Detec%ng and Analyzing Urban Regions with High Impact of Weather Change on Transport

Transcription

1 Detec%ng and Analyzing Urban Regions with High Impact of Weather Change on Transport Ye Ding, Yanhua Li, Ke Deng, Haoyu Tan, Mingxuan Yuan, Lionel M. Ni Presenta;on by Karan Somaiah Napanda, Suchithra Balakrishnan, Zhaoning Su

2 URBAN compu%ng connects urban sensing, data management, data analy%c and service providing *Urban compu;ng with taxis, MSRA *A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality, MSRA

3 URBAN compu%ng connects urban sensing, data management, data analy%c and service providing Focus Traffic conges;on Energy consump;on Pollu;on Based on data traffic flow human mobility geographical data infer the real-;me and fine grained air quality informa;on throughout a city iden;fy the hot spots of moving vehicles in an urban area propose a framework, called DRoF, to discover regions of different func;ons in a city try to sense the refueling behavior and citywide petrol consump;on in real-;me

4 The impact of inclement weather to traffic May slow down the traffic May cause conges;ons due to low visibility and high demand of May influence the transport performance,

5 the impact of inclement weather to traffic *Changes in Climate and Weather Relevant on US Transport, The impact of climate change and weather on transport: An overview of empirical fin

6 Mo%va%on *IBM Smarter Traveler Traffic Predic;on So^ware *Google Opera;ng System how can we identify those regions being highly influenced by weather change on transport?

7 Challenges lacking of effec%ve traffic monitoring system in city-wide scale To enable weather-traffic index throughout a city and factor analysis extract traffic informa;on from numerous taxis driving on roads due to its availability, wide-coverage and low-cost. A taxi tracking system con;nuously record the informa;on including loca;on, speed, occupancy status, and orienta;on of the taxis

8 Challenges lacking of effec%ve traffic monitoring system in city-wide scale Voronoi diagram

9 Challenges lacking of effec%ve traffic monitoring system in city-wide scale Equal-sized rectangles Ø some cells are highly dense and in others are highly sparse Voronoi diagram Ø seeds are the road intersec;ons Ø every cell include at least one road intersec;on and a number of roads connected to this intersec;on Ø if several road intersec;ons are very close to each other, for example within 50 meters, they are grouped together as a complex intersec;on *The Voronoi diagrams par;;ons in Shanghai. The under layer represents the road networks.

10 Ques%ons The average of taxi can reflect the traffic informa%on? In the area of residence community, everyone have their own car so they don t need the taxi By calcula%ng the average driving speeds of all taxis in each Voronoi cell can reflect the average speed? However maybe some cells have a lower average speed of taxis is just because people are likely get on or get off from the taxis here

11 Challenges how to disclose the key factors behind the weather-traffic index density of roads number of road intersec;ons number of POIs(points of interest) traffic volume average age of the household density of buildings and more in the surrounding regions

12 The Goal of this paper To develope weather-traffic index (WTI) system The first is to set up a weather-traffic index throughout a city, which indicates the impact of weather to traffic from light to heavy. The second is to reveal the key factors behind the weather-traffic index throughout the city and their rela;ve weights. Previous works mainly focus on the analysis of road segments; on the contrary, this paper is the first study on local traffic-weather sensi;vity throughout a city and the inves;ga;on to reveal the key factors behind the sensi;vity

13 OVERVIEW 1. Data Prepara;on 2. Weather-traffic Index Establishment 3. Factor Analysis

14 1. DATA PREPARATION i. Regional Par;;oning Voronoi Diagram ii. Source Data Road Network Traffic Data Regional features Weather Report Data

15 DATA PREPARATION - Regional Par%%oning VORONOI DIAGRAM Par;;oning of a plane into cells/ regions based on the distance between seeds Shape and size of each cells is different from each other SEEDS- road intersec;ons ROAD- INTERSECTION- ORIENTED Par;;oning Proper;es Even distribu;on of road networks Potray the rela;on of weather and traffic

16 DATA PREPARATION Source Data i. Road Networks ii. Traffic Data iii. Regional Features iv. Weather Report Data i. Road Networks - G(V, E) E- set of road segments V- set of road intersec;ons E- type, length, speed limit, two end points V- loca;on (la;tude and longitude), type

