every time she comes on I need to make her co-host, so, here we go.
Okay, so let's try again.
Yes Okay.
So, Cara you can try to share your screen and meanwhile I’m
going to do a brief introduction of Professor Cara Wang.
Cara is currently Associate Professor at RPI.
She got her bachelor’s degree and
MS from Tsinghua University and then also the PhD from UT Austin.
Cara is mostly working on the analysis of the interaction between land use,
transportation, energy and environment and
also the spatial dependence of travel behavior.
She has worked on a lot of the freight research, and I have known Cara for
a long time since we started to work together on one organization called
Chinese Overseas Transportation Association.
In the flyer, it shows that Cara is serving as a Vice-President,
actually that needs to be updated, Cara actually is the President of COTA,
which is the position that I served about two years ago.
So, without further ado now we see Cara and I will,
I think Cara already shared the screen.
Because of a technical problem, we have wasted some time but let’s get going and
please join me to welcome Cara and Cara the platform is all yours.
Okay.
Thank you YuYu for introduction. Can you hear me well?
Yes.
Okay, thank you.
Sorry for the technical issues.
So, I’m Cara Wang.
I’m from Rensselaer Polytechnic Institute.
Our school, RPI, is currently also hosting one of the VREF Center
of Excellence For Sustainable Urban Freight Systems.
So, one of the key research areas of our program is freight transportation.
So I have been very lucky to be involved in many of the research
projects related to freight transportation in the past few years.
So, ten years ago if I'd asked you what’s your reaction to freight transportation,
I think most people's reaction was “You’re talking about trucks and
then therefore you are talking about accidents,
pollution and energy consumption caused by trucks” right?
So basically, a lot of people view freight transportation as a nuance.
And then, at most, people will think “If you’re talking about
freight transportation, you’re probably talking about logistics, so
how to improve the efficiency and maximize the profit”.
And then maybe also try to reduce the externalities.
So, basically what I’m saying here is for a long time people probably know in
the back of their mind but not really recognize that they are themselves,
they are also part of the freight system.
And it’s their demand that generates the freight transportation.
So, thanks to the development of information technology and
the prosperity of e-commerce in recent years,
people’s perception of freight transportation is now gradually changing.
Because now we have more direct and
frequent interactions with other agents in the freight system.
So, more and more people start to realize that they are the generators
of the demand and they are really the driving force of the freight system.
And then, at the same time thanks to the information technology, shippers,
carriers and the associated service sector also get to interact with each other and
adopt some new practice to improve their efficiency.
So, as a system, we can compare the old transportation system and then
the new freight transportation system, like what I show here on the slide.
The left side is more of the traditional freight system.
It’s more like a pipeline with one direction with large volume.
And then now, the current freight system is more like a spider web.
So, everyone is connected with everyone else and
there is more complicated interaction between all the agents.
Or we should say the simple aggregated supply chain is now becoming
more individualized and complicated as the information technology
allows more information and then the goods exchange.
So, how fast are things changing now?
I want to first show some statistics showing the trend.
The upper left one, figure one, is a statistic on
the global e-commerce sales, the total volume.
We can see from 2016 to 2019,
on average we are experiencing 20% annual increase.
And then the figure below it is a country-by-country e-commerce growth.
So, if we see this breakdown by countries,
we can see China is expected to increase by 70% in five years increase and
then U.S. will experience about 45% of increase in five years.
And then the third one, the one on the upper right corner is
the breakdown of e-commerce by categories of goods and regions.
So, we can see here if we compare the regions Asia-Pacific,
Eastern Europe and North America, we see at least for now North America and
Europe do not have much grocery deliveries yet as compared to Asia.
And then the last one, in the lower right corner,
is a figure just for the top ten e-commerce sites in the U.S. So,
let’s take a closer look at the top e-commerce sites in the U.S.
Amazon and e Bay as expected are the top sites, but
interestingly, a lot of the traditional mortar and
brick stores are also picking up the online business.
Of course, these numbers in all these four figures are up to 2018/2019.
The last one is the latest, it’s up to February 2020.
Well, I believe this year due to the interruption caused by coronavirus,
we may expect some big changes in all these trends in terms of both volume and
the categories of goods.
