Spatio Temporal Analysis of Network Data. Dr Tao Cheng

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1 Spatio Temporal Analysis of Network Data and Road ddevelopments Dr Tao Cheng

2 Dr Tao Cheng Senior Lecturer in GeoInformatics Department of Civil, Environmental & Geomatic Engineering, UCL Data quality and uncertainty of spatial objects Multi-scale spatio-temporal data modelling and analysis Intelligent spatio-temporal temporal data mining Some relevant projects: 4D GIS for decision support system [1] Experimental modelling of changing activity patterns using GIS (HK) [2] Spatio-temporal data mining (PRC) [3] [1] [2] [3]

3 Artemis Skarlatidou EngD student Department of Computer Science, UCL Trust in web-based GIS/SDSS Case study: Site Selection of a nuclear waste repository in the UK How trust can be built and enhanced in the webbased GIS/SDSS context and what elements affect its trustworthiness? - using Human-Computer Interaction Methods

4 Adel Bolbol Fernández PhD Student Department of Civil, Environmental & Geomatic Engineering, UCL Modelling the Changing Surroundings of Everyday Life - Travel Mode Detection from Geotagged Data Modelling Spatio-Temporal Human Activity & Travel Behaviour Activity recognition Diaries Semi-SupervisedSupervised Unsupervised Location Information Location Information Sensor Data GPS,GSM, WiFi & RFID Mode of Transport by producing Activity Maps

5 Spatio Temporal Analysis of NetworkData and Road Developments Dr Tao Cheng Prof. Benjamin Heydecker, Mr Andy Emmonds, Team

6 Outline Introduction Background, aim and programme Methodology integrated ST Data Mining Statistical approach Machining learning Visualization Simulation Working Plan Progress

7 Background Large cities are increasingly crowded - population & mobility Traffic congestion affects both the economy and daily life. It is difficult and expensive to increase the capacity of the road network.

8 City of London Traffic levels in the Congestion Charging Zone are falling but congestion levels are rising. cost of congestion - 3 billion per year Mayor s traffic priorities reduce congestion and smooth traffic flows Remove western extension of CC (27/11/2008) Olympic Game 2012 travel time to London Olympic sites

9 Why is this? Thought to be due to a reduction in network capacity? Reallocations of capacity to other uses? Reduction R d in resilience of the network? Aim To quantitatively measure road network performance To understand causes of traffic congestion association between traffic and interventions traffic flow, speed/journey time incidents, road works, signal changes and bus lane changes Case study London

10 Challenge (1) Network kcomplexity 1) Dynamics 2) Spatial dependence 3) Spatio-temporal interactions 4) Heterogeneity To understand the traffic congestion in central London EP/G023212/1 /

11 Challenge (2) - Data issues massive 20GB monthly multi-sourced related to 5 different networks different scales (density & frequency) variable data quality contain conflicts, errors, mistakes and gaps

12 London Road Networks Cordons Central, Inner, Outer Screenlines Thames, Northern, five radials four peripherals

13 DATA COVERAGE

14 Traffic Flow Surveys NMC (National manual count annual data) ATC (Automatic Count) 20 MB different time periods, intervals and accuracy

15 Traffic speed (and hence journey time) data MCOS (Moving Car Observer Surveys) Centre, Inner, and Outer least accurate of the datasets ITIS (GPS vehicle tracking system) - 2GB major A roads and bus routes in town with 2000 probes medium accuracy ANPR (Automatic Number Plate Reading) 6 GB main roads in the central and west extensions of CCZs 5-minute intervals, 5 vehicle groups, high accuracy available since March 2008 At least 5 networks boundaries do not fully align

16 LTIS incident id and event data - 20MB works, hazards, accidents, signal faults, special events, breakdowns, security, and other causes DfT have all these data as map or as text files - Minimal, Moderate, Serious or Severe subjective? unrecorded? not geocoded? not broadcast on the traffic Link website, creating problems in analysis and reporting. There are uncertainties and gaps in the intervention data

17 Methodology - ISTDM

18 What s new - (1) data-driven, top-down Transition in data availability Data scarcity: Data abundance: High cost Low volume Intensive validation Transition in modelling approach Bottom-up: High volume Multiple kinds and sources Extensive application Top-down: Mechanistic Explicit Phenomenological representation of Describe system behaviour (origin, gross of all dest n, model, time behavioural ) responses System properties Direct to by aggregation objectives

19 What s new:(2) integrated space and time Existing ST analysis methods time series analysis + spatial correlation spatial statistics i + the time dimensioni time series analysis + artificial neural networks ST dependence space + time Integrated modelling of ST is needed seamless & simultaneous ST-association/autocorrelation

