of 32
8/3/2019 Mobility on Demand Introduction
1/32
mobility on demandFuture of transportation in cities
8/3/2019 Mobility on Demand Introduction
2/32
8/3/2019 Mobility on Demand Introduction
3/32
mobility on demand
Smart CitiesMIT Media LaboratoryJune 2008
Future of transportation in cities
8/3/2019 Mobility on Demand Introduction
4/32
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
8/3/2019 Mobility on Demand Introduction
5/32
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
2
6
10
12
14
15
17
18
20
23
24
26
Contents
mobility on demand
8/3/2019 Mobility on Demand Introduction
6/32
8/3/2019 Mobility on Demand Introduction
7/32
1
Mobility-on-demand systems
Mobility-on-demand systems
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.
8/3/2019 Mobility on Demand Introduction
8/32
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.)
Clean, compact, energy efficient vehicles
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
8/3/2019 Mobility on Demand Introduction
9/32
3
Clean, compact, energy efficient vehicles
8/3/2019 Mobility on Demand Introduction
10/32
Clean, compact, energy efficient vehicles
8/3/2019 Mobility on Demand Introduction
11/32
5
Clean, compact, energy efficient vehicles
> Mobility on demand users canemploy multiple vehicle typesthat are specifically suited totheir needs and a complement topublic transport.
8/3/2019 Mobility on Demand Introduction
12/32
Stacks and racks throughout the city
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.
8/3/2019 Mobility on Demand Introduction
13/32
7
Stacks and racks throughout the city
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
8/3/2019 Mobility on Demand Introduction
14/32
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
Stacks and racks throughout the city
8/3/2019 Mobility on Demand Introduction
15/32
8/3/2019 Mobility on Demand Introduction
16/32
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
Networks, queues and 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)
8/3/2019 Mobility on Demand Introduction
17/32
11
Networks, queues and latencies in the system
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)
8/3/2019 Mobility on Demand Introduction
18/32
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.
Mobility demand, pricing, and balancing supply and demand
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
8/3/2019 Mobility on Demand Introduction
19/32
13
Mobility demand, pricing, and balancing supply and demand
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
8/3/2019 Mobility on Demand Introduction
20/32
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
8/3/2019 Mobility on Demand Introduction
21/32
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
8/3/2019 Mobility on Demand Introduction
22/32
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
Combination with GPS navigation and personal mobility assistants
8/3/2019 Mobility on Demand Introduction
23/32
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.
Combination with GPS navigation and personal mobility assistants
Synergy with Transit Systems
8/3/2019 Mobility on Demand Introduction
24/32
Synergy with Transit Systems
Mobility-on-demand systems
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
Synergy with Transit Systems
8/3/2019 Mobility on Demand Introduction
25/32
19
Synergy with Transit Systems
Synergy with Clean Energy Systems
8/3/2019 Mobility on Demand Introduction
26/32
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
8/3/2019 Mobility on Demand Introduction
27/32
21
Road Safety Benefits
8/3/2019 Mobility on Demand Introduction
28/32
8/3/2019 Mobility on Demand Introduction
29/32
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
8/3/2019 Mobility on Demand Introduction
30/32
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
8/3/2019 Mobility on Demand Introduction
31/32
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
8/3/2019 Mobility on Demand Introduction
32/32