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An Ontology-Based Traffic Accident Risk Mapping Framework. Jing Wang and Xin Wang Intelligent Geospatial Data Mining Group Department of Geomatics Engineering University of Calgary, Canada Aug 24, 2011. Outline. Overview of Road Accident Problem.

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an ontology based traffic accident risk mapping framework

An Ontology-Based Traffic Accident Risk Mapping Framework

Jing Wang and Xin Wang

Intelligent Geospatial Data Mining Group

Department of Geomatics Engineering

University of Calgary, Canada

Aug 24, 2011

outline
Outline
  • Overview of Road Accident Problem
  • An Ontology-Based Traffic Accident Risk Mapping Framework
  • System Implementation and Case studies
  • Conclusions and Future Work
road safety problem
Road Safety Problem

1.18

  • World Health Organization estimates 1.18 million people were killed by road accidents in 2002.

(Source: WHO, 2004)

  • In Canada, about 3,000 are killed every year on roads.

(Source: www.RememberRoadCrashVictims.ca)

3,000

  • In Alberta, the average time between collisions is 5 minutes.

(Source: Tay, 2006)

5

How to reduce traffic fatalities and serious injuries on public roads?

accident concentration
Accident Concentration
  • The occurrences of traffic accidents
    • Seldom random in space and time, but form clusters in geographic space
  • These accident concentration areas or locations have increased likelihood for an accident to occur based on spatial dependency of historical data.
research goal
Research Goal
  • Generate road traffic accident risk maps showing the risk area
  • “Risk” not only reflect the accident numbers but also the degree of danger
weakness of t raditional a pproaches
Weakness of Traditional Approaches

1. Handle accident analysis at data level

  • Cannot generate different maps to meet users’ different needs

e.g. a map for the “downtown area” or the “rush hours” only

  • 4-year (1999-2002) accident statistics on the 16th Avenue of Calgary with the same time interval of the day
weakness of t raditional a pproaches1
Weakness of Traditional Approaches

2. Ignore the severity levels of accidents

Accident with Injury

Accident with property damage only

Fatalities and injuries put more strain on the network.

how to i mprove
MinPts = 5

Eps = 40

How to Improve?
  • How to help user select the proper dataset?
      • Naive option

- Translate users' requirements into traditional database queries [SQL] Select * from TrafficAccidentTable where AccidentCondition = “Rain” AND location = “DowntownArea”…..

      • Second option

- Handle users' requirements at the knowledge level

Ontology (provides domain knowledge include the non-spatial and spatial concepts and definitions relevant to the traffic accident. )

  • How to generate the traffic accident risk map?

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

slide9
ONTO_TARM Framework

ONTOlogy-Based Traffic Accident Risk Mapping Framework

Ontology-Based Traffic Accident Risk Mapping Framework

Domain Ontology

User’s goal

Clustering

Engine

Publishing

Module

Reasoner

User

Proper Datasets

Interface

Data

domain ontology
Domain Ontology
  • The Traffic Accident Domain Ontology (TADO)
    • A formal description of the classes of concepts and the relationships among those concepts that describe traffic accidents.
    • Based on a 7-tuple structure O:= {D, C, R, A, HC, prop, att}
      • domain context identifier D
      • Concept set C
      • The relation identifiers R
      • Attributes describe C and R A
      • A concept hierarchy classification HC
      • Function prop
      • Function att
traffic accident domain ontology tado
Traffic Accident Domain Ontology (TADO)

Country

Polygon

Province

Line

RoadCondition

City

GeometricThing

Point

LightCondition

County

GeopoliticalRegion

EnvironmentalCondition

GeopoliticalRegion

RoadSurfaceCondition

Community

GeographicalRegion

GeospatialThing

GeographicalRegion

WeatherCondition

EcologicalRegion

AccidentCondition

CitySection

GeoculturalRegion

Instant

Thing

TemporalCondition

Interval

Building

Expressway

FixedStructure

Accident_Records

DateTimeDescription

Station

Highways

RoadCondition

Roadway

Majorroad

LightCondition

EnvironmentalCondition

RoadSurfaceCondition

Localroad

WeatherCondition

reasoner
Reasoner
  • Input of the reasoner is a user’s goal, and the output is a set of properties dataset
    • Decompose into one or more spatial and non-spatial tasks
    • Assemble returned results
  • Example:
    • Risk map for “The accidents happened in rush hours with bad weather in downtown Calgary”
    • Subtask 1 (Spatial task): find the "downtown Calgary".
    • Subtask 2 (Nonspatial task): find the “rush hours with bad weather ”.
      • Subtask 2.1 (temporal condition task) find the “rush hours”
      • Subtask 2.2 (weather condition task) find the “bad weather”
finddowntownareatask
findDowntownAreaTask

