While Uber has challenged taxi drivers, multiple passenger ride sharing service can give us the both of best worlds: short travel times and low prices. They can provide the low prices of public transport with the short travel times of private cars or single passenger taxis. Different than Uber Pool or other crowd sourcing services, the key is for the startup to guarantee both short travel times and low prices, even if demand does not initially exist. This can be be done by having better data on the starting and ending points of travelers, which enables us to identify high demand routes and times and thus enable services that have few stops. The fewer stops enable short transit times and the multiple passengers in cars, vans, or mini-buses can reduce costs.

1. Multiple Passenger Ridesharing
Changes the Economics of Commuting
Provides both low price and short travel times, along
with greener transport
Jeffrey Funk
Division of Engineering and Technology Management
National University of Singapore

2. Traditional Approaches to Transport
 Private Cars
 Advantage: Lots of freedom! Usually fast speeds and short travel
times
 Disadvantages: cars are expensive, cities are filled with roads
and parking lots, much lost time in traffic during many parts of
day
 Public transportation
 Advantage: Inexpensive
 Disadvantages:
travel times are usually much longer than for private cars or
taxis
almost double those of private cars and taxis in Singapore

3. City Percentage Devoted to
Streets
Street Area (square feet) Per
Capita
New York 30% 345
Newark 16% 257
San Francisco 26% 441
Chicago 24% 424
Philadelphia 19% 365
St. Louis 25% 609
Pittsburgh 18% 455
Cleveland 17% 416
Miami 24% 778
Milwaukee 20% 724
Cincinnati 13% 573
Los Angeles 14% 741
Atlanta 15% 1,120
Houston 13% 1.585
Dallas 13% 1,575
Portion of Land Devoted to Streets
Source: John R. Meyer and Jose A. Gomez-Ibanez, Autos, Transit, and Cities, Twentieth Century Fund
Report (Cambridge: Harvard University Press, 1981).

4. Rank City Parking Area* Divided by Land Area
1 Los Angeles 81%
2 Melbourne 76%
3 Adelaide 73%
4 Houston 57%
5 Detroit 56%
6 Washington, D.C. 54%
7 Brisbane 52%
8 Calgary 47%
9 Portland 46%
10 Brussels 45%
Land for Parking in Urban Areas
Source: Michael Manville and Donald Shoup, “People, Parking, and Cities,” Journal of Urban Planning and Development, Vol.
131, No. 4, December 2005, pp. 233-245
* Includes all levels of all parking garages

5. This is the Reality of Many Cities
 Cars, cars, and more cars
 Private cars are primary mode of
transportation in most developed countries
 Particularly in U.S.
 But also in Japan and Europe
 They are parked 95% of the time
 When they are driven, they usually have a
single driver and are stuck in traffic
 Waste of time!
 And also energy
 Isn’t there a better way?

6. There is a Better Way:
Multiple Passenger Ride Sharing
 It combines the best of
 Short travel times (similar to private vehicles)
 Low cost, fewer private cars less congestion (similar to public transport)
 How can it do this?
 Many people have same starting and ending points, and times
 Entrepreneurs can offer services for high-density routes and times
 Information Technology enables us to do this
 Big data provides better data on common routes and times
 Smart phones enable interactions between riders and services
 GPS and fast computers enable vehicles to have very complex routes

7. Very Different from Uber Pool or
Crowdsourcing
 Entrepreneurs must take the risks
 They must guarantee short travel times and low prices
 Uber Pool has twice the travel times as Uber’s single
passenger services
 People want short travel times!
 Demand won’t emerge in the short run for Uber Pool!
 Entrepreneurs must offer services for specific times
and routes
 Even if there is initially low demand

8. Advantages of Multiple Passenger Ride Sharing
 Provides another choice for users
 Depends more on entrepreneurs than on governments
 If successful,
 Reduces cost of transport, with only small increase in travel time
 Increases income for drivers, good for them and economy
 Reduces congestion and thus travel times for everyone
 Reduces petroleum usage and air pollution, without expensive
subsidies for electric vehicles, solar cells, or wind turbines
 Can reduce need for car ownership, which represent second
highest cost for most low and mid-income families after homes
 Less car ownership means less need for parking lots and roads

