SMART CARD DATA IN A PERIPHERAL REGION: HOW SMART CARD DATA CAN BE USED TO ILLUMINATE THE FLOW OF PUBLIC TRANSPORT PASSENGERS IN NORTHERN JUTLAND

Transcription

1 IN A PERIPHERAL REGION: HOW CAN BE USED TO ILLUMINATE THE FLOW OF PUBLIC TRANSPORT PASSENGERS IN NORTHERN JUTLAND PRESENTATION FOR TØF BIG KONFERENCE 2 TH OF DECEMBER 2015 KRISTIAN HEGNER REINAU, ASSOCIATE PROFESSOR, HENRIK HARDER, VEJ- EU (FORMER )

2 INTERNATIONAL STATE OF THE ART FOR CAPTURING ON PASSENGERS GNSS (GLOBAL NAVIGATION SATELLITE SYSTEMS GPS, GLONASS, COMPASS) NEAR FIELD SYSTEMS (E.G. RFID, BLUETOOTH, ZIGBEE) COMMUNICATIONS (CELLULAR TELEPHONY GSM, 3G, 4G, WIFI, WIMAX (802.X)) TRANSACTION SYSTEMS (E.G. S) EMBEDDED SENSORS (ON PEOPLE, VEHICLES, INFRASTRUCTURE) SENSOR NETWORKS AND COOPERATIVE TECHNOLOGIES USER CONTRIBUTED CONTENT (E.G. SOCIAL NETWORKING ) CAMERA NETWORKS (EG. CCTV) SOCIAL NETWORKS (EG. FACEBOOK, TWITTER, FLICKR) ( ARMOOGUM ET AL., 2014)

3 NEW SCAPE (Armoogum et al., 2014)

4 FOUR TECHNICAL CHALLENGES OF BIG : 1) HOW DO WE PRACTICALLY COMBINE FROM SEVERAL DIFFERENT SOURCES, SUCH AS FACEBOOK, GOOGLE, TWITTER, GOVERNMENT AGENCIES ETC. (REDMOND, 2012)? 2) HOW CAN THE BE TRANSFERRED AND STORED ( KIRON, 2012; FERGUSON, 2012)? 3) HOW CAN THE BE MANAGED (MADDEN, 2012)? 4) WHAT KIND OF ANALYSIS SHOULD BE DONE ON IT (BATTY, 2012; CHESHIRE & BATTY, 2012; REDMOND, 2012)?

5 THE EMPIRIC SET A: NT ALL REJSEKORT TRIPS MADE IN NORTHERN JUTLAND FROM 1ST OF AUGUST 2013 UNTIL 31ST OF AUGUST 2014, I.E. 13 MONTHS, OF WHICH AT LEAST ONE LEG OF THE JOURNEY WAS DONE IN NT OPERATED VEHICLES FULL TRIPS IN THE 13 MONTH PERIOD, WITH 85 ATTRIBUTES TO EACH TRIP STAGE- TRIPS IN THE 13 MONTHS PERIOD, WITH 71 ATTRIBUTES TO EACH TRIP LEG. A FULL TRIP WILL ALWAYS CONSIST OF AT LEAST TWO STAGE- TRIPS DEFINED BY TWO ACTIVITIES (CHECK IN AND CHECK OUT).

6 THE EMPIRIC SET B: NT, DSB, ARRIVA, MIDTTRAFIK ALL REJSEKORT TRIPS MADE IN NORTHERN JUTLAND IN FULL TRIPS IN 2014, NT, DSB, ARRIVA ETC, WITH 85 ATTRIBUTES TO EACH TRIP STAGE- TRIPS IN 2014, WITH 71 ATTRIBUTES TO EACH TRIP LEG. A FULL TRIP WILL ALWAYS CONSIST OF AT LEAST TWO STAGE- TRIPS DEFINED BY TWO ACTIVITIES (CHECK IN AND CHECK OUT).

7 ANALYSIS AND VISUALIZATIONS 1) LINE VISUALIZING FLOWS OF PASSENGERS IN NORTHERN JUTLAND : TUESDAY THE 22 ND AND SATURDAY THE 28 TH OF MARTS ) MODULARITY AND PAGE RANK ANALYSIS OF IN NORTHERN JUTLAND MORE ADVANCED ANALYSIS OF THE FLOWS IN WHICH WE LOOSE THE GEOGRAPHY TO REVEAL NEW TRAVEL PATTERNS IN THE REGION. 3) VISUALIZATION OF ROUTE 200 DETAILED ANALYSIS OF ROUTES THAT SHOW WHAT AREAS THE ROUTES COVERS, THE MOST IMPORTANT STOPS, AND THE AREAS WHERE THE PASSENGERS COME FORM. 4) TRAVELS ON BUS AND TRAVELS ON TRAIN COMPARISON BETWEEN THE TWO MODES

