Mobility on Demand Introduction

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mobility on demandFuture of transportation in cities


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mobility on demand

Smart CitiesMIT Media LaboratoryJune 2008

Future of transportation in cities


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MIT Media Laboratory

William J. MItchell, Principal Investigator

Ryan Chin, ...

Mobility on Demand Text

William J. MItchell

Book design and layout

Christine Outram

Urban Design Team

Chi-Chao Chuang Christine Outram

Claire Abrahamse Quilian Riano

Sandra Frem

Business Model Team

Yaniv Fain Marcus Parton

Andres Sevstuk

Vehicle Designs

RoboScooter: Michael Lin

CityCar: Franco Vairani

Vehicle Design Team

Retro Poblano Wayne Higgins

Will Larke Ethan Lacy

Bo Stjerne Neri Oxman

Jamie Hu Somnath Ray

Tom Brown Brian Morris

Andrew Leone Charles Guan

Christine Lin Sung Hyuck Lee

Raphael Moyer Arthur Petron

Additional Collaborators

Joshua Fiala Carl Yu

acknowledgments

2008 Massachusetts Institute of Technology

William J. MItchell

ISBN xxx xxxx xxxxx

Special Thanks to

Ralph Glakenheimer

Chris Zegras


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Mobility-on-demand systems

Clean, compact, energy efficient vehicles

Stacks and racks throughout the city

Networks, queues and latencies in the system

Mobility demand, pricing, and balancing supply and demand

Latency and cost comparisons: private mobility vs mobility-on-demand

Demand information and realtime responsiveness

Combination with GPS navigation and personal mobility assistants

Synergy with transit systems

Synergy with clean energy systems

Road Safety Benefits

Urban design and quality of life benefits

Urban Implementation: Extended Case Studies

1

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6

10

12

14

15

17

18

20

23

24

26

Contents

mobility on demand


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provide stacks and racks of light

electric vehicles or bicycles

at closely spaced intervals

throughout a city. When you want

to go somewhere, you simply

walk to the nearest rack, swipe a

card to pick up a vehicle, drive

it to the rack nearest to yourdestination, and drop it off.

Users of mobility-on-demand systemshave the convenience and comfort ofprivate automobiles without the asso-ciated high cost, insurance require-ments, need to refuel, service and re-pair demands, or parking problems.

Key factors in the success of mobil ity-on-demand systems are the costs tousers and the system latencies that

is the times needed to walk from a triporigin to a nearby stack and pick upa vehicle, to travel to a stack near thedesired destination, and to drop off avehicle and walk to the actual desti-

nation. Well-designed and well-man-aged mobility-on-demand systemsshould be able to provide more attrac-tive combinations of costs and laten-cies than alternative systems such asprivate automobiles, taxis, and transitsystems.

Management is accomplished throughan innovative combination of:

Realtime, fine-grained mobility de-1.

mand sensing;

active realtime management to bal-2.ance vehicle (and parking space)supply and demand and meet la-tency targets at sustainable cost;and

sophisticated use of dynamic pric-3.ing for demand management. Themathematical model used for man-agement represents the system asa network of stacks and links, with

queues (maybe zero-length) of us-ers waiting to access vehicles andof vehicles waiting to access park-ing spaces at stacks, and dynami-cally varying latencies and priceson stacks and links.

Since mobility-on-demand systemsemploy lightweight electric or human-powered vehicles, they are energy-efficient, carbon-minimal, and silent.They are compact, and they have veryhigh utilization rates, so they minimizeurban traffic congestion and parkingspace requirements.

Thus the essential parts of mobility-on-demand systems, as described inmore detail below, are:

1. Specially designed vehicles;

2. Vehicle stacks and racks distrib-uted throughout the service area;

3. ICT infrastructure for sensing andcontrol;

4. Demand sensing and networkmanagement software;

5. Innovative electrical supply sys-tems that utilize clean, renew-able power sources and minimizetransmission losses.

These elements work in combinationto provide the benefits.


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The CityCar, developed by the SmartCities group at the MIT Media Labora-tory, is specifically designed to meet theneeds of mobility-on-demand systems .

CityCars are lightweight electric carswith in-wheel motors. They fold andstack like shopping carts at the super-market or luggage carts at the airport,making them extremely compact andefficient in the use of urban space.They are simple and modular in their

design (yet highly functional), robust,inexpensive, and easy to maintain. Theyrecharge automatically in their parkingspaces much as electric toothbrushesrecharge in their holders so they donot need very long ranges or to carryaround large numbers of batteries.

