1. Impact of rain on Daily Bus ridership: A Brisbane Case Study
Syeed Anta Kashfi QUT(Presenter)
Jinwoo (Brian) Lee QUT
Assoc Prof Jonathan Bunker QUT

2. Introduction Estimated population of 4.2 million in 2031
Busway network is fed by extensive network of routes
Heavily reliant on bus transit
Factors that affect the bus ridership are worthy of investigation.
Weather Effects transit ridership on daily basis
Weather is highly variable by season, due to sub-tropical nature.

3. Research Question Bus Ridership
Whole day rain
Morning peak hour
Day of week
Weekend
By Season
Rainfall amount
No research conducted in Australia on weather impact on bus transit ridership
Main goal is to investigate the effect of precipitation on daily bus ridership in Brisbane
What is the impact of rainfall on Daily Bus ridership in a sub-tropical city, Brisbane
Research Question

4. Outline Study area Results Conclusion Methodology Data collection
Seasonality Analysis
Seasonal Adjustment
Results
Conclusion

5. Study area Demographics of Brisbane Brisbane’s Weather
Located in the southeast corner of Queensland.
Comprising 189 suburbs, occupies only 0.1% of Queensland’s land area.
One quarter of Queensland’s total population lives in Brisbane area.
Public Transport system
TransLink provides public transport service in Brisbane.
Transport network divided in 23 zones. Brisbane encompasses 5 zones.
TransLink operates 394 routes originating from study area.
Brisbane’s Weather
“Sub-tropical” capital of Queensland.
Four distinct seasons observed in Brisbane.
Recent years continuous heavy rain for long period has been observed.

6. Methodology Data Collection Data collection Daily bus ridership data
Daily precipitation data
Daily sum of all passengers for 24-hr period.
TransLink , Department of Transport and Main Roads.
January 1, 2010 to December 31, 2012.
Hourly cumulative rain data.
Bureau of Meteorology, Climate Data Services.
Cumulative rainfall from 9:00am for 24-hr period.

7. Methodology
This study examines bus ridership with respect to the rainfall in two periods
Whole day Rain from 6:00 am to 9:00 pm.
Morning Rain from 6:00 am to 10:00 am.
Rainy day defined if at least 1 mm of rainfall occurs during analysis period.
Non-rainy day defined if it rains less than 1 mm.
Excludes public & school holidays as ridership is very low due to holiday activities
Analysed weekend ridership separately from weekdays.

8. Methodology Seasonality Analysis
Bus ridership fluctuates from day to day
Influenced by Day of week and Month of Year.
Important to analyse the seasonality effects.
Ridership was segmented into each weekday
Same method adopted for month of Year

9. Seasonality Analysis continued..
Methodology
ANOVA test used for statistical significance of mean variance
The mean ridership for each day of week compared against one another.
Only Monday ridership is statistically different (p = 0.00≤ 0.05) from other days.
More fluctuations are observed between the monthly ridership volumes.
January month’s ridership is statistically different from other months except December (p =0.88 > 0.05).
The same result was achieved for the month of March.
Confirms existence of seasonality in daily and monthly ridership patterns.

10. Seasonality Adjustment
Methodology
By day-of-week
By month-of-year
ANOVA test confirmed existence of seasonality in ridership data
Following two formulas are used for seasonal decomposition
The seasonal index (SI) is a measure of the degree of seasonality.

11. Effect of rain on daily bus ridership
Results
Effect of rain on daily bus ridership
Analysis result confirmed clear impact of rain on daily bus ridership.
Whole day rainfall reduced ridership by 2.58%
morning rainfall reduced ridership by 3.50%.

12. Effect of rain on daily bus ridership (cont..)
Results
T-test conducted for statistical significance of mean differences.
Period of rain
Ridership mean
Ridership change
t-test Significance
No-Rain
Rain
Whole day rain (6.00 am to 9.00 pm)
283,357
276,056
- 2.58%
0.00 (Sig)
Morning rain (6.00am to am )
282,695
272,720
- 3.50%
Both mean differences statistically significant with 95% confidence interval.
Rain in morning has greater influence on people’s mode choice.
Commuters tend to choose their transport mode in the morning period.

13. Effect of rain by day of week
Results
Effect of rain by day of week
Rain effect varies depending on the days of the week.
Graphs show the effect of rain as ridership reduction in each day of week
Observed ridership reductions are more significant with the morning rain.
Most significant reduction 3.80% and 2.22% with morning and whole day rain, respectively.

14. Results Effect of rain by day of week (cont..)
T-test conducted for statistical significance of mean differences.
Both mean differences statistically significant with 95% confidence interval.
Rain in the morning has greater influence on people’s mode choice.
Day of week
Whole day rain (6:00 am to 9:00 pm)
Morning rain (6:00 am to 10:00 am)
Ridership change
t-test Significance
Monday
-2.05%
0.031 (sig)
-3.6%
0.001 (sig)
Tuesday
-2.22%
0.037 (sig)
-2.8%
0.006 (sig)
Wednesday
-2.09%
0.044 (sig)
-0.8%
0.407 (non-sig)
Thursday
-1.90%
0.143 (non-sig)
-3.8%
Friday
-1.30%
0.504 (non-sig)
-0.6%