17 DATA PREPARATION Source Data Time Mean Speed/ Average Speed- Traffic Parameter Arithme;c mean of individual spot speeds that are recorded over a selected ;me period Extracted from taxi trajectories in each cell ii. Traffic Data Traffic Parameters of interest are extracted Average Speed Quan;ty measures Quality assessment measures Movement measures Composi;on/ Classifica;on measures Split into 7 classes <10 km/h km/h km/h km/h km/h km/h >110 km/h The average speed of one road segment is subject to the traffic parameter of that road segment only, not comparable with other road segments

18 DATA PREPARATION Source Data iv. Regional Features Four Categories a) Points of Interest b) Structure c) Density d) Community iii. Weather Report Data State of atmosphere Degree to which it is hot or cold, wet or dry, calm or stormy, clear or cloudy

19 2. WEATHER TRAFFIC INDEX ESTABLISHMENT Input Weather data and Traffic data Indicates the impact of weather to traffic in different cells Given a cell g, its value in the weather traffic index is the correla;on between traffic and weather, denoted as ρ(g). ρ(g) takes a value from the discrete range [1, 2, 3, 4, 5]

20 WEATHER TRAFFIC INDEX ESTABLISHMENT Correla%on Detec%on Correla;on between traffic speed F t and weather F w. Classifier is trained with F t and F w Input- Weather as a feature vector Output- one of the seven speed classes Inference accuracy Correla;on between F t and F w in that cell Inference accuracy Correla;on between F t and F w in that cell Weakness of this method, Does not consider other reasons that affect traffic

21 WEATHER TRAFFIC INDEX ESTABLISHMENT Correla%on Detec%on Granger Causality test whether one ;me series is useful in forecas;ng another A ;me series X is said to Granger-cause Y if it can be shown that those X values provide sta;s;cally significant informa;on about future values of Y. A variable X that evolves over ;me Granger-cause another evolving variable Y if predic;ons of the value of Y based on its own past values and on the past values of X are beuer than predic;ons of Y based only on its own past values.

22 WEATHER TRAFFIC INDEX ESTABLISHMENT Correla%on Detec%on Traffic Predic;on models are trained separately for different ;me Weather-traffic index value ρ(g) is assigned to each cell to indicate the extent to which traffic is impacted by weather Cells are organised in ascending order of traffic predic;on accuracy improvement and then divided into k- equal sized subsets k- quan;les show the correla;on between traffic and weather from weak to strong

23 WEATHER TRAFFIC INDEX ESTABLISHMENT Traffic Predic%on Classifica;on Problem Methods used Support Vector Machine (SVM) Logis;c Regression (LOGIT) Perceptron 10-fold cross valida;on SVM is applied and LOGIT and Perceptron is used to verify the output of SVM

24 3. FACTOR ANALYSIS AssumpRon weather traffic index of all cells have been certainly assigned This method iden;fies the key factors and their weights contribu;ng to the weather- traffic indices of cells. Discloses what regional features make the traffic in some cells vulnerable to inclement weather Steps: 1. Key Factor Verifica;on by Index Inference (KFVII) 2. Weight Es;ma;on of Regional Features

25 FACTOR ANALYSIS Key Factor Verifica%on by Index Inference (KFVII) Intui;on Weather- traffic index of one region can be inferred from the indices of its closely located (or adjacent) cells Given a set of regional features F r, if the inference accuracy is sa;sfactory with F r as input, it indicates that such set of regional features are the key features This model is not symmetric Naïve Bayes classifier

26 FACTOR ANALYSIS Key Factor Verifica%on by Index Inference (KFVII) Marginal DistribuRon Probability distribu;on of the regions contained in a similarity subset Probability of one region being the index of i given one of its adjacent regions with index j, if two regions have a certain similarity Cosine Similarity is used to describe the similarity m uv between two regions g u and g v Similarity ranges of k- groups b0 is minimum similarity Bk is maximum similarity

