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IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Trajectory Data Mining
Shaolin ZamanNafeez Abrar
Bangladesh University of Engineering and Technology
July 9, 2013
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
OutlineKeyWordWhat is our Data
Outline
1 Urban Computing with Taxi Trajectories
2 Where to find my next passenger
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
OutlineKeyWordWhat is our Data
KeyWord I
Urban Planning
Land Use planning
Transportation planning
Infrastructure planning
Ubiquitous Computing
Every person, vehicle, building used as computing component.
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
OutlineKeyWordWhat is our Data
KeyWord II
Taxicab
Vehicle for hire with a driver
GPS
space-based satellite navigation system that provides locationand time information
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
OutlineKeyWordWhat is our Data
What is our Data
Taxi TrajectorySequence of GPS points having
TimeLatitude and LongitudeState (Occupied/Non-occupied)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Urban Computing with Taxi Trajectories
Section 2
Urban Computing with Taxi Trajectories
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Main Objective
Traffic Modeling
model city-wide trafficconnection between region pairs
Flaw Detection
Detect Flawed Region pairRelationship between the flawed pairs
Real Evalution
Justify the effectiveness of proposed method
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Some Definition I
Taxi Trajectory
denoted as Tr
Time-ordered point sequence
Tr = p1 → p2 → · · · → pn
p = (lat, long , t, o)
Region
Map is partitioned by disjoint regions bounded by the majorroads.Denoted as r
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Some Definition II
Transition
Denoted as sDirectional transition from one region to another region.
Tr = p1 → p2 → · · · → pn
s : r1 → r2
pi : point of Tr falling in region r1
pj : point of Tr falling in region r2
where i < j
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Some Definition III
A transtion, s have the following attributes
Leaving Time, pj .tArriving Time, pi .tTravelled distance, d
d(pi , pj) =∑
i≤k<j
Dist(pk , pk+1)
speed of the transition
v =d(pi , pj)
|pi .t − pj .t|
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Some Definition IV
Region Pair
Attribute Notation
A pair of region (r1, r2)
Euclidian Distance CenDist(r1, r2)
Count of Transition |S |Expected Travelled Distance E (D)
Expected Speed E (V )
Ratio between actual travelleddistance and Central distance
θ
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Some Definition V
Region Pair
E (V ) =
∑si∈S si .v
|S |
E (D) =
∑si∈S si .d
|S |
θ =E (D)
CenDist(r1, r2)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Some Definition VI
<S ,E (V ), θ>
Represents the connectivity and traffic between two region
θ may be smaller than 1
Big θ means that people have to take a long route
Big S and small E (V ) implies heavy traffic
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Architecture of the model
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Traffic Modeling
1 Urban Computing with Taxi TrajectoriesTraffic ModelingFlaw DetectionEvalutionFuture Direction
2 Where to find my next passengerProbability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Traffic ModelingMap Partition
Partition the map into disjoint regions based on major roadsegments
Employ Connected Component Labeling (an image segmentmethod)
Why not based on road segments?
Region carries knowledge about people’s living and travel
Flaws represented by regions contribute to both land use andtransportation.
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Map Partition
Figure: Heat map of the partitioned regions in Beijing
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Traffic ModelingBuilding Region Matrix
Steps:
1 Temporal Partition
2 Transition Construction
3 Build the matrix
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Step 1: Temporal Partition
Partition the taxi trajectories according to
Work Day
Rest Day (weekend and public holiday)
Time Work day Rest day
Slot 1 7:00am-10:30am 9:00am-12:30pm
Slot 2 10:30am-4:00pm 12:30pm-7:30pm
Slot 3 4:00pm-7:30pm 7:30pm-9:00am
Slot 4 7:30pm-7:00am
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Step 2: Transition construction I
Pick out the trajectories with passengers
Project the trajectories onto the map
Construct transition between two regions
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Step 2: Transition construction II
Figure: Transfer a trajectory into transitions
Tr1 = r1 → r2
Tr1 = r1 → r2 → r3
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Step 3: Building Region matrix
Formulate matrix, M for each time slotEach item a(i , j) is a tuple representing a region pair whichhas three attributes <S ,E (V ), θ>If working days = x , rest days = y total number of matrix =4x+3y
Figure: Region Matrix
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw Detection
1 Urban Computing with Taxi TrajectoriesTraffic ModelingFlaw DetectionEvalutionFuture Direction
2 Where to find my next passengerProbability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw DetectionPrimary Steps of Detecting Flaw
Detect region pairs with big S, small V and big theta.