RoboScooters, developed by SmartCities in collaboration with ITRI andSYM, are also lightweight, folding, in-wheel-motor electric vehicles. Thesetwo-wheelers are smaller, lighter, less

expensive, and consume less energythan their counterpart enclosed, four-wheel cars. They also have shorter

Mobility-on-demand systems may

use a single vehicle type. However,

a more attractive option in larger

and more sophisticated systems is

to employ multiple vehicle types

providing users with choices among

combinations of cost, comfort, and

functionality. For example, a user

might choose to ride a bicycle tothe supermarket, leave it there, and

bring back a car to carry the bags

of groceries. (Many inefficiencies

in traditional urban mobility

systems for example, driving

an empty SUV to the supermarket

result from the fact that vehicle

types cannot be matched to trip

purposes. One size must fit all.)


range. They are particularly suitable foruse where weather conditions are good,individual transportation is the priority,and urban and economic conditions areless favorable to automobiles.

Bicycles can also be used in mobility-on-demand systems, as in the Vlosystem in Paris. They may be traditionalbicycles, or smart electrically assistedversions. These provide the lightest,cleanest vehicle options, but their use

can obviously limited by terrain, weath-er, and range and carrying capacity de-mands.

Segway personal transporters havesometimes been proposed for use inmobility-on-demand systems. Thesemay be suitable in shorter-range, lower-speed situations, and for indoor-outdooruse. (The Dutch Railways are currentlyexploring the possibility of Segway mo-bility-on-demand at railway stations.)

Walking400m

RoboScooter5km

Bicycle2km

CityCar25km

Suite of vehicles used in themobility on demand system:CityCar, RoboScooter, Bicycle,Segway

> The folding mechanism ofthe RoboScooter allows it to fitunobtrusively into the street

Range per mobility mode Effect of topography on mobility range


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> Mobility on demand users canemploy multiple vehicle typesthat are specifically suited totheir needs and a complement topublic transport.


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Stacks and racks are the vehicle

pickup and dropoff points in

mobility-on-demand systems.

(Of course, vehicles may

also be parked temporarily

at other locations.)

These points need to be distributed suf-ficiently densely around the service areato be always in close proximity to trip

origins and destinations. They not onlyprovide access to vehicles, but also en-able vehicle recharging, provide vehiclesecurity, and handle the vehicle pickupand dropoff transactions which mustbe electronic, quick, and seamless.They need suitable space and streetaccess, electric power supply, and net-work connectivity .

A crucial technical issue, in design ofstacks and racks, is the provision ofefficient, safe, convenient, and weath-

erproof connection between powersupply and parked vehicles. The con-nection might either be through con-tact or through induction. In any caseit should be automatic whenever the

vehicle parks, so that there is no needfor the user to plug in or perform anyother explicit action. The idea is that us-ers never have to think about refuelingor recharging; the system simply pro-vides charged vehicles .

These ubiquitous access points al-low the system to operate in one-wayrental mode, rather than two-way rentalas with traditional car rental systems.Instead of relying upon users to bring

vehicles back to pickup points, whichsimplifies management but greatly re-duces the flexibility and responsivenessof the system, the operator accepts theresponsibility (and reaps the rewards) ofmanaging the distribution of vehicles inthe system so that they are always avail-able to meet demand. (Two-way rentalcan be regarded as a restricted specialcase of one-way rental, implemented bymeans of price incentives to return ve-hicles to pickup points, and managedusing the same technology.)

Locations of stacks and racks will bedetermined by some combination ofurban design considerations, availabil-ity of suitable sites, and long-term pat-terns of demand. Often it makes sense

to combine them with existing servicepoints, such as convenience stores,coffee shops, hotels, or bank ATMs to the commercial benefit of both. Ob-viously, as well, they can usefully beplaced at major origin and destinationpoints, such as railway stations, officetowers and parks, and sports and enter-tainment facilities .

Some stacks and racks may be small,informal, and temporary. Others may be

large, permanent mobility interchangepoints incorporating retail and servicefacilities that take advantage of the traf-fic passing through. Larger nodes in asystem may serve as vehicle cleaningand maintenance points .

