Outline by linxiaoqin

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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
  –   Safety
  –   Mobility
  –   Energy
  –   Environmental
     IGERT Ph.D. program in
 Computational Transportation Science
Information Technology             Transportation




• 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
                      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

• 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
• 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
• 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: determine the current location
  – On mobile device
  – Based on incomplete trip trajectory


• Offline: Determine multiple past locations
  – Based on complete trip trajectory
                    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
                       Algorithm
                         Step1:Extract stays                     GPS data



                          Step2: Get street
  Map
                         address candidates



 Profile                 Step3: Get semantic                  Yellow pages
                          location candidates



             Step4.3            Step4.2           Step4.1
            Calculate         Calculate SA       Calculate
           profile utility        utility        SC utility



                             Step5: Decide the            Semantic
                             semantic location             Log file


User confirmation
              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           11                   2/2/2012
Wolfson, Huabei Yin,
 Step2 – Street address candidates
• Reverse Geocoding
     – Physical location
       (stay_position) -> street
       address
• Traditional geocoding                       Building B
  method                                   850 S. Halsted St

     – Nearest street address                  E


     – Incorrect result

     Street address candidates:
        the street addresses within
        k meters (graph distance)
        from      stay_position.
Juhong Liu, Ouri                      12                       2/2/2012
Wolfson, Huabei Yin,
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
• Semantic location
  – Location name (BestBuy)
  – Semantic category
    • Business type (electronics store; theater),
    • office
    • home
  – Street address
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
                      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
• 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
 Pda‟s, cell-phones, sensors, hotspots, vehicles, with short-range
 wireless
    A central server does not necessarily exist


                                                                 Short-range wireless networks
                              resource 8
                                                                   wi-fi (100-200 meters)
                                                                   bluetooth (2-10, popular)
                                           resource-query C        zigbee

Local query      resource-query A                                Unlicensed spectrum (free)
                 resource 1
Local database   resource 2                   resource-query B
                 resource 3                   resource 4
                                              resource 5         High bandwidth


“Floating database”                                              Bandwidth-Power/search tradeoff
Resources of interest
   in a limited geographic area
   possibly for short time duration
Applications coexist
     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?


• Develop a platform for building them
  Problems in data management

• Query processing
• Dissemination analysis
• Participation incentives
          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
Each vehicle communicates reports to other vehicles
using short-range (e.g. 300 meters), unlicensed, wireless spectrum,
e.g. 802.11


   2                            1                        1
                           3    2                        3
   5
                           6
                                4                        4
                       C        5   B                C   6
       B
                   A                             A

              1                            1
              4                            4

             (a)                           (b)
Travel-time map
 Multimedia info: view/hear traffic
conditions 1 mile ahead by a click
       on your smartphone.
       Query Processing Strategies
WiMaC paradigm: WiFi-disseminate,
                                                                             WiMaC Design Space
               Match
               Wifi/cellular-respond
               media            media              Q



    M-producer                           Z             Q-producer

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


                         Q



    M-producer                           Z             Q-producer

              (b) Z sends Q to M-producer via cellular
                                                                      Evaluation criteria:
                                media
                                                                      • Throughput
                                                                      • Response time
                                                                      • Wi-Fi communication volume
     M-producer                            Z             Q-producer   • Cellular communication volume
        (c) M-producer sends media to Q-producer via cellular
                            Comparison Results
                                            simulations

                          dominance analysis
                                                                                                                  1 (media)
                                                                                                                  push -media
                                                                                                                  3a (query)-WiFi
                                                                                                                  pull
                                                                                                                  7b MuM -cell
                                                                                                                  hy-(media,meta,query)-cell
                    7b (media,meta,query)-cell        6b (media,query)-cell                                       hy-(meta,query)-cell
                                                                                                                  6b meta -cell

WiFi-cellular
                         4b (media,meta)-cell                2b (meta)-cell
 strategies


                    5b (media,query)-cell             3b (query)-cell




                        1 (media)                          3a (query)-WiFi
WiFi-only
strategies
                              5a (media,query)-WiFi              2a (meta)-WiFi
                                                                                                      10




                                                                                  answer throughput
                                                                                                       8
                      4a (media,meta)-WiFi
                                                           6a (meta,query)-WiFi                        6
                                                                                                       4
                7a (media,meta,query)-WiFi                                                             2
                                                                                                       0
                                                                                                              1%        12.5%       25%     37.5%       50%

                X         Y: Strategy X dominates strategy Y                                                                penetration ratio

                X         Y: Strategy X weakly dominates strategy Y                                   1 (query)                       3a (query)-WiFI
                                                                                                      7b (media,meta,query)-cell
                      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




– Tracking the movements of all vehicles in the
  USA needs approximately 4TB/day (GPS
  receivers sample a point every two seconds).
 Trajectory Lossy-Compression

• approximate a trajectory by another
  which is not farther than ε.



         e

                e
Desiderata for Trajectory Compression



• bounded error when answering
  queries on compressed trajectories.
        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
• 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: bound on the error when answering spatio-
  temporal queries on compressed trajectories.
     Where_a When_a Intersect Nearest_ Spatial Join
     t       t                Neighbor
E2       No          No         No          No       Sound when
                                                     (a)   the distance
E3       No          No         No          No             function D of join is
Eu      Yes          No         Yes         Yes            metric
                                                     (b)   E is weaker than D.
 Et      No       Yes        No         No
• 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


• 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.
                      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
        B               B
                                a      b
    a
                        A
                  A


• A, B: road segments       • A, B: road segments
• a, b: GPS points          • a, b: GPS points
      Weight-based Matching
                                               t
• Compute the weight of each                              trajectroy
                                                                    p8, t8
  road segment (block)
          tj
              ( g traj (t )  g arc (t ))dt
                                                        p7, t7
                                                                      p6 , t 6

   W
        ti                                         t6                                     b5, t'5

                     | t j  ti |
                                                                           p5 , t 5
                                                                 p4, t4                b4, t'4        arc
                                                                                      b3, t'3       polyline
                                                            p3, t3                     b2, t'2

• Compute the shortest weight                      t2                 p2, t2           b1, t'1
  path between the start and the                         p1, t1

  end GPS points as the route                                                                                  x

  of the moving object          y
        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

• 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
– 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
                      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
              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?
                                              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 trajectories?
• Discrete vs. Continuous data

• Operators of the language that are natural
  in the domain

• Uncertainty
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
Uncertain trajectory model
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
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
Uncertainty in Language -
 Quantitative Approach
                              probability density
                              function
     database location

       Uncertainty interval
Probabilistic Range Queries

SELECT   o
FROM     MOVING-OBJECTS
WHERE    Inside(o, R)


         R

 Answer: (RWW850, 0.58)
         (ACW930, 0.75)
                      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



• Tradeoff :
   precision vs. resource-consumption
• Cost based approach
  (1 update = 2 units of imprecision)
• Dynamic cost minimization
     Information-Cost of a trip
Components:
• Cost-of-location-update
• Cost-of-imprecision
   • Cost-of-deviation                proportional to length
                                      of period of time for
   • Cost-of-uncertainty              which persist
Current location = 15 + 5

                    14      15
     10                      Uncertainty = 1020

          actual                   database
          location deviation = 1   location
                        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
• 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
Example Graph
             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


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
 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

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
            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
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 (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
• 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
   Can be                WHERE number-of-transfers (t) < k
 solved with             OPTIMIZE is the minimization of the sum of some
                         numeric edge attribute (e.g., length, duration)


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

								
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