CitiesandfleetsDamonWischik

UNIVERSITY OF CAMBRIDGE

Dept.ofComputerScienceandTechnology

DRIVER CITY§  canchoosewhichroutetotake§  wantsthefastesttraveltime

§  providesinfrastructure§  (hopestomaximizetotalutility)

Theclassicmodelofrouting...[Wardropequilibrium]

...canleadtoperverseoutcomes,e.g.thecitybuildsanewroad,andeveryone’straveltimegoesup[Braess’sparadox]Thisisthe“priceofanarchy”.

USER RIDESHAREFLEET CITY§  canchoosemodeoftransport

§  wantslowestprice

§  cansetorigin-basedsurgemultipliers

§  needstorebalancethefleet§  wantstomaximizeprofit

§  setspublictransitfares§  (hopestomaximizetotalutility)

Inaworldwithmoredecisionmakers...

...thereismoreanarchyhencemorewaysforthingstogowrong,&alsonewoptionsforjoined-upcitymanagement.

Whatcouldgetbetter?Afleetoperator(ifit’sadominantplayer)willinternalizethecostofanarchy,soitwillavoidBraess’sparadox.

A B

C D

cityreducesA→Brailfare

£3

£2

£6

£2

Whatcouldgowrong?§  Therecanbeperverseoutcomes,inthespiritofBraess’sparadox

A B

C D

totalrailpassengers=20totalrideshareprofit=£38

£6

£2

£6

£2

£0.1

£1

£0.1

10pax

10pax

12pax

12pax

totalrailpassengers=10totalrideshareprofit=£21.93

Relatedwork:IntheInternet,weimplementedmultipathloadbalancing.Withtheright‘price’signals,thenetworkachievescompleteresourcepooling.

Threeflowssharefourresources,asthoughthenetworkweremadeupofasingleresource

resourcepoolingofroads

Therearenewoptionsforjoined-upcitymanagement

Can’talltrafficproblemsbesolvedwiththerightcongestionpricing?

Singapore’sElectronicRoadPricing

§  Thecitysimulatesavirtualroadnetworkwhosecapacityis95%ofwhat’sreallythere,andmeasurescongestion

§  Fleetoperatorsagreetosetroutesandpricesaccordingtovirtualcongestion[orairquality,…]

§  Thecitysendsreal-timevirtualcongestionsignals,andthefleetssendenoughdatathatthecitycanverifycompliance

§  Thestreetsarekeptfree-flowing

§  Inreturn,thefleetsarepermittedspecialaccesstorestrictedzones

Therearenewoptionsforjoined-upcitymanagement

What’sthewayforward?•  Apps+algorithmsmovefasterthancities•  FleetoperatorshavecrackedUX

(thusthey’vegotgreatdatasets+levers)•  Eachcityhasitsown

issues,datasets,andcontrollevers

•  Weshouldn’tjustbedevisingmodels•  Weshoulddeviseadashboard

–adataplayground–foroperatorstoeasilyexploreperformance/policies/systemdesigns

•  Betterthancongestioncharging,citiesandfleetscansolveproblemstogether

Whatdoyoudoinadataplayground?

WhatarethemostcommonstrategiesI’veused(asastatistician/modeller/programmer)andwhattoolswouldhavemademyjobeasier?

explore“userstories”simulatenewscenarios

compareandoptimizepolicies

Whatdoyoudoinadataplayground?

§  Thedataplaygroundshowsgranulardata(reconstructed,ifneedbe,usingmachinelearning)sothattheoperatorcanseeandmeasureeverything

byIanLewis,directorofsmartCambridge

§  Seereal-timedatafeedsandinferences§  Workwithallsortsofdatasets,sinceeachcityisdifferent

§  Intuitivelyexplorerichdata,especiallyabout“userstories”Example:howdotouriststravel?

§  Intuitivelyexplorerichdata,especiallyabout“userstories”Example:howdotouriststravel?

tripstothenightsafari

§  Intuitivelyexplorerichdata,especiallyabout“userstories”Example:howdotouriststravel?

safari-goers

§  Intuitivelyexplorerichdata,especiallyabout“userstories”Example:howdotouriststravel?

safari-goers

§  Intuitivelyexplorerichdata,especiallyabout“userstories”Example:howdotouriststravel?

tripsbysafari-goers

§  Intuitivelyexplorerichdata,especiallyabout“userstories”Example:howdotouriststravel?

tripsbysafari-goerssafari-goertruefalse

§  andsupportthisexplorationwithmachinelearning

§  i.e.thedataplaygroundprovidesaUXforinteractingwithhierarchicaldataExcelandTableauareorientedaroundtabulardataGoogleAnalyticsetc.areorientedaroundhierarchicaldatasets,buttheiranalysesarehardcoded

users

trips

waypoints

§  “Deconstruct”thesimulatorandembeditinthedataplaygroundbytreatingitascomposableoperationsondata

§  “Deconstruct”thesimulatorandembeditinthedataplayground:interactvisuallywiththesimulator’sinputandoutput

§  Short-circuitthe“data→model→simulation”pipeline:useresampleduserstoriesetc.

Thecurrentdatasciencetoolbox:

•  emphasisonuserstories•  ML-powereddataclustering+highlighting•  composableempiricalsimulation

SQLplyr

pandasdata.table

ggplotd3

interactivevis

•  everyqueryhasanaturalvisualization•  interactwithviz⟺modifyquery

ThenextExcel:

spark

mlepdesim