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10 Δεκ 2013 (πριν από 3 χρόνια και 6 μήνες)

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U
SING

O
PEN

D
ATA

TO

D
EVELOP

M
ULTIMODAL

T
RIP

P
LANNERS

FOR

L
IVABLE

C
OMMUNITIES

Sean J. Barbeau

Edward L. Hillsman

Center for Urban Transportation Research @

University of South Florida


GIS in Transit Conference

St. Petersburg, Florida

September 14, 2011




Funded by the

Florida Department of Transportation and
t
he National Center for Transit Research

P
URPOSE


Advise on two
emerging technologies


Multimodal trip
planning


Crowd
-
sourced
data/applications


Explain state
-
of
-
the
-
art
and relationship to GIS

W
HY

MULTIMODAL

TRIP

PLANNERS
?


If you want to drive, the question is “How do I
get there?”


Road networks are dense, connected, complete


Google, Mapquest, Yahoo can easily tell you


For bike/walk/bus, the question is “Can I get
there (by a safe route)?”


Networks are sparse, incomplete, or both


Route
-
specific info is more important than when
driving


Multimodal


Options to mix modes for a
trip


Examples


Bike to bus, ride bus, bike or
walk to final destination


Drive/bike to park
-
and
-
ride,
take bus


Wheelchair
-
accessible routes


Various access to/from bike
-
sharing, car
-
sharing

T
RIP

PLANNING

SOFTWARE

TYPES


Unimodal


Similar to what Google
Maps/Transit/Bikes, Yahoo
Maps, Mapquest offer


One mode per trip:

only
only
only
only
+
+
+
+
P
ROPRIETARY

T
RIP
-
PLANNING

SOFTWARE


Custom
-
built software and data are expensive


Goroo® in Chicago cost more than $1 million and
is still being improved


Web
-
based software is proprietary and closed


Google, Yahoo, etc. are free to use, but


Services depend on the needs and desires of the
providers


Providers limit use and presentation of their systems
(frequency, branding)

O
PEN
T
RIP
P
LANNER


Free, open
-
source software
-

opentripplanner.org


Development spearheaded by Tri
-
Met in Portland,
with grant funding (2009
-
present)


Active worldwide developers’ group


Available for anyone to download, install, modify


(and, with approval, contribute back)


Non
-
profit
OpenPlans

can provide installation,
customization, maintenance support


OpenPlans will be giving Keynote on OTP status
and roadmap on Thurs. morning at 8:30am

O
PEN
T
RIP
P
LANNER



T
RUE

M
ULTIMODAL


USF’s OTP Demo for Tampa, Fl
-

http://opentripplanner.usf.edu


Example: Bike
-
>Bus
-
>Bike

O
PEN
T
RIP
P
LANNER



I
NTERLINING

BETWEEN

TRANSIT

SYSTEMS


HART

USF

Bull
Runner

W
HY

DON

T

WE

JUST

USE

G
OOGLE

M
APS
?


In USF community, Google Maps can’t find USF building names or abbreviations


Google Maps gives walking directions on Alumni Dr. (where there were no sidewalks)
and using a cross
-
street (instead of the nearby crosswalk)

Google Maps

OpenTripPlanner

© 2011 Google


Map data © 2011 Google

Data CC
-
By
-
SA OpenStreetMap

OTP W
HEELCHAIR

ACCESSIBLE

ROUTING

OPTIONS

Regular route with stairs

Wheelchair
-
accessible route

OTP W
HEELCHAIR

ACCESSIBLE

ROUTING

OPTIONS

GIS D
ATA


To provide this kind of service, you need data


Transit routes and schedules


Street network


(plus addresses, points of interest for geocoding)


Bicycling facilities


(lanes, routes, parking)


Sidewalks, crosswalks, and other pedestrian
infrastructure


Future: Park
-
and
-
ride lots, car
-
sharing, and/or
bike
-
sharing stations

12

O
PEN

D
ATA

S
OURCES

FOR

O
PEN
T
RIP
P
LANNER

General Transit Feed Specification (GTFS)


Over 140 agencies in US have transit data in this format,
more than 447 world
-
wide


Most agencies did this to get on Google Transit


But, GTFS is open
-
data format that anyone can use


Used by many mobile apps


OpenTripPlanner


Becoming a
de facto

standard


See “GTFS Data Exchange” for list of agencies with GTFS
data


http://www.gtfs
-
data
-
exchange.com/



Or, ask your local agency


Major transit scheduling software packages can prepare
GTFS

O
PEN

D
ATA

S
OURCES

FOR

O
PEN
T
RIP
P
LANNER

OpenStreetMap.org


Think “Wikipedia for
geographic data”