8 LINE VISUALIZING FLOWS OF PASSENGERS IN NORTHERN JUTLAND (SET A)

9 LINE VISUALIZATION OF TRIPS

10 TUESDAY THE 28 TH OF MARTS TRIPS

11 TUESDAY THE 28 TH OF MARTS YOUTH TRIPS

12 TUESDAY THE 28 TH OF MARTS RETIREMENT AGE TRIPS

13 TUESDAY THE 28 TH OF MARTS RETIREMENT AND YOUTH

14 TUESDAY THE 28 TH OF MARTS TIMING OF TRIPS

15 TUESDAY THE 28 TH OF MARTS TIMING OF TRIPS

16 TUESDAY THE 28 TH OF MARTS TIMING OF TRIPS

17 TUESDAY THE 28 TH OF MARTS TIMING OF TRIPS

18 TUESDAY THE 28 TH OF MARTS TIMING OF TRIPS

19 SATURDAY THE 22 ND OF MARTS TRIPS

20 SATURDAY THE 22 ND OF MARTS RETIREMENT TRIPS YOUTH 768 TRIPS

21 MODULARITY AND PAGE RANK ANALYSIS OF IN NORTHERN JUTLAND (SET A)

22 MODULARITY ANALYSIS MODULARITY ANALYSIS IS AN APPROACH DEVELOPED IN NETWORK THEORY, AND APPLIED TO NUMEROUS THEMES, FOR EXAMPLE SOCIAL NETWORK ANALYSIS, TWITTER, WEB- SITE ANALYSIS, ETC., TO IDENTIFY CLUSTERS IN THE NETWORK, ALSO CALLED COMMUNITIES

23 MODULARITY ANALYSIS THE MODULARITY OF A NETWORK IS ALWAYS WITHIN THE RANGE OF - 0,5 TO 1.0, POSITIVE INDICATING THAT THERE ARE MORE EDGES AMONG THE NODES WITHIN THE CLUSTERS THAN THERE WOULD BE IF EDGES WERE RANDOMLY DISTRIBUTED AMONG ALL THE NODES. Seminal Karate Club network, identified by (Zachary 1977) Cluster 1 Cluster 2

24 MODULARITY ANALYSIS SINCE THE MODULARITY ALGORITHM IDENTIFIES CLUSTERS USING AN STARTING VALUE, WHICH INDICATES HOW MANY CLUSTERS TO LOOK FOR, A HIGH VALUE RESULTING IN A A DETAILED PICTURE, A LOWER VALUE RESULTING IN LARGER CLUSTERS, THE IDENTIFICATION PROCESS IS AN ITERATIVE PROCESS, WHERE DIFFERENT STAT- VALUES ARE TESTED UNTIL A RESULT WHICH SUITS THE GRAPH VISUALIZATION, IN OUR CASE A FORCE- FIELD VISUALIZATION IS SATISFYING. THE FOLLOWING TWO SLIDES SHOWS FORCE FIELD VISUALIZATIONS, WITH DIFFERENT AMOUNTS OF CLUSTERS IDENTIFIED USING THE MODULARITY ALGORITHM. THEN THE MODULARITY WAS CALCULATED USING GEPHI. THE APPLIED ALGORITHM WAS THE LOUVAIN MODULARITY ALGORITHM, IN AN IMPLEMENTATION WHICH INCLUDES EDGE WEIGHTS IN THE CALCULATION.

25 MODULARITY ANALYSIS MARTS 18 CLUSTERS

26 MODULARITY ANALYSIS MARTS 31 CLUSTERS

27 MODULARITY ANALYSIS MARTS 21 CLUSTERS

28 PAGERANK ANALYSIS ONE THING IS TO CLASSIFY THE STOP POINTS INTO CLUSTERS, ANOTHER THING IS TO IDENTIFY THE MOST IMPORTANT STOPS IN THE NETWORK. THIS IS DONE USING THE PAGERANK ALGORITHM, ORIGINALLY INVENTED BY GOOGLE TO IDENTIFY KEY WEB- SITES IN NETWORKS CONSISTING OF LINKS AND WEBSITES. WE USE THE SAME METHOD, IN THE IMPLEMENTATION IN GEPHI, WHICH TAKES INTO CONSIDERATION THE WEIGHT OF THE EDGES.

29 MODULARITY ANALYSIS 21 CLUSTERS AND PAGERANK FOR MARTS

30 GEOGRAPHICAL VISUALIZATION OF 21 CLUSTERS

31 GEOGRAPHICAL VISUALIZATION OF 21 CLUSTERS, WITH PAGE RANK

32 CLUSTER 5: A CASE

33 TRIPS WITHIN CLUSTER stops trips between stops in cluster trips out of cluster trips into cluster 5

34 TRIPS WITHIN CLUSTER stops trips between stops in cluster trips out of cluster trips into cluster 5

35 TRIPS OUT OF CLUSTER stops trips between stops in cluster trips out of cluster trips into cluster 5

36 TRIPS INTO CLUSTER stops trips between stops in cluster trips out of cluster trips into cluster 5

37 IMPLICATIONS THE IDENTIFIED CLUSTERS CALCULATED ON THE BASIS OF A MODULARITY ANALYSIS OF THE NETWORK OF FULL- TRIPS SUGGEST THAT THE SYSTEM OUGHT TO BE PLANNED WITH HIGH CONNECTIVITY AND TRAVEL EASINESS WITHIN THE IDENTIFIED CLUSTERS AS AN OBJECTIVE!

