Effects Of Weather And Climate On Bike Commuting Patterns Among Students And Diversified Population: A Study Review

EGH404 Research in Engineering Practice

Question

Write a succinct reverse outline of the paper using the dot-point style presented in class.The two studies discuss how weather and climate affect bicycle commuting among students in tertiary institutions.The study by (Max, 1999) outlines the findings of the effects of variation in weather and climate on bicycle commuting patterns.

The study examines the assumption that particular conditions of weather are seen by the bicycle commuters as non- viable.

The study demonstrates that the group that was surveyed does not support the assumption.

The study by (Max, 1999) revealed that cycling for the normal activities is affected by both practical and cultural practices.

The study further revealed that the unpredictable nature of the daily weather conditions of Melbourne may cause the bicycle riders to be stranded.

However, the unpredictability of the weather conditions is not a major deterrent for group surveyed. The average travelling time is 14 minutes and the mean distance of 7km.

About 75% of the trips are less than 10 Km and less than 20 minutes. Cycling is lowest in June- July.

Only 2 % of those surveyed felt that weather is a deterrent to cycling.

The other study by (Brian, Greg, Justine, & Lisa, 2012) had participants of diverse bicycle use, gender and age.

The participants had a relatively better education than in the study that has been discussed above.About 34% of the participants travel to work on a bicycle.The study revealed that the absence of rain increases the number of those travelling to work on a bicycle.

The other factors that have an effect on the number of people who are commuting to work on bicycle include distance, gender and age.The factors that have significant influences on bicycle use include participation, temperature, wind and snow conditions.

The two studies use different groups of participants: one, relatively well-educated; the other, tertiary students. Comment on the main differences between these two groups and highlight any concerns or uncertainty that these groups bring to the conclusions of these studies?

According to (Max, 1999), the study consisted of a small number of the tertiary students (only 2%) felt that weather and climate were deterrent to cycling. However, it is likely that weather and climate conditions in Melbourne maybe they are more likely young and relatively healthy (therefore the participants are presumably more likely to confront extreme climate and weather conditions). Similarly, the participants have limited access to personal cars that they can use as an alternative means of transport but the participants generally have a relatively flexible schedule in comparison with the other groups of the employed people.

For example, the effects of absence from classes and being late for the classes are usually not severe.  These are the factors that give this group of participants an advantage on commuting with bicycles over the other groups of the employed people. Moreover, the students are not obliged to commute during certain extreme weather conditions according to the Australian academic.

On the other hand, according to (Brian, Greg, Justine, & Lisa, 2012), the major factors that are identified as greater influencers of bicycle commuting among the participants surveyed include the distance involved, characteristics of the individual and the commuting mode. Moreover, the weather conditions that are indicated as major influencers of bicycle commuting among the participants include the wind patterns, conditions of the snow, temperature and the precipitation.

Answer

What are the main limitations of the two studies?

The limitation of the study by (Max, 1999) is limited studies that have been done in relation to the conditions of urban areas in Australia except for Newcastle by Keay (1992). The study about Newcastle was meant to count the number of those commuting on a bicycle for the recreational purpose at a given point during a specific weather condition.  

On the other hand, the limitation according to the study by (Brian, Greg, Justine, & Lisa, 2012) is that there was a general lack of local details for the data that were collected from the weather stations. Moreover, such data have more focus on the hours of commuting than on the number of commuters at each particular time frame.

The common limitation for the two studies is that there was a lack of existing literature from previous studies

Your Director does not understand what is captured by an “odds ratio” in the latest study.
Briefly describe this to her in simple terms, and explain why Gender (men vs woman) has such a high odds ratio?

An Odds Ratio is the ratio of the possibility of happening of a particular event to the possibility of non- occurrence of an event. The gender (men vs Women) odds ratio is, therefore, the ratio of the likelihood that men will use a bicycle to the likelihood that women will not use a bicycle. The gender odds ratio is high because the majority of the men are likely to use a bicycle.  Furthermore, there were more male participants (102) than the female participants (61).

Comment on the main differences, if any, on the mean travelling times between the two studies?

The study by (Max, 1999) reveals that the mean travelling time was around 14 min, with a mean distance 7 km, while 75% of trips were less than 10 km and less than 20 min.

On the other hand, the study by (Brian, Greg, Justine, & Lisa, 2012) reveals that the mean travelling time is between 7 am and 9 am with an average distance of 7miles.

Nankervis (1999) makes a clear distinction between weather versus climate deterrents to commuter cycling. What are the main findings they present about this distinction?

The study by (Max, 1999) reveals that generally, winter was perceived to be the period with the worst weather conditions. Moreover, the conditions of cycling were perceived to be worst during the winter periods. The rainfall patterns show an even distribution of the precipitations throughout the year while the lowest temperatures are recorded during the months of June and July. The lowest temperature recorded during the months of June and July is 6 degrees. The study clearly demonstrates that weather and climate conditions affect bicycle commuting, a finding that is consistent with other studies (other studies reveal that weather and climate have a 21% chance of influencing bicycle commuting in Australia). 

Table 5 of Nankervis (1999) reports on the significance of the relationships between weather elements and daily bike numbers. Your Director is not familiar with p-values or Pearson’s R. Give her a brief overview of what these mean, and highlight the key messages from this table?

