DYNAMIC TREE DATA VISUALIZATION METHOD FOR GRADE CROSSING ACCIDENTS
Jacob Mathew Juan C. Medina Rahim F. Benekohal University of Illinois Urbana-­‐Champaign USA Introduc=on
•  Detailed grade crossing accident database is maintained by FRA (in the USA) •  OHen used for safety analysis •  ExtracKng useful informaKon is crucial to determine appropriate safety improvements •  This task is difficult because •  The number of accidents is of the order of 2000 per year within the USA •  The number of accident aPributes for each record is over 100 •  No data visualizaKon method is available •  This project developed a methodology to visualize accident data Sta=c Tree
•  Accident data visualizaKon •  Done manually •  Using 7 aPributes with a predetermined hierarchy Previous Study (Sta=c Tree) example (Daily train volume: passenger 64 and freight 4; AADT=15400)
•  Crossing (173887G) with 9 Accidents in 10 years (2002-­‐2011) •  4 EB trains hit 4 SB Vehicles •  It points to a possible angle issue? Image and sketch of Crossing 173887G
Improvement to Sta=c Tree
•  StaKc method: the aPributes and their hierarchy are predetermined •  An aPribute lower in the hierarchy may reveal accident trend bePer •  Accident trends may be lost due to excluding an aPribute •  Dynamic method: the hierarchy is determined based on data •  APributes are ranked based on their ability to find cluster of accidents •  Discover more accident trends •  Dynamic considers more aPributes •  22 aPributes considered in dynamic method (vs 7 in StaKc method) •  But, dynamic method has to be computerized •  All possible ordering of aPributes needs to be considered to determine hierarchy •  It is not pracKcal to do this manually and there is a chance for human error •  The computerized process can be used for single crossing, corridor, or region AZributes Considered in Dynamic Method
Three Methods for Determining Hierarchy
•  Method A (Absolute Sor0ng) •  Distribute the total number of accidents into the sub-­‐aPributes for each of the 22 aPributes •  Sort the aPributes based on the largest sub-­‐aPributes to determine the hierarchy •  Establish a main branch of the tree by sequenKally dividing the largest sub-­‐aPribute for APribute1 into sub-­‐aPributes for APribute 2 based on the established hierarchy; ….and so on •  Advantages •  Simple and hierarchy relies on the distribuKon of accidents into subcategories for this crossing •  Disadvantage •  Another hierarchy may idenKfy the trend in the main branch “bePer” than Method A by keeping a higher number of accidents in sub-­‐aPribute at comparable level Three Methods for Determining Hierarchy
•  Method B (Nested Sor0ng) •  Establish the highest ranking aPribute as in Method A •  Divide the accidents in the largest subcategory of APribute 1 into subcategories of the un-­‐selected aPributes to determine the 2nd highest ranking aPribute •  This stepwise procedure is conKnued to determine the 3rd, 4th, 5th… highest ranking aPributes •  Advantages •  Keeps the trend in the main branch of the tree bePer than Method A •  This is because only those accidents are considered for further classificaKon •  Disadvantage •  There may be Kes in ranking of the aPributes •  This focus on the main branch and does not consider the accident on other branches Three Methods for Determining Hierarchy
•  Modified Method B (Modified Nested Sor0ng) • 
• 
Use historic accident data to resolve Kes in establishing the hierarchy It finds “Crossing Cluster” which is the sum of no. of accidents in all branches of an aPribute for the sub-­‐aPributes that idenKfied the trend in main branch •  Advantages •  Keeps accidents clustered together without breaking trend •  Shows “Crossing Cluster” informaKon to give a big picture •  Can be used to idenKfy trends on various crossings •  This method is selected and used Dynamic Tree Example
•  Modified Nested SorKng method used to determine hierarchy •  Crossing with 9 accidents (173887G) between 2002 and 2011 •  StaKc method also showed this Observa=ons from Dynamic Tree
