Prepared for the ESRI Public Sector Conference April 11-13, 2017, West Palm Beach, Florida

Transcription

1 Prepared for the ESRI Public Sector Conference April 11-13, 2017, West Palm Beach, Florida By: John Lee, PhD Student, Ga. Tech Phoenix Associates Consulting, Inc. Urban Planning Professionals 1609 Lenox Rd, NE Atlanta, GA Rhonda Joyner, GISP DeKalb County GIS 330 West Ponce de Leon Ave. Ste. 400 Decatur, GA

2 Current Challenges Analysis, Assessment & Hypothesis Current research methodology Conclusions Future goals & expectations Questions & Answers 2

3 John M. Lee EDUCATION: 1) Kennesaw College - BS Computer Science 85 2) Kennesaw State University MBA Economics and Finance 98 3) Southern Polytechnic State University, BS Surveying and Mapping 13 4) Georgia Institute of Technology MS Geographic Information Science Technology 14 5) Georgia Institute of Technology PhD Urban Planning 18 Rhonda A. Joyner EDUCATION: DeVry University - BS Computer Information Science 03 3

4 Current Challenges Current Challenges First Responders (Police, Fire, etc.) Day of the event Unpredictable Time-on-scene Incomplete site awareness Potential Fire / Electrical / personal safety issues Erratic / Uncooperative behavior by public Inclement local weather conditions Potential Traffic volume Significance of road segment on emergency corridor operations plan After the event Detective s repetitive site visits Goals / Public Expectations First Responders (Police, Fire, etc.) Day of the event Zero Time-on-scene After event Perfect Open Records Info 4

5 Current Challenges On-going operations GIS, Public Works, Transportation, Public Relations Current Challenges Different & incomplete datasets from different sources Reactive responses / Silo d proactive analysis Limited ability to alert public to current events 5

6 Purpose of the research Goal of my research project Consolidated research / analysis Improved Predictive analysis Reduced time-on-scene & exposure of first responders Reduce road segment closure time Eliminate redundant & incomplete data collection 6

7 - Analysis Examining 2007 through 2016 data for Dekalb County, we found that: 1) Fatalities occurred at the same locations year-on-year 2) There was no obvious or significant pattern other than location 3) Previous research activities were limited to specific departments 4) Economic Impact costs were rising 7

8 - Analysis Additionally: 1) First Responders remain at great risk using current practices 2) Increasing departments are suffering an inability to respond adequately to Open records request 3) There is an inefficient usage and sharing of existing data across departments 4) Road segments are constantly changing (from design to construction to maintenance), they interconnect and they influence each others performance. 8

9 Assessment Activities 1) Incident Data was plotted for the period of time from 2007 to 2016 for Dekalb County, Georgia. 1) Data was collected from GIS, Transportation and Police. 2) Current practices and procedures for data collection were documented. 1) Open records request results from different departments were compared. 9

10 Assessment Activities 1) Subject matter experts examined existing data attributes and life-cycle 1) Age / freshness, source, accuracy, precision and storage media type 2) Producers and consumers 2) Interviews were conducted with each department 1) Directors, managers, supervisors, officers, technicians, staff 3) Review of existing traffic incident reports 1) Pattern recognition (location, time-of-day, participant type, phase of the moon) 2) Past control and treatment activities 10

11 Hypothesis 1) Drivers behave in a manner consistent with their assessment of the surroundings and their perception of the current risk levels 2) Drivers who have been driving on a high speed connector for a prolonged period of time will continue to drive at a higher rate of speed after leaving that connector. Thus, my hypothesis is that: 1) The locations where accidents occur that result in a fatality are statistically more likely to be located within a specific distance of a high speed connector. 11

12 Hypothesis 1) Drivers behave in a manner consistent with their assessment of the surroundings and their perception of the current risk levels 2) Drivers who have been driving on a high speed connector for a prolonged period of time will continue to drive at a higher rate of speed after leaving that connector. Thus, my hypothesis is that: 1) The locations where accidents occur that result in a fatality are statistically more likely to be located within a specific distance of a high speed connector. The drivers were Velocitized 12

13 - Proof 1) Using Spatial Analysis and Spatial Statistics analysis on longitudinal data, we found that road segments with higher fatality frequencies were located within a particular distance of a high speed connector at a statistically significant rate. 1) In our sample, we found that 397 out of 426 incidents were within sixty seconds travel time (less than 3500 feet) from a road segment with design speeds of 40 MPH or greater. 13

14 Current Investigation Process 1) First responders arrive 2) Upon discovery of a fatality, police closes road segment 3) Policeman (Detective) investigates & documents location with tools 6) Medical Examiner's Office escorts the deceased to the Medical Examiner's Office 5) Ambulance escorts the injured to hospital if needed 4) Policeman (Detective) notifies state DOT office, clerk of record at courthouse of fatality 7) Tow truck company relocates vehicles involved in the accident 8) Contractor (tow truck company) removes debris out of right of way 9) Policeman opens up intersection or road segment 10) Detective responds to hospital or to family residence to make notification. 14

15 Data Sets Roads Shapefile and Network Parcel Shapefile Accident Data [Excel Spreadsheets and scanned documents] Contour Shapefile LiDAR Data [3D Point Cloud of County and of Incident Site] Drone Data [Video and Geo-referenced Photography] 15

16 Basic Process Receive and clean up data Create buffers around roads Definition query of roads with a speed limit of 40 or more mph Buffer test, ran definition query of fatalities that were in the road buffer Run Kernel Density analysis to confirm where we should look into further Run an Optimized hotpot analysis (highlights clustering) Discuss Results 16

