Some of my previous posts have considered how Auckland compares to the Netherlands in terms of general consideration for pedestrians. Based on the results of this discussion it seems fair to suggest that the largest divergence in engineering practices between Auckland and the Netherlands occurs in the following areas:
- Left-turning vehicles in Auckland are often provided with slip lanes that allow them to maintain high speeds even as they turn.
- Pedestrian crossings in Auckland are commonly removed in order to expedite vehicle flows (with little to no regard for how pedestrians can subsequently cross the road).
- Vehicle lanes in Auckland are very wide, which in turn increases vehicle speeds and the distances pedestrians must walk to cross intersections.
Taken together this means that 1) vehicles travel relatively fast in Auckland and 2) walking in Auckland is relatively difficult and unpleasant.
In response to my wailing some people suggested that Auckland’s preferential treatment of vehicles was warranted because relatively few people in Auckland actually walk, at least compared to places like the Netherlands. And if this were true then this may indeed warrant different priorities. After all, it would not be prudent to invest large amounts of resources (either directly in the form of infrastructure or indirectly in the form of additional delays to vehicles) in order to meet the needs of relatively few pedestrians.
But is it true that Auckland has low rates of walking compared to the Netherlands? To investigate this issue I collated some data on journey to work mode share for several European cities, which I then compared to the walking mode share in Auckland City, as illustrated below.
These journey-to-work mode share statistics reveal something quite special: Auckland’s walk mode share is actually twice that found in Amsterdam. This statistic on its own surely puts lie to the suggestion that Auckland’s inferior treatment of pedestrians is somehow related to less demand than is found in the Netherlands. In fact the reality is quite the opposite – within the former Auckland City area we actually support higher rates of walking than several, much larger, European cities, such as Berlin and Hamburg amongst others.
As with most analyses, however, this result just raises a further question: If Auckland does indeed treat pedestrians so poorly (as I have suggested) then why are rates of walking not lower than all these other cities? It’s a good question, but one that may also have an answer. My hypothesis in response to this question is this: Notwithstanding Auckland’s poor pedestrian infrastructure, we are still able to sustain relatively high levels of walking because of our reasonably favourable climate.
In order to test my hypothesis I collated some additional climate statistics, namely annual precipitation, annual sunshine hours, and annual mean daily temperature, for all the cities included in the table above. These relationships are illustrated in the following three graphs.
These results suggest a relatively strong positive correlation exists between walk mode share and mean annual temperature (R2 = 11%) and annual sunshine hours (R2 = 32%). There also seems to be a strong negative correlation between walk mode share and annual precipitation (R2 = 24%). If you look closely you can see that in all three charts I have highlighted the point for Auckland in red. This in turn shows that Auckland is below the trend (compared to other cities) in terms of the level of walking we support based on temperature and sunshine hours, but above the trend based on our levels of precipitation.
But of these three factors which is the most important? To answer this question we need to move from the single variable relationships shown above and instead analyse multiple variables simultaneously. My multi-variate regression results are summarised in the following table (NB1: who ever said economists have no sense of visual aesthetics?!? NB2: Econometric wizardy such as this is something that all transport planners should try to pick up – it’s incredibly useful).
For those who are not accustomed to the beauty that is regression analysis let me clarify these results as follows:
- The overall “goodness of fit” of the model is indicated by the “R-squared” variable, which in this case is 36%. This suggests that our regressors (namely “rain” and “sun”) are able to explain 36% of the variation that is observed in walk mode share. Stated differently, 64% is explained by other factors. This is actually not to bad because there’s a lot of factors aside from climate that can influence walk mode share (which is discussed in more detail below).
- The left hand column lists our variables. Walk is the dependent variable, i.e. it is the variable we are trying to explain in terms of the regressors, namely “rain” and “sun”. The “cons” variable is simply the constant of regression and is meaningless (it just describes the hypothetical walk mode share one would expect in the event that you lived in a city that had both zero rainfall and zero sunshine).
- The “Coef.” column signifies the direction of the relationship between our dependent variable (“walk”) and the regressors. So we see that walk is negative related to “rain” and positively related to “sun”. More specifically, a 1mm increase in annual precipitation reduces walk by -0.00402%, whereas a 1 hour increase in annual sunshine increases walk by 0.00288%.
- The P>|t| column essentially describes the probability that our regressors are not statistically significant. We see that there is a 17% and 3.8% chance that our rain and sun regressors are not statistically significant. Thus the balance of probabilities suggests there is a statistically significant correlation between walk and our chosen regressors.
Note that in this regression I have omitted temperature because it is (as you would expect) strongly correlated with sunshine hours – as such including both temperature and sunshine hours as regressors would really just dilute their individual effects to the point where neither were statistically significant.
The final step is to use these regression results to “predict” the walk mode share for Auckland. This is calculated simply as follows:
Walk mode share (Auckland) = -0.0000402*Rain + 0.000028*Sun+0.0765143 = -0.0000402*1240mm+0.000028*2007hrs+0.0765143 = 8.45%
So how does our predicted mode share compare to actual mode share? Well, the latter is 7.84%, which is not too far off what we have predicted above. Nonetheless, when considering Auckland’s climate we would actually expect an even higher walk mode share than what we actually have, holding other factors constant. Auckland is, based on this analysis, actually under-performing slightly in terms of our walk mode share, at least given our relative climatic advantages.
But what other variables (not included in our model) might explain Auckland’s slightly lacklustre walk mode share? Some obvious ones include:
- Poor pedestrian infrastructure, as mentioned at the start of this post;
- Auckland’s relatively low average density (2,900 peeps per sqkm versus an average of 3,900 peeps per sqkm for the cities in our sample);
- Geography, namely our relatively hilly central city area.
But I’m sure people out there have other their ideas of their own, which I’d be interested to hear about. Before we end this post let’s just recap what this all means:
First, Auckland seems to have relatively high levels of walking when compared to similar sized cities in Europe, which in turn suggests that our poor treatment of pedestrians is unlikely to be explained by underlying differences in demand. Instead, it seems more likely that our relatively poor pedestrian infrastructure reflects underlying differences in our traffic engineering practices – the needs of pedestrians simply are not given as much weight in New Zealand as they are overseas. And I think that’s a real shame.
Second, and in a more positive vein, Auckland seems to have some natural climatic advantages that support high levels of walking. That suggests to me that if we are able to get our traffic engineering practices on par with those found in these European cities (i.e. avoid slip lanes, install pedestrian crossings on most approaches to intersections, narrow the width of vehicle lanes, provide raised pedestrian tables at intersections, and improve general street amenity) then we can look forward to even higher walk mode share in the future.
There you go – we did end up finishing on a positive note .