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未來城市的任意⾨門Mobility on Demand for Future Cities
Shih-‐Fen Cheng 鄭世昐Associate Professor of Information Systems
Deputy Director, UNiCEN Corp LabSingapore Management University
2016臺灣資料科學愛好者年會, July 17, 2016
Dream of Urban Planner
Photo Credit: http://doraemon.wikia.com/wiki/File:Dokodemodoa.jpg
A 50-‐Lane Traffic Jam Near Beijing*
京港澳⾼高速公路 (G4),2015年⼗十⼀一連假的收假⽇日。
* Number 5 on the Mega-‐City list.
A Traffic Jam near Jakarta* that Kills 12
* Number 3 on the Mega-‐City list.
At the end of 2016 Ramadan. Traffic jam reached 20-‐km long near Brebes Timur.12 dies of fatigue and fume poisoning.
Cities are Growing Larger• Cities are growing larger at unprecedented rate(54% urban today ➞ 66% urban in 2050)1.
• Megacities (> 10m population):– 1950: Only New York City.– 2015: 35 globally; with 27 in developing nations2.
• Nightmare for urban planners everywhere.
1 UN World Urbanization Prospects 20142 See https://en.wikipedia.org/wiki/Megacity
Come up with attractive “alternatives” to private transport.
Why is Private Transport Bad?• Inefficiency in road space usage• Pollution• Parking space– Across the world cars seem to be parked at least 92% of the time and typically ~96% of the time1.
– For every car in the United States, there are approximately 3 non-‐residential spots2.
• Every collective car removes more than 10 privately owned cars from the street3.
1 http://www.reinventingparking.org/2013/02/cars-‐are-‐parked-‐95-‐of-‐time-‐lets-‐check.html2 https://mitpress.mit.edu/books/rethinking-‐lot3 http://trrjournalonline.trb.org/doi/abs/10.3141/2143-‐19
A Tale of Two Cities
Taipei Metro AreaSingapore
Population Land Area (km2)6,669,133 2,324
Population Land Area (km2)5,469,700 718.3
A Tale of Two Cities
PopulationLand Area (sq km) Automobiles Motorcycles
Taipei 2,702,315 272 787,676 980,577
New Taipei 3,966,818 2,053 987,361 2,191,138
Taipei Metro Area 6,669,133 2,324 1,775,037 3,171,715Singapore 5,469,700 718.3 827,011 145,026
MRT Bus TaxiOperating KMs Train KMs
Daily Passenger Trips Bus KMs
Daily Passenger Trips Population
Average daily trips
Taipei
129.2 21,330,255 1,861,661 195,620,000 1,421,868
30,130
11.9
New Taipei 22,765
Taipei Metro Area 52,895Singapore 154.2 28,178,000 2,762,000 329,120,500 3,751,000 28,736 20
Data Source: Taipei: 台北市交通局交通統計年報 / 中華民國統計資訊網Singapore: LTA Annual Report / Singapore Department of Statistics
Road Traffic Condition (Singapore)Express Way: 64.1 km/hArterial Roads: 28.9 km/h
The Role of Taxi Industry• A particular form of car-‐sharing.– Dynamic: move on the road instead of parked at designated spots.
– Providing driving as a service.• We call it “Mobility-‐on-‐Demand” service, and it covers more than just taxis.– E.g., All Uber-‐like services fall under similar category as well.
• We focus on taxis as it is usually the most inefficient in the MOD sector.
Burning Issues in Taxi Operations
• Supply/Demand mismatch:– Demands might appear anywhere, and stay undetected (for street hail and taxi queues).
– Drivers might not be able to position themselves at the right place at the right time.
• Insufficient capacity during peak hours.
• Uber-‐like services can be much more efficient as they only cater to the “Booking” service mode, and can use price surge to incentivize (direct) drivers.
Street Hail Taxi Queue Taxi Booking
ObjectivesProject signed with Land Transport Authority (LTA) in April 2016, for the following objectives:• Balance taxi demand and supply dynamically, i.e., reduce empty taxi cruising time.– Anticipate where demands would most likely be.– Provide guidance to drivers on where to go.
• Enable taxi ride-‐sharing for last-‐mile servicesand crowd dispersion.
Based on real-‐world data; aim to develop working technology.
Taxi Industry in SG• Almost all taxis (~28K) are owned by 5 operators; largest
operator has ~60% of market share.– Companies are free to set their own fare structures.
