- Good Afternoon everyone.
Welcome to the ITS Friday seminar.
Today we have a change of flavor in terms of topics covered.
and we have Dr. Neil Maizlish [who] is a epidemiologist with 30 years of research
experience and he worked in various work locations: academia,
government, unions and non-profit. He's the visiting research professor here at Davis and
also, he works for the Public Health Institute in Oakland so he will talk
about the cool benefits of active transportation as mitigation strategy for climate change.
Welcome, Dr. Neil Maizlish
- Thank you.
Little known fact, I actually started my career
in 1984, right here in UC Davis. Just starting out a public
health program which is now grown into a pretty big program in conjunction with
the medical school as well so it's always nice to sort of like come
home because this is where I started my career. So glad you're all here. What I'm
going to talk about today is the health co-benefits of active transportation
as a strategy for greenhouse gas mitigation. So I think we're all aware of
the challenges that we face on the climate front. Both North and South
California have been recently devastated by fires. From a public health standpoint
we know that climate change is the number one existential threat in the 21st
century for Public Health. We saw this just a few years ago in the Paradise
fire where it was very obvious. Over 85 people lost their lives. I know we have
Sarah who worked on our project, who's doing follow-up to the issues around
transportation and evacuation. There's a whole spectrum of activities that
climate change intervenes on when it comes to the transportation systems.
California is among the top 20 carbon emitters in the world - somewhere in that
top 20. It's been as high as 14 maybe its lowest is 20, but the point is that
roughly 425 million metric tons of carbon that go out every year - 40% of
that - roughly 175 million metric tons are from the transportation sector. It is the
leading source of carbon emissions in California. So what we do related to the
transportation system has a big impact not just on carbon mitigation but as
you'll see, on public health as well. From the standpoint of mitigating carbon,
increasing the efficiency of fuels and vehicles, that's one of the strategies
that is being pursued. Also just reducing vehicle miles, could be carpooling, could be
active transportation, could be a number of different strategies that we're well aware of
to reduce carbon emissions. What you may be less aware of is how it's linked to
public health and that's really what the topic of this conversation is. So on one
side you see the greenhouse gases going up the stacks. On the other side you see
chronic disease and really what the connection is between those. I should
just point out that chronic disease is what's driving our health or the health
burden in California. It's things like cardiovascular disease, cancer,
diabetes, and things like that. That's like 93 percent of the health
burden are those things. They're not communicable diseases. They're not
measles. They're not other kinds of infectious diseases. That's the big enchilada when
it comes to our health status. We can act as if these two issues, our climate
Crisis, and our chronic disease crisis are totally disconnected and pursue
solutions that are totally diametrically opposed to one another. That's the mewls you know
trying to get to their hay and pulling in different directions. Or we could
actually think of ways in which the strategies actually are synergistic.
That's really where the word co-benefits and what its meaning is really all about-
is finding a win-win situation where we can both mitigate greenhouse gases and
improve public health at the same time so really the questions become
you know, what are the strategies that generate these co benefits? Are there any
harms involved? We got to be aware of that. You know unintended consequences and how
do we even begin to measure the health code benefits of different
strategies. So that's what the next pieces of modeling work that is called
the "integrated transport and health impact model" or ITHIM and it looks at
three main ways the transportation system impacts human health. The first is
through physical activity and that's where active transportation is all about.
It's the biking and walking and it's also linked to transit because that
first and last mile has to be biked or walked. Anyway it also takes into
account air pollution. So we know that vehicle emissions using combustion
engines generate emissions. Those are breathed in and they cause a lot of health harm.
Also traffic injuries is the third main pathway that this model
integrates into a quantification of deaths and premature life loss. It could
be dying before your optimal life expectancy or living with disability - sub
optimal health. Conditions like diabetes and other kind of chronic conditions
they can persist for years and they don't contribute to optimum life
compared to someone who doesn't have those conditions. So the model takes into
account both mortality and morbidity and that's actually one of its great
advantages. It also quantifies the carbon emissions
from car travel as well as it monetizes the health benefits in terms of live,
you know deaths averted or the length of healthy life preserved. As
far as what the inputs of the model are some of these ...since I know the room is
filled with people who are starting their career or somewhere in the middle
of their career in transportation or civil engineering we use a lot of the
tools of your trade as part of this model. How many of you are aware of
the California Household Travel Survey. Almost everyone put up their hand. Great! That's
one of the inputs to the model. How many of you are aware of activity-based
or four-step travel demand models? Your hands go up. Those are inputs to our public
health outputs. So, the tools of your trade are the inputs to this model.
That's where we're kind of joined at hip because I use a lot of the tools
that you guys use to quantify the travel behaviors especially around active travel that
are tied to physical activity, road traffic injuries or air pollution. I
should just point out that there are other ways the transportation system
impacts our health. We know noise is associated with cardiovascular disease,
sleep disturbances, and other things like that. So I don't want to make it sound
like this model is the do-all and be-all of all models. There's many other ways in
which transportation system impacts human health.
When major roadways sever a community, there's all kinds of issues of access to
resources that that creates and disruptions of social... so there's many
other things that are related to social cohesion that it would be great if we
could model them in this model, but right now we're picking sort of the main
direct pathways. I just want to acknowledge that there's other ways the
transportation system impacts human health. By the way I think we're
gonna have plenty of time that if you guys have questions you can interrupt me
with a question during this rather than saving it for the end. Michael has a question
- "You have traffic collisions as an input. So we
collect traffic equation statistic but for future scenarios and
alternative scenarios, I'm not aware of these models predict you know
estimate the collisions. How do you get them?
