A goal of the research was to analyze the suitability of ORcycle data to analyze LTS and route comfort level. This subsection explores how comfort level can be estimated as a function of trip or user characteristics.
The response to the “route comfort” question can be used as a dependent variable utilizing a cumulative logistic regression approach. This approach has been used in several levels of service models (Jensen 2007; Ali et al. 2012; Foster et al. 2015). Logistic regression models are used to
model categorical dependent variables. Cumulative logistic regression models (also known as ordinal logistic regression models) are used to model categorical dependent variables of an ordered nature. The cumulative logistic regression model results presented herein were
constructed using the R package “ordinal”52, which offers many tools for statistically modeling ordinal outcome variables.
In all of the models tested, the route comfort rating was the dependent variable. For continuous variables, a single variable cumulative logit model was tested for each variable to assess the relationship of that variable to route comfort (in terms of significance, magnitude, and direction) alone. For categorical variables, the Chi-Square test of independence was used to test for a statistically significant relationship between the variable of interest and route comfort. In this test, the null hypothesis is that the variable of interest has no relationship with route comfort; which would be rejected in the case of the Chi-Square statistic being statistically significant. The following independent variable groups were explored separately (one variable at the time): (1) trip attributes (length, duration, and average speed), (2) trip temporal characteristics, (3) user- reported trip characteristics (e.g. trip purpose and route stressors), and (4) user attitudes and socio-demographics.
This is just an exploratory study and the reader should be reminded that the results presented herein may not hold in a model with multiple variables and interactions. Each of these variables can be correlated with other variables in the ORcycle dataset and more advanced specifications such as non-linear models or segmentation should be also explored. The reader is also reminded that correlation or statistical significant does not necessarily mean causality. Finally, this is the first study of route comfort utilizing revealed GPS route data and that winter cyclists are largely represented in the sample data. Hence, results must be interpreted with due caution.
6.5.1 Trip Attributes
Three trip attributes were calculated: trip length (miles), trip duration (minutes), and average trip speed (miles per hour). A script was run to remove trip ends where the user forgets to stop the application. These trip attribute variables were tested for significant relationships with route comfort and trip length and average speed had significant and negative signs as shown in Table 60. Results suggest that longer trips tend to have lower comfort levels and that routes with a higher average speed tend to be less comfortable. Later results show that routes that are chosen because they are direct and fast tend to have a negative coefficient as well. It is not possible to determine a causality direction and these results should be taken with caution as this is just an exploratory study.
Table 6.9 Results suggest that longer trips tend to have lower comfort levels and that routes with a higher average speed tend to be less comfortable. Later results show that routes that are chosen because they are direct and fast tend to have a negative coefficient as well. It is not possible to determine a causality direction and these results should be taken with caution as this is just an exploratory study.
Table 6.9: Trip attribute variable definitions
Variable Description
Data Range z-statistic in single variable cumulative logit
Statistical Significance
Trip length Min: 0.30 miles Max: 29.67 miles -2.389 p<0.05 Trip duration Min: 2.51 minutes Max: 166 minutes 0.087 Not significant Average speed Min: 0.63 mph Max: 16.83 mph -2.282 p<0.05
6.5.2 Temporal Characteristics
The impacts of time of day and day of the week on comfort levels were also tested. The time a trip started was used to categorize these temporal variables into two groups representing weekday/weekend travel as well as peak and off-peak time travels. The corresponding variable definitions are outlined in Table 6.10; only weekday was significant and had a negative
relationship. Given the large number of commuter trips in the sample, the weekday variable may indicate that traveling during days/times with high traffic volumes tend to decrease route
comfort.
Table 6.10: Temporal characteristics variable definitions
Variable Description
Possible Values Chi-Square, DF Statistical Significance Trip day-of-
week category Weekday Weekend
10.57, 8 p<0.05 Trip start time
category Off-Peak Night (6:30 PM to 7:00 AM) Peak AM (7:00 AM-9:00 AM) Off-Peak Day (9:00 AM to 4:30 PM) Peak PM (4:30 PM to 6:30 PM)
8.65, 18 Not significant
6.5.3 Trip Route Choice
Many route choice factors were significant; results are contained in Table 6.11. As mentioned previously, routes chosen because they are fast and direct tend to be less comfortable. When users do not know or have another alternative route comfort levels are also lower. On the other
hand, comfort increased when routes were chosen because they: had good bike facilities, were good for families, had enjoyable or nice scenery, had low traffic volumes or speeds, or had few busy intersections.
Table 6.11: Route Choice Factors
Variable Description Possible Values of Variable (range for Continuous variables) z-statistic in variable group cumulative logit model Statistical Significance User chose this route because
it was direct or fast. True/False -8.49 p<0.001
User chose this route because it has good bicycle facilities.
True/False 4.08 p<0.001
User chose this route because it is enjoyable or has nice scenery.
True/False 1.97 p<0.05
User chose this route because it is good for a workout.
True/False -0.54 Not
significant User chose this route because
it has low traffic or low vehicle speeds.
True/False 3.51 p<0.001
User chose this route because it has few busy intersections.
True/False 2.76 p<0.01
User chose this route because it has few and/or easy hills.
True/False 0.64 Not
significant User chose this route because
it has other riders/people.
True/False 1.64 Not
significant User chose this route because
it is good for families/kids.
True/False 3.71 p<0.001
User chose this route because they do not know another route.
True/False -3.24 p<0.01
User chose this route because they found it online or using their phone.
True/False 1.28 Not
significant User chose this route for
some other reason.
True/False -0.82 Not
significant
Intuitive results were also obtained regarding route stressors. Routes without any stressors were significantly more comfortable than routes were users chose a stressor such as traffic,
commercial vehicles, or other cyclists; variable definitions and results are shown in Table 6.12. With the exception of cycling frequency, all user demographic and attitude questions were also significant after performing a Chi-square test.
Table 6.12: User question response variable definitions
Variable Description Possible Values of Variable (range for Continuous variables) z-statistic in variable group cumulative logit model Statistical Significance
User indicated that on this route they were not concerned with traffic stressors.
True/False 4.23 p<0.001
User indicated that on this route they experienced discomfort as a result of auto traffic.
True/False -2.81 p<0.01
User indicated that on this route they experienced discomfort as a result of large commercial vehicles/trucks.
True/False -8.11 p<0.001
User indicated that on this route they experienced discomfort as a result of public transport.
True/False -1.57 Not significant
User indicated that on this route they experienced discomfort as a result of parked vehicles.
True/False 0.92 Not significant
User indicated that on this route they experienced discomfort as a result of other cyclists.
True/False 2.17 p<0.05
User indicated that on this route they experienced discomfort as a result of pedestrians.
True/False 1.62 Not significant