A p-value is the probability value that shows the significance of the test. For the regression analysis test, a p-value is the probability value that shows whether the variables have a significant relationship or not. A person’s R on the other side is a value that shows the strength and nature of the relationship that exists between the variables. The 0.363 Pearson’s correlation between daily temperature and daily bike numbers is a weak positive regression coefficient and the p-value is 0.00, meaning this is a significant relationship.

This is an indication that an increase in daily temperature would lead to an increase in the number of bikes and vice versa. There exist a weak negative relationship between daily wind category and a daily number of bikes. This is an indication that an increase in the daily wind category will cause a decrease in the daily number of bikes and vice versa.

You have been provided 3 years’ of data from BCC and BOM (Bureau of Meteorology) on daily commuter numbers and associated weather data in Brisbane.

1. Are the seasonal trends observed in Melbourne in the Australian study (Nankervis, 1999) consistent with what’s observed in the Brisbane data?

The figures below show the trends observed in the data between the years 2014 and 2016.  The trends here is that there is a general fluctuation in the various climatic conditions.  This is a similar observation according to the study by (Max, 1999).

1. What role does daily extreme weather events appear to play in commuter numbers in Brisbane? What are the limitations of the given data to answer this question?

Different daily extreme weather events reduce or increase the number of daily commuter numbers in Brisbane. Extreme high solar exposure encourages bicycle commuter hence the number of bicycle commuter will increase and vice versa. Extreme high rainfall will discourage individuals from cycling. Therefore, when there is high rainfall, the number of bicycle commuter will immensely reduce. The limitation of extreme rainfall and solar exposure is that they can lead to the total extinction of bicycle commuter as they may not be suitable for bicycle commuter.

1. Is the impact of daily weather conditions consistent across the entire Brisbane network? Provide statistical measures and/or regression outputs/plots to support your findings. 

The investigation of the impact of daily weather conditions on Brisbane network can be determined using regression analysis.  The tables below are the output of the regression analysis for each weather condition. A regression analysis reveals whether a variable (s) has an effect on the other as well as indicating the nature of the relationship. The outputs below shows that there is a regression coefficient in the summary tables. The regression coefficients (R) indicates the nature of the linear relationship or impact between the variables.

1. Daily Solar Exposure

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.532165
R Square	0.283199
Adjusted R Square	0.279239
Standard Error	5.284562
Observations	365

ANOVA
	df	SS	MS	F	Significance F
Regression	2	3994.108	1997.054	71.51083	6.72E-27
Residual	362	10109.43	27.92659
Total	364	14103.53

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	7.210898	0.97231	7.416256	8.63E-13	5.298813	9.122983	5.298813	9.122983
2015	0.228257	0.04754	4.80135	2.31E-06	0.134767	0.321746	0.134767	0.321746
2016	0.376526	0.044232	8.51254	4.59E-16	0.289542	0.46351	0.289542	0.46351

Daily Maximum Temperature

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.72341
R Square	0.523322
Adjusted R Square	0.520644
Standard Error	2.552387
Observations	359

ANOVA
	df	SS	MS	F	Significance F
Regression	2	2546.162	1273.081	195.4173	5.3E-58
Residual	356	2319.226	6.514679
Total	358	4865.388

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	7.085084	1.020277	6.944278	1.81E-11	5.078557	9.091611	5.078557	9.091611
2015	0.26614	0.047881	5.558362	5.35E-08	0.171975	0.360305	0.171975	0.360305
2016	0.472598	0.047289	9.993908	6.83E-21	0.379598	0.565598	0.379598	0.565598

Daily Minimum Temperature

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.833029
R Square	0.693937
Adjusted R Square	0.692237
Standard Error	2.631505
Observations	363

ANOVA
	df	SS	MS	F	Significance F
Regression	2	5652.245	2826.122	408.1151	2.79E-93
Residual	360	2492.934	6.924817
Total	362	8145.179

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	0.480868	0.588083	0.817687	0.414077	-0.67564	1.637377	-0.67564	1.637377
2015	0.434081	0.043496	9.979768	7.21E-21	0.348543	0.519619	0.348543	0.519619
2016	0.524791	0.048389	10.84529	6.66E-24	0.429631	0.619952	0.429631	0.619952

1. Daily Rainfall

SUMMARY OUTPUT
Regression Statistics
Multiple R	1
R Square	1
Adjusted R Square	1
Standard Error	0
Observations	356

ANOVA
	df	SS	MS	F	Significance F
Regression	2	26828.2	13414.1	#NUM!	#NUM!
Residual	353	0	0
Total	355	26828.2

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	0	0	65535	#NUM!	0	0	0	0
2015	0	0	65535	#NUM!	0	0	0	0
2016	1	0	65535	#NUM!	1	1	1	1

References

Brian, S. F., Greg, S. D., Justine, S., & Lisa, A.-H. (2012). Weather factor impacts on commuting to work by bicycle. Journal of Preventive Medicine, 122-124.

Max, N. (1999). The e€ect of weather and climate on bicycle commuting. Journal of Transportation Research, 417-431.