- I'll get to that. Yeah, it's a really interesting
and novel approach that has a lot of predictive power that is totally lacking
in what I've seen other approaches. I'll get to that. So how many of you are
familiar with SWITRS data? You know what SWITRS are? It's a Statewide
Integrated Traffic Reporting System. It is the biggest source. So whenever there
is a collision on a public road and there's some law enforcement agency that
goes out to investigate what happened. They file the 555 form. These
forms are sent to the California Highway Patrol. They're cleaned
up to put in a database and the UC Berkeley safe trek program... how many of you folks know
about Safe Trek? okay They're TIMS. They're Traffic
Injury Management System. .. know what it's all about. Actually geocodes, the serious and
fatal portion of those injuries and that is inputs to the model. I didn't talk
about the other inputs but we have health surveys - California Health
Interview Survey. We also have lots of health statistics, final statistics that
the California Department of Public Health provides, census data on
population growth and actually the Department of Finance data. The whole
point of this is, that there's all these data sources that represent state
investments that you wouldn't think they're somehow connected, but here in this
model they're all brought together. It's also a plug for how important these data
sources are for research and applied research. So anytime we hear that you
know California is not going to do another California Household Travel
Survey I get very nervous because that is a real important source of data for
calibrating the model and it's also used for calibrating models without the MPO landscaping health form.
All right so let me talk a little bit about the outputs a little bit more
explicitly. So for the health outcomes we have the annual number of deaths that
would be prevented if we went to a more active transportation scenario.
There's a concept called disability adjusted life years which if
you die before your optimal life expectancy, there's a certain
number of years that are lost. So if everyone fell a little bit short and
they contributed X number of those short years into like a common kitty, we went
around the room and that happened, there would be a certain number of lost life
years. The optimum would be zero. Everyone gets to their optimal life span.
No disease, no illness, and then you're dead. That would be in a way ideal. Or you
exceed the limit, but you wouldn't be penalized if you exceed the limit but if
You fall short of your optimum life. So
that's what those years of life loss is. The years of living with disability also
takes into account how long. You know some conditions, let's say
you're hit by a car when you're a kid and it resulted let's say
an amputated limb. You're gonna go through X number of years with your
lifespan with some level of disability. So again if that were to happen in a
population, the years in which that occurs are added to a common kitty but
it recognizes that some conditions are more disabling than others. So there's a
waiting factor where one is you're dead so that's the most severe disability but
anything less than that... so really severe chronic conditions like you know a
severe kind of cancer, that would have a high weight closer to one than to zero.
You know something like maybe a, I don't know, an ear infection that comes
and goes would have a weight closer to zero. So anyway the idea is that the
duration of time that you're living with a disability is also... what's important
about this is not just some kind of like interesting methodological issue, it
turns out that if you only count deaths, which is actually a very popular thing
to do, a lot of researchers you know... because people don't challenge deaths usually
as far as not being serious outcome but for certain things
that are really prevalent in the society like mental illness, you usually don't
die of mental illness but it is a very disabling condition that if you don't
take that into account you're going to be underestimating the burden of
disease in a society. So there are certain conditions in which you need to
have a measure of morbidity rather than mortality to get the full picture of the
impact of health outcomes. As far as the specific outcomes, remember this is sort
of the mantra - physical activity, road traffic injuries, and air pollution. Those
are the three pathways. It turns out that heart disease, diabetes,
dementia, depression, and colon and breast cancer, are very much linked to physical
activity. You increase your levels of physical activity [on a] population
basis, you will be decreasing the prevalence and incidence of these
conditions. That's how those are weighed. Specific diseases that are linked to
the fine particles, so this is a particular matter with an aerodynamic diameter of
2.5 microns. These are tiny tiny little particles that are capable when
they're breathed in, they penetrate deeply into the lungs. They can cross the
blood barrier and get into your system and they are carcinogenic and they have
all kinds of other effects. They do produce lung cancer but they also impact
some of the same diseases that physical activity does as well
so cardiovascular disease, but also non cancerous lung conditions like asthma
and emphysema and chronic obstructive pulmonary disease. It affects those
Things as well. Road traffic injuries again is those outcomes I mentioned
before. As far as monetizing the health benefits, you can just count up the
number of diseases and there's a concept called the value of a statistical life.
If you've heard about ...anyone heard about that? Few people here. It's basically
saying the value of one death is X million dollars and there's different
methodologies to arrive with that, but for regulatory purposes, US EPA and other
Agencies, Caltrans, assigns a specific dollar amount and it varies by agency.
Right now I think it's up to like 9.8 million dollars per death. So if you have
more physical activity and reduce air pollution you can actually be preventing
deaths. Each death that you prevent you can multiply by whatever it is 9.8
million dollars. You come up with a large pool of money, a way of monetizing
that. The other method which is much more conservative. It says you add up all the
medical bills for diagnosis and treatment. Remember those premature
years of life lost? Well they represent productivity loss
because if you are working during those years or you had earning potential that
money that you lost for the X number of years that you died prematurely, that
gets added to the kitty. So everyone who lost lives prematurely
in their working ages would be contributing a certain amount of money of the
productivity losses. So that's a different way of adding it up, but what I
did is I sort of bracketed. Those are the two most popular methodologies for
quantifying or monetizing health benefits, to give people a range because
each one has a pro and a con to it. Anyway just so you know what it is. And
then there's car carbon emissions which is the other major output.
Just again to emphasize the importance
of what we're doing here is that this is a recent tally of deaths in California.
You want to know what the burden of disease in California is by specific
Causes, here it is. Heart disease, this is the top ten
causes. What I did is I made bold the causes that are actually modeled in IFIM.
So you can see the first seven are all in the top you know ten and those are
ones that are being modeled and part by IFIM. You have heart disease,
cancer, stroke, Alzheimer's disease, chronic lower respiratory disease (that
that's where you get your asthma), and unintentional injury. That's a big
category of which motor vehicle collisions is a part. So it's not just
like you know slips and falls and other things like that. Also
unintentional injury would be the unintentional drug overdoses would be in
that category. That's a really big category but motor vehicle accidents
contribute to probably around a third of that and then diabetes. So just
to re-emphasize this model is picking diseases that
have major impact on population health for California.
I'm now going to shift into sort of the methodology. I'm not going to go really
overboard because you know I can show integral calculus and all
this stuff and press everybody, but I think I'd probably lose most people as well, but if
you want a more rigorous treatment of this comes see me afterwards or read the
papers because all of this stuff has been published already.
