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Computational Transportation Science. Ouri Wolfson Computer Science. Vision. Take advantage of advances in Wireless communication (communicate) Mobile/static Sensor technologies (integrate) Geospatial-temporal information management (analyze) To address transportation problems Congestion

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computational transportation science

Computational Transportation Science

Ouri Wolfson

Computer Science

vision
Vision
  • Take advantage of advances in
    • Wireless communication (communicate)
    • Mobile/static Sensor technologies (integrate)
    • Geospatial-temporal information management (analyze)
  • To address transportation problems
    • Congestion
    • Safety
    • Mobility
    • Energy
    • Environmental
slide3
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

outline
Outline
  • 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 peer-to-peer networks. 
  • Managing spatial-temporal data: compression.
  • Software tools: Databases with
    • spatial,
    • temporal,
    • uncertainty

capabilities for

    • Tracking,
    • analysis,
    • routing; 
introduction location information
Introduction – location information
  • Location information
    • Physical location
      • Provided by positioning systems
        • GPS: (122.39, 239.11, 11:20am)
      • Unreadable by users
    • Semantic location
      • Not directly provided by positioning systems
        • Dominick’s grocery store, 1340 S. Canal St.
        • Dermatologist’s office
        • Home
      • Useful to users
introduction problem statement
Introduction – problem statement
  • Physical location -> semantic location
  • Devices
    • Outdoor positioning systems
    • Internet access
  • Application examples:
    • context awareness of mobile devices (autocomplete)
    • Reminder applications
    • “Total Recall” by Gordon Bell
main input and output
Main Input and Output
  • Input: Trajectory: T =(x1, y1, t1), (x2, y2, t2), …, (xn, yn, tn)
  • Output 1: Semantic location
    • Location name (BestBuy)
    • Semantic category
      • Business type (electronics store),
      • office
      • home
    • Street address
  • Output 2: Semantic location log file
    • (date, begin_time, end_time, semantic location)
online and offline versions
Online and offline versions
  • Online: determine the current location
    • On mobile device
    • Based on incomplete trip trajectory
  • Offline: Determine multiple past locations
    • Based on complete trip trajectory
auxiliary inputs
Auxiliary inputs
  • Profile
    • Calendar – (event date, semantic location)
    • Address Book – (phone number, semantic location)
    • Phone Call List – (calling date, semantic location)
    • Web Page List - (visiting date, semantic location)
    • Destination List – (searching date, address)
    • User’s Feedback
      • Confirmed list
      • Denied list
step1 stay extraction
Step1 - Stay extraction
  • Stay
    • Loss of GPS signal
    • To spend at least min_time in an area with the diameter no larger than d.
  • (stay_position, date, stay_start, stay_end)

Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

step2 street address candidates
Step2 – Street address candidates
  • Reverse Geocoding
    • Physical location (stay_position) -> street address
  • Traditional geocoding method
    • Nearest street address
    • Incorrect result

Street address candidates: the street addresses within k meters (graph distance) from stay_position.

Juhong Liu, Ouri Wolfson, Huabei Yin, UIC

step3 semantic location candidates
Step3-semantic location candidates
  • Street address candidates ->

semantic location candidates

    • Yellow pages
      • Such as switchboard.com
    • Profile
      • Calendar, Address Book, Phone Call List, Web Page List, Destination List, User\'s Feedback
at end of step 3 a set of semantic location candidates
At end of step 3: A set of Semantic Location candidates
  • Semantic location
    • Location name (BestBuy)
    • Semantic category
      • Business type (electronics store; theater),
      • office
      • home
    • Street address
step4 three utilities calculation
Step4- three utilities calculation
  • For each semantic location SL in set of candidates compute:
    • Semantic category (SC) utility: likelihood of semantic category, given semantic log (history)
    • Street address (SA) utility: likelihood the street address, given the stay location
    • Profile (P) utility: Likelihood of SL, given profile P
outline1
Outline
  • 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 peer-to-peer networks.
  • Spatial-temporal data: compression.
  • Software tools: Databases with
    • spatial,
    • temporal,
    • uncertainty

