Research Article

Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

Table 5

Association rules by weather condition.

LHSRHSItemsetαβLift

{Cause: drowsy, Segment: mainline, Time: t12–17}{Weather: fine}4-itemset0.02580.84661.3941
{Cause: drowsy, Horizontal alignment: straight, Time: t12–17}4-itemset0.02350.83121.3688
{Cause: poor loading}2-itemset0.02140.81241.3378
{Crash type: vehicle- vehicle, Horizontal alignment: straight, Number of vehicles: multi-vehicle involved, Time: t12–17, Vertical alignment: no slope}6-itemset0.02260.81171.3367
{Cause: drowsy, Driver age: 20s}3-itemset0.02410.80241.3215

{Cause: over-speeding, Month: JULY}{Weather: rainy}3-itemset0.02130.78794.3533
{Cause: over-speeding, Crash type: vehicle-facility, Number of vehicles: single, Segment: mainline, Vehicle weight: >3.5 t and<8.5 t, Vehicle type: cargo truck}6-itemset0.02100.76484.2256
{Cause: over-speeding, Crash type: vehicle -facility, Number of vehicles: single, Segment: mainline, Vehicle weight: >3.5 t and<8.5 t}6-itemset0.02810.75354.1632
{Cause: over-speeding, Crash type: vehicle -facility, Segment: mainline, Vehicle weight: >3.5 t and<8.5 t, Vehicle type: cargo truck}6-itemset0.02300.75264.1580
{Cause: over-speeding, Crash type: vehicle -facility, Horizontal alignment: straight, Number of vehicles: single, Vehicle weight: >3.5 t and<8.5 t}6-itemset0.02460.73014.0338