Performance Measures for Maintenance of Roadside Features
1-amend_odometer(OdometrMarker).R
fix odometer markers in order of postmiles
2-bind_df(LEMO_WorkOrder, LCS).R
clean up LEMO, Work Order, and Lane Closure System data sets. Merge LEMO and Work Order. Print for conversion of postmiles to odometer
3-bind_files(AADT, Truck).R
row bind AADT and Truck AADT data sets for different years into a single dataset
4-bind_files(LCS).R
5-bind_files(LEMO).R
6-bind_files(SWITRS).R
7-bind_files(WorkOrder).R
8-bind_odometer_geocode(SWITRS).R
adding odometer and coordinate values to the SWITRS database
9-bind_odometer(LEMO_WorkOrder_LCS_AADT_TRUCK).R
10-bind_odometer(LEMO_WorkOrder_LCS).R
11-Classification_decisionTrees(SWITRS_wzOnly).R
Classification code implementing decision tree for feature selection on SWITRS data set for work zone collisions
12-Classification_ElasticNet(LogReg).py
13-Classification_ElasticNet(Multinomial).R
14-Classification_RFE.py
15-Classification_REF(LogReg).R
16-Classification_RFE(Multinomial).R
17-Classification_RFE(Ordinal).R
18-Classification_xgBoost(binary).R
19-Classification_xgBoost(Multiclass).R
17-Extract_pdfTable(IMMS).R
extract activity tables from the IMMS pdf files
18-FUNC_clean(FinalDataSet).R
function to clean up the final joint data set
19-FUNC_match(LEMO_WorkOrder_LCS, AADT, TRUCK).R
function to match LEMO_WorkOrder_LCS data set with AADT and TRUCK AADT data sets based on route, odometer and date
20-FUNC_match(LEMO_WorkOrder, LCS).R
funcion to match LEMO_WorkOrder data set with Lane Closure System based on route, odometer, and date
21-generate_collisionDensity(SWITRS).R
generate collision densities for every 2 mile segment of each road using all collisions between 2011-2018
22-generate_testData(FinalDataSets).R
generate test data set for the classification modesl
23-HeatMap(SWITRS_wzOnlyRuralPop).py
generate a heatmap on Google Maps showing the density of work zone collisions by population code
24-MATCH(FinalDataSets).R
merge all the data sets, LEMO, WorkOrder, LCS, AADT, TRUCK AADT, CleanRouteFile based on matching keys
25-MATCH(LEMO_WorkOrder, CleanRouteFile).R
extract matchink keys for LEMO_WorkOrder and CleanRouteFile data set based on location
25-MATCH(LEMO_WorkOrder, ClosureMatches).R
26-MATCH(LEMO_WorkOrder, collisionDensity).R
27-MATCH(SWITRS, CleanRouteFile).R
28-MATCH(SWITRS, WorkOrder).R
29-MATCH(LCS, CHP).R
30-Pipeline(LEMO_WorkOrder_LCS_AADT_TRUCK).R
a pipeline to match LEMO_WorkOrder, LCS, AADT, and TRUCK AADT calling appropriate MATCH functions
31-pm_odom_geocode_query.py
script to define HTTP request for Caltrans API to convert postmiles to coordinates
32-pm_odom_query.py
script to define HTTP request for Caltrans API to convert postmiles to odometers
33-pm_submit_queries.py
a script to build xml request and translate xml responses
34-print_forOdometerConversion(CleanRouteFile).R
print the neccessary fields of CleanRouteFile for odometer conversion
35-print_forOdometerConversion(SWITRS).R
36-Sankey(IMMS Crew classification).R
generate a sankey diagram for IMMS and Crew classification
37-STAT(LEMO_WorkOrder_LCS).R
generating some highlevel statistical observation of the joint LEMO_WorkOrder_LCS dataset
38-STAT(LEMO).R
39-STAT(SWITRS_CleanRouteFeatures).R
40-STAT(SWITRS_wzOnly_LEMO_LCS_AADT).R
41-STAT(SWITRS_wzOnly)
42-STAT(SWITRS).R
NOTE: The files are not numbered in order of operation