capabilities for

    • Tracking,
    • analysis,
    • routing; 
problem
Problem
  • Most information systems are client/server
  • Nearby mobile devices are inaccessible
    • Parking slot info
    • Video of road construction
    • Malfunctioning brakelight
    • Taxi cab
    • Ride-share opportunity
environment

resource 8

resource-query C

resource-query A

resource 1

resource 2

resource 3

resource-query B

resource 4

resource 5

Environment

Pda’s, cell-phones, sensors, hotspots, vehicles, with short-range

wireless

A central server does not necessarily exist

Short-range wireless networks

wi-fi (100-200 meters)

bluetooth (2-10, popular)

zigbee

Unlicensed spectrum (free)

High bandwidth

Bandwidth-Power/search tradeoff

Local query

Local database

“Floating database”

Resources of interest

in a limited geographic area

possibly for short time duration

Applications coexist

mobile local search applications
Mobile Local Search: applications
  • social networking (wearable website)
    • Personal profile of interest at a convention
    • Singles matchmaking
    • Games
    • Reminder
  • mobile advertising (coupons, rfid-tag info)
    • Sale on an item of interest at mall
    • Music-file exchange
  • Transportation
  • emergency response
    • Search for victims in a rubble
  • military
    • Sighting of insurgent in downtown Mosul in last hour
  • asset management and tracking
    • Sensors on containers exchange security information => remote checkpoints
  • mobile collaborative work
  • tourist and location-based-services
    • Closest ATM
how to enable mobile p2p applications
How to enable Mobile P2P applications?
  • Develop a platform for building them
problems in data management
Problems in data management
  • Query processing
  • Dissemination analysis
  • Participation incentives
floating probe car data
Floating (Probe) car data
  • Periodically the ITA on a vehicle generates
  • a velocity report:
    • Vehicle id IL391645
    • Average speed 45mph
    • Time 3:49:45pm
    • Location (12345.25, 4321.52)
    • Travel direction east

・・・

A Segment of the road network

p2p method
P2P method

Each vehicle communicates reports to other vehicles

using short-range (e.g. 300 meters), unlicensed, wireless spectrum, e.g. 802.11

query processing strategies
Query Processing Strategies

WiMaC paradigm: WiFi-disseminate,

Match

Wifi/cellular-respond

media

media

Q

Z

M-producer

Q-producer

(a) media and Q are initially disseminated. They collocate at Z.

Q

Z

M-producer

Q-producer

(b) Z sends Q to M-producer via cellular

media

Z

M-producer

Q-producer

(c) M-producer sends media to Q-producer via cellular

WiMaC Design Space

  • Evaluation criteria:
  • Throughput
  • Response time
  • Wi-Fi communication volume
  • Cellular communication volume
comparison results
Comparison Results

7b (media,meta,query)-cell

6b (media,query)-cell

WiFi-cellular

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

WiFi-only

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

outline2
Outline
  • 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 peer-to-peer networks. 
  • Spatial-temporal data: compression
  • Software tools: Databases with
    • spatial,
    • temporal,
    • uncertainty

capabilities for

    • Tracking,
    • analysis,
    • routing; 
data compression motivation
Data Compression -- Motivation
  • Tracking the movements of all vehicles in the USA needs approximately 4TB/day (GPS receivers sample a point every two seconds).
trajectory lossy compression
Trajectory Lossy-Compression
  • approximate a trajectory by another which is not farther than ε.