So, basically the change of people’s shopping behavior and the new trends in
the logistic system both suggest that the freight demand is quickly evolving.
Many new features have emerged and many new problems need to be addressed.
So, in this webinar I’m just going to talk about some of
the special features related to the evolving freight demand.
Most of these studies have already been published.
There are also some ongoing research then some of
the findings may be further updated.
And then I also want to acknowledge the contributions of all my co-authors,
and this is more of a synthesis of several related research.
The New Freight Demand co-authored by me and
then my colleagues Professor Holguin-Veras and
then my PhD students WooJung Kim, Yiwei Zhou,
Dapeng Zhang and many others.
Okay, so the key features, I’m going to discuss five of them.
The first one is about the dynamics and heterogeneity in this evolving freight
demand and then I’m going to talk about peer influence and then interaction
between decision makers and then I’m going to talk about how freight deliveries and
person-trips will interact- will they substitute each other or
complement each other, and then at the last point I’m also going to talk about
some further behavioral changes that we should think about.
Okay, starting from the first one Dynamics and Heterogeneity.
As I’ve already shown in the first several slides, things are rapidly changing and
here is a quick comparison between home delivery frequency in 2009 and
then 2017 in the U.S. These numbers come from National Household Travel Survey.
So, starting from the NHTS 2009, the survey had
two additional questions related to home deliveries.
It asked people “Do you shop online?” and
then if they answered yes then there is a following question “So how many
deliveries do you receive, have you received in the past month?”.
And these questions were asked again in the NHTS 2017.
So, with this two years NHTS data we get to compare
the trends of home deliveries at the national level.
So, you can see from 2009 and
2017 there is a big shift to the right.
So, in 2009 there’s still lots of zeroes and
then there is very low frequency on the high demand people.
And then in 2017 we see the number of zeroes has reduced significantly and
we also see there are many frequent home deliveries,
more than eleven deliveries per month.
Okay, I believe the changes to the home delivery,
may be he largest single reason why online shopping has become so frequent.
As we all know, Amazon Prime existed in 2009 with free two-day delivery,
but it was relatively uncommon back then.
But in 2017, it was estimated that nearly two thirds of U.S. households subscribe to
Amazon Prime, so they have access to the two-day free delivery.
And then similarly,
Walmart also has introduced free two-day shipping with no membership requirement.
So, many of the other retailers also joined in this competition,
because of this free two-day delivery,
it has somehow spoiled our consumers.
So, many consumers now, they rely more and more on the online shopping and
thus home delivery.
And so therefore, we are going to see a surge of home deliveries.
And this one, this slide is a break down of how the home
delivery frequency changes by different type of people.
So, we can see in 2009 and 2017, there is some consistent
trend – higher education level implied more home delivery frequency.
But of course, this difference is more significant in 2017 as compared to 2009.
And then in terms of gender, in 2009 there is not much of a difference but
in 2017 on average, female receives more home deliveries than male.
And then in terms of the races, so
in 2009 it seems that the Asian-American
tend to have the highest home delivery frequency and
then in 2017, the Whites on average have
the highest home delivery frequency.
And then the last one in the lower right corner
is a breakdown of home delivery frequency by the types of MSA’s,
so from the generally rural area to the more populated metropolitan areas.
So, we can see there is some difference, but
the difference is not that big, at least not as big as I had expected.
And then here I borrowed a slide contributed by my colleague
Professor Holguin-Veras about a long view of the freight trip generation.
So, back in 1963 when we talk about freight generation,
it’s mostly deliveries to commercial areas or to the businesses.
And then in 2009, we can see the rise of home deliveries.
So, the freight trip generated by households have increased significantly.
And then there is this significant rise between 2009 to 2017,
so the daily deliveries generated by household per
person increased from 0.04 in 2009 to 0.12 in 2017.
This number we feel is still relatively low compared to most of
the numbers in Asia-Pacific region.
So, the red dot here shows a comparison to the frequency
of freight trip generation compared to Seoul,
South Korea which has a generation of deliveries to
households of 0.2 deliveries per day.
So, I believe in the next few years we are going to see
a fast increase of the home deliveries especially with
the continuous trend of the on-demand deliveries.