Stacks and racks may be deployed in-crementally as a mobility-on-demandsystem grows, increasing both areaand density of coverage. And locationsmay be adjusted, over time, in responseto experience of operating the system.

Stacks and racks are modular, and (un-like subway stops, for example) are notnecessarily locationally tied to fixed in-frastructures .

< In Taipei City, the ubiquitous7-11 network at 5 minute intervalsprovides an excellent opportunityfor the insertion of scooter stacks

> In Florence, the scale of stacksand racks could ranges fromlarge storage areas outside ofthe historic center, key mobilitynodes at the traditional city gates,semi-permanent nodes thatrelate to the existing piazzas andportable snap on street elementsthat could be adjusted once thesystem is developed.


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Large vehicle storage areascan lie outside of the dense

historic center

Major mobility nodes exist atthe traditional city gates

Minor mobility nodes arealigned with piazzas and

existing transportation hubs

Minor snap-on stacks andracks can be placed in streets

and adjusted over time

0 100

250

500

1000m


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Placing mobility on demandpoints in the narrow streetsbetween traditional Lilonghousing in Shanghai would freeup valuable public real estate thatis usually consumed by parkedcars

Mobility on demand outside theMIT Media Laboratory



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When mobility-on-demand

pickup and dropoff points are

located within a city street

system they form a mobility

network. The pickup/dropoff

points are nodes, and the streets

provide the links among them.

Each node can park some finite numberof vehicles. At any moment, a node mayor may not have vehicles available, andit may or may not have empty parkingspaces available. Ideally, whenever apedestrian walks to a node there is a ve-hicle available for pickup, and whenevera driver approaches a node there will bea vacant space to drop off the vehicle.In practice (particularly when the systemis heavily loaded), pedestrians and driv-ers will sometimes have to queue to get

access.

It is possible to gather information on thelengths of pedestrian queues at pickuppoints. There might be some sort of sen-sor system. Waiting users might punchin to signal that they are waiting for avehicle at that location. Or users mightemploy their cellphones, as in calling fora taxi, to inform the system that they willwant a vehicle at a particular locationand time. (This is functionally equivalentto making a reservation.) In any case,

the system operator will want to man-age the system in such a fashion that itdirects vehicles to pickup points wherethere are queues of waiting customers just as a taxi dispatcher might.

Similarly, it is possible to gather informa-tion on the lengths of car queues wait-ing to park at dropoff points. (Vehiclesdriving along the road towards a dropoffpoint are implicitly queued they donthave to be lined up at the dropoff point,waiting to get in.) Most obviously, thiscan be harvested from destination in-formation that users punch into GPSnavigation systems. Queue length for adropoff point can also be inferred fromthe numbers of vehicles originating at

other pickup points that are now in thevicinity of the dropoff point.

The network therefore forms a queu-ing system, somewhat analogous to apacket-switching network such as theInternet. Vehicles travel from node tonode; there are varying numbers of ve-hicles present at nodes; and there arevarying-length (maybe zero) queues ofpedestrians and vehicles waiting to ac-cess nodes.

Thus there are three types of latenciesto manage in a mobility-on-demandsystem: pickup latencies, transit laten-cies, and dropoff latencies. The totallatency for a trip is the sum of these. Us-

ers will care both about mean latenciesand variances since they want not onlyto minimize their trip times, but also topredict them with reasonable accuracy.

In general, the larger the number of ve-hicles in the network, and the larger thenumber of parking spaces, the shorterthe queues and associated latencieswill be. (You can always solve latencyproblems with capacity.) However, thecosts, parking space demands, and

road space demands will also rise.Therefore the operators goal, in man-aging a mobility-on-demand system, isto meet user requirements for low-laten-cy service without spending an unsus-tainable amount on vehicles and park-ing spaces. Software tools to facilitateachieving this goal are key elements ofmobility-on-demand systems.