e

e

desiderata for trajectory compression
Desiderata for Trajectory Compression
  • bounded error when answering queries on compressed trajectories.
relational oriented queries
Relational-Oriented Queries
  • Point queries:
    • Where (T,t): where is the moving object with trajectory T at time t
    • When (T,x,y): when is the moving object with trajectory T at location (x,y)
  • Range queries (R,t1,t2,O): retrieve the moving objects (i.e. trajectories) of O that are in region R between times t1 and t2.
  • Nearest neighbor (t,T,O): retrieve the object of O that is closest to trajectory T at time t
  • Join queries (O,d): Retrieve the pairs of objects of O that are within distance d.
distance functions
Distance Functions
  • The distance functions considered are:
    • E3: 3D Euclidean distance.
    • E2: Euclidean distance on 2D projection of a trajectory
    • Eu: the Euclidean distance of two trajectory points with same time.
    • Et: It is the time distance of two trajectory points with same location or closest Euclidean distance.
  • #(T\'2) ≤ #(T\'3) ≤ #(T\'u), which is also verified by experimental saving comparison.
soundness of distance functions
Soundness of Distance Functions
  • Soundness: bound on the error when answering spatio-temporal queries on compressed trajectories.
  • The appropriate distance function depends on the type of queries expected on the database of compressed trajectories.
    • If all spatio-temporal queries are expected, then Eu and Et should be used.
    • If only where_at, intersect, and nearest_neighbor queries are expected, then the Eu distance should be used.
aging of trajectories
Aging of Trajectories
  • Increase the tolerance ε as time progresses
  • Aging friendliness property: If ε1ε2 then

T’ =Comp(Comp(T, ε1 ), ε2) = Comp(T, ε2)

(associative)

Theorem: The DP algorithm is aging-friendly, whereas the optimal algorithm is not.

outline3
Outline

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 peer-to-peer networks. 

Spatial-temporal data: compression.

Software tools: Databases with

spatial,

temporal,

uncertainty

capabilities for

Tracking,

analysis,

routing; 

matching methods straightforward snapping
Matching Methods ---- Straightforward Snapping
  • A, B: road segments
  • a, b: GPS points
  • A, B: road segments
  • a, b: GPS points
weight based matching
Weight-based Matching
  • Compute the weight of each road segment (block)
  • Compute the shortest weight path between the start and the end GPS points as the route of the moving object
matching variants
Matching Variants

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

experiments offline
Experiments ---- Offline

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)

results
Results

On average, weight-based alg. is correct up to 94% of the time, depending on the GPS sampling interval.

It is always superior to the straightforward closest-block snapping.

Correct matching decreases significantly when GPS sampling intervals are larger than 120 seconds

outline4
Outline
  • 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 peer-to-peer networks. 
  • Spatial-temporal data: compression.
  • Software tools: Databases with
    • spatial,
    • temporal,
    • uncertainty

capabilities for

    • Tracking,
    • analysis,
    • routing; 
basic element of a moving objects database a trajectory
Basic element of a moving objects database: a trajectory

Time

3d-TRAJECTORY

Present time

X

2d-ROUTE

Y

Future Trajectory: Motion plan

Past trajectory: GPS trace

why are traditional databases inappropriate to manage trajectories
Why are traditional databases inappropriate to manage trajectories?

11

R

sometime

always

10

10

11

Retrieve the objects that are in R sometime/always between 10 and 11am

SELECT o

FROM MOVING-OBJECTS

WHERE Sometime/Always(10,11)

inside (o, R)

why are traditional databases inappropriate to manage trajectories1
Why are traditional databases inappropriate to manage trajectories?
  • Discrete vs. Continuous data
  • Operators of the language that are natural in the domain
  • Uncertainty
uncertainty operators in spatial range queries
Uncertainty operators in spatial range queries

possibly and definitely semantics based on

branching time

SELECT o

FROM MOVING-OBJECTS

WHEREPossibly/Definitely Inside (o, R)

R

definitely

possibly

uncertainty interval

possible motion curve pmc and trajectory volume tv
Possible Motion Curve (PMC) and Trajectory Volume (TV)
  • PMC is a continuous function from Time to 2D
  • TV is the

boundary of the

set of all the PMCs (resembles a slanted cylinder)

predicates in spatial range queries
Predicates in spatial range queries

Possibly – there exists a possible motion curve

Definitely -- for all possible motion curves

  • possibly-sometime = sometime-possibly
  • possibly-always
  • always-possibly
  • definitely-always = always-definitely
  • definitely-sometime
  • sometime-definitely
slide50

probability density function

database location

Uncertainty interval

Uncertainty in Language - Quantitative Approach

probabilistic range queries
Probabilistic Range Queries

SELECT o

FROM MOVING-OBJECTS

WHERE Inside(o, R)