And then, so this is about the dynamics of home deliveries.
Now let’s talk about the heterogeneity that I just mentioned.
So, what do I mean by heterogeneity?
Now with the very individualized shopping activity and
the home delivery activity, if we look into individual data,
we can see a lot of factors could contribute
to people’s home delivery frequency.
So, this is a summary of our findings from NHTS 2009,
which we call it base case, and then NHTS 2017.
So, we can see some general trends and
changes between 2009 and 2017.
First, we started from a model including regional factors,
household factors and individual factors.
But most of the regional conditions, like whether or not it’s a rural, urban or
housing unit density, it turned out to be not significant.
So, we can claim that these regional conditions have very limited influence on
the residential freight trip generation.
And then the most influential factors are still individual and
household characteristics.
And then out of the individual characteristics we can see people who
are female, self-employed, white, have high education level, have high
house income, have children tend to have more freight deliveries to their home.
And then in terms of, we also did some comparison of this home delivery
modeling across different states and which I will show in the next slides.
We see that state specific analysis also indicates variation across states so
meaning behavior heterogeneity and then some behavior inertia.
And then, let’s first take a look at just basic statistics
on the deliveries per month at different regions on the U.S.
So, the national average of monthly deliveries as of 2017 is 2.7 so
we see some regional heterogeneity from these numbers.
So, the West Coast and
East Coast tend to see the highest deliveries as compared to the Midwest and
we also further develop models for each different region and
then here from this light we can see a summary of the differences across regions,
so basically we can see even the different factors have different
affects based on which regions we’re talking about.
Let me pick one of these.
So, let me see.
For example, here we are talking
about a number of adults.
So, as compared to the base case which we used South Atlantic,
we can see the number of adults have a more pronounced
impact in the West North Central region as compared to the other areas.
So, all these findings basically point to one conclusion that is,
the behavior can be different from region to region.
And then, it can be very different across even different people so when we analyze
the home delivery frequency, we need to collect quick and dis aggregate data.
So, the current survey approach,
trying to collect data is still very useful, but it may not be enough.
We need to integrate different data sources including public and
private data sources.
In certain cases, we may need to also purchase data from private sources and
then with the support of such data we need to develop disaggregate model
which consider individual household and regional characteristics.
And then the next key feature I want to talk about is the peer influence.
So, here are some posts from the UPS Pulse of the Online Shopper.
One-third of all shoppers and
one-half of Millennials said purchase decisions are influenced by social media.
I think we all have similar feelings.
When we are happy or extremely unhappy with some of our order,
we are going to talk about that.
And then, in this area when we talk about something, we don’t just,
we do not only talk to our family and friends, we also share it on Twitter,
Facebook- and this implies a larger scale of peer influence.
And, so some of the businesses have already
recognized the power of peer influence so
we see some businesses have already tried to send their
commercials through Instagram, Twitter and then Facebook.
So, what does that mean for us?
That means maybe we should also integrate our studies on social network and
then on shopping behavior.
And, we also need to recognize the peer influence in our demand modeling.
This includes three key points.
Individual decisions could be influenced by their peers.
And then, second, peers can be identified over social media, and then third,
mutual influence between peers change with individual decisions.
So, for example, this is a model that I
developed to recognize that people’s
behavior could influence their peers.
The first equation here is a basic utility function.
We can interpret this as utility function for
people’s shopping behavior if we are talking about consumers.
Of course, it can be extended to many other binary decision models.
So, you see this is the latent variable, the only thing different
is we recognize that this utility is also influenced by its peers’ utility.
This rho*W recognizes the influence of the peers.
W is a weight matrix.
By the way, this concept is adapted from spatial econometrics so originally,
in spatial econometrics,
we use weight matrix to capture the influence of the nearby locations.
The assumption is if two locations are closer to each other then they
tend to have stronger influence to each other.
Now we adopt this concept to the peer influence in social media.
Now this w has similar concept.
That means if two observations,
two individuals are similar to each other in terms of the socioeconomic features,
they tend to have stronger influence to each other.
So, this is captured by the weight matrix W.
And then rho is called the spatial coefficient,
spatial influence coefficient.
So, it captures the overall magnitude of peer influence.