Pickup and dropoff nodes arelocated throughout a cities streetsystem creating a network ofmobility-on-demand points. Totaltrip time can be calculated byadding latencies in the system


Cost

Latency

QueuingTheoryModel

Dynamic Pricing

Vehicle Location DataGPS, Parking Space Sensors

Real-time Mobility Demand DataCredit Card transactions,

cellphones, system history

Number of vehicles(at each node)

Optimal SystemPerformance

Maximize vehiclepick-up availability

Optimize vehicle drop-off

Minimize totalcustomer travel time

Minimize cost tosystem operator

System Balancing Actions(redistribution trucks)


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NODESparking capabilities anddynamically varying customerqueues and wait times

TOTAL TRIP TIME

=

PICK-UP LATENCY

+

TRANSIT LATENCY

+

DROP-OFF LATENCY

DROP-OFF LATENCYdrop-off wait timeplus walking time

PICK-UP LATENCYwalking time plus wait time

ZONEServiced by node

LINKSDynamically varying travel

times (transit latencies)


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Mobility demand manifests itself as

queues forming at pickup points. A

mobility-on-demand system must

be designed to respond to this

demand effectively (from the users

perspective) and economically

(from the operators perspective).

Cleverly managing the spatial

distribution of vehicles in thenetwork, as the spatial and temporal

distribution of demand fluctuates, is

the key to success. In other words,

with a given stock of vehicles

and parking spaces, the system

operator must try to keep supply

and demand of vehicles in optimal

balance across the system.

Under certain ideal conditions for ex-ample, in high-density, mixed-use urbanareas with random distributions of tripdemand mobility-on-demand systemsmay be essentially self-organizing. Inother words, the inflows of vehicles tonodes generally match outflows, so thatthere are never too many or too few ve-hicles at a location for the current de-mand.

In practice as with other types of net-

works, such as e lectrical power, packet-switching, and delivery route networks there will be spikes and irregularitiesin mobility demand patterns . This re-quires active management intervention either by means of automatic controlalgorithms, by skilled operators whomonitor and adjust the system, or somecombination of the two to keep supplyand demand appropriately balanced.

You dont want all of the vehicles on oneside of the city when all the demand ison the other.

Since people make cost and conve-nience tradeoffs in their mobility behav-ior, and generally have some flexibilityabout when and where to go, much ofthis management can be accomplishedthrough tools of dynamic pricing. If pick-

up price at a node is currently low, it willmotivate users to go to that node, but ifit is high, it will motivate users to seeka slightly less convenient alternative. Ifdropoff price is low it will attract vehiclesto that node, but if it is high it will pushvehicles out to alternatives. If price/timeis low, it will encourage users to maketheir trip now, but if price/time is high,then it will encourage them to make thetrip earlier or later .

If there is an available pool of appro-priate labor, negative pricing may alsobe used. In other words, users can getcash or credit for moving vehicles towhere they are urgently needed. Thismay be appealing, for example, toyoung people with time on their hands,the under-employed, and those whojust want to explore the city or get somebicycle exercise.

Note, incidentally, that users at differentnodes across the city may have differ-

ent preferences for cost-latency com-binations. In lower-income areas theremay be a preference for lower-costservice with higher latencies. In higher-income areas, conversely, there maybe a preference for lower latencies at ahigher price. Pricing can also be used to

implement public policy, for example bysubsidizing the daily commute of low-income service workers to areas wherethey are needed .

However, pricing strategies may not al-ways suffice to keep the system in anoptimum state of balance between ve-hicle supply and demand. In this case,it becomes necessary for the operatorto physically move empty vehicles fromlocations of current low demand to loca-

tions of current high demand. Obviouslythis is costly, and a management goalis to minimize it. Emerging techniquesfor efficiently moving driverless vehi-cles, such as virtual towing of trains ofvehicles, and low-speed autonomousdriving late at night, can assist with this.So (at least on a fairly small scale) cansimply throwing vehicles on trucks.

It is not necessary to invent from scratchstrategies and algorithms for balanc-ing supply and demand in mobility-on-

demand systems. The task is closelyanalogous to some well-known, exten-sively studied tasks such as airline fleetmanagement, and delivery vehicle fleetmanagement. There is a lot of existingtheory, technology, and experience todraw upon.