9. Singapore’s Taxis are Concentrated in a Few Places (bright
red) Throughout a Typical Day (this data is for Sunday)
Midnight
3AM 6AM
9AM
Noon 3PM
6PM
9PM

10. How Much Space Does Bright Red Occupy? 1% of Space? Two
Major Areas/Routes for Most of the Day (Green Boxes)
Midnight 3AM 6AM 9AM
Noon 3PM 6PM 9PM

11. Taxis are Operating on Same Routes at
the Same Time
 These taxis can be shared with little increase in travel time
 One main route along east coast
 A second route from south central to central
 Simple calculation for Singapore
 28,000 taxis or 39 taxis per km2 (total area of 710 km2 )
 If taxis are operating in 1% of area: 3,900 taxis per km2
 So many chances for shared taxis
 Singapore is not unique!
 Similar situations probably exist in many cities

12. Travel Time
Price
Multiple Passenger Ride Sharing can Change the
Economics of Commuting
Private vehicle
or private taxi
Multiple
passenger
ride sharing
Public
TransportBEST: want
low price,
short time

13. Design Services that Better Match Real Demand
 Use big data to understand
 People’s actual starting and ending points by time of day
 Provide direct services for high density routes and times
 Fewer stops reduce travel times, thus increasing user value
 Increase number of vehicles if demand emerges
 Vehicles follow multiple routes during day, facilitated by GPS
 Real densities and demand should determine fixed routes
 Vans and cars follow demand as it changes from commuting to shopping
during middle of day and back to commuting in evening
 During non-peak commuting times, vehicles can also be used for other
transport needs, such as deliveries (see below)

14. Such Private Bus/Car Mobile Apps are Emerging
 Many transportation apps are emerging
 Mostly private taxis
 Uber, Didi Dache-Kuaidi Dache, Ola Cabs, Lyft, Grabtaxi
 All are valued at >$1Billion, Uber >$50 Billion
 Some transport multiple passengers in same vehicle
 Uber Pool, LyftLine, Via (in NY) and Split
 Driver receives requests via real-time routing algorithm, which maps pickups
and drop-offs into most efficient route
 Problems is most services have long travel times, because there aren’t
enough people using the services
 Most people want to plan their routes, not depend on dynamic algorithm
http://bits.blogs.nytimes.com/category/special-section/?_r=1
http://districtsource.com/2015/05/split-a-new-ridesharing-app-is-out-to-shake-up-d-c-s-on-demand-transportation-scene/

15. Fixed Route Services Can Have Bigger Effect
 Fixed route services transfer risk from passenger to service
 Services must provide short travel times (and low prices)
through small number of stops, perhaps one or two at each
end
 Dynamic services will not provide short travel times until
the number of users is high
 Fixed route services can provide shorter travel times
 Initially number of passengers may be small and thus service
might lose money
 Depends on choice of routes
 Services must target routes with high densities of users

16. Fixed of Fixed Route Services are Emerging
 Examples
 San Francisco area: RidePal, Chariot, Split, Potrero, Richmond,
Loup, Sunset
 Bridj in Boston and Washington
 Services make multiple stops only at beginning and end of route
 Since no need to access car from parking garage, travel times
almost as fast as private vehicles, but can be much cheaper
 The challenge is to find starting and ending points with lots
of demand; Big Data analysis will help
 Most current services based on crude observations, not real data
 Better data on starting and ending points will lead to better
services http://bits.blogs.nytimes.com/category/special-section/?_r=1; http://www.bridj.com/welcome/#how
http://www.theverge.com/2015/3/23/8279715/san-francisco-bus-leap-loup-chariot

