Computational Transportation Science. Ouri Wolfson Computer Science. Vision. Take advantage of advances in Wireless communication (communicate) Mobile/static Sensor technologies (integrate) Geospatialtemporal information management (analyze) To address transportation problems Congestion
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Funded by the National Science Foundation ($3M+)
Train about 20 Scientists
Will develop novel classes of applications
Colleges: engineering, business, urban planning
$30K/year stipend, international internships
IGERT Ph.D. program in
Computational Transportation Science
Transportation
Information Technology
capabilities for
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
Street address candidates: the street addresses within k meters (graph distance) from stay_position.
Juhong Liu, Ouri Wolfson, Huabei Yin, UIC
semantic location candidates
capabilities for
resourcequery C
resourcequery A
resource 1
resource 2
resource 3
resourcequery B
resource 4
resource 5
EnvironmentPda’s, cellphones, sensors, hotspots, vehicles, with shortrange
wireless
A central server does not necessarily exist
Shortrange wireless networks
wifi (100200 meters)
bluetooth (210, popular)
zigbee
Unlicensed spectrum (free)
High bandwidth
BandwidthPower/search tradeoff
Local query
Local database
“Floating database”
Resources of interest
in a limited geographic area
possibly for short time duration
Applications coexist
・・・
A Segment of the road network
Each vehicle communicates reports to other vehicles
using shortrange (e.g. 300 meters), unlicensed, wireless spectrum, e.g. 802.11
WiMaC paradigm: WiFidisseminate,
Match
Wifi/cellularrespond
media
media
Q
Z
Mproducer
Qproducer
(a) media and Q are initially disseminated. They collocate at Z.
Q
Z
Mproducer
Qproducer
(b) Z sends Q to Mproducer via cellular
media
Z
Mproducer
Qproducer
(c) Mproducer sends media to Qproducer via cellular
WiMaC Design Space
7b (media,meta,query)cell
6b (media,query)cell
WiFicellular
strategies
1 (media)
push

media
4b (media,meta)cell
2b (meta)cell
pull
3a (query)WiFi
7b (media,meta,query)cell
hy

MuM

cell
5b (media,query)cell
3b (query)cell
hy
6b (meta,query)cell

meta

cell
1 (media)
3a (query)WiFi
WiFionly
strategies
5a (media,query)WiFi
2a (meta)WiFi
4a (media,meta)WiFi
6a (meta,query)WiFi
7a (media,meta,query)WiFi
X Y: Strategy X dominates strategy Y
X Y: Strategy X weakly dominates strategy Y
simulations
dominance analysis
capabilities for
e
e
T’ =Comp(Comp(T, ε1 ), ε2) = Comp(T, ε2)
(associative)
Theorem: The DP algorithm is agingfriendly, whereas the optimal algorithm is not.
Abstraction of concepts from sensor data: extracting semantic locations from GPS traces.
Coping with imprecision and uncertainty: map matching.
Mixed environments: information in vehicular and other peertopeer networks.
Spatialtemporal data: compression.
Software tools: Databases with
spatial,
temporal,
uncertainty
capabilities for
Tracking,
analysis,
routing;
Offline
Find the overall route of a vehicle after the trip is over
Online Snapping
Real time, i.e. every 2 minutes (online frequency)
Determine the road segment on which the vehicle is currently located
Evaluation method
Edit Distance
The smallest number of insertions, deletions, and substitutions required to change the snapped route to the correct route
Correct matching percentage (OFFcorrect)
OFFcorrect = 100(1 – ed/n)
On average, weightbased alg. is correct up to 94% of the time, depending on the GPS sampling interval.
It is always superior to the straightforward closestblock snapping.
Correct matching decreases significantly when GPS sampling intervals are larger than 120 seconds
capabilities for
Time
3dTRAJECTORY
Present time
X
2dROUTE
Y
Future Trajectory: Motion plan
Past trajectory: GPS trace
11
R
sometime
always
10
10
11
Retrieve the objects that are in R sometime/always between 10 and 11am
SELECT o
FROM MOVINGOBJECTS
WHERE Sometime/Always(10,11)
inside (o, R)
possibly and definitely semantics based on
branching time
SELECT o
FROM MOVINGOBJECTS
WHEREPossibly/Definitely Inside (o, R)
R
definitely
possibly
uncertainty interval
Uncertain trajectories? trajectory model
boundary of the
set of all the PMCs (resembles a slanted cylinder)
Possibly – there exists a possible motion curve
Definitely  for all possible motion curves
probability density function trajectories?
database location
Uncertainty interval
Uncertainty in Language  Quantitative Approach
SELECT o
FROM MOVINGOBJECTS
WHERE Inside(o, R)
R
Answer: (RWW850, 0.58)
(ACW930, 0.75)
capabilities for
precision vs. resourceconsumption
(1 update = 2 units of imprecision)
Components:
• Costofdeviation
• Costofuncertainty
Current location = 15 + 5
proportional to length of period of time for which persist
14
15
Uncertainty = 10
10
20
actual location
database location
deviation = 1
capabilities for
lets me stop at a grocery store for 30 minutes
ALL_TRIPS( originvertex, destinationvertex)
Returns a nonmaterialized relation of all trips (sequences of vertices) between the origin and destination
SELECT *
FROM ALL_TRIPS(origin, destination)
WHERE
<WITH STOP VERTICES> (florist, grocery)
<WITH MODES> (Bus, boat)
<WITH CERTAINTY> (0.8)
<OPTIMIZE>) (time, distance, cost, #transfers),…)
SELECT *
FROM ALL_TRIPS(work, home) AS t
WITH STOP_VERTICES v1, v2
WITH CERTAINTY .75
WHERE "pharmacy" IN v1.facilities
AND "florist" IN v2.facilities
AND DURATION(v1) > 10min
AND DURATION(v2) > 10min
AND MODES(t)containedin {pedestrian, rail, bus}
MINIMIZE numberoftransfers
With a certainty greater than or equal to .75, ﬁnd a trip home from work that uses public transportation and visits a pharmacy and then a ﬂorist (spending at least 10 minutes at each) and has minimum number of transfers
From the set of trips that satisfy:
Select the optimal (according to single criteria)
Select *
From All_Trips (work, home) as t
WITH STOPVERTICES v1
WHERE pharmacy in v1.facilities, and
modes(t) containedin {train, bus}, and
begin(t) > 8pm, and
arrive(t) <10pm, and
duration(v1) > 10mins
WITH CERTAINTY 0.9
MINIMIZE NUMBEROFTRANSFERS
For each trip from work to home create a mapping from v1 to vertices of t:
t1…. (t1,map1) map1: v1 > UnionStation
t1…. (t1,map2) map2: v1 > CentralStation
t2…. (t2,map1) map1: …..
.
.
For each (ti, mapj) evaluate WHERE condition and if satisfied with CERTAINTY > 0.9 put pair in RESULT.
From RESULT return the pair that MINIMIZES the number of transfers.
∫S f(y1,z1,y2,z2)dy1dz1dy2dz2
SELECT *
FROM ALL_TRIPS(source, dest) AS t
WITH STOP VERTICES is empty
WHERE numberoftransfers (t) < k
OPTIMIZE is the minimization of the sum of some numeric edge attribute (e.g., length, duration)
Can be solved with
A. Lozano and G. Storchi. Shortest viable path algorithm in multimodal networks. In Transportation Research Part A: Policy and Practice, volume 35, pages 225–241, March 2001.
capabilities for