Troughs and peaks of bikeavailability experienced by theParis bicycle system over 1 day

> Effect of dynamic pricing ondesirability of pick up and dropoffnodes

Mobility Demand

high

low throughput(storage)

Inflow(sink)

Outflow(source)

highthroughput

hig

h

PICKUP COSTS

DROPOFFCOSTS

low

low

Cost/minute


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Desirability of nodes at equal prices

5min

10min

15min

20min

Node Desirability

High Desirability

Low Desirability

x-dollars

10min

15min

20min

The effect of dynamic pricing on node desirability


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7am 8am 9am 2pm 3pm

leaveparkinggarage

findstreetpark

arriveofficebuilding

arrivemeeting

arriveatdestination

leavedowntowncore

leaveparkinggarage

arrivecar

leavedesk

arrive

parkingg

arage

arrivecitiesedge

leavegarage

leavehouse

arriveatdesk

9 10 5 5

20km/hr

avg. 45km/hr

60km/hr

3km 7km1.25km

parking $10

235 5 6

15175

5 4

$39.00

7c

train fare$2.50

15km/hr

70km/hr

30km/hr

parking $6

66

arrivedowntown

leavetrainstation

arriveatdesk

6

arrivetrainstationanddropo

ffCityCar

leavehouse

pick-upCityCar

55 5 25

655

5

5 25

32

$18.56 7c

latency (minutes)

total cost by modalityper person*

timeaverage speed km/hr

distance (kms)

location

latency (minutes)

total cost by modalityper person*

timeaverage speed km/hr

distance (kms)

location

mobilityon

demand

privatemo

bility

46 mins

26 kms

$17.99*

52 mins 37 mins

$24.63*

11.25km26km

$49.12

hire fee

$2*hire fee

$2*

6c 1c

0.4km

parkCityCarinrackatdestination

arriveofficebuilding

arrivemeeting

leavedowntowncore

pickupCityCar

leavebuilding

leavedesk

3 4 5

3km 6.4km

9127

470km/

hr30km/

hr

66

$9.40 6c

27 mins

$11.46*

10km

23km 3km

5km 20km400m 480m

15

$10.35$5

OfceHome Meeting


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15

5pm 6pm 7pm

arrivegym

findparking

arrivegymp

arkinglot

leavestreetparking

leavemeeting

5 3

arrivehome

arrivegarage

leavegymp

arkinglot

arrivecar

leavegym

5

9 5710 6 8

6

leaveofficebuilding

6 4 4 84

7c

60km/hr

60km/hr

5km/hr

5km/hr

5

freeparking

$9.90*

25 mins

$10.00*

6km

hire fee

$2*

5.6km 0.4km

arrivegym

ParkCityCar

pickupCityCar

leavemeeting

6 6

leaveofficebuilding

460km/

hr

$5.40

5.4km0.4km

21 mins

5.8km

$7.54*

Gym Home

7c

7c 3c $13.20* $13.24*

8.2km

20 mins

0.2km 8km

4c

6

arrivehome

drop-offCityCar

pick-upCityCar

leavegym

5

56 869 6

860km/

hr

$7.60* $7.73*

8km

19 mins

7.6km 0.4km

7c 6c

* Cost Estimates in US dollars per vehicle kilometreVictoria Transport Policy Institute Spreadsheet for Transport Cost Analysis, 2002

Total cost incl udes values for:

Vehicle Ownership Congestion Vehicle Operation Road FacilitiesOperating Subsidy Land ValueInternal Crash Traffic ServicesExternal Crash Transport DiversityInternal Parking Air PollutionExternal Parking NoiseResource Externatilies Barrier EffectLand Use Impacts Water PollutionWaste

1996 US dollars per mobility mode

Average Car $1.65/kmCityCar $1.00/km

Average motorcycle $2.50/kmRoboscooter $??

Bicycle $0.42/kmWalking $0.14/km

otal costs per day: private mobility vs mobility on demand

Costs

Time (mins)Distance (km)Cost (1996 US$)Pick up latency (mins)Travel latency (mins)Drop off latency (mins)

Private Mobility

13451.45$96.99

276344

Mobility on Demand

11349.8

$44.20275531

Latency and cost comparisons: private mobility vs mobility-on-demand

< Latency, time and cost analysisbetween private mobility andmobility on demand systems.

Cost analysis based on theVictoria Transport Policy Institutespreadsheet for Transport Cost

Analysis, 2002

All costs are in 1996 US dollars

D d i f ti d lti i


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Obviously the effectiveness of

mobility-on-demand systems

depends upon having very good,

spatially and temporally fine-

grained information about varying

patterns of mobility demand,

and upon responding swiftly and

appropriately to these patterns.

(Traditional population density and

trip origin/destination data, as used

in transportation planning, may

provide a useful starting point for

approximately estimating demand,

but it will not suffice.) There are

several potential ways maybe best

used in combination to obtain

the necessary fine-grained, up-to-

the-minute demand information.