You possibly have heard this in the popular press. This idea of... it's called
the attributable fraction or it's the percentage of deaths that could be
Prevented only if . . . we stop smoking. I mean,
we meaning everyone in the United States stop smoking or we had a healthier
lifestyle through better diets or more physical activity. Or we didn't use
firearms on each other so much. So this is now an estimate of ...it is sort of
you know a yes or no. If we could just eliminate the risk factor entirely how
many deaths would we prevent? So here you see poor diet and physical activity that
often go together. They're like a leading cause. In other
words we could prevent 25 percent of all deaths in California if we could just
have healthier lifestyles with more physical activity and better
diets. Tobacco is always on the top of the list. The reason why poor diet and
physical activity come out on top of the smoking is because in this particular
journal article I got this from, they combined the two. Anyway you've heard how
many thousands and thousands of deaths could be prevented. So there is actually
a methodology behind the percentage. I won't go into all the details but the
point is again I've bolded where I think this model is. So the physical inactivity,
fine particulate matter and motor vehicles. These are among the top
preventable diseases or injuries that is driving health
care costs. So now we're gonna get a little bit more into the details but I'm
not gonna get a super super technical unless someone asks for it. Ok so we're really
talking about, in a sense, what are the inputs or what are the exposures? What are the
the independent variables that drive health outcomes? That I'm sure you all
got. So what we're trying to do with physical activity is change the
level of transportation related physical activities. You're walking, bicycling,
you're alone, or in combination with. I think you're probably aware that the
minutes of physical activity engaged in transportation probably looks like a log
normal distribution. There's a lot of people who do virtually nothing or very
very little and then there's this tale of people who are out on their
bicycles doing centuries every weekend. You know [people] like Dr. Jang that are bicycling every day
and just getting part of the tale you know in the sense of a lot of physical
activity. It's a log normal distribution. What we're trying to do is
shift that distribution more push it to the right where there's more physical
activity and a higher percent of the population. That's the scenario that
we're trying to achieve by increasing physical activity. Again it's a
population phenomenon trying to impact the entire distribution. Change in
air pollution levels from shifting short car trips to active transportation. Again
that's going to change the distribution of pollutants. It's going to interact
with the entire airship. So 20 sources or stationary sources are still going to be
there. They'll be interacting with the transportation related ones but
ultimately changing the entire air shed that everyone is breathing, not just
active travelers but people in cars who are breathing the exhaust in the car in
front of it. Lastly this is getting to what Dr. Jang was talking about is the road
traffic injuries. We're shifting distances. I'll get into a little bit
more detail here because this is a novel approach. It takes two
to tango right? So that means when there is a collision, there is a victim vehicle
and then there is a striking vehicle. To accurately portray the risks of an
injury, you need to know how far the striking vehicle goes and how far the
victim vehicle goes. You can construct an injury rate that as the denominator
per mile traveled is the product of the victim vehicle and the striking vehicle.
To be a little bit more technical, it is the person miles traveled of the victim
and the vehicle miles traveled of the striking vehicle. So if you know what the
PMT and the VMT is and before I lose everybody you know what I'm talking
about? Yep? Everyone? Yes, okay we've got a few
people who aren't sure what a PMT and a VMT is but they're related by occupancy.
A car with two occupants goes one mile. The VMT
is one mile, but each of those people experienced one mile as well, so there's
two person miles to the one vehicle mile make a ratio of PMT to VMT, you get
occupancy. I said there were two people in the car to begin with okay. So again, if you're
a modeler you need to be aware of the difference between the two and
incorporate both PMT and VMT. That's really novel because when you look at
most of the injury statistics that come out of the MPO models and stuff, they
look at VMT. Sometimes think ...They don't even consider the two-to-tango piece
they put you know parties who are really at risk to a collision in
the middle of the injury rate and what I mean there is, the IFIM model also
stratifies by roadway type. It's a sort of a surrogate for speed and volume. So
it actually stratifies the injuries so every pairwise combination of a striking
vehicle and a victim is put into a matrix and the miles that they each
travel for that particular cell in the matrix is further stratified by
roadway type: local road, arterial, and highway. For certain
modes like bicycle and ped, you're not going to need to... there's going to be so
little highway miles out there that they're not going to be a risk. Yet some
people create injury statistics where all the car highway miles are in the
denominator and they put a ped or a bicyclist in the numerator. There's a lot
of those miles that are not even at risk. This is a novel approach that takes
into account the miles traveled by both victim and striking vehicle and it also
takes into account safety in numbers. How many folks have heard of safety in numbers?
This is just an empirical observation that as mode share increases,
especially for active travel, the biking and walking, that injury rates to
pedestrians and cyclists tend to go down in an exponential manner. Safety in
numbers implies motorists seek hoards of bicyclists and pedestrians out there and
they anticipate you know not banging into them by slowing down. That's one
interpretation. Other people have said that's actually a case of reverse
causality, its actually all the safety improvement that allowed people to
walk and bike in the first place and that's really why.
For the purposes of the IFIM model I don't need to resolve that,
but those miles basically have a square root function that represents
sort of the mid-point of the power function of how injury rates decline with
increasing mode share. So we have two questions, let’s do it and then we'll come back to you.
[audience member] Well Neil I'm Peter Jacobs and I wrote the paper statement
and actually I work with David Ragland of Berkeley and you see what it looks like is prevalence.
Human beings are having a real difficulty seeing their things ...
Think of it as the baggage screener at the airport. Say he's trying to find a weapon that's really hard to find
1 in 1000 weapons. The baggage he's looking at is really hard to spot.
But if there are 1 in 10 or 1 in 100 he'll spot most of them.
Likewise with a radiologist looking at cancer. It's really hard to spot just 1 in 1000.
1 in 100, you'll spot most of them. 1 in 1000, you'll miss most of them.
So it all has to do with causality. We have again...
I agree with most of that but I also
think that there is another component that could be explained by safe public health.
Also, where it's true for biking and walking, it's also true for other modes as well. So that's maybe
a weaker effect but it's actually true for cars and trucks and other things. So you see how
things were in argument but anyway the point is that what's nice about
the model is it does take safety and safety in numbers into account in the
modeling of the injury component. So all these features make the model a little bit
more acceptable because it seems to be incorporating more realistic elements of
how the transportation system works. Ok let me just keep on going.