For each matrix, M, select the region paris with transitionsabove the average.
Find the skyline set from the selected region pairs.
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw DetectionWhat is Skyline
A point dominates another point if it is good or better in alldimension and at least better in one dimension
Skyline is the point which is not dominated by any other point.
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw DetectionExample of Skyline Detection
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw DetectionExample of Skyline
Three kinds of region pairs fall in Skyline:
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw DetectionPattern mining from Skyline
1 Formulate skyline graph
2 Mining frequent sub-graph pattern
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw DetectionFormulating Skyline Graph
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw DetectionMining frequent sub-graph pattern
g = subgraph
G = SkylineGraphcontainingg
G = CollectionofSkylineGraphs
Support(g) =|G |g ⊆ G ,G ∈ G |
numofdays
Support(g1 ⇒ g2) =|g1 ∪ g2|
numofdays
Confidence(g1 ⇒ g2) =|g1 ∪ g2||g1|
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Flaw DetectionMining Skyline Graph Example
Figure: Mining frequent skyline patterns
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Evalution
1 Urban Computing with Taxi TrajectoriesTraffic ModelingFlaw DetectionEvalutionFuture Direction
2 Where to find my next passengerProbability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
EvalutionReal Evaluation
Select some new urban planning (such as new road) andcheck if they reduced the flaw.
Check whether some flaws have been detected by city plannerin future.
[Two dataset of Trajectories of year 2009 and 2010 in Beijing havebeen used for evaluation]
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
EvalutionEvaluation Result
Some flawed planning occured in 2009 disappeared in 2010because of newly built roads
Number of defected regions have been increased in 2010
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Traffic ModelingFlaw DetectionEvalutionFuture Direction
Future Direction
Studying geographic features of a region
The purpose of people’s travel i.e shopping, sports, work etc.
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Where to find my next passenger
Section 3
Where to find my next passenger
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Purpose
Improve utilization of taxi
Reduce energy consumption
Recommend a Taxi driver some locations highly probable topick up passenger and maximize profit
Recommend people some locations highly probable to findvacant taxi
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
How does it fill the purpose
Detect parking places
Devise Probabilistic model to formulate time-dependent taxibehavior
Provide just-in-time recommendation to both taxi driver andpeople seeking taxi
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Some Definition I
Road Segment
Denoted as rDirected edge r .dirTwo terminal points : r .s and r .eTravel time : r .t
Route
Sequence of Road segmentsdenoted as
R : r1 → r2 → . . .→ rn where rk+1.s = rk .e (1 <= k < n)
R.s = r1.s and R.e = rn.e
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Some Definition II
State.
State Taxi Status
Occupied (O) Taxi is occupied by passenger
Cruising (C) Taxi is travelling without passenger
Parking (P) Taxi is waiting for passenger
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Some Definition III
Trip A taxi trip is a sub-trajectory which has a single state(C/O/P)
Figure: Taxi Trajectory and Taxi Trip
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
System Overview
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability Calculation
1 Urban Computing with Taxi TrajectoriesTraffic ModelingFlaw DetectionEvalutionFuture Direction
2 Where to find my next passengerProbability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Notations
Let , P : A parking placeR : r1 → r2 → . . .→ rn : A RouteARP : an action that a driver drives along R until finds passengertmax : maximum waiting time at PQuestion:
How likely driver will get passenger
If finds then what is the expected duration