•  7 out of 8 accident involved an EB train •  In 6 out of the 7 accidents, train hit the highway vehicle only in Dynamic tree •  In 5 out of the 7 accidents, vehicles were stopped on crossing •  4 out of the 5 accidents were SB vehicles •  AcKon of motorist in all 4 accidents was “other”, Also by StaKc method •  “Other” meant the motorists stopped on crossing before the gates descended •  Dynamic tree reveals more info compared to staKc tree •  Frequently EB train is involved •  Frequently Train hits vehicles •  Frequently vehicles are stopped on the crossing Dynamic Tree Plus Crossing Cluster
•  Dynamic Tree Method also finds “Crossing Cluster”, once hierarchy of aPributes are found •  Crossing Cluster shows how many of the total no of accidents belongs to that aPribute •  Crossing Cluster shows the trend that may be obvious when following a branch of the tree •  Example Crossing Cluster for a crossing with 9 accidents (811479J) between 2005 and 2014 is presented next Crossing Cluster •  Grade Crossing 811479J Charlie Rd and Railroad St, Industry City, Los Angeles, CA •  With 9 accidents between 2005 and 2014 •  AADT 48000 •  Train 40 per day (al freight) Observa=ons from Dynamic Tree & Crossing Cluster
Both DT and CC show: •  All accidents involved a southbound HW user •  8 of the accidents occurred during PM hours •  Majority of vehicles were stopped on crossing CC also shows that: • 
• 
• 
• 
3 vehicles were trapped on the crossing 5 accidents involved when vehicle was stopped 5 accidents involved trucks 5 vehicles stopped on crossing Comparison of Dynamic vs Sta=c Method
•  Dynamic hierarchy is based on accident data for that crossing, but StaKc method is based on a pre-­‐determined hierarchy •  Dynamic method kept the trends shown by StaKc method AND revealed more info •  Even when the same aPributes are considered, the ranking of the aPributes can be different in two methods •  Using a computer program makes process quicker with no chance for errors Dynamic Tree Analysis for Mul=ple Loca=ons
•  Corridor Analysis •  Example Corridor •  Northeast Illinois Regional Commuter Railroad •  23 accidents at 8 crossings between 2002 and 2011 Dynamic Tree Analysis for a Corridor
•  Modified Nested SorKng method used to determine hierarchy Observa=ons from Corridor Analysis
•  DisproporKonate distribuKon of accidents between NB and SB trains within the 17 accidents involving a train himng a HW vehicle •  All 6 accidents (at the end of the tree) involving a motorist moving over the crossing involved the driver running around the gate •  These observaKons gives ideas as to where further examinaKon should be done •  Frequent south bound train accidents •  Motor vehicle accidents involving vehicle moving over crossing •  These observaKons cannot be made by analyzing individual due to low number of accidents in each individual crossing Conclusions and Recommenda=ons 1.  This study developed a visualizaKon tool to display accident data •  It is bePer than data in tabular format because accident trends could be easily seen 2.  The staKc method was developed to achieve this task •  Accident trends could be observed using it, but need to know the hierarchy •  Some trends remain hidden due to limited number of aPributes 3.  The dynamic method overcame the limitaKons of staKc method •  More accident trends were revealed using this method •  Could be expanded to mulK accident locaKons, such as corridors •  Reduced manual effort and computaKon Kme •  Create Crossing Cluster that shows overall trend of accidents Conclusions and Recommenda=ons 4. Use dynamic tree method to visualize accident trends at single crossing, corridor and regional levels 5. Bring in aPributes from other data basis to obtain more comprehensive explanaKons for the accidents •  For example, Grade Crossing Inventory database carries informaKon regarding angle of the crossing, proximity of the crossing to another intersecKon that could reveal more details explanaKon about the accidents. Thank you! Sta=c Tree example
•  Crossing with 9 Accidents (811479J) between 2005 and 2014 •  8 accidents involved motorized vehicles, most of them were stopped on the crossing (5 accidents) •  Southbound vehicles involved in all accidents Map and Sketch for example crossing