Post on 06-Apr-2018
8/3/2019 Traffic Dynamics Intelligence
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Lasisi Saheed Abiola
Msc SAI 2011
CSC 7502: Ambient Intelligence and Pervasive Systems Course
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1. Introduction
2. Motivation and Goals
3. Overview
4. Implementation
5. Evaluation
6. Related Works
7. Conclusion
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Roads
GPS
Vehicles
Driver
Mobiles
TrafficTraffic
Traffic DynamicsIntelligence
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Roads
GPS
Vehicles
Driver
Mobiles
Traffic Dynamics Intelligence :
Collective Intelligence derived from:
Traffic flow and Patterns
Driver Behaviors
Weather
Roads
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Roads
GPS
Vehicles
Driver
Mobiles
Urban Vehicular traffic:
Massive
Not expected to reduce
Going Forward:
It is important to derive
intelligence from this
occurrence as well as propose
solutions to make this
nightmare a wonder. 5
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Roads
GPS
Vehicles
Driver
Mobiles
Cloud-based system for
computing realistically fast
routes for users engaging the
use of:
Taxi GPS Data
Environmental data from Internet
Sources
Aggregation and mining of data from
sources.
Discovery of knowledge and
Intelligence 6
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Roads
GPS
Vehicles
Driver
Mobiles
Aspects of the System Study:
Utilization of taxi drivers and
traffic patterns intelligence .
Inferring future traffic conditions
using mth-order markov model.
Utilization of a real datatset of
33,000 taxis over3 months
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Roads
GPS
Vehicles
Driver
Mobiles
Preliminaries:
Taxi Trajectory: Sequence of GPS point pertaining to a trip.
TR
P1
P2
P3
PN
p: Longitude, latitude and Timestamp
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GPS
Vehicles
Preliminaries(2):
Road Segment (r): a directed edge that is associated with :
direction symbol (r.dir)
two terminal points (r.s, r.e)
list of intermediate points describing the segment
Road Segment (R):A Route R is a set of consecutive roadsegments.
R: r1
r2
.-rn
r.e
r.s
r.e
r.s
r.k
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GPS
Vehicles
Knowledge Discovery :
Offline Mining: mining of accumulated historical data from:
Taxis Trajectories
Weather Condition Records
Runs seldom (Monthly)
Online Inference
Calculation of Real time traffic on landmark edges
Inference of future traffic conditions
Runs every 10-20 minutes
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GPS
Vehicles
Service Provision:
User query (start point, destination, departure time, custom factor)
Route computation
Computed driving route and travel times retrieval
GPS phone records a GPS trajectory
Computation of new custom factor
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GPS
Vehicles
Architecture:
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GPS
Vehicles
Offline Mining:
Modeling Taxi Trajectories:
Taxi Usually report their location every 2-5 minutes
This leads to a degree of uncertaintySpeed Pattern cannot be ascertained
This is not goodenough.
1. Partition GPSlog
2. Employ IVMMAlgorithm
3. Detect top-kfrequentlytraversedlandmarks
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GPS
Vehicles
Matched taxi
Trajectories
Detected
LandmarksLandmark Graph
This aims to solve the stuck taxi problem. 15
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GPS
Vehicles
Mining Taxi Drivers knowledge:
Time Distributions from raw data
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GPS
Vehicles
How ?Mining Taxi Drivers knowledge.
Gathering a transition set Suv and a landmark edge euv Estimating the time-dependent travel timeIdentify and discover contexts
From the Figure above:
(a.) It is observed that travel times gather around some value:
We can therefore infer that:1. Different number of traffic lights encountered by different drivers.
2. Different routes taken by different drivers
3. Drivers personal behavior, skills and preferences.
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GPS
Vehicles
Differentiating Taxi Drivers Experiences.Because
they knowthe smart
routesSome driversare smarter !
How ?
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GPS
Vehicles
Differentiating Taxi Drivers Experiences.