R

Answer: (RWW850, 0.58)

(ACW930, 0.75)

outline5
Outline
  • Databases with
    • spatial,
    • temporal,
    • uncertainty

capabilities for

    • Tracking,
    • analysis,
    • routing; 
  • compression of spatial-temporal data; 
  • query and dissemination of (possibly multimedia) information in vehicular and other peer-to-peer networks; 
  • extracting semantic locations and activity knowledge from GPS traces;
  • map matching. 
adapt uncertainty to update frequency
Adapt Uncertainty to Update frequency
  • Tradeoff :

precision vs. resource-consumption

  • Cost based approach

(1 update = 2 units of imprecision)

  • Dynamic cost minimization
information cost of a trip
Information-Cost of a trip

Components:

  • Cost-of-location-update
  • Cost-of-imprecision

• Cost-of-deviation

• Cost-of-uncertainty

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

outline6
Outline
  • Databases with
    • spatial,
    • temporal,
    • uncertainty

capabilities for

    • Tracking,
    • analysis,
    • routing;
  • compression of spatial-temporal data; 
  • Databases in vehicular and other peer-to-peer networks; 
  • extracting semantic locations from GPS traces;
  • map matching. 
example queries
Example queries
  • Find a multimodal route that will get me home by 7pm with 90% certainty.
  • Find a route that will get me home by 7pm with 90% certainty, and

lets me stop at a grocery store for 30 minutes

all trips
ALL_TRIPS

ALL_TRIPS( origin-vertex, destination-vertex)

Returns a non-materialized relation of all trips (sequences of vertices) between the origin and destination

general query structure
General Query Structure

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),…)

example query
Example Query

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)contained-in {pedestrian, rail, bus}

MINIMIZE number-of-transfers

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

query semantics
Query Semantics

From the set of trips that satisfy:

  • the non-temporal constraints, and
  • the temporal constraints with the required certainty (remember probabilistic travel times)

Select the optimal (according to single criteria)

semantics
Semantics

Select *

From All_Trips (work, home) as t

WITH STOP-VERTICES v1

WHERE pharmacy in v1.facilities, and

modes(t) contained-in {train, bus}, and

begin(t) > 8pm, and

arrive(t) <10pm, and

duration(v1) > 10mins

WITH CERTAINTY 0.9

MINIMIZE NUMBER-OF-TRANSFERS

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.

evaluation of where condition w on t i map j
Evaluation of WHERE condition W on (ti,mapj)
  • Evaluate non-temporal conditions and if W = ‘true’ or ‘false’ , then done.
  • Otherwise split trip into legs: L1, v1, L2
  • L1 has departure y1 and duration z1
  • L2 has departure y2 and duration z2
  • y1>8pm, y2+z2<10pm, y2-y1-z1>10mins defines a region S in R4.
  • Assume that we know the joint density function f(y1,z1,y2,z2).
  • Then we compute the probability of W as the integral

∫S f(y1,z1,y2,z2)dy1dz1dy2dz2

plug and play query processing
Plug-and-play Query Processing
  • Based on a framework
    • Algorithms are chosen based on the structure of the query

SELECT *

FROM ALL_TRIPS(source, dest) AS t

WITH STOP VERTICES is empty

WHERE number-of-transfers (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.

conclusion
Conclusion
  • 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 peer-to-peer networks. 
  • Managing spatial-temporal data: compression.
  • Software tools: Databases with
    • spatial,
    • temporal,
    • uncertainty

capabilities for

    • Tracking,
    • analysis,
    • routing; 
ongoing work
Ongoing work
  • Autonomous driving
    • Grand Cooperative-Driving Challenge
    • high precision maps
  • Database platform for intellidrive applications (nsf grant)
  • Competitive routing
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