The most obvious is to use the informa-tion generated by the mobility systemitself. Nodes can keep precise track ofqueue lengths and actual vehicle pick-ups and dropoffs, and GPS navigationsystems in vehicles can track vehiclelocations. In this way, it is possible tobuild up very detailed historical picturesof expressed demand on the system,and over time, these provide an increas-ingly precise and reliable foundation foreffectively responding to demand .

Cellphone operators now track the loca-tions of handsets with increasing preci-sion either by their association to celltowers, or through GPS location. Aggre-gate cellphone location data providesrealtime census snapshots of howpeople are distributed throughout thespace of a city. This provides an effec-tive basis for predicting emerging mo-bility demands .

Credit card transactions are recordedby time and location, so these can also

provide a great deal of useful, real-time information about the distributionand activities of people in the city, andhence about likely patterns of mobilitydemand.

In general, mobility-on-demand systemsdraw upon large-scale, fine-grainedhistorical databases to establish long-term patterns of mobility demand. Theyaugment this with realtime sensor andtransaction data reflecting short-termfluctuations and perturbations due, forexample, to special events or emergen-cies. They apply sophisticated analysistechniques to mine significant infer-ences from the data.

It should be fairly straightforward toestablish the structures of the queuingand demand prediction models. Thesemodels will have many parameters, andinitially the estimates of parameter val-ues will probably be very rough. But,with experience over time, it should bepossible to refine these values and thus

develop very powerful and accuratemodels. Possession of these modelswill be a competitive advantage to ex-perienced operators of mobility-on-de-mand systems, and lack of them will bea barrier to new entrants.

When a mobility-on-demand system isbeing planned, detailed databases ofdemand patterns, vehicle movements,and latencies do not exist. However, itis possible to simulate system operationin order to develop initial strategies forresponding to demand, balancing thesystem, and minimizing latencies. Then,as the real system comes up, thesestrategies can be modified incremen-tally in the light of real data.

Demand information and realtime responsiveness

> Callphone data providescensus snapshots of howpeople are distributed in a city.(Taken from senseable cities,Graz project)

>> The onboard locationand guidance system on the

roboscooter, provides thecustomer with data and directionsto the nearest scooter rack, aswell as monitoring the location ofthe scooter.

>>> Users of the mobility-on-demand system could employtheir cellphones to check onwaiting times or numbers ofvehicles available



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17Mobility-on-demand vehicles

are most effective when they are

equipped with GPS navigation

augmented with traffic density

data. From the users perspective,

this enables efficient navigation

to destinations. From the

operators perspective, it enables

tracking of vehicle locations and

provides realtime information

about vehicle densities and

speeds. Furthermore, destinations

entered by users into navigation

systems constitute flight plans

that enable operators to predict

parking space demands at

arrival points and availabilities

of vehicles, at these points, to

meet near-future demands.

An even better option is to integratemobility-on-demand systems with theemerging idea of personal mobilityassistants (PMAs). PMAs are wire-lessly networked, location-aware,handheld devices. They know aboutstreet networks, traffic conditions,transit routes, and transit schedules.They allow users, with minimum cog-nitive load, to plan and execute mul-timodal trips that may combine walk-ing, mobility-on-demand, and transit

even when they are unfamiliar withthe urban terrain that they are travers-ing.

Location-awareness also opens upthe potentially lucrative possibility ofintegrating location-based advertis-ing, allowing users to plan shoppingtrips, combination with social net-working, scheduling, and meeting co-ordination, and so on. Opportunitiesfor innovative, add-on services suchas these are likely to be importantparts of mobility-on-demand busi-ness models.

The availability of high-quality tripplanning facilitates sophisticated,dynamic pricing and the use of pric-ing to manage demand. Users canchoose among combinations of vehi-cles, links, pickup and dropoff points,and overall latencies and prices. Theymay choose to optimize whatevercombination of monetary cost, energyconsumption, carbon footprint, andoverall latency is important to them. Inaddition to minimizing resource use in

this way, they might also want to max-imize quantities like touristic interestor protection from the weather.


Synergy with Transit Systems


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generally are not replacements

for transit systems. Instead,

they operate effectively as

partners of transit systems,

and enhance the efficiency and

attractiveness of these systems

by solving the first kilometer

and last kilometer problems.