• How to drive a taxi:– Singapore citizen, at least 30 years old.– Hold a taxi vocational license.– Cost:
• Daily rent (from any operator) is around S$75 ~ 130.• Fuel cost: around S$30-‐40 (diesel).
• Primary drivers (who hold contracts with the operator) are allowed to identify a secondary driver to share the daily rent.– How to divide driving time is up to them; but drivers usually split shift
to be 6am – 4pm and 5pm – 4am.– Drivers can also negotiate on how to share the taxi rental.
Taxi Industry in SG
LTA regulates the taxi industry tightly:• Monitors various indices on service quality:
– Percentage of taxis on the roads during peak periods.(7-‐11am, 5-‐11pm: 85%; 6-‐7am, 11-‐12pm: 60%)
– Percentage of taxis with minimum daily mileage of 250km(85% on weekdays, 75% on weekends & public holidays)
• Sets fleet size for each operator depending on its performance on the above indices.
• Asks operators to provide all sorts of data to help with the above evaluation.
• Strong desire to make taxi service even more efficient.
The Taxi Dataset• For each active taxi (fleet size 28,000), following information is
sent every 30 seconds:– Taxi ID: unique ID for each taxi– Timestamp: date & time– GPS coordinate: latitude, longitude– Taxi state: free, occupied, on-‐call, busy, etc.
• Size:– ~1.6B records per month– ~57M records per day– ~2.5M records per hour– ~42K records per minute
• Not particularly large, yet very challenging to process– Contains both “spatial” and “temporal” components– Lots of noises and errors
Derived Information• Based on state transitions, different types of taxi trips can be inferred, e.g.,:– Free ➞ Occupied: Street hail– On-‐call ➞ Occupied: Booking thru operator– Busy➞ Occupied: Booking thru 3rd-‐party App
• Trip information:– Time and coordinate of “origin”– Time and coordinate of “destination”– Estimated distance / fare
Trip Counts Over the Hours
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Workday
Holiday
Trip Counts Over the Weekdays
480,000
500,000
520,000
540,000
560,000
580,000
600,000
620,000
640,000
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Trip Origins Over the Hours
Distribution of Trip Distances
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 15 20 25 30 35 40
% of Total Running Sum of Count of Distance
Taxi Availability vs Taxi Bookings
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
availability booking-‐ratio
Daily Income Distribution
Average net daily earning by drivers.
The Tale of Two Taxis: 1251 vs 13335
6am – 5:30pmApril 30, 20151251: made 65 trips13335: made 15 tripsAverage: ~19 trips
The Art of Taxi DrivingThe “spatial” and “temporal” patterns of taxi demands are pretty predictable.• An experienced driver should know where and when to look for passengers.
• So why is driver’s income varying so greatly?!
The Art of Taxi Driving• Drivers have to constantly decide where to go and what to do; with mostly local information.– Cannot see out-‐of-‐sight demand– Cannot see out-‐of-‐sight competition
???
The Need for GuidanceTo better understand supply/demand mismatches, we divide Singapore into 87 zones, and monitors: 1) incoming taxis (supply), and 2) outgoing trips (demand)
• Taxis are available even during peak hours
• Demand and supply mismatches are highly dynamic
The Need for Guidance
Challenges in Making Guidance System• Only booking demands can be observed, while street-‐hail demands and demands at most queues need to be inferred.– Most existing approaches use only historical information, and not responsive to real-‐time information.
• Even with known demands, generating decisions for “ALL” drivers is not easy.
Making a Case for Guidance SystemWhy we believe guidance would work:• By providing taxi queue information to drivers at the Changi airport (from Dec 2009), we notice significant increase in productivity.
• Key: To provide “relevant” and “easy-‐to-‐process” information.
The new Taxi Management System is part of Changi Airport Group’s on-going effort to
improve airport’s operations and passengers’ experience. Taxi companies and drivers
were consulted on the design of the new system to ensure its relevance to their needs.
###
New Taxi Management System Display Boards
Master Display Board at Airport Boulevard Road Display Board after Terminal 3 Departure kerbside
About Changi Airport Group
Changi Airport Group was formed on 1 July 2009 as a result of the corporatisation of Singapore
Changi Airport. As the airport company managing Changi Airport, one of the world’s best
airports, Changi Airport Group undertakes operational functions focusing on airport operations
and management, commercial activities and airport emergency services. Through its subsidiary
Changi Airports International, the Group invests in and manages foreign airports to spread the
success of Changi Airport far and wide.
