A Case Study on Traffic Violations in the City of Colombo Udara Perera Sandun Silva Oshada Senaweera Yogeswaran Akhilan Amani Subawickrama

Introduction  Driving is very important for working, social life, entertainment, economic, recreational and other reasons  Number of registered vehicles in Sri Lanka have risen from 3.1 million in 2007 to 5.6 million in 2014  During last two decade approximately 25,000 km road were added to the national grid.  Violation in traffic laws are very common in Sri Lanka  Traffic law violations are a contributing factor to the majority of road accidents that occur in Sri Lanka

Objectives of the study  Identify the most frequent traffic law violations in Colombo  Examining the factors that influence traffic law violations  Identify the relationship between traffic law violations and other factors  Build a suitable model to predict the probability of doing a traffic law violation

Data Collection  Response variable Violation type  Predictor variables Location, vehicle type, gender of the driver, age of the vehicle, time, number of passengers  Target population Motor vehicles using the roads in Colombo area  Sampling technique Stratified sampling based on the type of location

Data Collection (ctd)

 Data collection method Observational study

Analysis and Interpretation  Univariate Analysis

Analysis and Interpretation (ctd)

 Relationship Analysis  Relationship between violation and road type H 0 : There is no relationship between violation and road type H 1 : There is a relationship between violation and road type P value = (<0.05) Reject H 0 at 5% significance level. Violation depends on the road type

Analysis and Interpretation (ctd)  Relationship between violation and vehicle type H 0 : There is no relationship between violation and vehicle type H 1 : There is a relationship between violation and vehicle type P value = (<0.05) Reject H 0 at 5% significance level. Violation depends on the vehicle type

Analysis and Interpretation (ctd)  Relationship between violation and age of the vehicle H 0 : There is no relationship between violation and age of the vehicle H 1 : There is a relationship between violation and age of the vehicle P value = (>0.05) Do not reject H 0 at 5% significance level. Violation is independent of the age of the vehicle

Analysis and Interpretation (ctd)  Relationship between violation and gender of the driver H 0 : There is no relationship between violation and gender of the driver H 1 : There is a relationship between violation and gender of the driver P value = (<0.05) Reject H 0 at 5% significance level. Violation depends on the gender of the driver

Analysis and Interpretation (ctd)  Relationship between violation and time H 0 : There is no relationship between violation and time H 1 : There is a relationship between violation and time P value = (<0.05) Reject H 0 at 5% significance level. Violation depends on the time

Analysis and Interpretation (ctd)  Relationship between violation and number of passengers H 0 : There is no relationship between violation and number of passengers H 1 : There is a relationship between violation and number of passengers P value = (>0.05) Do not reject H 0 at 5% significance level. Violation is independent of the number of passengers

Model Fitting  Binary logistic regression model Logit (Pi) = Time(1) – Location(1) Location(2) – Location(3) – Location(4) Here, Pi = Probability of violating a traffic lawTime(1)= Peak hours Location(1)= One-wayLocation(2)= Two-way Location(3)= T-junctionLocation(4)= Cross junction Eg:- Consider a vehicle at a one way road in peak hours Logit (Pi) = (1)-0.857(1)+0.051(0)-0.127(0)-0.174()) Log (Pi/1-Pi) = Pi/1-Pi = exp(-1.126) Pi = 0.245

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