People contribute data
under a Creative
Commons Attribution
-
ShareAlike

2.0 license


Edit online, using
custom GPS traces, or
programmatically


Anyone can download
and use the
data

(not
just the maps)


O
PEN

D
ATA

S
OURCES

FOR

O
PEN
T
RIP
P
LANNER

National Elevation Dataset (NED)


Provides elevation data for biking/walking in OTP


Currently used to produce elevation graph, and for
some biking routing decisions



O
PEN

D
ATA

S
OURCES

FOR

O
PEN
T
RIP
P
LANNER

Geographic Information
Systems (GIS) files


OpenTripPlanner can also
support loading GIS (e.g.,
.
shp
) files


Local government sources:


City


County


Special Districts (parks, etc.)


Ask your local government
what data might be available


Especially if there isn’t much
OpenStreetMap activity in
your area


OPEN ISSUES

Multimodal trip planning is a new field, and there are still . . .

17

P
EDESTRIAN

S
IGNALS

& C
ROSSINGS


“Implicit” vs. “Explicit” data coding of
pedestrian infrastructure in OpenStreetMap


Implicit


less work when sidewalks are always
present and follow roads (e.g., downtown):




Explicit


less work when sidewalks are sparse,
or don’t follow roads:


18

Street
Sidewalk
Street
Sidewalk is attribute of street
(
highway
=
footway
)
19

Stairs
Footway
Street A
Street B
Node
1
(
Pedestrian Crossing Coding
)
Node
2
(
Curb Cut Coding
)
Crossing
1
Crossing
2
Node
3
(
Vehicle Traffic Signal Coding
)
Curb Cut coding
(
e
.
g
.
,
Sloped Curb
,
Tactile Paving
)
Legend
Footway
(
i
.
e
.
,
Sidewalk
)
Highway
Crosswalk
Pedestrian Crossing Coding
(
e
.
g
.
,
Type of Marking
,
Accessibility
,
Pedestrian Signal
)
Vehicle Traffic Signal Coding
Explicit example

20

Stairs
Footway
Street A
Street B
Node
1
(
Pedestrian Crossing Coding
)
Node
2
(
Curb Cut Coding
)
Crossing
1
Crossing
2
Node
3
(
Vehicle Traffic Signal Coding
)
Stairs
Footway
Street A
Street B
Node
1
(
Pedestrian Crossing Coding
)
Node
2
(
Curb Cut Coding
)
Crossing
1
Crossing
2
Node
3
(
Vehicle Traffic Signal Coding
)
Curb Cut coding
(
e
.
g
.
,
Sloped Curb
,
Tactile Paving
)
Legend
Footway
(
i
.
e
.
,
Sidewalk
)
Highway
Crosswalk
Pedestrian Crossing Coding
(
e
.
g
.
,
Type of Marking
,
Accessibility
,
Pedestrian Signal
)
Vehicle Traffic Signal Coding
Explicit coding example

21

"highway=footway”


"footway=crossing”

"highway=crossing”

+

"crossing=pedestrian signals“

"marking=zebra”

"wheelchair=yes”


Stairs
Footway
Street A
Street B
Node
1
(
Pedestrian Crossing Coding
)
Node
2
(
Curb Cut Coding
)
Crossing
1
Crossing
2
Node
3
(
Vehicle Traffic Signal Coding
)
Explicit coding example

22

"highway=footway” (normal sidewalk tag)

"footway=crossing" (new tag)

"highway=crossing”

+



P
EDESTRIAN

S
IGNALS

& C
ROSSINGS

"crossing=pedestrian signals”

"marking=zebra”

"wheelchair=yes"

FOR OTP ROUTING:

Stairs
Footway
Street A
Street B
Node
1
(
Pedestrian Crossing Coding
)
Node
2
(
Curb Cut Coding
)
Crossing
1
Crossing
2
Node
3
(
Vehicle Traffic Signal Coding
)
P
EDESTRIAN

S
IGNALS

& C
ROSSINGS


How to support implicit coding routing, and
locations where explicit/implicit
codings

merge?