What is the expected duration of next trip
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of picking up passenger I
Let, S = an event a driver succeeds in getting passenger if takesaction ARP
t(max) = maximum waiting time at Parking place
S =n+1⋃i=1
Si where i = 1, 2, . . . , n and current timeT0 (1)
Sn+1 = event that driver picks passenger at Parking place P (2)
The probability that taxi gets passenger at road segment ri andtime T0 + ti is:
pi = Pr(C O|ri ,T0 + ti ) (3)
p∗ = Pr(P (0,t(max)] O|T0 + tn) (4)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of picking up passenger II
The probability that a taxi succeeds at picking passenger at P is
Pr(Si ) =
p1 i = 1
Pi∏i−1
j=1(1− pj) i = 2, 3, . . . , n,
p∗∏n
j=1(1− pj) i = n + 1
(5)
Pr(S) = 1− Pr(n+1⋃i=1
Si )
= 1− (1− p∗)n∏
j=1
(1− pj)
(6)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of picking up passenger III
Let random variable T = duration from current time T0 tobeginning of next trip
T = TR + TP{TP = 0, if TR ≤ tn
TR = tn, if TP > 0(7)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of picking up passenger IV
The probability mass function
Pr(TR = ti |S) =Pr(TR = ti , S)
Pr(S)
=
{Pr(Si )Pr(S) i = 1, 2, . . . , n − 1,Pr(Sn)+Pr(Sn+1)
Pr(S) , i = n
(8)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of picking up passenger V
Conditional Expectation of TR is
E [TR |S ] =n∑
i=1
Pr(TR = ti |S)
=1
Pr(S)(
n∑i=1
tiPr(Si ) + tnPr(Sn+1)
(9)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of picking up passenger VI
Let W = event that driver waits at PSo probability of waiting at parking place
Pr(W ) =n∏
j=1
(1− pj)
Now let we break TP i.e (0, tmax) into m buckets.
t0 = 0
4t∗ =tmax
2mt∗j = (2j − 1)4t∗, j = 1, 2, . . . ,m,
(10)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of picking up passenger VII
So, probabiilty that waiting time TP belongs to j − th bucket is
pj∗ = Pr(P t∗j −4t∗,t∗j +4t∗ O|TP > 0,T0 + tn) (11)
p∗ =m∑j=1
pj∗ (12)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of picking up passenger VIII
The conditioanl Probability The conditional expectation of TP is
E [TP |S ] =Pr(W )
Pr(S)
m∑j=1
pj∗t∗j
The conditional Expectation of TR is
E [TR |S ] =1
Pr(S)(
n∑i=1
tiPr(Si ) + tnPr(Sn+1)
The conditional Expectation of T is
E [T |S ] = E [TP |S ] + E [TR |S ]
=
∑ni=1 tiPr(Si ) + tnPr(Sn+1) + Pr(W )
∑mj=1 pj
∗t∗j
Pr(S)
(13)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of travel time of Next Trip I
Let
DN = Distance of next trip if driver takes ARP conditioned S happens
qji = Probability that dj−1 < DN ≤ dj when Si happens
dmax = maximum distance of next trip
i = 1, 2, . . . , n + 1 and current time T0
qji = Pr(dj −4d < DN < dj +4d |Si ,T0 + ti )
Now, if distance is splitted into s buckets as earlier then
d0 = 0
4d∗ =dmax
2sd∗j = (2j − 1)4d∗, j = 1, 2, . . . , s,
(14)Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of travel time of Next Trip II
The conditional probability that DN is dj is
Pr(DN = dj |S) =n+1∑i=1
Pr(Si )qji
Pr(S)(15)
The conditional expected distance of next trip is
E [DN |S ] =1
Pr(S)
s∑j=1
(dj
n+1∑i=1
Pr(Si )qji )
=1
Pr(S)
n+1∑i=1
Pr(Si )(s∑
j=1
djqji )
(16)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability of getting taxi for passenger I
Let Pr(C ; r |t) : Probability of a vacant taxi at road segment r attime t So the suggested road segment for a passenger will be
r = argmaxr∈σPr(C ; r |t)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Parking Place Detection
There are three steps
Candidates detection
Filtering
Parking place clustering
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Candidates detection I
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Candidates detection II
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Filtering I
Why Candidates can be generated due to traffic jam.
How Design supervised model to detect real parking places
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Filtering II
Filtering Features
Spatial-Temporal features
Minimum bound ratioAverage DistanceCenter DistanceParking DurationLast Speed
POI feature
Point of interest i.e shopping mall, theaters etc.
Collaborative feature
Historical state of the candidate sets.