17. RidePal
It offers a number of
fixed route services that
connect starting and
ending points with high
demand
Picture shows SF and
Sunnyvale
Also provides services
for specific companies
(they know addresses of
their employees)
https://www.ridepal.com/#/

18. Chariot
 Runs 14 passenger vans across San Francisco on five set routes
during morning and afternoon rush
 Rides cost between $3 and $5
 Passengers book from smartphones and use mobile phone apps to
monitor van location
 Free WiFi also available
 Total of 5,000 rides provided each week
 Introduced tool to determine new routes, “Roll your Route”
 Users can submit their optimum bus route and commute times
 Can then recruit friends and neighbors to vote for the route
 If route meets certain threshold, the service starts within a week
http://www.bloomberg.com/news/articles/2015-04-22/silicon-valley-private-bus-service-chariot-gets-more-vc-funding

19. Examples of Possible Services in Singapore
 Consider NUS (National University of Singapore) night classes
 500 to 1000 students attend classes on weeknights (6-9PM) in
Faculty of Engineering
 Probably similar numbers in other faculties and universities
 Some of these students are
 coming from the same place about 6PM
 going to the same place at about 9PM
 Can we offer cars, vans or mini-buses for them? Thus reducing
 use of single passenger private vehicles
 travel time for students who typically use public transportation
 The more we know about starting and ending points by time of
day, better services can be offered

20. Many other Examples in Singapore and
Other Places
 Any location with many low- and mid-income people is one
end point
 Low price retail outlets
 Popular shopping malls
 Popular weekend destinations: beaches, parks, theme parks
 Schools, universities, and government offices
 Entrepreneurs can connect these end points with residential
locations that are close and are densely populated
 Learn from results, expanding successful routes and reducing
unsuccessful routes
 Data base will improve over time

21. What About Other Transport Demands
 Ride sharing vehicles/vans are wasted when they are parked
 Are there transportation demands during non-peak hours,
such as 10AM to 4PM?
 Can vehicles and vans be used for other types of transport
services?
 Use them for deliveries and other applications?
 Uber wants to do other applications, why can’t others?
 Many store-owned vehicles sit 90% of the time
 The following slide suggests there is large demand for
transport in non-peak hours
 Understanding the demand through big data is essential

22. 0
50
100
150
200
250
0 5 10 15 20
Relative Traffic On All Roads, Great
Britain, by Time of Day
Ride sharing
cars can
also service
high off-peak
demand
May be lots of potential
during non-peak times
Ride sharing cars and vans
can be used
for other transport
applications during
middle of day,
when there is less
commuting
We need better info on
starting and ending points
during non-peak hours
Peak
Commuting
Times

23. Challenge is to Find High Density Routes
 Taxi companies have good data,
 because pick-up and drop-off points are often close to user starting
and ending points
 Same with Waze, a community-based traffic and navigation app
(acquired by Google)
 Train and bus companies also have data on boarding and alighting
points but,
 these points may be far from actual user starting and ending points
 Ride sharing companies are collecting data and will likely guard it
closely
 Employers and governments also have data for specific people on
 residential
 employment locations

24. Other Types of Data? (1)
Residential Retail Total
Densities Employment Employment
http://simulacra.blogs.casa.ucl.ac.uk/2011/04/running-spatial-interaction-models-in-java/
 Employment, residential,
and shopping densities
are known in many cities
 Can this data can be
used to build a rough
map of high density
routes and times?
 Many trips are between:
 Employment centers
 Retail centers
 Residential centers
 Entertainment centers

25. Other Types of Data? (2)
 Can this rough map be used to
devise a travel model for a city?
 Can we assume travel times for work
and shopping activities?
 Would time-of day road, train, bus,
and taxi usage data or retail data
provide a better model?
 Can this model help us devise ride
sharing routes and schedules?
 Can simulations help us identify the
best combinations of routes and best
schedules?
Where should vehicles stop and at
which times?