23

Street A
Street B
Sidewalk is attribute
of street here
...
but separate feature here
O
PEN

I
SSUES



C
ROWD
-
SOURCING

L
EVEL

OF

S
ERVICE


Having traffic characteristics for roads would
help in pedestrian/biking routing decisions


However, traditional road traffic metrics (i.e.,
traffic volume, width of lanes) are
difficult/dangerous to crowd
-
source


Need better objective metrics to define bike
and walk

"level
-
of
-
service" (i.e., how "good" an
OSM way is for walking or

biking) that can
easily be recorded by a casual observer

24

O
PEN

I
SSUES



P
ERSONALIZING

B
IKING

D
IRECTIONS


Level
-
of
-
service metrics must translate to
subjective judgments for whether a

cyclist would
be comfortable riding on a specific road


Different for every cyclist:


Some expert cyclists would be comfortable riding

on
high traffic roads where other beginner cyclists would
not


Also depends on presence of bike lanes, shoulder, etc.


What does an “ideal” user interface look like to
meet everyone’s needs, but not be overwhelming?


Should we customize based on some self
-
assessment of skill/comfort level?


25

O
PEN

I
SSUES



S
PARSENESS

OF

OSM D
ATA


Many areas of U.S. are still sparsely populated
in OSM


We believe OTP is a “game
-
changer”


now
OSM contributors can see direct benefits of
their work in OTP routing


What are the motivations/profiles of current
U.S. contributors?


How can we leverage this knowledge, and
visibility of benefits in OTP, to motivate a larger
crowd of OSM contributors?


26

GO
-
Sync

A Software Tool to Synchronize Transit Agency
GTFS Datasets with OpenStreetMap


Coded by
Khoa

Tran

GO
-
S
YNC

M
OTIVATION


Shortcomings of official transit GTFS datasets


Inaccurate bus stop locations


Lack of transit data in OSM for many U.S. cities


Goal


create a tool that can:


Share transit agency data with
OpenStreetMap

community


Leverage social mapping model to improve bus
stop inventory, and allow agency to retrieve these
improvements

C
HALLENGES


Need to respect work by other OSM users


Avoid overwriting existing OSM data


Lack of a strict tagging system in OSM


Ex: “route”, “routes”, “
route_id




route_ref



Need to avoid duplicating OSM data


Ongoing updates to GTFS data


Integration of crowd
-
sourced data into transit
agency internal datasets

GO
-
S
YNC


General Transit Feed Specification (
G
TFS)


OpenStreetMap

(
O
SM)
Synch
ronization


http://code.google.com/p/gtfs
-
osm
-
sync/


Open
-
source, under Apache 2.0


GO
-
Sync is an open
-
source tool that can
synchronize GTFS datasets with OSM


Performs “Point
-
conflation”, or merging, for bus
stops in OSM

1) Input GTFS data and Agency Info

GO
-
Sync analysis, allowing user changes before upload


33


34

E
VALUATION

IN

T
AMPA



On July 2010, 3,812
new
HART stops uploaded

(133 stops previously existed)



By January 2011, 173 modifications were made

Example:

moved

E
VALUATION

IN

T
AMPA

0
10
20
30
40
50
60
0-5m
5-10m
10-15m
15-30m
30-70m
70-120m
120-400m
Number of

stops moved

Distance of Moved Stop from Original Location

Bus Stop Location Movement Distribution

GO
-
S
YNC

S
UMMARY


GO
-
Sync can help you leverage crowd
-
sourced
edits for your bus stop inventory


Available to download from Google Code


http://code.google.com/p/gtfs
-
osm
-
sync/


Caveats:


Must have the GTFS owner’s permission before
upload!!!


It’s a prototype


read the instructions carefully!!


May not be appropriate for all transit agencies


Knowledge of OSM is highly suggested


Respect others work!


We would welcome improvements by other
contributors!


37

CONCLUSIONS

What should I take away from today’s presentation?

38

T
AKEAWAYS


Open
-
source multimodal trip planners are a reality


Get your GIS data together for your community


GTFS


OpenStreetMap


Local GIS


Think about multimodal data connections


Bike/walk is part of trip, not whole trip


Park
-
and
-
Ride lots,
carsharing
,
bikesharing


Intersection data


How might you benefit from crowd
-
sourced data?


Benefits of open software/data


No vendor lock
-
in


Community add
-
ons (USF students created OTP Android app,
USF
BullRunner

GTFS data)


C
ONTACT

I
NFORMATION


Project Website:


http://www.locationaware.usf.edu/ongoing
-
research/projects/open
-
transit
-
data/


OpenTripPlanner

Tampa Demo:


Opentripplanner.usf.edu


Sean Barbeau, M.S.

(OpenTripPlanner/Android)

barbeau@cutr.usf.edu

(813) 974
-
7208

Ed Hillsman, Ph.D.

(OpenStreetMap)

hillsman@cutr.usf.edu

(813) 974
-
2977

40