Why is it Hard to Guide ALL Drivers?• Say we are recommending either A or B to a driver John.
• By going to A, John has 50% of chance getting a passenger.
• By going to B, John has 100% of chance getting a passenger.
üRecommendation: B
A
B
John
Why is it Hard to Guide ALL Drivers?• Yet this recommendation will fail if we have 5 or more drivers.
• E.g., if we have 5 drivers, 1 should go A, and 4 should go B.
A
B
John
A Multi-‐Taxi RecommenderRecommendations are generated…• every 30 minutes (using both historical information and most recent supply/demand information).
• for all zones, all time periods (i.e., where should a taxi go if it is in a particular zone in a particular time period).
• considering both revenue potential and fuel cost.• as a probability distribution (30% drivers are sent to A, 50% are sent to B, 20% are send to C).
A Multi-‐Taxi RecommenderSome more details:• When a taxi is hired, the rider decides where to go!(driver cannot make decision when occupied)
• Traveling between different zones takes time.
The recommendation should work even with thousands of taxis.• And following the recommendation should always be better!
A Multi-‐Taxi Recommender• How do we know if the recommender is good?– By testing the generated recommendation against historical data.
–What should be the “comparison baseline” that is representative of a typical human decision maker?
A Multi-‐Taxi Recommender• From historical data, we can quantify each driver’s strategic reasoning capacity.
• Driver’s strategic reasoning capacity can be measured using Cognitive Hierarchy (CH) Model:– Level 0: random– Level 1: best response to level 0– Level 2: best response to levels 0 & 1– …– Level n: best response to lower levels
Limitations of Human Decision Maker
1.68 1.77 1.85
• From the data: the more you think, the better you perform.
• With sufficient computation efforts, our algorithm can reason with infinite depth.
Ride-‐Sharing: Connecting Last Mile
• Optimize usage of taxis as a dynamic bridging service for public transport.– Through ride-‐sharing–Develop and experiment with service process that could be dynamic and sustainable
• To ease congestion at high-‐demand locations or events.
LM-‐MOD: Connecting Last Mile
20% (30%) of all taxi trips are within 2 (3) km!
319.5
Short trips outside of central region mostly originate from MRT stations.* Yishun station is the station that has the highest LM demands.
LM-‐MOD: The Case of Yishun
Khoo Teck PuatHospital
Condos
By analyzing short-‐distance taxi trips, we can detect neighborhoods that can benefit from better FM/LM connection services.
LM-‐MOD: The Case of Yishun
• Demands are recurrent.• Yet demands are not high enough to warrant regular connection services.• Taxi sharing can lower demand pressure in these areas.• We focus on LM demands as all demands depart from the same location, making it
easier to arrange service.
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
April, 2015LM
FM
Ride-‐Sharing Last-‐Mile Service
• Step 1: Travelers to submit their LM requests (destinations) via mobile phones or at a service counter (kiosk).
• Step 2: The real-‐time planner determines the “LM demand clusters” to be served by individual vehicles.
• Step 3: The service sequence and associated payment for each LM request in a cluster is determined, i.e., route guidance to drivers to serve multiple destinations
Hub
S1: Submit demands
S2: Demand clustering
S3: Determine service order and individual payments.
p1p2p3 p4
p7
p6
p8p5
Will People Share Taxi Rides?• A pilot study was performed 20-‐27 Dec 2015 at the
Suntec Convention Centre in Singapore• Major findings:
− Young people are more open to sharing taxis with strangers.− Female passengers are more open to ride sharing.− For shorter travels, major concern is total journey time (waiting + travel). For longer travels, major concern is cost.
− The importance of waiting time increases with rider’s age.− Bus riders would consider shared taxis if price is right (rider source: 64% taxis, 31% buses, 5% MRT).
Conclusions• Guidance can improve driver’s performance.• Preparing for the realization of a “car-‐lite” city.– Mass transit– Mobility-‐on-‐demand• Shared vehicles• Autonomous vehicles
We are Hiring!Fujitsu-‐SMU Urban Computing & Engineering Corporate Lab• A 5-‐year, S$27m center supported by both Fujitsu & NRF• Research and solutions to address urban and social issues, with
focus on crowd and congestion• Goal: To develop industry-‐relevant applications• Openings:
– Research Engineer (BS/MS)– Research Fellow (PhD)
• General Enquiry: Shih-‐Fen Cheng ([email protected])