traffic dynamics intelligence

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]

traffic dynamics intelligence

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