Progression in Experience
A landmark edge euv wastraversed by N Drivers
The progress of a drivers
familiarity with the landmarkedge can be defined as:
-ni is the time taken by the
Driver
-ani + b is a linear
transformation [ni =>(Min,Max)]
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GPS
Vehicles
Online Inference
We Infer the traffic condition at a future
time (F) :
:Of landmark graphs:From historical data (H)
:real-time traffic flow
calculated based on
:near real-time Taxi trajectories
(R).
The online inference problem is modeled
as an mth-order Markov chain.
Aftermining
what dowe do ?
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Implementation
Modeling Traffic condition
Tracking traffic condition Xt1, Xt2, , Xtn , at each time-stamp ti,
- X is the average verlocity of vehicles traversing a road segment or
average travel time over a landmark edge.
Tracking traffic condition Xt1, Xt2, , Xtn , at each time-stamp ti,
- X is the average verlocity of vehicles traversing a road segment
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Implementation
Service Providing
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Query Sending:
A user sends a query (qs,qd,td,)
Route Computation
Chooses a proper landmark graph
Two Stage Routing Algorithm
Route Downloading
Path Logging
Adapting Custom Factor Travel Time Distribution
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Evaluation
TEXT TEXT TEXT TEXT
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Datasets
33,000 taxis trajectories over a period of 3
months
106,579 Road Nodes and 141,380 segments for
Adaptive routing
Updated traffic data with frequency of 26
minutes of 50 road segments
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Evaluation
TEXT TEXT TEXT TEXT
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Prediction
Prediction on landmark edge and road
segments
Compare H+R approach with previously
existing :
H Method (Time Driven)
R Method
Accuracy of traffic inference measured with
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Evaluation
TEXT TEXT TEXT TEXT
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Prediction
RMSE w.r.t time of the
day (interval=90)
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Related Work
TEXT TEXT TEXT TEXT
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Traffic Estimation
Some related work aim to learn historical traffic patterns, estimate real-time traffic
and forecast future traffic condition on some road segments .
Using:
GPS Trajectories
WIFI
Ontologies for Prediction
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Related Work
TEXT TEXT TEXT TEXT
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Smart Routing
To optimize taxi driver income , some works have proposed
route recommendation services for a taxi driver by analyzing
fleet trajectories and inferring profitable routes.
Some other Works aims to provide personalized routes
according to a users driving preferences in choosing a road,
using UCI or implicit modeling.
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Related Work
TEXT TEXT TEXT TEXT
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Floating Car Data 1.UsesDatabase
as an
Historical
dataset.
2. Taxitransmits
data 4 tx a
Minute.
3.
Trajectorie
s arecalculated
based on
time frame
and
points.
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Related Works
TEXT TEXT TEXT TEXT
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Individual Travel services and Traffic
monitoring
Dynamic Routing and Navigation
Taxi FCD historic traffic flow pattern
during rush hour vs Navtech Speed
types.
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Related Works
TEXT TEXT TEXT TEXT
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Individual Travel services and Traffic
monitoring
Figure
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Related Works
TEXT TEXT TEXT TEXT
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Emission Monitoring
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Conclusion
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It is possible to gain a whole lot advantage from
traffic dynamics if put into use in different context and
applications.
Other data collection techniques can be explored to make the
prediction mechanism richer such as Control Area Networks,
VANETs etc.
Other Inference based systems can also be deployed such asOntology based dynamic routing system with an ontology model
based inference engine .
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References
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Driving with Knowledge from the Physical World,
[Jing Yuan, Yu Zheng,, Xing Xie, Guangzhong Sun]
Monitoring Traffic and Emissions by Floating Car Data
[Astrid Ghnemann et al]
A traffic information system by means of real-time floating-car data
[Ralf-Peter Schfer, Kai-Uwe Thiessenhusen, Peter Wagner]
Traffic Known Urban vehicular Route Prediction based on
Partial Mobility Patterns[Guangtao Xue, Zhongwei Li et al]