So now I'm going to talk about the kinds of scenarios, so
you know we're talking about shifting the distribution of physical activity,
miles travel, and air pollution. We are contrasting these
distributions with a baseline. The baseline for the travel is largely based
on what's been reported in the California Household Travel Survey for
walking and biking and for certain modes like bus miles and it also
takes advantage of MPO outputs for some of the other miles, especially trucks
and things like that which aren't part of the California Household travel survey. So
we have a 2010 baseline for all those distributions. Now I'll just explain what
some of the state agency goals are that have been put out for different purposes.
So the Air Resources Board, it's part of its 2017 update of what's called the
Scoping Plan. It's a scoping plan for carbon reduction for California so it's
part of the legislation that was passed in 2006, the Global Warming Solutions
Act 1832. So ARB is the agency that's responsible for coordinating and
implementing the state strategy on carbon mitigation. They came up with a
recommendation of goal which is to quadruple the amount of walking and
transit and a nine-fold increase in cycling compared to the 2010 baseline
and achieve that by the year 2030. That's a very lofty goal that they have.
They didn't say what the health implications were but you'll be
seeing that shortly. Caltrans has what you might consider a more modest, but it's
also a more intermediate plan of doubling walking and transit and tripling
cycling. Same 2010 baseline but do it by 2020 which is next year. Then there's the
sustainable community strategy. How many people have heard of SB375? The
sustainable community strategy? Okay so how many people have heard of regional
transportation plans? A little bit. So California
as well as many other states have been creating regional transportation plans.
They charge certain recognized entities called metropolitan planning organizations. How
many people have heard of MPOs? Okay good, so the MPOs have been doing these
regional transportation. They're long-term transportation plans. I mean
they have a 20-25 year you know time horizon out into the future but they're
constantly updating these. Every four years they have to update them with new
projects and other things like that, new strategies. In 2008 the SB375
added a greenhouse gas reduction component to these regional
transportation plans, did other things as well but you know for the MPOS they had
to incorporate greenhouse gas reductions in those plans and other requirements
you know, accommodating housing and building within the urban footprint and
a bunch of other stuff and increased air pollution. So it was a number
of other requirements they had but that was new. So I took their plans and what
they said is as far as the travel patterns input them into the IFIM model and
came up with what the health benefit would be. Some of the individual MPOs did that on
their own, but I did it for all the major MPOs in California.
Then there's the US Surgeon General recommendation. So this is our US Surgeon
General making recommendations on physical activity for decades. Turns out
to optimize your health, if you have 150 minutes of moderate physical activities,
physical activity that is enough to make you breathe hard, if you do
That, you will decrease your risk of all those nasty chronic diseases that I put
up on the screen before. So that's a recommendation and sort of like you know
a health optimum goal - health based goal rather than a mobility goal. Then
the IFIM model, also you guys could play with I'll talk about that at the very
end, but it allows what-if scenarios. What if we increased active
transportation by some other thing than the three, four, or nine multiples. Let's
say just one at five percent, you want to know what the health benefits would be.
You could do that. Or you could just pick an absolute amount of physical activity.
I want 15 minutes a day and see what that does.
I want to do 10 minutes a day and see what that does. The model allows you to do
that. Another kind of what-if scenario is what if we could replace a lot of those
short car trips which is like a a third of our trips are really low five
miles and distance. So we could just replace half of them I'm not talking
about you know coming home with a refrigerator on your back from the
hardware store. That trip you're not gonna make on a bicycle one but a lot of 0:36:22.950,0:36:27.120 those short trips you probably could get you're shopping or whatever you're
socializing done. So the idea is that you'd replace trips that
are less than you know one mile walking in between one to five miles that
would be cycled. Then there's this low-carbon scenario which isn't about
physical activity but it reflects the policy that we're going to solve our
problem by electrifying the auto fleet. So there's actually no change in
physical activity there because if we're just swapping out propulsion systems,
electric for fossil fuel base, there's gonna be no change. So that's a scenario
but it has a very dramatic effect obviously
on carbon reduction. Then you can design your own scenario if you know
what the inputs look like, you can upload for the different modes the travel
patterns and you can quantify that. So just to give you a flavor for what we're talking about,
here's a spectrum of those scenarios. You can see the baseline. The top part is
time. So we're talking about the median. I said you know it's a log normal
distribution. The mean is a little bit misleading as far as you have a tail
that's dragging up the mean. So I just think the median or
geometric mean to describe what the distribution looks like but what IFIM
actually does is it takes the distribution into five quintiles and it
models each one of those quintiles. So it actually is approximating the entire
distribution but for purposes of just describing the scenario, I'm just going
to give you the median. This is the median per person per let's see this
is per day okay so the baseline. This is a commentary on us folks. The median
amount of travel time okay and I broke it down by bike and walk. So the red bar
is bicycling and blue bar is walking but the total amount of the median minutes
per day per person is on top of the bar. So in 2010, California population spent a
grand median of 2.7 minutes per person per day with walking and
biking and transportation, two minutes. I can't remember what I was doing
two minutes ago, it wasn't that long ago. We're not talking about a lot of time. So
the Caltrans strategic management plan, that was the one with doubling walking
transit and tripling cycling that brings that basically doubles it. Okay so we're up
to 5.4 minutes per person per day as the median. Short trips, we got
another big boost there. We're up to 11 minutes per person per day. The Air
Resources Board, again you can see how in the short trips we're getting
a lot more cycling. In the CARB, we're getting a lot more
walking. Even though CARB, that the bicycling act was a nine-fold increase
but that nine-fold little sliver on the baseline is what we're multiplying nine by.
When you have a very low baseline you when you talk
about relative change you can totally exaggerate what the absolute impact is.
That's sort of another commentary about goal-setting and how you approach this.
Finally, the US Surgeon General is 115 minutes a week. That's 21.4 minutes a
day. It's equally divided between biking and walking. That was just the
decision I made. You can change the mix however you wanted but just to
give it an equal amount. That's roughly because a lot of people you know they
think about you know time. It's roughly 400 miles walk per year and
about a thousand miles cycled per year roughly at that level.
- The baseline is from the most recent travel survey?