38 MODULARITY ANALYSIS JULY 22 CLUSTERS AND PAGERANK

39 JULY 22 CLUSTERS In July, the top of Northern Jutland, including Frederikshavn, appears to be one cluster This could raise the hypothesis that this is due to missing work related travel between Frederikshavn and Aalborg!

40 VISUALIZATION OF ROUTE 200 (SET A)

41 ROUTE 200: 13 MONTHS TRIPS

42 ROUTE 200: 13 MONTHS TRIPS

43 ROUTE 200: 13 MONTHS TRIPS

44 TRAVELS ON BUS AND TRAVELS ON TRAIN (SET B)

45 EXPLORING THE TRANSPORT COMPANY USED THE FIRST OPERATION WAS TO LINK ALL STOPS IN THE TO CITIES. THIS WAS DONE THROUGH AN OVERLAY ANALYSIS IN GIS LINKING THE COORDINATE POSITION OF EACH STOP POINT TO A VECTOR MODEL DESCRIBING THE GEOGRAPHICAL EXTENT OF ALL CITIES IN NORTHERN JUTLAND (KORT10). THIS ANALYSIS RESULTED IN A CITY NAME BEING LINKED TO EACH STOP POINT. THIS WAS THEREAFTER JOINED TO THE START- POINT AND END- POINT INFORMATION IN THE FULL- TRIPS BASE WITH THE FULL TRIPS CONDUCTED IN NORTHERN JUTLAND IN THEREAFTER A NUMBER OF QUERIES WERE DONE IN THE RESULTING BASE TO EXTRACT ON HOW MANY FULL TRIPS THERE WERE CONDUCTED BETWEEN CITIES IN THE REGION AND THE OPERATORS ON THESE TRIPS.

46 EXAMPLE OF STATISTICS ON TRIPS BETWEEN TWO CITIES From City To City Operator Passagers Skørping Aalborg DSB Skørping Aalborg DSB+NT 5875 Skørping Aalborg NT 61 Skørping Aalborg NT+Midttrafik 1 Aalborg Skørping DSB Aalborg Skørping DSB+NT 5088 Aalborg Skørping NT 222

47 ALL TRIPS BETWEEN THE LARGER CITIES SOUTH OF THE LIMFJORD TRIPS

48 PURE DSB (TRAIN) TRIPS BETWEEN THE LARGER CITIES SOUTH OF THE LIMFJORD TRIPS

49 PURE NT (BUS) TRIPS BETWEEN THE LARGER CITIES SOUTH OF THE LIMFJORD TRIPS

50 PURE DSB (TRAIN) TRIPS BETWEEN THE LARGER CITIES SOUTH OF THE LIMFJORD TRIPS

51 PURE NT (BUS) TRIPS BETWEEN THE LARGER CITIES SOUTH OF THE LIMFJORD TRIPS

52 ALL TRIPS BETWEEN THE LARGER CITIES NORTH OF THE LIMFJORD TRIPS

53 PURE DSB (TRAIN) TRIPS BETWEEN THE LARGER CITIES NORTH OF THE LIMFJORD TRIPS

54 PURE NT (BUS) TRIPS BETWEEN THE LARGER CITIES NORTH OF THE LIMFJORD TRIPS

55 CONCLUSION All previous research in smart card data has focused on larger metropolitan areas, there are no studies yet which explores the use of this data source in a peripheral geographical context. This research has investigated how this type of data can be used to illuminate the flows of passengers in a peripheral region like Northern Jutland. The transport field have moved into a new datascape, and the use of smart card data entails both technical and legal challenges. However, if these are overcome, and this is possible as illustrated through the analysis in this report, smart card data holds potential for optimizing the transport systems in peripheral regions. The analysis and visualizations examples in the report shows how the data can be used to both establish the overall flows of passengers in Northern Jutland, to classify the stops in the region into coherent clusters in which good connectivity should be planned, and finally to explore the catchment areas of specific routes in detail. Finally it should also be noted that the travel card data us more dynamic. As explored in relation to the cluster analysis, there may be an monthly variation in the travel patterns in the region, and smart card data holds the potential to explore such temporal variations in greater details than ever before, be that weekly, monthly or seasonal, and the data can as such be used to design more efficient and flexible public transport products.

56 W2 THANK YOU FOR YOUR ATTENTION