In general, transit systems are very ef-ficient for moving large numbers of pas-sengers, at relatively high speed, be-tween fixed points. Their difficulty is thatboarding points are rarely exactly whereyou want to begin your journey, anddropoff points are rarely exactly whereyou want to end. You have to get to theembarkation point (the first kilometerproblem) and from the dropoff point(the last kilometer problem ).

Mobility-on-demand systems solvethese problems by providing stacks andracks at transit stops.

One possible combination is with met-ropolitan transit networks, such assubway and bus rapid transit systems.Commuters might ride the suburbantrain home in the evening, pick up a ve-hicle at the stop, keep it overnight, andbring it back to the station in the morn-ing. (There might be a price incentive torecharge the vehicle, at a home station,overnight.) At the city end, commutersmight take vehicles from the station tothe workplace and back again.

Another possibility is combination withinter-city high-speed rail or air transport,in which vehicles are picked up anddropped off at train stations and airports.This combines the long-distance speedand efficiency of transit systems with theshort-range convenience of mobility-on-demand systems. And it eliminates the

need to design mobility-on-demand ve-hicles to meet the requirements of long-distance, high-speed highway driving.

Transit systems are least efficient wherepopulation density is low and stops aresparse, and in off-peak times, whenthey have to move around large vehiclescontaining few passengers. In thesecontexts, through use of small vehiclesand availability on demand, mobility-on-demand systems may cost-effectivelysubstitute for transit .

Mobility-on-demand systems can alsoprovide virtual rings to supplement ra-dial suburban transit systems. In thesesystems, it is usually necessary to gointo the center and out again to movecircumferentially. The problem getsworse towards the periphery, as theradial lines spread apart. Mobility-on-demand stacks in suburban areas canenable efficient circumferential move-ment instead .

> Transport islands in SanFrancisco. These areas do nothave adequate transport facilitiesand are ideal places for mobilityon demand systems as a wayto augment the existing publictransport network

< Transport islands in the city ofShanghai



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19


Synergy with Clean Energy Systems


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The use of electric vehicles and

bicycles eliminates tailpipe

emissions, local pollution, and

traffic noise. However, this does

not necessarily reduce dependency

upon non-renewable energy

sources. This depends upon the

source of electricity. If the source

of electricity is old-fashioned

coal-burning power plants,

for example, then the shift to

electric vehicles merely displaces

(though maybe with at least some

advantage) carbon emissions.

But if the source is hydro, then

carbon emissions are eliminated.

y gy gy y

A general problem with todays electricgrids is that they lack storage capac-ity. This makes it difficult for them torespond effectively to demand spikes,and it makes them unfriendly to clean,renewable, but intermittent sourcessuch as solar, wind, and wave. Howev-er, since electric-powered mobility-on-demand vehicles are always connectedto the grid when parked in stacks andracks, they throw a large amount ofbattery storage capacity into the grid.This opens up the possibility of vehiclesbuying and selling electricity much ashas been proposed for plug-in hybrids.Trading strategy would respond to cur-rent electricity prices and expectationthat they would need electricity for travelin the near future .

For example, vehicles parked at homescould recharge inexpensively at off-peak times during the early hours ofthe morning, and might sell electricityback to the grid if they happened to beparked at home on a sick day duringpeak travel hours.

Carbon emissions and CO2comparisons show environmentalperformance of vehicle types

This also deals with the problem of in-termittency in solar, wind, and wavegeneration systems. Cars can chargebatteries while the sun shines or thewind blows, and then sell electricitywhen these sources are not producing.There is particular promise, in sunnyclimates, in combining vehicle batterycharging with distributed, rooftop solarpanels, since this minimizes transmis-sion losses .

Charging and discharging batteries isnot cost-free, and the costs of charg-ing and discharging currently availablebatteries limit the immediate practicaleffectiveness of this attractive strategy.But improvements in battery technol-ogy will probably make it increasinglyfeasible.

Increasing political and economic pres-sure related to the geopolitics of en-ergy supply, the need to reduce carbonemissions and global warming (to whichgasoline-powered urban mobility is a

major contributor), and the need to shiftto clean, renewable energy systems, willcreate increasingly powerful incentivesfor local and national governments tosupport mobility-on-demand systems.