- Yup, that's the California Household Travel Survey. Okay and now to give you a sense
of distances that we're talking about. So the baseline we're talking about 0.4 tenths
of a mile per person per day which is not very much walking and biking. Doubling of
that through the Caltrans program short trips boost that up to actually almost
three miles per person per day. The CARB scenario again because it's more walking
than bicycling, it's intermediate and then you get a whopping five miles per
person per day with the US Surgeon General. Again we're not talking about
marathons here folks. We're talking about five miles per person per day. So that's
like two and a half miles there and two and a half miles back.
Just to give us you know... what are we asking
potentially people to do? We're not asking them to run marathons. It's
something that should be achievable, although there's a lot of work between
setting of the goal and actually achieving it. All right so here's the meat.
I [ran] the model and now I'm going to give you the results, the main
results. What I've done here is to try to contextualize
the information by putting what the California burden of disease is on the
left axis. So roughly 23,000 deaths per year in California are attributable to
the lack of physical activity. Roughly 9,000 are due to particulate matter and
I should just point out it's not just [from] a transportation system this is
from all sources. So the same thing with the physical activity. That's all, it's
leisure time, that's occupational, it's home, it's
transportation. So it's just whatever the source of physical inactivity is,
that's what the overall burden is and then road traffic injuries is roughly
3,000 per year. Just to give you a sense of what the order of magnitude is.
That it's this chronic disease piece or the physical activity piece. A lot of
that 9,000 in the air pollution is also chronic disease as well, but you see on
the right side of the graph are the different scenarios that I laid out.
Those are the state agency one's, the Surgeon General short trips, [etc], so you get a
sense of this. I did it as a stack bar so you can immediately get a sense
of what is contributing to the reduction in mortality because what this is, is that
anything above the zero point which is this line down here - that actually
means you're adding to the burden. You're making you're making more deaths.
Anything above that is you're actually preventing deaths. So immediately you can
look back and say hey wait a minute, some of these scenarios are actually
increasing the road traffic injuries. Whoa! I don't know if these state
agencies actually thought that through? Or obviously the contracting community to
this work so they know now. The point is that, I don't think
that was widely appreciated, but if some of these scenarios were put out there.
There's some really interesting nuances. Even in the ones that have a
little bit of red on the top meaning that they prevented those deaths. There's
some really interesting subtle things that I'll get to because there's some issues
of mode equity or the peds, cyclists, and cars are actually experiencing the
benefits in a very different way. So let's just go through here. It's hard to
see that there's a little thin blue line here that reflects the air pollution
benefit. So just again let’s step back and look at the big picture. I mean you
can hardly see it. I mean it's there, but the big benefits are really about
physical activity not air pollution, yet it is air pollution that tends to drive
our conversation about electrifying the auto fleet benefits there. We seem to be
ignoring the big enchilada which is chronic disease reduction through physical
activity increases and that's where the health code benefits reside and active
transport not so much. I mean every bit counts. So air pollution yeah it is really
important to reduce that, but it is not the major source of health benefits and as you
see here road traffic injuries, depending on the scenario, could increase or
decrease. Michael has a question.
- So Neil, for the short trips, you basically have half the short trips being walking
or bicycling. - Right
- So in that scenario the VMT will be reduced and
then the active transportation mode will increase and since the red is the
road traffic injury so that means in that scenario the road traffic injury risk
gets higher. - Yep
- But your risk factor was the VMT and the PMT so in
that case the... - We'll get to this because
this is something that model predicts
which is not intuitive because you have all the different modes interacting. So one
of the biggest threats to car occupants is other
cars. That's where all the crashes, I mean a lot of the cars crashing happens between each
other. So [if] you get a big VMT reduction of cars, you're actually reducing the car to
car collision rate substantially. That's where the benefit is for scenarios that
have a large substitution. One thing I should just point out, again it gets into
some of the methodological issues, is that each one of these scenarios has the
same level of mobility over all modes. So it's roughly ten thousand miles per
person per year over all modes. So I've kept, to simplify things, so there's
actually no more or less overall travel. I haven't restricted any overall
mobility. All of these scenarios have the same total mobility over all modes. So
that it really is when you increase active travel you're decreasing car.
You're increasing transit you take that away from car. So depending on the extent
of substitution, so the more ambitious scenarios are obviously taking more of
the car miles traveled. So for their injury component, remember you have two things
in the denominator. You'll have an increase in the PMT so that's the victim
of more walking and biking but then you'll have a decrease in car travel. So you're
going to have this tension between the two in the denominator and depending on
which one overcomes the other, the overall injury rate will change.
That's in part a technical explanation of why you're seeing in some
cases you're seeing increases in the other cases decreases.
- So that means the road traffic injury will involve more pedestrians than bicyclists right
because the other side is reducing and they you have a total number of increasing.
- I will show a slide that will get into the details of that for one of the
scenarios because again it's not totally intuitive and because
the safety in numbers we're actually taking the square root of this, it has a
differential impact. So actually this is one of the really more powerful things
about IFIM because I've read a lot of the
MPO plans and this is totally missed by all of them. I think you know again
when you see, you know the national highway, the federal highway industry just came out with
the most recent statistics. What do they show?
Decreasing car to car injuries increases in head [which is] exactly what is being predicted here
by having more active transportation because we know that is
occurring. There is more biking and walking so that exposure part, more miles
traveled is happening in that denominator but we're also seeing
something going on with that other part that's contributing which is the
car miles. It's not clear to me that it's actually VMT reduction there, may just be
the safety of vehicles is greater. - I'm wondering rate and risk are not
the same. - Yeah I'm going to get to a
slide that will just illustrate that in
a very... it will be really interesting and somewhat
counterintuitive, but when you do the math it actually makes sense. Okay,
so that was the health outcome. So I just want to go back again and say
the US Surgeon General missed 150 minutes
per week. When you look at the green over here and compare it to the green over there
Obviously, we would be making a big dent in chronic disease reduction if we
could implement the scenario with that level of ambition. This is where the
MPOs are. This tiny little piece of green over here. Every bit counts, but more ambition gets more
health benefit. So really there have been sort of the lowest level of ambition and
there are other scenarios that if they were achieved would
generate far more health benefit and I'll get to the carbon reduction in a
second. I mean the other message is that really you're seeing that air
pollution makes a contribution. It's very modest compared to the other sources and
since the air pollution discussion tends to sort of dominate in our
discussions around the transportation and climate change I just wanted to point
out how from a policy standpoint getting more active transportation is going to
have a lot of benefit. Here are some of the other outputs of the model. Again you
have your blend of scenarios in a rank order. This is again the
two different methods for quantifying the health benefits. One is the cost of
illness which is counting up old medical bills and the lost productivity. The other is
assigning, in this case I used an old EPA figure only 7.4 million
dollars whether 7.4 or 9.8, you generate huge
annual savings. So those several hundred deaths that were averted turn
out to be for the value of statistical life 67 billion dollars in monetized
health benefit per year. So again that's not trivial that's actually a lot of
money which is what plied back into the transportation system.