Pseudocode of spatial query task findDowntownAreaTask

Sub-task: findDowntownAreaTask

defgoal find Calgary Downtown Area

Input:

(object (is-a City) (object?ci) (hasName "Calgary"))

(object (is-a CitySection) (object?cs)

(hasName "Downtown Area") (insideOf?ci))

(object (is-a community) (object?co) (insideOf?ci)

(belong-section?cs))

Output:

(object (is-a $?community) (object? co))

findaccidentconditiontask
findAccidentConditionTask

Pseudocode of Nonspatial query task findAccidentConditionTask

sub-task: findAccidentConditionTask

defgoal find Accident Conditions

Input:

(object EnvironmentalCondition?ec

(RoadSurface-condition "dry"), (RoadCondition "straight" || "curve"), (WeatherCondition findSevereWeatherTask()) (LightCondition "artificial"||"nature"))

(object TemporalCondition?tc

(Interval? findRushHoursTask()))

(object (is-a AccidentCondition) (object?ac) (include?ec & tc))

Output:

(object (is-a $?AccidentCondition) (object?ac))

risk index
Risk Index
  • How to define the risks?

Assign different weights to accidents with different severity levels

With in a given accident dataset D,

i - ith severity level

n – the total number of different severity levels

Count() - a function to get the total number of accidents at that level

Wi - the weight assigned to the ith severity level

risk index model
Risk Index Model
  • Converted into Equivalent Property Damage Only (EPDO) accidents
    • EPDO = W1* Fatal + W2* Injury + W3* PDO
      • PDO: property damage only crashes
  • e.g. PIARC (Permanent International Association of Road Congresses) recommended formula:

W1=9.5; W2=3.5; W3=1; EPDO = 9.5 * Fatal + 3.5 * Injury + PDO

Different jurisdictions use different weighting schemes:Model Ratio Source

1 1:1:1 Simple Total Crash Count

2 9.5:3.5:1 PIARC

3 76.8:8.4:1 North Carolina DOT

4 136.13:4.94:1 Ohio DOT

5 779.9:13.88:1 Transport Canada

6 1300:90:1 Federal Highway Administration

DOT: Department of Transportation

modified dbscan for traffic accidents
Modified DBSCAN for Traffic Accidents
  • Density-based Clustering for Traffic Accident Risk (DBCTAR)

Fatal

Injury

MinPts = 5

Eps = 40

PDO

MinRisk

=10

RiskIndex = W1* Count(Fatal) + W2* Count(Injury) + W3* Count(PDO)

9.5

3.5

×6

1

×1

×1

19 =

> RiskIndex Threshold: MinRisk

main interface
Main Interface

Global Setting

Menus

Quick Setting Panel

Tool bar

Map Area

Layer Control

Status bar

case studies
Case Studies
  • Dataset
    • Reported collisions on the road within Alberta province (770,000+ records)
    • Network street dataset is clipped from Street Map of North American in the ArcGIS 9.3 Media Kit
    • Community boundary dataset is from census subdivisions
  • User’s goals
    • Case 1: to find a risk map "at rush hours in the morning of downtown area of Calgary"
    • Case 2: to find a risk map “between 8:00-10:00pm in the downtown area of Calgary”
    • Case 3: to find a risk map “on the Deerfoot Trail in Calgary”
results
Results

Risk model = PIARC, MinRisk = 8, Eps=45, MinPts=3

(Case 2) Risk map between 8:00-10:00PM of Calgary downtown area

(Case 1) Risk map in rush hours (7:30-9:00AM) of Calgary downtown area

(Case 3)Risk Map of Deerfoot Trail (extract)

results1
Results

Road Accident Risk Mapping Web Publishing Platform

slide22
Results

A risk map generated based on user’s requirement “Calgary downtown area under snow condition”, published with online platform

MinRisk

evaluation
Evaluation

Detail Comparison of two mapping results

Site 1

Site 2

Kernel Density Estimation (KDE) Result (radius set to 40 meters and cell size is 10X10 meters.)

