Strategic Crash and Citation Analysis Using a State-Wide Dataset. Alex Wagner Center for Leadership in Public Service

Transcription

1 Strategic Crash and Citation Analysis Using a State-Wide Dataset Alex Wagner Center for Leadership in Public Service

2 Main Members of Project Team Alex Wagner, Fisher College Christopher Bruce (consultant) Barbara Rizzuti, Massachusetts Highway Safety Division Diana Danelian, Fisher College Victoria Guay, Fisher College

3 Project Background Uses state-wide dataset for MA Data is from online Massachusetts Traffic Records Analysis Center (MassTRAC) Funding from Commonwealth of Massachusetts Traffic Records Coordinating Committee Any views or opinions expressed are solely those of the presenter and not of the Commonwealth of MA

4 Data from MassTRAC MassTRAC provides online users with the ability to: Create map-based displays and perform spatial queries of crash locations. Evaluate crash data and relate it to underlying road network characteristics. Generate statistical summaries and tabulations of crash, citation, person and vehicle characteristics. Generate standard reports to be used in the development of various highway safety plans.

5 MassTRAC Available for free to Law Enforcement All that is needed is an MOU Great resource for departments without own analysis or mapping resources Especially for regional approaches

6 Data Sources MassTRAC integrates data from a number of different sources: Crash data (details, people, vehicles) from the Registry of Motor Vehicles (RMV) Citation and violation data from the Merit Rating Board and the RMV Geographic data and files from MassGIS and ESRI Demographics from the US Census

7 MassTRAC Background Oracle Relational DB Data provided in set views aka queries Users are limited in what they can see online Data not current, strategic rather than tactical use

8 Predetermined Filters For Views

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15 Project Goals Include: Depicting how accurate and complete the data is Demonstrate to law enforcement agencies on the richness of the data by depicting what data is accessible and promoting the use of the data Provide insights into trends and patterns of the data Highlight causal relationships between variables and sets of variables Provide insights into use of traffic safety data for policy making

16 Took the available data out More advanced analysis possible Exported data for three years as CSV files Imported data into: Access (relational DB Schema) SPSS (flat file) ArcGIS (geocoded data or shape files)

17 Crashes (PK Crash ID) Tables Citations (PK Citation ID, FK Crash ID) Violations (PK Violation ID, FK Citation ID, FK Crash ID) Vehicles (PK Vehicle ID, FK Crash ID) Persons (PK Person ID, FK Crash ID, FK Vehicle ID) Subset of person e.g. driver, passenger, non-motorist

18 Data Linkage Possible via foreign keys/common keys Relational DB recreation not an issue Flat file creation in SPSS more challenging Multiple citations Multiple violations Etc. Excel could also be used for data subsets, perhaps more as I found out yesterday (see LSU presentations) Focus on crashes with citations for this phase

19 Project data 3 years of MA traffic records (closed data) About 2 million citations About 370,000 crashes About 2.9 million violations Linked 60,712 crashes with citations so far

20 Simplified Access DB Schema

21 Strategic Analysis Examples This data allows for systematic strategic evaluation of not just own data but neighboring communities, the region, or state Compare enforcements in one area to displacements in another and account for time difference (days, weeks, or months) New ways to look at traffic data to deploy enforcement resources

22 Strategic Analysis Examples DDACTS displacement effects can be systematically evaluated Police deployment can be based on more advanced analysis of data than might be done currently, if any is done at all Data driven support for policy development

23 Contrast to State or other Areas

24 Exploring the data Exploratory data mining techniques Found decision trees to be useful Unsupervised techniques Methods determines how data hangs together Combines if there is no statistically significant differences Splits if there is Exploring, not predicting, that comes later via model building and testing

25 Crash data example

26 Citation data example

27 Linked data example

28 Linked data example

29 Can easily become crowded!

30 Questions? Any additional questions: Please contact: Alex Wagner Follow up questions on MassTRAC and/or MA: Barbara Rizzuti Thank you very much