The problem is the health benefits are
accrued by a different group of people than people who have the capital expenditures when
they increase. So it's called the wrong pocket syndrome where the people
who have to shell out the money, meaning the transportation agencies, are not the
ones reaping the benefit. On a societal level this is occurring but on a more
transactional basis, so how from a policy standpoint can you somehow plow these
benefits back into the system is a real challenge from the policy standpoint.
The next slide is Carbon Reduction because that was my basic premise is that we
should be thinking about active transportation
as a strategy for carbon mitigation. So let's take a look at how much carbon we're
actually reducing. So you can see again I did it in red. I just want to point
out the low carbon driving. It was based on work that was
done here by Nick Lutsee. I don't know if people remember Nick. I don't know
where he is now but he was a graduate student. Published a paper several
years ago, in which he basically looked at market share hybrids, electric vehicles,
biofuels all kinds of strategies that that were likely to occur
from 2010 to 2045 and he generated a family of curves of carbon
reductions based on how ambitious adoption of electric vehicles, lightening
up the weights, and everything else that that can be done from an engineering standpoint
to make lower carbon emissions. So I just used his (inaudible). Basically
he predicted by 2045 if they used all those strategies you could reduce the carbon
emissions by roughly a third from the 2000 baseline. That's what I'm using for
the projections here. So yeah you get big reductions. So the baseline is roughly 90,
and again, I'm only focused on car travel not the other
modes because again, the substitution that we're doing is between car and bike
and walk. Yeah there's gonna be some transit stuff but I didn't take that
into account. So there's roughly 95 million metric
tons per year in 2010 from car emissions in California. You can see that there's
modest reductions from that 95 million metric tons and this is where the carb
scenario comes in. So it's roughly about a 20 million metric tons per year
reduction which isn't so bad. It's making a contribution. Maybe not as much
as the low carbon driving. These things aren't necessarily mutually
exclusive. You could consider both the active transportation plus the low
carbon driving cumulatively as a way to reach our goals rather than putting
all of our eggs into the low carbon driving task. Now I want to get back to
the injury piece because this is really important and I'm only comparing one
scenario with the baseline. This is the Air Resources Board system where we
did quadruple walking transit and nine-fold increase in bicycling. What
I've done here is I put the absolute number which is sort of what you're getting at
when you talk about risk, but also a rate. This rate is per mile traveled of the
victim. So we're talking about pedestrian injuries in the numerator, we're talking
about miles traveled in the denominator as a pedestrian. So I've taken that rate
and broken it up a little bit and just looking at it from the victims
standpoint. So, when you look at the baseline which is in the darker bars
and the blue bars are the scenario for walking. The absolute numbers actually go up for walking
and cycling. The absolute numbers for cars go down and that reflects largely
the car to car collisions are going down because you're reducing their miles
traveled. When you look at it from the standpoint of a rate though... so this now
is looking at, the numerator is the injuries, the denominator is the miles
traveled of that mode. You actually see big reductions in injury risk per 10
million miles traveled for both walking and cycling. There is a decrease
for cars, but you hardly see it because the amount of car travel is so
overwhelming that even when you take out a little bit for walking and biking, it
doesn't change that much. So here you have a really interesting, almost
counterintuitive situation where it's getting safer in this scenario, per mile
travel, but the absolute numbers go up and it won't be until these really big
reductions in car travel and substitution with active transportation
or transit that you see the numbers go down as well.
That's what places like the Netherlands have already achieved. They're not contrary
to one another but when you increase the exposure to biking and walking dramatically,
unless you have inordinate amount of attention to safety, you will be generating these
kinds of results. It's exactly what we're seeing right now.
Okay so we have a question, go ahead.
"Does this the model account for traffic injury rates in different geographies?
Or is it like a constant number for the entire Californian region?"
I'll answer your question a couple ways. One:
micro geography and this is only in the sense that we stratify the injuries by
roadway type. So we have local roads, arterioles, and highways. So that matrix,
you can imagine a matrix with every mode crossed with every other mode and all the
two-way combinations in which they can collide where if one axis is striking
vehicle the other is the victim. So you have to imagine that
matrix has been repeated three times. One for local roads, one for arterioles,
and one for highways. So that is like a micro geography and it accounts for,
it's a circuit for volume and speed because we know what the
consequences are, pretty much what the likelihood of a fatality
when you are traveling at a particular speed. So that's one way geography is
accounted for. The other is the model is calibrated by region so that specific
travel patterns that are specific to a region. That gets into the California
Household Travel Survey having adequate sample size so that the regions are the
nine Bay area counties, that could conform to the MPO region. I've done it partly
because I want to make sure that MPOs have something that they can use.
So it follows their business practice. We have the 9 bay area counties which is part of
MDC. That's the Bay Area MPO. We have San Joaquin Valley which is largely based on the Fresno cog
but it's been expanded to the population of the eight San Joaquin Central Valley
counties. Then we have SANDAG which is the San Diego County. It's its own MPO.
Then SCAG region which is the eight counties in Southern California and then
the Sacramento cog which has what I think six or eight counties as well. So
those are the regions. It does do some geographic downscaling
to the 30 counties that are part of those MPO regions and it
uses the regional health benefit but it scales it to the population
size and the age and gender structure of a specific County. It's not
ideal, but it's useful. So that's how geography... so really the most refined
geography this will work at is at a county level.
