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Computational Transportation Science

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Title: Computational Transportation Science


1
Computational Transportation Science
  • Ouri Wolfson
  • Computer Science

2
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

3

IGERT Ph.D. program in Computational
Transportation Science
Information Technology
  • 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

4
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 

5
Introduction location information
  • Location information
  • Physical location
  • Provided by positioning systems
  • GPS (122.39, 239.11, 1120am)
  • Unreadable by users
  • Semantic location
  • Not directly provided by positioning systems
  • Dominicks grocery store, 1340 S. Canal St.
  • Dermatologists office
  • Home
  • Useful to users

6
Introduction problem statement
  • Physical location -gt semantic location
  • Devices
  • Outdoor positioning systems
  • Internet access
  • Application examples
  • context awareness of mobile devices
    (autocomplete)
  • Reminder applications
  • Total Recall by Gordon Bell

7
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)

8
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

9
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)
  • Users Feedback
  • Confirmed list
  • Denied list

10
Algorithm
11
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)

12
Step2 Street address candidates
  • Reverse Geocoding
  • Physical location (stay_position) -gt street
    address
  • Traditional geocoding method
  • Nearest street address
  • Incorrect result
  • Street address candidates the street addresses
    within k meters (graph distance) from
    stay_position.

13
Step3-semantic location candidates
  • Street address candidates -gt
  • semantic location candidates
  • Yellow pages
  • Such as switchboard.com
  • Profile
  • Calendar, Address Book, Phone Call List, Web Page
    List, Destination List, User's Feedback

14
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

15
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

16
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 

17
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

18
Environment
Pdas, 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
19
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 gt remote checkpoints
  • mobile collaborative work
  • tourist and location-based-services
  • Closest ATM

20
How to enable Mobile P2P applications?
  • Develop a platform for building them

21
Problems in data management
  • Query processing
  • Dissemination analysis
  • Participation incentives

22
Floating (Probe) car data
Periodically the ITA on a vehicle generates a
velocity report
Vehicle id IL391645
Average speed 45mph
Time 34945pm
Location
(12345.25, 4321.52)
Travel direction east

A Segment of the road network
23
P2P method
Each vehicle communicates reports to other
vehicles using short-range (e.g. 300 meters),
unlicensed, wireless spectrum, e.g. 802.11
24
Travel-time map

25
Multimedia info view/hear traffic conditions 1
mile ahead by a click on your smartphone.
26
Query Processing Strategies
  • WiMaC paradigm WiFi-disseminate,
  • Match
  • Wifi/cellular-respond

WiMaC Design Space
  • Evaluation criteria
  • Throughput
  • Response time
  • Wi-Fi communication volume
  • Cellular communication volume

27
Comparison Results
simulations
dominance analysis
28
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 

29
Data Compression -- Motivation
  • Tracking the movements of all vehicles in the USA
    needs approximately 4TB/day (GPS receivers
    sample a point every two seconds).

30
Trajectory Lossy-Compression
  • approximate a trajectory by another which is not
    farther than e.

e
e
31
Desiderata for Trajectory Compression
  • bounded error when answering queries on
    compressed trajectories.

32
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.

33
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.

34
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.

Where_at When_at Intersect Nearest_Neighbor Spatial Join
E2 No No No No Sound when the distance function D of join is metric E is weaker than D.
E3 No No No No Sound when the distance function D of join is metric E is weaker than D.
Eu Yes No Yes Yes Sound when the distance function D of join is metric E is weaker than D.
Et No Yes No No Sound when the distance function D of join is metric E is weaker than D.
35
Aging of Trajectories
  • Increase the tolerance e as time progresses
  • Aging friendliness property If e1?e2 then
  • T Comp(Comp(T, e1 ), e2) Comp(T, e2)
  • (associative)
  • Theorem The DP algorithm is aging-friendly,
    whereas the optimal algorithm is not.

36
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 

37
Matching Methods ---- Straightforward Snapping
  • A, B road segments
  • a, b GPS points
  • A, B road segments
  • a, b GPS points

38
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

39
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

40
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)

41
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

42
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 

43
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
44
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)

45
Why are traditional databases inappropriate to
manage trajectories?
  • Discrete vs. Continuous data
  • Operators of the language that are natural in the
    domain
  • Uncertainty

46
Uncertainty operators in spatial range queries
  • possibly and definitely semantics based on
  • branching time
  • SELECT o
  • FROM MOVING-OBJECTS
  • WHERE Possibly/Definitely Inside (o, R)

R
definitely
possibly
uncertainty interval
47
Uncertain trajectory model
48
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)

49
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

50
Uncertainty in Language - Quantitative Approach
51
Probabilistic Range Queries
  • SELECT o
  • FROM MOVING-OBJECTS
  • WHERE Inside(o, R)

R
Answer (RWW850, 0.58) (ACW930, 0.75)
52
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. 

53
Adapt Uncertainty to Update frequency
  • Tradeoff
  • precision vs. resource-consumption
  • Cost based approach
  • (1 update 2 units of imprecision)
  • Dynamic cost minimization

54
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
55
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. 

56
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

57
Example Graph
58
ALL_TRIPS
  • ALL_TRIPS( origin-vertex, destination-vertex)
  • Returns a non-materialized relation of all trips
    (sequences of vertices) between the origin and
    destination

59
General Query Structure
  • SELECT
  • FROM ALL_TRIPS(origin, destination)
  • WHERE
  • ltWITH STOP VERTICESgt (florist, grocery)
  • ltWITH MODESgt (Bus, boat)
  • ltWITH CERTAINTYgt (0.8)
  • ltOPTIMIZEgt) (time, distance, cost, transfers),)

60
Example Query
With a certainty greater than or equal to .75,
?nd a trip home from work that uses public
transportation and visits a pharmacy and then a
?orist (spending at least 10 minutes at each) and
has minimum number of 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) gt 10min
  • AND DURATION(v2) gt 10min
  • AND MODES(t)contained-in pedestrian, rail, bus
  • MINIMIZE number-of-transfers

61
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)

62
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) gt 8pm, and
  • arrive(t) lt10pm, and
  • duration(v1) gt 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 -gt
    UnionStation
  • t1. (t1,map2) map2 v1 -gt
    CentralStation
  • t2. (t2,map1) map1 ..
  • .
  • .
  • For each (ti, mapj) evaluate WHERE condition and
    if satisfied with CERTAINTY gt 0.9 put pair in
    RESULT.

63
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
  • y1gt8pm, y2z2lt10pm, y2-y1-z1gt10mins 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

64
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) lt 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 225241, March 2001.
65
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 

66
Ongoing work
  • Autonomous driving
  • Grand Cooperative-Driving Challenge
  • high precision maps
  • Database platform for intellidrive applications
    (nsf grant)
  • Competitive routing
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