SIG2012 Group 1 by AHMAD RASHID BIN MOHAMED YAZID

Interval Data Type Example

sii1900031 — 2021-10-25 14:30:36 UTC

Time of high traffic, time of low traffic, rate of accidents, vehicle density in one road, average road segment speed

Ratio Data Type Example

sii1900031 — 2021-10-25 14:30:58 UTC

Time spent in traffic (comparing between time of high traffic with low traffic)

For instance, Driving through the LDP Highway at 8 am takes twice as longer when driving thorugh it at 11 am

Ordinal Data Type Example

sii1900031 — 2021-10-25 14:33:31 UTC

Level of Congestion ( 1=jam, 2= heavy traffic, 3= light traffic)

average vehicles velocity ( 1=(60-70km/h), 2=(71-80km/h), 3=(> 81km/h)

Binary Data Type Example

sii1900031 — 2021-10-25 14:34:02 UTC

Overlimit of speed, Driver Status (Probationary/Competent)

Overlimit speed- for instance the limit is 90km/h, 1- >90km/h, 0- < 90km/h

Categorical Data Type Example

sii1900031 — 2021-10-25 14:34:12 UTC

Light vehicles/Heavy vehicle, Local/Import Cars, Weekdays/Weekends/Public Holiday/School Holiday
-different type of car
(Hatchback, sedan, MPV, SUV, coupe, pickup, crossover, station wagon, mini, Luxury Cars, supercars, hypercars, muscle cars)
Driver (male/female driver)

Continous Data Type Example

sii1900031 — 2021-10-25 14:34:18 UTC

Time of high traffic, time of low traffic , the length of jammed road , the time taken to repair damaged roads,

Discrete Data Type Example

sii1900031 — 2021-10-25 14:34:23 UTC

Number of vehicles, number of accidents, number of gas stations available in a certain state, number of Rest stop places available, number of speedtraps

Nominal Data Type Example

sii1900031 — 2021-10-25 14:34:27 UTC

Bus, lorry, van, car, motorcycle, street lamp, road sign, toll, petrol station, tree, rest stops, speed trap, billboard,

What analysis can be done?

sii1900031 — 2021-10-25 14:39:37 UTC

Can correlate heavy traffic with time ( what time does heavy traffic always occur i.e. 7-9 am)

Can correlate what types of vehicle often involved in accidents

Can correlate heavy traffic occurence with day (either weekdays - weekends - public/school holiday) - which day have high occurence of heavy traffic

Able to determine how much is maintenance cost based on what type of vehicle passes through

Able to estimate the revenue based on the traffic. For example, the revenue decreased 2% year-on-year to RM504 million in 2020 due to MCO. A drop of 460,000 vehicles in 2019 to 447,000 vehicles in 2020.