So you know you have to use your judgment in the sense that if it's a
county that is the dominant population center in the whole region then it's
like double counting. I mean it's probably more valid there, but if it's
only 10% of the overall region you just have to ask yourself "is
there something about this county, its transportation system or
something else, that's so different than the region that would make people
question the validity of making that assumption?" It's a question I get
all of the time. So I'm going to summarize. We've been working on the
ITHIM model in conjunction with the ITS. Carol Rodier helped me calibrate the model
with some graduate students almost ten years ago already. So it's something that
we've been working on for quite some time and here are some of the use cases
and publications. So obviously government and transportation agencies, they're
falling along the spectrum of health co-benefits from active travel. Again
physical activity is the primary driver of those health benefits. You get, you know
depending on the scenario, you can have either very little or very large
monetization of that. It's not money to sneeze at. The SES's are the
most modest. Let's face it folks they're not going to be moving the
needle very much if they keep doing what they're doing now. There would need to
be a huge ramping up of those sustainable community strategies to
achieve health co-benefits as well as the VMT reductions. There's been activity in
the state legislature to try to change that. I don't think it successfully... I think
they held the bill this year by being re-introduced to put more emphasis on solving that issue.
While the net health benefit can be positive, we have to dig deeper into the
safety because this issue of mode equity. I mean we might be decreasing the
overall and creating an overall health benefit
because an overall reduction in collisions, but it's not looking at the
heterogeneity of modes. There could be a big overwhelming benefit from car to car
collision reduction which is not mutually exclusive from or doesn't
contradict the possibility of there being increases in ped and bicycle
fatalities. So that's really important and it's also a note to the MPOs that
the way they're going about modeling this right now is probably not adequate
because every MPO EIR that I read did not even acknowledge that this was a
possibility and that I think needs to be looked at more carefully. Again we're
looking at different strategies for greenhouse gas mitigation. You know
physical activity is one pathway and air pollution is another that we need to
think about. All policy options including doing things that will
increase physical activity in transportation and not just focus on
electrifying vehicles because that will
generate some benefits but you saw that air pollution slicing there, it's tiny
compared to physical activity, this will just represent a huge opportunity lost to
again do the right thing and make population healthier and reduce green
House gases. The other thing, from our Publications, is that strategies
that optimize bicycle travel are really good for health, but they're also the
best strategies for reducing carbon. That's just because they cover more
territory, they're more competitive with cars as far as the distances that they
can cover. We want a balanced portfolio but we need to think about how
the specific role of cycling is in carbon reduction. It has a health benefit,
but it also has a bigger impact on carbon reduction. It has been pointed
out in the climate world, peddle now or paddle later. So having a
strategy that emphasizes cycling is really working. I wanted to also give you a
sense of it's not just research that lives on a shelf and journal articles,
it's actually had an impact already in California. This one aspect of this
work which I call policy education. The findings of the model for close to 10
years now have been brought to the legislature and brought to legislative
hearings to educate policymakers about the role that active travel plays
in our transportation and greenhouse gas dilemma. Advocacy groups have just
latched on to this whether it's the bicycle advocacy or the walking groups and
climate groups, some of the environmental justice groups, have all latched on to this
because it really helps advocate their position. So, it's been very useful
to shift the dialogue. You know this is not Denmark. This is not some abstract
place. This is California and I think that helps change the nature of the
conversation. We've also done stuff, actually not with my work here but my job
at the Public Health Institute. We actually created policy briefs down to
the level of a legislative district for both the Senate and assemblies where we
create little ITHIM profiles of what could be gained through active
Transportation and things like that. The other used cases, I mean it's really been used by MPOs and
and state agencies to quantify health benefits so they can advocate internally
because a lot of these state agencies are not monoliths.
You have big agencies that feel like "oh we got to engineer our way
out of this" and others say "no we need to think about land use."
So, it provides information so that within these agencies they can
have more informed discussions about what the benefits
are of different strategies. I think that's been very helpful.
Obviously, the California Department of Public Health, as an advocate for Public
Health has been the champion for leading the charge on educating other state
agencies on the health benefits of active travel and climate health equity program. I
was part of it years ago. It's continuing and they're trying to sort of do that
inside. You know there's the inside-outside paradigm of trying to
promote change. So they're doing it from the inside using the scientific evidence
for this. As I mentioned MPOs have actually used the model. So four of the five big MPOS
have actually used the model to quantify the health benefits. They've just been
nagged to death by their advocacy groups where they live.
They say, "do more to increase the health content of your transportation plans and
we want to see you do something." So, they came running to me because they felt
under attack politically that they need to respond and there just aren't that many
models out there that do this. So, by default I guess I'm the one to
help them, but the idea is that they're trying to respond. I've
worked with the MPO staff and the MPO staff is different than their
governance bodies. Many of the MPO staff are very much committed to trying
to increase active transportation, but they're operating in a political
environment that makes it very difficult for them to implement all of their most
ambitious plans. MTC is probably unique among the MPOs.
So when they run their travel demand Models, they run it system-wide and they
quantify the health benefit of their large projects. I’ll get to you in a second.
So right now in the left cycle, any project that was
capitalized at a 150 million dollars or more, that project was
run through their activity-based model to quantify all
kinds of things; travel time, congestion, you know the usual stuff,
but they also want to know what the health benefit was. So they used I think sort of as a
post-processor to come up with that and they monetized it. It actually
shifted priorities. They rent I think 150, well more than that I think,
initially it was like 750 projects, but only the top ones floated to the surface,
the big ones. So the large, usually it's the transit regional transportation
projects, they became to the top of the list because we can monetize the
health benefits. They were overwhelming compared to the other
benefits that were also monetized. So it's sort of an interesting approach
that they've now integrated the ITHIM model into their business process where
they're trying to justify the prioritization of projects using health
as one of the criteria. So I think that's a huge success. They don't
advertise it broadly but you know when you think about impacts you have, to me
that's like "wow that's a big success" because it's now you know what do
they say "when the extraordinary becomes the ordinary
that's when you know you've gotten success." So this was a very novel thing
a decade ago at MTC and Fresno cog, I mean they're doing it too. So it
doesn't have to be just big, well resourced MPOs. Other ones are doing
it as well so it's more of a political role.