27 FACTOR ANALYSIS Key Factor Verifica%on by Index Inference (KFVII) Pairs of cells in the group of [b i-1, b i ] are in marginal distribu;on matrix B i Rows of B i - weather traffic indices of g u Columns of B i - weather traffic indices of g v p ij - probability that the weather-traffic index of g v is ρ j when the weather- traffic index of g u is ρ i

28 FACTOR ANALYSIS Key Factor Verifica%on by Index Inference (KFVII) Index- Index Inference From marginal distribu;on, weather traffic index of a par;cular cell is inferred from adjacent cell using naïve Bayes classifier. Given a cell g u, the marginal distribu;on allows naïve Bayes classifier to infer which value the weather- traffic index of g u is most likely to be, based on the weathertraffic indices of its adjacent cells ρ(g 1 ), ρ(g 2 ),

29 FACTOR ANALYSIS Weight Es%ma%on of Regional Features Consider a regional feature with nontrivial impact to weather-traffic index, 1. Remove this regional features 2. Using KFVII method test if remaining features have high overall accuracy 3. If not, then removed regional feature is very important 4. δ(f i r) weight of the regional feature F i r by compu;ng the difference of the inference accuracy with and without F i r

30 EMPIRICAL STUDY Data Sets Road Network Data of Shanghai Taxi Trajectory Data Weather Report Data (of the same period of ;me as taxi trajectory data) Regional Informa;on Data

31 More about the Data Road Network Data Obtained from the government 7 levels of roads: Na;onal Expressway City Expressway Regular Highway Large Avenue Primary Way Secondary Way Regular Road Minor Roads Major Roads Weather Report Data Obtained from Weather Underground (wunderground.com) Includes 14 weather features For data alignment, recorded during the same ;me frame as taxi trajectory.

32 More about the Data Trajectory Data Spa;al-Temporal points Provides addi;onal informa;on including average speed. Regional Informa;on Data Real Estate Data & POI (Points of Interest) data. Real estate (soufun.com) provides informa;on about loca;on, price and residen;al communi;es. Dianping.com provides local merchant informa;on and other aurac;ons around the locality

33 Weather Traffic Index What is the WTI Average speed is inferred with/without weather. Difference indicates sensi;vity of the cell at that ;me slot. Robustness of the model: Accuracy of traffic predic;on is test with 3 models SVM gives 0.5 Perceptron and Logis;c regression give low accuracy, hence they will be used to test the robustness of WTI (with/without weather). A Posi;ve value indicates high impact or weather and a nega;ve value indicates low impact.

34 Robustness Evalua;on The above diagram shows most of the cells have an average accuracy of 0.5 for SVM. The above diagram gives changes in the predic;on accuracy which is the least for SVM. Thus, SVM can be used as the base model and Logis;c Regression and Perceptron are used to test. The changes in predic;on accuracy are close to mean for SVM and Logis;c Regression and it s a regular distribu;on where as for Perceptron is greater.

35 Valida%on The five labelled regions indicate high impact of weather on traffic. Most of them are tourist aurac;ons. Not all tourist aurac;ons have high impact due to inclement weather. eg: Xin;andi is not influenced by weather

36 Valida%on The rain aspect has been separated to show that it has significant impact on traffic. Example area 1: It shows that weather and rain both have significant impact on traffic. Rain label gives more informa;on about rain affec;ng traffic than weather affec;ng traffic. Example area 2: Average speed is more on rainy days.

37 Effec%veness Effec;veness verifies the regional features and es;mates their weights. Reciprocal method is used to evaluate It is considerably a fairer method than maximum likelihood since it widens the gap between different likelihoods. For this, Naive Bayes, Ar;ficial Neural Networks and Random Guess is used. Bayes performs the best, followed by ANN and then finally Random Guess.

38 Factor Analysis Four categories of regional features are tested: POI, structure, density and community. Figure shows that community regional features are more important than structure regional features. House Age & Number of Neighboring Cells are important factors.

39 Conclusion This paper fills the gap in the study on the impact of weather on traffic. The effec;veness of the system is verified. Regional factor analysis has significant impact: Regional House Age is an important factor The factors retrieved from this study can be applied to other ci;es to generate same knowledge. Further developments are expected since research in this field is always emerging.

40 Ques%ons?