And then, so this W as I said is no longer a measurement of
distance as in spatial econometrics.
It measures more of how close people are or
the level of, to measure how close two peers are.
And then, therefore we further model this W,
the elements in this weight matrix W, as functions of
people’s demographic and economic characteristics.
So how close these two features are.
And then we also recognize that these features could also change,
they're dynamic, so
the change of people's behavior will change how close two people are.
And then, this dynamic peer influence will further change people’s decisions.
So this is a model that we can use to analyze people's
interaction when they decide to send all online
purchase orders and then receive home deliveries.
Another one key feature that I want to talk about in this evolving freight demand
is the interaction between different types of decision makers in the freight system.
So, again I borrow this slide from my colleague, Professor Holguin-Veras, and
then this is a summary of the interaction of different types of freight agents
in the traditional freight system – suppliers, carriers and receivers.
There are several potential implications of the evolution of online shopping,
so something new now has been added to this interaction.
So, this interaction pattern still holds true but
now we have more competition and more transparent, more frequent
interactions between agents thanks to again to the information technology.
Some of new freight form we have seen for example,
we see now the demand can be sent out, shared by the shippers,
and then the carriers can bid for this shipper's order and
then only when the shippers and the carriers are happy
with each other, they can then decide to make a deal and
then they jointly then determine many of the features
in the freight demand such as frequency and then mode.
So, what I’m talking about here is the demand,
the freight demand now is often determined by first, by the matching,
and then after the matching is achieved, then the joint decision
making of receivers, suppliers and carriers can be achieved.
So, this is similar to the passenger transportation world similar to Uber,
so now the driver and the passenger need to be matched and
then they together get to decide when to pick up and then where they go.
So how do we recognize this integrated process of matching and
then joint decision making?
So, we can generalize this to the pair up behavior of seller and buyer.
The seller has some orders and then the buyer will bid for them.
So, on one side, the seller is saying “So I have all these goods,
do you like it?” and then the buyer, they also have a list of
sellers then can chose from so this is a mutually selective process.
So, the supplier, they want to select their customers,
of course they need to consider the price they can offer, or their revenue.
They also want to consider the customer’s credibility, and then for
the customers, they, of course they are still very sensitive to the cost,
they also need to consider “Are the suppliers reliable?” and
then “How fast can they make the delivery?”.
So, this mutual selection process is sort of like online dating.
So, it’s like one side wants to choose the other side,
the other side also want to choose, also want to have their best option.
The only thing different if we’re talking about online dating is
mostly one-on-one matching, and then for suppliers and
customers is more of many-to-many matching.
But something in common is, for each side, they always want to choose
their best option and then only when the best option is not available,
they will move down to the next best option so this is for the traditional
utility maximization process, we know people always choose their best
option when the best option is available but this is more of a one-sided choice.
Now for the matching, we’re talking about two sides,
they are going to select each other.
So, we need to extend the old utility maximization
model to more of the utility ranking for pairs so
these are the mass market forms but in short,
the behavior background of this is as I said,
each side want to select the best partner.
But that also depends on the partners availability,
only when the best partner is available they get to get their best partner,
otherwise they're going to move down the list to the next one in the ranking.
So, basically after we finish this matching process and
then we allow the matched pairs to make joint decisions,
so this is also integrated process.
So basically, we observe the joint decision making of matched pairs and
then if we don’t integrate it there will be certain bias in the estimation,
so this is the mathematical form.
We have the first module as the matching process,
the second module is the joint decision-making process, and
then we also recognize that these two modules are correlated.
The next key feature I want to talk about is the inter-dependency
between freight demand and then the person trips.
There has always been this question “So now I do more online shopping and
I receive more home deliveries,
does that mean that I will reduce my shopping trips?”.
So maybe with increase of freight demand we can reduce the passenger traffic flow.
So, they can offset each other.
Maybe in the end the increase of home deliveries is not a bad thing.
So, many researchers have looked into this question and
then they have collected data from either local regions or
some small-scale community-based survey and
then we are also very interested in this question as we also
recognize that people are doing Omnichannel retailing so
that is when people shop online, they actually will compare and
sometimes they will compare and then sometimes
they will also go to the physical stores to try them and
then if people are unhappy with their order sometimes they
will return it and then they may return it by mailing it back,
they may also return it to the actual store.