20 What s new:(3) Hybrid Approach combine regression analysis with machine learning - improve the sensitivity and explanatory power study the heterogeneity and scale of road performance - optimal scale for monitoring Quantitative assessment of network performance - Sensible decision making & policy evaluation

21 Principle of ST Modelling Space-time data = global (deterministic) space-time trends + local (stochastic) space-time variations Z ( t ) = μ ( t ) + e ( t ) i i i Z(t)=u(t)+e(t) Zi=ui+ei z i ( t ) - the observation of the data series at spatial location i and at time t; μ i (t) - space-time patterns that explain large-scale deterministic space-time trends and can be expressed as a nonlinear function in space and time. e i (t) - the residual term, a zero mean space-time correlated error that explains small-scale scale stochastic space-time variations.

22 Model 1 STARIMA Spatio Temporal Auto Model 1 - STARIMA - Spatio-Temporal Auto- Regressive Integrated Moving Average = = = = + = p k q l n h h lh m h h kh i l k t l t W k t z W t z ) ( 0 ) ( ) ( ) ( ) ( ) ( ε ε θ φ (Pfeifer P E and Deutsch S J, 1980)

23 Our approach Integrated modelling of ST Model 1 STARIMA z p m q n k l ( h ) ( h ) i ( t ) = φkh W z ( t k ) θ lh W ε ( t l ) + ε k= 1 h = 0 l= 1 h= 0 define weights based upon spatial distance and spatial adjacency consider anisotropy able to model spatially continued phenomena ( t ) Tao Cheng Jiaqiu Wang Xia Li 2010A hybrid approach for space-time Tao Cheng, Jiaqiu Wang, Xia Li, 2010 A hybrid approach for space-time series of environmental data, Geographical Analysis (subject to minor revision)

24 Model 2 - ANN - Artificial Neural Networks (Mandic D P and Chambers JA, 2001) SFNN spatial interpolation DRNN time series analysis ( a) static neuron ( b) dynamic neuron ẑ i = n j= 1 iw ij z j + b ẑ ˆ( (t) = iw z(t) () + lw ẑ(t ˆ( 1) + b

25 ANN for space-time trend analysis n μˆ = i ( t) f ( β k f ( i, t ) + β 0 ) k = 1 Tao Cheng Jiaqiu Wang 2009 Accommodating Spatial Associations in Tao Cheng, Jiaqiu Wang, 2009, Accommodating Spatial Associations in DRNN for Space-Time Analysis, Computers, Environment and Urban System, 33,

26 Model 2 - STANN Space-Time Neuron n (1) (0) ẑz i (t) = iw + ẑ + ji z j(t 1) lw zi(t 1) b One step implementation of ANN+ STARIMA Accommodate ST associations i in ANN Deal with nonlinearity & heterogeneity in BP learning j= 1 Jiaqiu Wang, Tao Cheng, STANN Modeling Space-Time Series by Artificial Neural Networks, International Journal of Geographical Information Science, under review

27 Model 3 SVM - Support Vector Machines SVC & SVR (Vapnik et al, 1996)

28 J.Q. Wang, T. Cheng*, J. Haworth Model 3 - STSVR Nonlinear Spatio-Temporal Regression by SVM Develop ST kernel function Overcome over-fitting in STANN Deal with errors Model nonlinearity & heterogeneity Jiaqiu Wang, Tao Cheng, James Haworth, Space-Time Kernels, submitted to Spatial Data Handling (SDH) 2010, Hong Kong, May

29 Other methods Geographically Weighted Regression (GWR) -> STWR? Permutation Scan Statistics (PSS) > STPSS? (or STC) Exploratory Visualization + ST -> STEV? Simulation (Multi-scales) -> STMSS?

30 James Haworth Department of Civil, Environmental & Geomatic Engineering, UCL Multi-scale analysis of road network performance Using spatio-temporal temporal data mining techniques to look for patterns in congestion at varying spatial and temporal scales What patterns can be observed in inbound and outbound congestion... Daily? Weekly? Seasonally?... Identification of recurrent and non-recurrent congestion in London EP/G023212/1 /

31 Berk Anbaroğlu Mphil/PhD Student Detection of Emerging Spatio-Temporal Outliers on Network Data Faulty Sensor Detection Better Modelling

32 Garavig Tanaksaranond Mphil/PhD Student Visualization of dynamic traffic data Research Objectives To provide visualization tools with contributing ti methods to help better understanding of traffic data. Visualization techniques should help to cope with the characteristics of traffic data that limit the ability to extract useful information and to understand traffic data. How? Modify M existing i depiction i techniques Visualization + Other techniques: Data D aggregation and summarization i Extract patterns prior to visualization (ex. data mining i techniques)