Synergy with Clean Energy Systems


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21

Road Safety Benefits


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23Currently, gasoline-poweredautomobiles weigh approximately

25 times the weight of the driver,

and run at speeds of at least 150

km/hour. This combination of

mass and velocity adds up to

enormous inertia in crashes, and

automobiles must be designed

with safety systems to withstand

this greatly increasing weight,complexity, and cost, and

reducing energy efficiency .

By making use of lower-speed, lower-mass vehicles, mobility-on-demandsystems can enormously reduceoverall levels of inertia in urban mo-bility systems thus reducing energyconsumption and embodied energyand the weight and complexity ofsafety systems, and potentially reduc-ing overall road deaths and injuries.

Ideally, a mobility-on-demand systemhas a range of vehicles, from bicyclesand Segways with low vehicle weight /

passenger weight ratios to four-wheel,fully enclosed automobiles with high-er ratios and safety cages, crumplezones, seatbelts, and airbags. Us-ers can choose the combinations ofcosts, weights, and safety levels theywant for particular conditions. Policymakers can also set general param-eters .

As under todays conditions in mostcities, there will be mixtures of ve-hicle sizes and weights on streets,and this will put smaller vehicles at adisadvantage in collisions with largervehicles. But light vehicles formingmobility-on-demand systems willnot need to mix with heavy vehiclesunder highway conditions, just asbicycles do not go on the freewaystoday. They will create demand andjustification for higher levels of vehiclesegregation by mass and speed, forexample by establishing urban corezones that exclude or heavily penalizeprivate automobiles and rely solelyupon mobility-on-demand. And, bygreatly increasing the percentage oflight, relatively low-speed vehicles onthe streets, they will reduce the aver-age energy of collisions. The benefitsto pedestrians (at 1/1 driver/vehicleweight ratio, unless they wear armor)are particularly significant.

< Zones of modality in Lisbon

^ Weight and size comparisonsbetween transport modes clearlyshow the space benefits of amobility on the demand CityCar

Urban Design and Quality of Life Benefits


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The private automobile hasbrought many benefits to city

dwellers, but also many negative

externalities. The effect of these

externalities increases with

automobile density. Streets become

congested and slow to travel,

noisy, polluted, and dangerous,

and increasing proportions of

valuable urban real estate mustbe devoted to car parking. From

an urban design and quality of

life perspective, the problem is to

retain the benefits of the private

automobile (particularly those

that are realized at low automobile

densities) while eliminating

the negative externalities.

Mobility-on-demand systems accom-plish this by providing equivalent orgreater access to mobility with muchmore compact and benign vehicles,with far fewer parked vehicles occupy-ing space, and with far fewer vehicleson the streets creating congestion.

The footprints of mobility-on-demandvehicles even the electric cars aremuch smaller than the footprints ofgasoline-powered automobiles. Fur-thermore, they pack tightly together in

stacks and racks. This allows parkingcompression ratios of between 3:1 and8:1 for cars, and even more when two-wheelers replace cars.

One possible response to this gain isto pack many more vehicles into thesame amount of parking space. Thismay occasionally be appropriate in ar-eas where mobility demand is extremelyhigh, but generally it will not be neces-sary. In most contexts, the introductionof mobility-on-demand systems, com-bined with the removal of traditional carparking, will result in freeing of on-streetand off-street parking for other uses.Urban designers may take advantageof this to introduce greenery and otheramenities into streets, to remove park-ing from piazzas and return them topublic pedestrian use, and so on.

Although mobility-on-demand vehicleshave lower top speeds than todaysprivate automobiles, they can providehigher throughput in urban areas be-cause they generate less congestionand provide through their navigationsystem for automatic routing aroundblockages and congested areas. Fur-thermore, since they are intelligent andwirelessly networked, they support so-phisticated techniques of intelligent traf-fic flow management at merges, inter-sections, and constrictions. In general,they should be able to make highly op-timized use of available street and roadcapacity.

>^ Increased number of carparking spaces possible on atypical Lisbon block with thereduced footprint of the CityCar

> Possibility for increased urbanamenity when car parking spacescan be reduced to 1.75m inwidth

Urban Design and Quality of Life Benefits


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25

71m

Typical block in Lisbon with 11standard parking spaces

Typical sidewalk and parkingbay configuration in Lisbon

Typical block in Lisbon with potentialfor 37 CityCar parking spaces

Potential for enlarged sidewalkand street planting with reduced

CityCar parking bay of 1.75m

3m 3.75m2.5m 1.75


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Mobility on Demand Introduction

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