So, there is the interaction between the online shopping behavior and
then people’s trips to the stores.
So, one thing for sure, their shopping travel demands, and
then the freight demand are intertwined.
The question is how do they influence each other?
So, the first study we conducted was a few years ago,
we used NHTS 2009, we developed a structural equation model and
then we looked into the mutual influence between people’s propensity for
online shopping and propensity for making shopping trips and
then I’ll jump to the conclusions we have.
So, here are the results of the structural equation model,
basically what we observe from NHTS 2009 was on national level,
people making more shopping trips tend to shop online less frequently but
this relationship is not symmetrical.
We also notice that frequent online shoppers tend to make
more shopping trips at the same time.
So, because the data is cross-sectional,
we cannot really claim this as the change of behavior but
something at least we can claim is that those frequent online
shoppers are also people who like to go to the actual stores.
So, because the data is cross-sectional, we cannot really claim this as the change
of behavior but something at least we can claim is that those frequent online
shoppers are also people who like to go to the actual stores. So, frequency of
shopping trips and online shopping are neither pure substitution nor
pure complement.
And then this may also imply that doing a lot of online shopping may not necessarily
reduce the personal shopping trips. People who like to go to the actual stores so
frequently offer shopping trips and all my shopping on either pure substitution or
pure. And then this may also imply that doing a
lot of online shopping may not necessarily reduce the personal shopping trips.
So, now this year, in the last few years,
things are also rapidly changing. And
then due to competition from other businesses, several stores and
restaurants in many market segments are also starting to offer home deliveries,
and then this has become common now in supermarkets either as store run
service such as those operated by Shop Rite or it can be served by
some third party platform like Instacart. So,
over the past few years, we see more and more deliveries,
food deliveries, and grocery deliveries. And then, so
we are also interested in how the food and grocery deliveries
may change people’s personal trips. So,
with survey data from New York City DOT,
New York City mobility survey conducted in 2018,
we also conducted some similar study using structural equation
model to look into the connections between deliveries and
then trips. Now, only this time we separated them into
three types – retail, mainly packages, and then grocery, and
then the prepared food. And then we also split the trips into two
different modes – trips by driving and trips by walking.
This is the first time that we get to take
a look into the different trips by different modes and
then I think, thanks to the data from New York City,
because it’s probably one of the special cases where we can get
a lot of walking trips to retail stores, to grocery stores and
then to restaurants. So, this is still an ongoing research.
The findings may be updated in the future.
I just want to show you some of
the current findings. So we still used
the structural equation model to look into frequency of deliveries and
then the personal trips by different modes and how they interact with each other.
Here are our key findings.
So, in short, we can say the relationships
between online shopping and
then the person trips may vary by not only
delivery type but also trip mode. So,
again something we find again is the retail delivery
is possibly correlated with the in-store trips,
so people who like to receive packages at home
a lot also tend to go to the actual stores but
only by driving. This impact is not significant for
the walking trips, so this is for the first one.
And people who like to go to in-store
shopping either by driving or walking, they tend to shop less online and
receive less home deliveries. So, this is consistent with our
findings from NHTS 2009. And then, the similar approach is used
to analyze the relationship between person trips and deliveries for,
this is for grocery and this is for prepared food from restaurant.
So, we notice that if people like to
receive groceries online, they also like to go to,
drive, to the grocery stores. Okay, so again this does
not really reduce people’s person trips. And then, the only thing,
the only category different is the food delivery.
So, people who like to receive food at
home frequently tend to reduce their walking
trips to the restaurants which I think is totally meaningful.
And then, but its impact on the in-store
trips by driving is not significant. So,
still that means even for people who like to receive food at home,
they do not necessarily reduce their driving trips
to the restaurant. What is our conclusion when we put
everything together? So, in short we can say the online
shopping, in general do not really reduce people’s
person trips at least by car, or we can say the increase
to freight demand do not necessarily reduce the VMT
generated by passenger transportation. So,
in the future, we may want to integrate the travel survey,
we may want to incorporate both delivery pick-up information and
personal travel information to try to look into
the connections between online behavior and
in-store behavior in more regions beyond New York City.