(A) KDE

Site 1

Site 2

(B) DBCTAR

23

evaluation1
Evaluation

Site 1

Site 2

conclusions
Conclusions
  • Ontology is first time integrated into a traffic accident risk mapping framework to generate different risk maps based on users' goals
  • A density-based spatial clustering method for traffic accident risk (DBCTAR) is proposed
  • To demonstrate the framework, a prototype of proposed framework has been implemented
  • The preliminary results from the case studies are promising
future work
Future Work
  • Improve ontology reasoner and map generator
  • Provide recommendations for the weight model
  • Adopt other properties in the risk index model
  • Explanations from civil experts
slide27
References
  • WHO, 2004. World Health Organization, World Report on road traffic injury prevention, World Health Day Publication.
  • RememberRoadCrashVictims.ca 2009. RememberRoadCrashVictims.ca
  • Anderson, Tessa K. 2009. Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention 41, no. 3 (May): 359-364.
  • Borruso, G. 2005. Network density estimation: analysis of point patterns over a network, Osvaldo Gervasi, Marina L. Gavrilova, Vipin Kumar, Antonio Laganà, Heow Pueh Lee, Youngsong Mun, David Taniar, Chih Jeng Kenneth Tan (Eds.): Computational Science and Its Applications - ICCSA 2005, International Conference, Singapore, May 9-12, 2005, Proceedings, Part III. Lecture Notes in Computer Science 3482, 126-132.
  • Flahaut, B., Mouchart, M., Martin, E.S., and Thomas, I. 2003. The local spatial autocorrelation and the kernel method for identifying black zones a comparative approach, Accident Analysis & Prevention, 35, 991-1004.
  • Okabe, A. and Yamada, I., 2001. The K-function method on a network and its computational implementation. Geographical Analysis 33 3, pp. 271–290
  • Okabe, A. Satoh, T. and Sugihara, K. 2009. A kernel density estimation method for networks, its computational method and a GIS-based tool. International Journal of Geographical Information Science 23(1):7 - 32.
  • Shino S. 2008, Analysis of a distribution of point events using the network-based quadrat method, Geographical Analysis 40 (2008), pp. 380–4000.
  • Steenberghen, T., Dufays T., Thomas,I. and Flahaut. B. 2004. Intra-urban location and clustering of road accidents using GIS: a Belgian example. International Journal of Geographical Information Science 18(2): 169 - 181.
  • Xie Z., Yan, J. 2008. Kernel density estimation of traffic accidents in a network space.. Computers, Environment and Urban Systems, 32, pp. 396-406.
  • Ester, M. Kriegel, H. Sander, J. & Xu, X. 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. Second International Conference on Knowledge Discovery and Data Mining: 226-231. Portland: AAAI Press.
  • Wang, X., Gu, W., Ziébelin, D. and Hamilton, H. 2010. An Ontology-Based Framework for Geospatial Clustering, International Journal of Geographical Information Science, 24(1): 1601 - 1630
  • Gruber, T. R, 1993. A translation approach to portable ontologies. Knowledge Acquisition, 5(2) (1993) 199-220
slide29
System Implementation

Prototype Implementation

29

ontology implementation
Ontology Implementation

Using Protégé OWL

mapping
Mapping

Find the suitable parameters

Limited to the Network

Publish

Evaluation

Buffer & Intersect

Clustering

spatial clustering
Spatial Clustering

MinPts = 5

K-dist (p):

distance from the kth nearest neighbour to p

Eps = 40

q

Sorting by k-dist (p)

threshold minrisk
Threshold - MinRisk

The kth-distance of p (k=30)

threshold minrisk1
Threshold - MinRisk

The risk index value of the Eps neighbours of p (Eps=100)

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