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Parking Place Clustering
Parking place is detected for each single trajectory
Use density-based clustering method (OPTICS) to discoverthe parking places
This method is used because clustered region may havearbitrary shape
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Time-Dependent Probablities
1 Urban Computing with Taxi TrajectoriesTraffic ModelingFlaw DetectionEvalutionFuture Direction
2 Where to find my next passengerProbability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Time-Dependent Probablities
Assumed that probability is stable during interval [t, t +4t]
Partion-and-group approach is developed for computing theprobability
Day is partitioned into K small time intervals of width τ
So k − th interval is
Ik = [(k − 1)τ, kτ ], k = 1, 2, . . . ,K
So we learn the probability of each Ik offline
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability w.r.t Road Segments I
Conduct road segment clustering to integrate road segmentswith similar features
Let,
r a road segmentr̃ : The cluster r belongs to#k(C ; r̃): The number of trips with state C during Ik on allsegments of cluster#k(O; r̃) : The number of trips with state O during Ik on allsegments of cluster
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability w.r.t Road Segments II
Probability that there exists taxi cruising on r at time t is
Pr(C ; r |t) =
∑b(t+4t)/τck=bt/τc #k(C (̃r)∑b(t+4t)/τc
k=bt/τc∑
r̃∈r̃ (#k(C ; r̃) + #k(O; r̃))(17)
Pr(C O; r |t) =
∑b(t+4t)/τck=bt/τc #k(C O; (̃r)∑b(t+4t)/τck=bt/τc
∑r̃∈r̃ (#k(C ; r̃))
(18)
Pr(da < DN ≤ db|r , t) =
∑b(t+4t)/τck=bt/τc #k(da, db; r̃)∑b(t+4t)/τck=bt/τc #k(0, dmax ; r̃)
(19)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Probability w.r.t Road Segments I
Pr(P (ta,tb] O|Tp > 0, t) =∑b(t+4t)/τck=bt/τc #k(ta, tb,P O; P)∑b(t+4t)/τc
k=bt/τc (#k(P O; P) + #k(P C ; P))
(20)
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Online Recommendation
1 Urban Computing with Taxi TrajectoriesTraffic ModelingFlaw DetectionEvalutionFuture Direction
2 Where to find my next passengerProbability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Taxi Recommender
Retrieve set of parking places
For each parking place, Generate the Route R with minimumPr(SR)
Recompute Pr(S) according to the query time
Rank the parking places
Recommend top-k parking places
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Passenger Recommender
Perform query for obtaining region within walking distance
If region has parking places, recommend k nearest parkingplaces
Otherwise recommend road segments with k largest probablity(Pr(C ; r |t)) of having vacent taxi
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Evaluation
1 Urban Computing with Taxi TrajectoriesTraffic ModelingFlaw DetectionEvalutionFuture Direction
2 Where to find my next passengerProbability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
EvaluationEvaluation Dataset
Road network:
road network of Beijing106,579 road nodes141,380 road segments
Trajectory:
over 12,000 taxisperiod of 110 days.total 20 million trips, among which 46% are occupied trips and53% are non-occupied
use 70 days data to build our system and evaluate the methodusing the rest (40 days) data.
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Evaluation IParking Place Detection
Ask three local people to label 1000 parking candidates
Features Precision Recall
Spatial 0.695 0.670
Spatial+POI 0.716 0.696
Spatial+POI+Collaborative 0.725 0.706
Spatial+POI+Collaborative+Temporal 0.909 0.889
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Evaluation IIParking Place Detection
Conduct survey of more than 20 users for submitting knownparking places
received 70 Parking places uniformly distributed in Beijing
Recall reaches to 81%
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Evaluation IStatistical Learning
Calculate overall time-dependent distribution for both Parkingplaces & road segments.
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Evaluation IOnline Recommendation
Extract high-profit drivers
Segment them to O/C/P trips
Before each O/P trip, randomly select 10 points
For each query point top-k parking places are recommended
The recommendation is evaluated based on real case.
Precision of recommendation reaches to 67
Shaolin Nafeez Trajectory Data Mining
IntroductionUrban Computing with Taxi Trajectories
Where to find my next passenger
Probability CalculationParking Place DetectionTime-Dependent ProbablitiesOnline RecommendationEvaluationFuture Direction
Future Direction
Incorporate real-time Traffic information to provide betterroutes toward the parking places
Shaolin Nafeez Trajectory Data Mining