The last thing I want to talk about is further behavioral changes.
So, the last four key features are something that I have studied or
am currently studying based on the available data,
but there are a lot of more interesting things that we can investigate.
Some studies in economics and
then in marketing have already looked into the related issues.
Some studies in economics and then in marketing have already looked into the
related issues. For example,
shoppers today have greater choice, it’s not just limited choice as before and
then it’s very easy for them to switch from one store to another,
to switch from one shopping behavior to another,
to switch between one home delivery frequency to another and
then they also have access to rich product review information so
they have better knowledge now, they can probably make more informed choice.
And then,
another thing is power of ‘free’ again from the UPS Pulse of the Online Shopper
claims that 58% have added items to their shopping carts to qualify for
free shipping, 83% are willing to wait an additional two days for
delivery if shipping is free. I don’t know about you but
I also have this tendency, if I was told I can get a free shipping
then sometimes I will add into my cart something that I don’t really need so
the change from $2 shipping cost to zero sometimes means a lot to people,
at least it means more than the reduction of shipping fee from
$4 to $2 although in both cases the change of fee are $2 but
just people feel different when it’s free. So
how to use this power of ‘free’ to maybe change people’s expectations or
behavior when they receive their home deliveries and thus we can use this to
try to further improve the efficiency of home delivery and
then to the reduce the externality is something that we should look into.
But the current behavior data collection
does not really support such analysis so that calls for
new data collection approach beyond stated preference.
We may need to consider uncertainty and
agents’ bounded rationality and then instead of equilibrium,
we may also need to collect data that can recognize people’s
decision-making process in the disequilibrium or
more partial equilibrium conditions. So,
in conclusion, things are changing rapidly,
the freight demand, there are a lot of new features and
this requires us to develop new data collection approaches,
to be innovative and creative on how we acquire data and
then with all different forms of data,
we need to fully understand these new changes and
then only with these full understanding of the changes
we can develop more efficient and more sustainable
integrated transportation system. And then, with that,
I’m going to end my presentation. Again,
I want to recognize the contributions of my colleagues and
most of the findings I presented here come from studies that
are co-authored by me and my colleagues Professor Holguin-Veras,
my PhD students Woojung Kim, Robyn Marquis, Josh Schmid,
Dapeng Zhang and Yiwei Zhou. Now, I open to the floor,
thank you for attention. Thank you so much for
the presentation, especially after surpassing so many
technical difficulties, thank you so much. Thank you.
So, for all attendees,
if you have any questions please use the function, the Q&A in the Zoom and
type in your questions. So, I have received several questions,
I’m going to read the question and so Cara please answer them.
So, the first question is regarding
the store pick-up, you know we talked about delivery but
now lots of stores also provide pick-up options to the customers.
So, the question form Dr.
Kelvin Cheu is that store pick-up has become an emerging trend and
instead of delivery problem also for example the UPS has some
pick-up problem by the buyers as well. So, the question is,
can these kinds of trips be considered as a freight since the trips' purpose and
the objects of the transport are still the goods?
This is a very good question,
pick-up is a new trend in the home delivery field.
Now, whether or not pick-up should be categorized as person trips or
freight trips, there is not a very clear definition yet.
Personally, I think pick-up,
I would categorize it as person trips because a rough way for
us, at least for now, to categorize which trips
are freight which trips are passengers are mostly
type of vehicles we use, and then who’s the driver.
So, this pick-up from the stores is sort of still similar to people’s
shopping trips only that the duration of the shopping has been
significantly reduced but it’s still carried by people and
then carried by the passenger vehicles so if I have to choose one
type between these two, I would put it into still the person trip type.
Thank you for the answers.
And there is another question from Irina Benedyk and she says thank you for
the presentation, it is a very interesting and promising area of research,
especially due to the current events.
So, in your matching model, how have you captured many-to-many relationships,
what is the model outcome, is it allocation of shares of order to
different carriers or is it different many-to-many relationships?
Okay, so I think one thing I should make clear is my matching model is not
an optimization model, it’s econometric model.