The other kinds of ... the other benefits, so I'm the only one out here
doing this. I've coached other people. So the LA County Health Department which is
a little bit unique among health departments it's almost like a state
within a state. I trained them on the model. They ran it and they analyzed
their metro, LAC metro plan. They have a transportation plan. The
2035 mobility plan. So they applied the ITHIM model. So it can be used at a
county level for different purposes. Health impacts of autonomous
vehicles. That's something I gave the reference here, but that's something
that our own UC Davis investigators are doing besides me. So that's Miguel,
Jaller, and Carol Rodier, myself, and a graduate student Elam. There you are.
This is the first time I'm actually seeing you, meeting you.
"I just want to ask, I was going to talk with you after."
Anyway, I just want to acknowledge that there's other kinds of use cases
and very elegantly we applied that to using the actually MTC travel activity
based model output. It's an input to ITHIM with several scenarios and the findings
are really interesting. Right now the paper that will be presented to the annual
meeting of TRB. So anyway, we're doing other kinds of use cases. I'm going to
show that again we're not alone. Yeah there's a lot..
this is implementations of ITHIM. As you can see it's heavy on the west coast. You can see the
different regions, but Oregon has been running this every year for the last
seven or eight years. It's part of their routine. The Health Department and their
DOT just runs the model on an annual basis to see how well they're doing. So
they've routine eyes there. There has been implementations in Nashville, Tennessee.
There's interests in Atlanta. Maryland, I helped implement there with the folks from the
National Center for Smart Growth. I don't know if you have connections
with Garret Nap or any of the folks there, but anyway
the team there has implemented as well as in Delaware there's been an
implementation. Massachusetts has calibrated the model. They haven't gotten
money to run scenarios yet, I just helped form on a tiny little
state like Vermont. There's like no excuse for other states that have a
lot more resources. So there's a number of implementations. There's also academic
centers. So, UC Davis has a big flag there for being one of the
academic centers. University of Wisconsin-Madison is also an academic
center. Alex Coroner who's a former graduate student here is now at the
University of Texas. He helped implement ITHIM for the Sacramento region a
couple years ago. There's a loose network in the United States of folks
who are working on this as well as an international
collaboration at the University of Cambridge.
It was really James Woodcock's group that go the whole thing moving
in 2009 and he's seeded groups around the world through this.
So I've helped people in New Zealand, Australia, and groups all over the place.
Why don't we take your question.
"I have a question about some of the interactions within the air pollution and the
active transportation. I've seen in some in some recent EIRs where
they were looking to not let the state of motor vehicles drop below 25 miles
an hour to keep the emissions down. For a project being built now,
I'm wondering how valid that is given that there's a changing progression
of the change in the vehicle mix over the life of the project?"
I'm not sure I understand the question. Are you saying that lower speeds are
increasing pollution?
"Yeah, they were saying they didn't want the motor vehicle speeds
below 25 miles an hour because there's more emission if they go slower."
I've heard a lot of things I haven’t heard that one yet.
I don't know how to respond to that. I mean you could quantify the difference.
If you wanted to know the two competing Issues, because I mean above 25 miles an
hour definitely increases the mortality risk, but then they're saying... I haven't heard
that. I mean it sounds pretty counterintuitive, but most people are talking about reducing speeds
and reducing the injury risks. There is a
concern , just the knowledge of that, "Oh Neil aren't you endangering people you know the
bicyclists and pedestrians by having them bike and walk in your busy roadways?
And because their respiration rates you're walking and you're biking and you're
breathing heavier, your actual intake of pollutants is higher, aren't you negating
the benefits of physical activity?" and the answer is yes if you're a pedicab
driver in New Delhi and you're doing this six hours a day in an atmosphere
that's probably 150 micrograms per cubic meter of PM 2.5 or greater, but in the
kinds of exposures that we experience in California which is largely below 50
micrograms per cubic meter which is even considered a hot spot, you would never
have enough minutes per day to have an inhaled dose that would negate the
effect of being out there and being physically active. So there's two things
Going, what's going on with the active travelers, but also there's basin-wide
reductions in the emissions like having that substitution. I've heard that
argument, but this issue of speed is not... but you could theoretically
quantify what that is because they're increasing the risk of injuries that
potentially and arguing that they're decreasing the risk of air pollution. I
don't know. I would have to see what that really looked like to believe it.
"I also wondered what the project is like now and if it will likely last.
I don't know if 50 or however many years it will last.
It's an interesting argument. I just haven't heard it before.
(inaudible question from audience)
I'm not denying that that's not the case but again, if it's a question in
isolation because what the ITHIM model is doing is its integrating road
traffic injuries, the air pollution and the physical activity. So, if there's a
question about active travelers and stop-and-go traffic, I mean there's other
things too and it makes the assumption that the active travelers are right
there rather than having a route choice that could be just a block away that
drastically reduces their exposure. It also is compared to what? So, you take
that bicyclist and put him in the car behind, they're getting that you know
high levels of exposure, maybe the respiration rates aren't as high but
they're getting high doses. So, there's a whole bunch of literature that has
just come out looking at air pollution and what the relative
contribution is of exposure to other modes and we need to consider all of
those things before we categorically say one thing is better than the other because
you know what's going on inside the cabin of a car could actually be more
more deleterious than that pedestrian who is just walking a block away from
busy traffic. If there is no block away because it's you know really constricted
access then there's an issue there, but you know there's a bicycle boulevard
that's parallel. I mean these are design things that we can mitigate the
exposures. We have some other questions.
"I think the time is up so we will stop
here but Neil will be here for about half an hour and if you
have burning questions you can talk to him afterwards.
okay. Let’s thank Neil."
(applause)
This model is accessible to you guys. I've made a big splash
it's really up to the air resources board to make a clean splash, but the
URL is available. So, if you want to go and play with the model there's the
right ITHIM page. It's very interactive. There's a ton of decision support material on
there. It recycles a lot of Susan Handy's work so UC Davis is very much important
in the decision support material. Especially on VMT reductions. I'd invite
you to go ahead and play with it and see what revelations you can come up with. Thanks!