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Understanding Pedestrian vs Train Accidents - a Machine Learning Approach

On May 11, 2021, I proposed to my colleagues a senior-engineering project on understanding why and how pedestrians keep getting hit by trains. While the work explored Artificial Intelligence / Machine Learning and how it can be applied in railroad industry work, the effort is ultimately towards implementing more effective prevention of pedestrian trespassing along railroad rights-of-way.

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Understanding Pedestrian vs Train Accidents - a Machine Learning Approach

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  1. Understanding Pedestrian vs. Train Incidents Data Science/Machine Learning/Artificial Intelligence Approaches Oliver Garcia, PE MAY 11, 2021

  2. Addition Training Data Data 1 1 3 0 Multiplication Training Data Data 1 Data 2 1 3 0 Data 1 2 Data 2 3 Data 2 2 4 1 Output 3 7 1 Output 2 12 0 2 4 1 OR Data 1 2 Data 2 3 OR Data 2 3 Output 5 Output 5 Data 1 2 Output 6 Output = Data 1 + Data 2 (e.g. UPRR’s Regression Model)

  3. ILLUSTRATIVE DATA ONLY Trains Pedestrians Warning Device Other Features Hazardous 2 Medium Passive 14 Low Gates 35 High Flashers 40 10 70 1 0 1 Trains Pedestrians Warning Device 21 Medium Feature X ? Hazardous ? Flashers

  4. Railroad Subdivision Line: 496 Open XINGs, (green dots) 202 Pedestrian incidents (brown dots) TIME DAY XING 35 ROW 90 ~15 indicated as suicide NIGHT 17 60

  5. APPLY KNN (k-nearest neighbor) to “designate” incidents to a crossing. = 1 (incident-prone) = 0 (not incident-prone) 111 out of 496 CROSSINGS ARE INCIDENT-PRONE (ILLUSTRATIVE DATA ONLY)

  6. DATA QUALITY – CROSSINGS INVENTORY 1 0 2 ? 3 ? Low High Med 0 ? 2 1 Gates Other Passive Flashers ? ? 0 55

  7. Primary Record Record Type Record Id CPUC Crossing Number Crossing Status County Code Crossing Roadway Name DOT Crossing Number Num INCRs Hazardous 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Yes XING MER-1030 001B-150.70 Open MER CANAL ST 765929C 1 Yes XING FRE-1007 001B-202.50 Open FRE MCKINLEY AV 757321A 1 Yes XING FRE-1048 001B-205.80 Open FRE MONO ST 757332M 3 Yes XING FRE-1052 001B-206.70 Open FRE S VAN NESS AV 757337W 1 Yes XING FRE-1053 001B-206.80 Open FRE FLORENCE AV 757388G 1 Yes XING FRE-1054 001B-207.20 Open FRE CHURCH AV 757389N 1 Yes XING FRE-1011 001B-211.10 Open FRE WILLOW AV 756879C 1 TRAIN/TEST ALGORITHM ON RANDOM 30% OF DATA SET Yes XING FRE-1021 001B-217.20 Open FRE MANNING AV 756867H 1 Yes XING KER-1020 001B-280.40 Open KER 13TH AV 757273M 1 Yes XING SAC-1042 001AH-102.50-C Open SAC ROCKINGHAM DR 753854W 0 Yes XING SAC-1081 001AH-97.36 Open SAC WATT AV 753620T 0 Yes XING STA-1001 001B-106.20 Open STA KIERNAN AV 752844M 0 Yes XING MER-1045 001B-142.90 Open MER APPLEGATE RD 766165P 0 Yes XING MAD-1037 001B-183.50 Open MAD 3RD ST 760980H 0 Yes XING MAD-1022 001B-184.10 Open MAD E 9TH ST 760975L 0 Yes XING MAD-1025 001B-184.30-C Open MAD N O ST 760992C 0 Yes XING MAD-1031 001B-184.60-C Open MAD HOWARD RD 760995X 0 Yes XING FRE-1004 001B-198.50 Open FRE SHAW AV 757316D 0 Yes XING FRE-1016 001B-214.80 Open FRE MARIPOSA ST 756872E 0 Yes XING FRE-1017 001B-214.90 Open FRE MERCED ST 756871X 0 Yes XING FRE-1022 001B-217.80 Open FRE DEWOLF AV 756868P 0 Yes XING FRE-1024 001B-219.30 Open FRE HIGHLAND AV 750708F 0 Yes XING FRE-1029 001B-220.60 Open FRE 1ST ST 750702P 0 Yes XING FRE-1064 001B-225.50 Open FRE DRAPER ST 750630N 0 Yes XING TUL-1023 001B-228.40 Open TUL AV 384 750613X 0 Yes XING TUL-1019 001B-249.60 Open TUL CROSS AV 756977T 0 Yes XING SAC-1126 001BEL-42.64 Open SAC 14TH AV 761735H 0 Yes XING SAC-1161 001BEL-43.64 Open SAC FRUITRIDGE RD 752761Y 0 Yes XING SAC-1160 001BEL-44.74 Open SAC ELDER CREEK RD 752760S 0 Yes XING SAC-1150 001BEL-49.84 Open SAC SHELDON RD 752751T 0 Yes XING SAC-1149 001BEL-50.64 Open SAC ELK GROVE-FLORIN RD 752750L 0 RUN PREDICTIONS ON 70% OF DATA SET Yes XING SAC-1143 001BEL-60.44 Open SAC TWIN CITIES RD 752892C 0 Yes XING SAC-1142 001BEL-60.94 Open SAC SPRING ST 752743B 0 Yes XING SAC-1140 001BEL-63.04 Open SAC A ST 752741M 0 Yes XING SJ-1119 001BEL-66.84 Open SJ JAHANT RD 752922S 0 Yes XING SJ-1105 001BEL-78.34 Open SJ MORADA LN 752908W 0 Yes XING STA-1049 001DC-122.60 Open STA J ST 753096W 0 Yes XING SJ-1167 001DE-105.30-C Open SJ TURNER RD 753126L 0 ILLUSTRATIVE DATA ONLY Yes XING STA-1230 075C-5.60 Open STA SPRUCE AV 865142F 0 Yes XING STA-1215 075C-5.70 Open STA LAUREL ST 865145B 0 Yes XING STA-1217 075C-5.75 Open STA LOCUST ST 865147P 0 Yes XING STA-1249 075C-5.85 Open STA 5TH ST 865151E 0 Yes XING SAC-1402 083E-11.76, 001AH-101.50 Open SAC MATHER FIELD ROAD 753532H 0 Yes XING SAC-1406 083E-13.54, 001AH-103.30 Open SAC OLSON DRIVE 753650K 0 Yes XING SAC-1397 001AH-103.18-D Open SAC CORDOVA TOWN CENTER PED XING #1 927823G 0 Yes XING SJ-1014 001B-104.00-C Open SJ S INDUSTRIAL AV 753085J 0 Yes XING STA-1033 001B-107.50 Open STA DAKOTA + MURPHY AV 752845U 0

  8. ML Algorithm: CHAID (Chi-Squared Automatic Interaction Detection) Source: IBM Watson Studio ILLUSTRATIVE DATA ONLY

  9. Source: KNIME Analytics Platform

  10. ILLUSTRATIVE DATA ONLY

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