So, by that I mean the model is more comparable to the discrete choice model,
it’s like I have already observed the current matching results,
I’ve already seen who’s matching with whom and I want to understand which factors
will lead to such matching, so it’s not the output is not allocation or
shares of orders to different carriers, that is more of the optimization,
this direction so my model is the reverse direction,
I try to understand which lead to these end results, does that make sense?
Okay, and Dr. Chanyoung Lee has a question which is also my own question...
Okay, Irina has a follow-up,
it says you have discussed carrier matching that is ‘observed’,
do you mean there is a platform similar to Uber exist for freight?
Yes, there are quite a few forms available, sort of like Uber,
Uber freight is one and then in China, the Truck Alliance,
I actually work for them for, I mean help them with some data processing.
The process was, as I explained,
the shipper was sent out several orders and then the carriers will bid for
these orders and then there will be a mutually selective process,
what we can observe is eventually who’s going to pick who’s order.
So, what we want to understand is what factors lead to this mutual choice.
Yeah, now I think we have the Instacart, we have the Shipt,
we have all these kinds of platforms, they actually do the process as well.
I guess especially now with this COVID events,
America will see lots of changes in behavior.
Ok, I mentioned that Dr. Chanyoung Lee has a question that is also my
question as well that is, any conjecture about how the COVID-19 may
impact the freight demand patterns both temporarily and
permanently and for myself, I purchased a membership with Shipt and
Instacart because of this outbreak of the COVID-19 and it’s $99 per year,
so I believe that later, even as things are getting better, I will keep on
using this service because I already paid so definitely there will be impact but
from your point of view, you know what is your kind of thoughts on this?
We, here in New York, we definitely have a similar situation if not worse.
I have been user of Instacart for two years but I did not really use it
that frequently although I have been a member, the reason is I still
like the experience of going to grocery store once every week and
then pick the things that I like, the process of picking really satisfies me.
But because of this corona virus now I solely rely on Instacart
to send the deliveries to me and then I think I start to be spoiled or
I don’t care so much about the quality of the groceries that much so
now I place an order every two days so even for me,
like a long term user of Instacart, I think internally
I have changes my perception of this delivery service,
I have lowered some part of my expectation and
I am more reliant on the service now and then my husband is also very happy,
he said that it just saves a few hours every weekend to go to
the grocery store is a life changer for him.
And then I have seen statistics related to Instacart,
some reports claim that the subscription to Instacart has
increased like tenfold just in the past several days so
I think all these changes will imply a short term change and
I think will extend to also long term.
As I earlier mentioned, if we compare the U.S.
and Europe, if we compare it to Asia-Pacific region, the percentage of
grocery deliveries achieved by online shopping is still pretty low.
I think maybe after this shock this time the corona virus problem,
we may see a large increase of the grocery delivery.
Yes, I think currently there’s still lack of shoppers,
you know you probably put an order but not enough shoppers to help you.
And another question is, so you talked about the interactions
between the passenger transportation and also home delivery and
now with more people are purchasing those memberships, you know Instacart,
Shipt, and there’s a requirement says if you shop no less than $35,
then you can get free delivery with a membership but you still have to
pay kind of the service fee, it’s kind of the tip to the shoppers so
combining with what you have mentioned, do you think that the passenger
transportation would be affected by the increase of the home delivery,
especially the membership increase of the Instacart?
That question has several layers, let me try to disentangle it.
So first you’re talking about how the passenger trips,
the personal trips will be influenced by the online shopping and
then home deliveries, and then you asked about the increased service fee right?
No, I’m talking about, I don’t know,
what is the average amount of money that we usually pay for
one grocery shopping [trip]?
For myself, I usually go at least shop more than $100 but now for
Instacart, Shipt, etc., etc., if you purchase a membership,
you’re only required to purchase at least $35, so
my question is whether that kind of increase the frequency of
the shopping and then what that will affect?
Okay, so you mean usually we go to the store and
make about a $100 purchase and then ship them back at one time and
then now the threshold is only $35 for free shipment so
maybe we are going to break it down into some small deliveries making
the deliveries leading to smaller, but more frequent deliveries right?
Yeah, that’s totally possible, so that’s what I mentioned, the power of ‘free’.