33 Ed Manley EngD Candidate Multi-Scale Traffic Simulation Microscopic (Individual) Mesoscopic (Link) Macroscopic

34 Team (July 2009 June 2012) UCL Dr Tao Cheng (PI), Senior Lecturer in GIS Prof. Benjamin Heydecker (Co-I), Director of CTS Dr Jingxin Dong, Pattern analysis (Congestion modelling) Dr Jiaqiu Wang, Prediction (STARIMA, STANN) Mr James Haworth, Scales in ST (SVM/GWR) Mr Berk Anbaroglu, Outlier detection (STPSS) Ms Garavig Tanaksaranond, Dynamic visualization i Mr Ed Manley, EngD, Simulation (Agent-based) 3 visiting scholars, each 2 months TfL RNP&R Mr Andy Emmonds, Principal Transport Analyst Mr Mike Tarrier, Head of RNP&R Mr Jonathan Turner, Performance Analyst

35 Working Plan

36 Stage 1 - Traffic pattern Analysis (recurrent/non-recurrent) recurrent) GPS (formerly ITIS, now Traffic Master) - speed ANPR (Automatic Number Plate Recognition) journey time/speed ATC (Automatic Traffic Counts) flow The problems how to associate ANPR data with the links defined in ITN map (GPS data). Flow with speed?

37 Prediction (JW - STARIMA) Outlier (BA Cluster, STARIMA) Visualization (GT animation inbound/outbound speed) Patterns (JD - Congestion movement) Scales in ST (JH - Kernel/GWR/SVM) Simulation (EM flow+behaviour)

38 Stage 2 Cause-effect analysis of congestion (Spring 2010) LTIS (London Traffic Information System) Time, Location, Event Type, Event description, Event Duration Others for validation Cordons and screen lines Geometric description of screen lines, preferably defined in core link map or ITN map. Flows crossing screen lines: Time, Traffic Counts, Vehicle type SCOOT Time, Link ID, Lane ID, Traffic Volume, Location

39 LTIS data Cordons and screen lines SCOOT

40 Stage 3 - Detailed traffic modelling (later stage) OD matrix (calibration of journey time) Observation Time, Origin, Destination, Number of Trips the description of traffic zones Mobile phone data (traffic simulation) Time, Current HandOff position ID HandOff position ID, Link ID, Coordinates(x,y)

41 GUI: A Web-base Platform for Dynamic Visualization, Simulation, Analysis (OLAP) Tool Boxes: Integrated Spatio-Temporal Data Mining (Matlab+..?) Pattern JH/BA Clustering Pattern 2.1 Transition 2.2 Intervention Analysis 2.3 Performance Prediction 2.4 Model Updating 2.5 DRNN GWR SVM STARIMA Database/Platform(Oracle + ArcGIS) (ANPR, GPS, ITLS,. based on ITN) STANDARD Platform Structure

42 Preliminary Results

43 Study area London Arterial Road Map

44 Pattern analysis of journey time (a) Journ ney time (miin) (b) Jourrney time (m min) 1 The distribution plot of 33 Mondays journey times link 605 during 07:00 to 19:00

45 Periodic and Space-Time Autocorrelation Analysis (a) Space-time Autocorrelation Function after 48th order seasonal difference 0.2x 10 6 Space-time Autocorrelation (a) R605 Periodogram Function after one order non-seasonal difference Space-t ime Autoco orrelations 0.5Space-tim me Autocorr relations (b) maximum at freq= period= frequency (b) R605. Cumulative Periodogram frequency Lag Lag

46 Prediction results Actual min prediction Aug Aug Aug Actual min prediction Aug Aug Aug Aug Aug Aug Aug 24 Actual Actual Actual 10 min prediction min 5 min prediction prediction Actual 20 min prediction

47 Figure 1. Delay at 9 am on 12 th April 2009

48 Figure 2. Delay at 9:15am on 12 th April 2009

49 Figure 3. Delay at 9:15 am and 3:45 pm on 12 th April 2009

50 DATA COVERAGE

51 Wall Map

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54 LCAP 15 January :00-10:00 am

55 LCAP(Inbound) 15 January :00-10:00 am Bus lane users Non bus lane users

56 Isosurface High delay value (red color)

57 Sideview Topview

58 Traffic Master Data

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61 STANDARD Website The STANDARD Project website is currently under development. It contains information regarding the objectives and progress of the project and details of the various methods that are used. There is a STANDARD blog There is a STANDARD blog which will soon be made available for interested people to leave comments and engage in discussion.

62 Can we predict/migrate i t emergence (congestion) of Road Network? Understand Detect Model Simulation

63 Acknowledgements National High-tech R&D Program (863 Program) NSF China