH PER SQUARE METER MAJOR ROAD LENGTH PER SQUARE METER # OF INTERSECTIONS # OF RESIDENTIAL COMMUNITIES

14. CONCLUSION  A systematic approach has been proposed for establishing weather-traffic index throughout a city  A novel method has been proposed to successfully address the impact of weather to traffic from many other reasons  A supervised learning method have been proposed to disclose the key factors and their weights contributing to weather-traffic index throughout the city  We conduct empirical study in the largest city of China using large-scale real-life data Dissecting Regional Weather-Traffic Sensitivity throughout a City 14

15. Thanks! Dissecting Regional Weather-Traffic Sensitivity throughout a City Presented by Ye Ding Dissecting Regional Weather-Traffic Sensitivity throughout a City