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Ride Sharing, Congestion, and the Need for Real Sharing

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Current ride sharing services are not financially sustainable. Although they provide more convenience than do taxi services, they are experiencing massive losses because they have the same cost structure as do taxis and thus must compete through subsidies and lower wages. After all, they use the same vehicles, roads, and drivers, and only GPS algorithms and phones are new.
They also increase congestion. Just as more private vehicles or taxis on the road will increase congestion, more ride sharing vehicles also increase congestion.
These slides describe new ways to use the technologies of ride sharing to reduce congestion along with costs while at the same time keeping travel time low. This can be done through changing public transportation systems or allowing private companies to offer competing services. For instance, current bus services, whether they are private or public, need to use the algorithms, GPS, phones and other technologies of ride sharing to revise routes, schedules and the premises that currently underpin public transportation. There is no reason a bus should be certain size, stop every 200 meters, or follow the same route all day. Algorithms and phones enable new types of routes in which designers simultaneously minimize time travel and maximize number of passengers transported per vehicle.hour.

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Ride Sharing, Congestion, and the Need for Real Sharing

  1. 1. Ride Sharing, Congestion, and the Need for Real Sharing TECHXLR8 ASIA Jeffrey Funk Consultant and Retired Professor
  2. 2. Tech Companies Are Changing, But Why
  3. 3. Why Are Tech Companies Changing  Ride-sharing services like Uber and Lyft making car congestion worse ..  Services like UberPool are making traffic worse, study says  They add 2.6 to 2.8 new vehicle miles for each mile of personal driving they eliminate  Studies are increasingly clear: Uber and Lyft congest cities Other Recent Newspaper Article Titles
  4. 4. Schaller found that while options such as UberX add 2.8 new vehicle miles for each mile of personal driving they eliminate, the inclusion of options such as UberPool and Lyft Line adds to traffic at only a marginally lower rate: 2.6 new miles for every mile of personal driving reduced.
  5. 5. Your next Uber could be a BIKE as ride- sharing company moves away from cars Coming soon to the Uber app: bikes, rental cars, and public transportation Ride-hailing companies are diversifying away from their core business, but right into more direct competition Recent Newspaper Article Titles
  6. 6. Why Does Ride Sharing Worsen Congestion?  Some users moving from public transport  Public transport requires less land per rider than do cars  Even users moving from private cars require more land per rider  Ride sharing vehicles don’t have passengers all the time  Time to find new passengers, drivers need breaks  Result is that ride sharing vehicles require more land per rider  Driverless Vehicles will make problem worse for near future  Removing driver will reduce costs, but increase congestion  Eliminating private cars will help, but people won’t sell until new services succeed
  7. 7. What About Uber Pool?  Not enough users for algorithm to generate efficient routes  Result is that vehicles  must drive additional miles for each rider  In addition to miles finding new passengers  2.6 new miles for every mile of personal driving reduced (vs. 2.8 for regular Uber Service)  More users might lead to better routes but this will take a long time
  8. 8. We Need Better Fixed Route Services  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 students  coming from same place about 6PM  going to 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
  9. 9. 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  Fixed route services 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
  10. 10. Another Look at Opportunities: 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
  11. 11. 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
  12. 12. What Prevents Better Fixed-Route Services?  Governments Don’t want to cannibalize public transport services So they oppose fixed route services Even as ride sharing services are stealing users away from public transport services  Answers: Allow more competition from private companies Remake public transportation services
  13. 13. Information Technology Helps Us  GPS and fast computers enable vehicles to have complex routes  Buses don’t have to run same route all day long, stopping every one minute  Can change routes according to time of day, stopping infrequently  Smaller vehicles can be used for some routes  Big data helps us plan better  Find best routes and vehicles for best times  Governments can use employment, residential, and other data to plan, or can open it up to private companies  Smart phones enable interactions between riders and services  Users can find schedules on phones, without looking at bus stop information boards
  14. 14. So Much Data, Hidden Away in Computers and Filing Cabinets  Taxi companies have data on pick-up and drop-off points  Train and bus companies have data on boarding and alighting points but,  Employers and governments have data for specific people on  residential  employment locations  Shopping and entertainment businesses also have data on users
  15. 15. What will Cities Do?  Many early adopters of ride sharing will restrict ride sharing services, but make few changes to public transportation  Non-adopters of ride sharing will be convinced they were correct to not have allowed ride sharing  A few will change their public transport services  A few will also allow more private services, and will likely be the most successful  What will your city do?
  16. 16. 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).
  17. 17. 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
  18. 18. 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?
  19. 19. Examples for Singapore 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
  20. 20. 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?
  21. 21. We Need Real Sharing: We Need Better Fixed Route Services  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
  22. 22. Better Fixed Route Services are Needed  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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. Centralized Cities/Rail Lines (with multiple centers emerging)
  27. 27. We Need Real Sharing: We Need Better Fixed Route Services  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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. 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)
  33. 33. 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/
  34. 34. 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
  35. 35. 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
  36. 36. 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/#/
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. 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
  41. 41. 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?
  42. 42. 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
  43. 43. 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
  44. 44. 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
  45. 45. 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
  46. 46. 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
  47. 47. 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
  48. 48. 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

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