17 Current research toolset Spatial Statistics tools Analyzing Patterns->Multi-Distance Spatial Cluster Analysis Mapping Clusters->Hot Spot Analysis (Getis-Org Gi*) Mapping Clusters->Grouping Analysis Mapping Clusters->Similarity Spatial Analyst tools Distance->Euclidean Distance, Distance->Path Distance Density->Kernel Density Multivariate->Maximum Likelihood Classification Surface->Hillshade, Surface->Viewshed, Surface->contour with Barriers 17

18 1 st Reviewed data & populated missing latitude & longitude values Saved as a.csv file, added into workspace Right clicked csv table and click on Display X,Y Set the Spatial Reference (Geographic Projection, North America, Georgia NAD 1983) 18

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20 Clicked on Analysis (top) Selected Optimized Hotspot For input features, selected fatality point feature class. For Analysis field, selected Fatalities (number of fatalities) and a distance band of 1000 ft Entered a file name for the output 20

21 Clicked on Analysis (top) Selected Kernel Densityle name for the output For input features, selected fatality point feature class. For Population field, selected None. Entered the search radius, selected units, output cell values as densities and used a geodesic method Entered a file name for the output raster 21

22 Right-click on the roads feature class Selected a definition query of roads 40 or more mph Created buffer, saved buffer as a feature class ( Definition Query to limit road data to high speed connectors 22

23 516 vehicle fatalities existed within one minute of a high speed corridor (516 / 553 total incidents. Multiply result by 100 to get percentage. Hence, 93.3% vehicle accidents within one minute of a high speed (40 or more mpg) Zoomed in view 1500 road buffer 23

24 546 vehicle fatalities existed within one minute of a high speed corridor (546 / 553 total incidents. Multiply result by 100 to get percentage. Hence, 98.7% vehicle accidents within 30 seconds of a high speed (40 or more mpg) Zoomed in view 3500 road buffer 24

25 Optimized Hotspot map Optimized Hotspot map (reduced radius size) 25

26 Optimized Hotspot map (Zoomed in) 26

27 546 vehicle fatalities existed within one minute of a high speed corridor (546 / 553 total incidents. Multiply result by 100 to get percentage. Hence, 98.7% vehicle accidents within 30 seconds of a high speed (40 or more mpg) 27

28 Buffer showing distance from high speed connector at 30 seconds 28

29 Optimized Hot Spot Analysis Optimized Hot Spot for Incidents that resulted in a fatality

30 Zoom In to examine repeat sites 30

31 Examine using 30 second buffer 31

32 Buffer showing distance from high speed connector at 60 seconds 32

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34 Kernel Density Hot Spot Analysis 34

35 3D Kernel Density 35

36 3D Kernel Density 36

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42 Conclusions Results On average for 2007 through 2016, 56 deaths per year can be explained by the theory that proximity to high speed connectors is a contributing factor with a statistical significance of 90% First Responders time-on-scene is directly related to the number of vehicles involved and the complexity of the incident 42

43 Conclusions Results (continued) Departments that regulate planning, design, construction and maintenance of the road network would benefit from greater sharing of information. Public Service (Police, Fire and EMS) organizations could adjust existing resources to compensate for the impact of these road segments deficiencies 43

44 Conclusions Results (continued) Elected official could optimize tax dollar expenditures by directing predictive research activities to improve the safety of both the public and the first responders With an application of the appropriate treatment variables, potentially less people would die from traffic accident incidents 44

45 Future Goals and Expectations Time-on-scene reduction The inclusion of drones into existing practices and procedures will facilitate a ten to forty percent reduction in the time spent during and after the event at the incident site performing data collection surveys. We estimate that the use of smart apps to create and supply precomputed detour plans for first responders of any potential road segment closure will facilitate a ten to twenty percent reduction in traffic delays. We recommend that the municipality should investigate the integration into their emergency services operation control center procedures the ability to alert external traffic guidance systems (Wazes, MapQuest, Google Maps, 511) 45

46 Future Goals and Expectations Information Sharing (Endogenous and Exogenous) We estimate that the use of integrated data collection and sharing protocols will reduce field data collection activities by approximately fifty percent over time. We recommend that data collected by incident site teams should be shared with other departments for future planning and design review, maintenance scheduling, and to improve the precision and accuracy of GIS along with other data repositories. Newly augmented data could be utilized by economic development and GIS departments for resell to external consumers from the business and land development industries. 46

47 Future Goals and Expectations Courtroom review of incident site data in 3D 47

48 Future Goals and Expectations Courtroom review of incident site data in 3D 48

49 Future Goals and Expectations Courtroom review of incident site data in 3D Incident Site documentation technologies will improve 49

50 Future Goals and Expectations Courtroom review of incident site data in 3D Incident Site documentation technologies will improve 50

51 Future Goals and Expectations Courtroom review of incident site data in 3D Incident Site documentation technologies will improve 51

52 Future Goals and Expectations Courtroom review of incident site data in 3D Incident Site documentation technologies will improve Drones will become standard data collection platforms 52

53 Future Goals and Expectations Courtroom review of incident site data in 3D Incident Site documentation technologies will improve Drones will become standard data collection platforms 53

54 Future Goals and Expectations Courtroom review of incident site data in 3D Incident Site documentation technologies will improve Drones will become standard data collection platforms 54

55 Future Goals and Expectations Courtroom review of incident site data in 3D Incident Site documentation technologies will improve Drones will become standard data collection platforms 55

56 Questions and Answers 56

57 Links

58 Thank You 58