26. Can Employers Help?
 For example, should employers provide anonymous
data on home addresses to help design transport
services for them?
 Well designed services could
 dramatically cut travel times for users
 increase employee satisfaction
 reduce traffic on trains during peak demand
 Should governments require employers to provide
data, in order to reduce peak demand traffic on
trains?

27. Multiple Passenger
Ride Sharing can
Change Conventional
Wisdom about
Energy Usage: High
urban densities
(and centralized
cities)
are needed for low
energy consumption
in transport
Newman P, Kenworthy J 1989. Cities and
automobile dependence : a sourcebook.
Aldershot Hants England: Gower Technica

28. Conventional Wisdom About Lower Energy Usage
 High urban densities are necessary for low energy usage
 Shorter distances to travel
More walking and bicycling in dense than in less dense cities
Vehicle, bus, and train trips are shorter
 More public transportation partly because better economics
of public transportation
 Both lead to lower energy usage in transportation
 Examples of extremes
 Long car commutes in Los Angeles
 Short bus or train commutes in Hong Kong

29. 0
20
40
60
80
100
0 50 100 150 200 250 300 350
Asia
Canada
Australia
US
Public
Transport
(%)
Density
(per hectare)
Public Transport Usage (%) is Higher in Dense Cities
(Asia, Canada, Australia, US)
Newman P, Kenworthy J 1989. Cities and automobile dependence : a sourcebook. Aldershot Hants England: Gower Technica

30. 0
5
10
15
20
25
30
0 5 10 15 20 25 30
A More Detailed Look at Canada, Australia, and US
New US Cities
Decentralized
Designed for Cars
Old US
Cities
Australia
Canada
Density
(per hectare)
Public
Transport

31. Why the Differences?
 Public Transportation tends to be more economic when
 Population is large, population density is high
 Cities are designed around walking (and not cars)
 Cities are centralized and commuting is one direction (e.g., Tokyo)
 Public Transportation is often designed for centralized one
direction commuting during peak hours
 Easy to design; just bring people downtown for
work and then back home
 Train and bus routes are fixed, repeat same routes
 Routes are repeated with only changes
in frequency of service by time of day

32. Centralized Cities/Rail Lines
(with multiple centers emerging)

33. Some cities have multiple centers,
particularly in the U.S. where growth
has occurred in the South-West
(California, Texas, Arizona) and Florida
Multiple Centers

34. Multiple Passenger Ride Sharing Will Overturn
this Conventional Wisdom
 Can increase the number of passengers per vehicle and thus
reduce energy usage
 Even lightly congested cities can do multiple passenger ride
sharing
 First find high density routes and times and offer services
 Then work towards lower density routes and times and offer
services
 The end result can be lower energy usage along with
 Lower cost and time of transport
 Less congestion and thus travel times for everyone
 Lower car ownership, which represent second highest expenditure for
most low and mid-income families after homes
 Less car ownership means less need for parking lots and roads

35. Conclusions
 Multiple passenger ride sharing can change the economics of
transport
 How can it do this?
 Many people have same starting and ending points, and same times
 We just need to identify those routes and times
 Information Technology enables us to do this
 Big data provides better data on common routes and times
 Smart phones enable interactions between riders and services
 GPS and fast computers enable vehicles to have very complex
routes

36. Conclusions (2)
 Not just Singapore and other high density
populated cities!
 Smaller and less dense cities can also do this
 Cities should provide more data, to help
services identify common routes
 Cities have data, so they can help
 Much cheaper than building train lines and
buying buses

37. Conclusions (3)
 Even Los Angeles can do this
 Cars, mini-buses and vans are used for high demand routes
 Big data can find these routes and times
 This will cause users to depend more on ride sharing,
reducing private vehicle usage and ownership
 Can we reduce number of vehicles on roads by more
than half during peak hours?
 Can we reduce the number of cars per family from two to one?
 Can Los Angeles have lower energy usage than Tokyo currently does?
 Perhaps, because no empty trains and buses running in opposite directions
 And fewer empty trains and buses during off-peak hours
 Instead, many full ride sharing vans and mini-buses