So, if you offer something free,
people are going to abuse this system for sure.
So, if we are concerned of the future externalities generated by
the home deliveries especially by such frequent and small deliveries,
we may want to on one side educate the customers to let them realize their
demand will generate these externalities and then on the other side we
may also want to try some programs with the private sector, for example,
I think Amazon’s Subscribe & Save program is a good one showing us how
we can use the power of ‘free’ or power of discount to encourage people
to consolidate their deliveries rather than having them into small deliveries.
Right, right, okay, great,
so this is the last call for any questions.
Yes, Oliver go ahead.
Thank you YuYu.
Cara, thank you, that was a great talk, you know we haven’t talked for
quite a while, I really enjoyed your presentation.
So, sounds like you use question folks a lot in the demand side so
here I have question that has two aspects.
What would you think with the future development of all
these urban logistics and the delivery trend,
what would that imply for the relationship between the carrier and
the shipper especially with the emergence of all these platform economy, so
for example if you think of Amazon right, Amazon you know with its platform and
Amazon is also getting to these kind of carriers business,
so in that case, and also Amazon last year bought
100,000 electric pick-up trucks for delivery so
how or what role would technology play in changing the horizon and
relation between the carriers and the shippers?
Okay, so I think I see two diverging trends,
the one trend is the winner takes all.
So, as you said, Amazon is now also getting into the, becoming a logistic
company so they are purchasing their own fleet, this trend could happen and then so
we might see some monopoly in the future so some big companies are going to be in
charge of everything, every stage of the logistics system. So, the other trend
though is, I also see a trend of ground deliveries. So, as you said,
Amazon is now also getting into the, becoming a logistic company so
they are purchasing their own fleet, this trend could happen and then so
we might see some monopoly in the future so some big companies are going to
be in charge of everything, every stage of the logistics system.
So, the other trend though is,
I also see a trend of ground deliveries.
So, especially in the more progressive
type of communities when we want to encourage more people,
more local people to be involved in this new business mode,
they want to encourage local people to get into this delivery system so
they have part-time or full-time drivers or
just sort of like the Uber for passenger system,
we have people to pick up the delivery on their way home and
then send the deliveries. So, how the platform changes
the relationship between shippers and carriers,
there will definitely be changes but of these two diverging trends,
which will dominate in the future, I cannot tell yet.
Yes, thank you, I think that’s a very realistic capture of what is going on.
So, do you have an idea, so what is the percentage of the fleet
that has been, or has the potential to be electrified for
the freight and urban logistics sector?
I think, Oliver you’re an expert in the transportation related pollution issues so
I think you will agree with me that in the ideal world,
we want all the fleet to be clean and then to be more energy efficient,
and the thing is, the current system does not really
recognize the value of the reduction of externalities yet
especially us, the real driving force of the freight demand,
we don’t recognize that because it’s our demand that will
generate these freight deliveries that will lead to these
externalities and then unless we are working in this field,
most people do not think “Why should the trucks change to be electrified?”.
So, if one day, say I would rather pay two more dollars shipping
cost if you tell me that the service fleet are all electric then that day,
we may see a higher percentage of trucks being electric.
So, I think technology wise it’s not really a problem,
it’s more of the need and then how we see the value.
Thank you, Cara, I think that’s a very good point,
I think essentially you are saying that there might be some space for
us to think about consumer driven policy and initiatives.
And then I also want to mention, at RPI, we are working for
DOE on a project related to this,
we’re going to look into how we can change the demand side,
especially the receiver side behavior to try to induce changes
in the carrier’s behavior and the shipper’s behavior to make
the whole system more energy efficient and then produce less pollution.
That sounds wonderful, thank you.
Thank you, Oliver for asking those questions, thank you Cara for
answering those.
I want to thank again for all attendees.
So, today I know some attendees left a moment ago but
in total we had 41 attendees with five panelists, so this is pretty good
size of the crowd for today’s seminar and I want to thank you all again,
thank Cara for delivering this presentation and stay tuned,
we will have more virtual transportation seminars in the near future.
So, stay safe and healthy and hopefully in the near future,
we can see each other in person at some venues.
Hopefully soon, thank you for having me.
Thank you.