Some Recent GIS Applications in Transportation and Logistics New York Metropolitan Transportation Co

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Some Recent GIS Applications in Transportation
and LogisticsNew York Metropolitan
Transportation Council November 19, 2008Fan
YangCity College of New Yorkfyang_at_ce.ccny.cuny.e
du
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Outline

• Introduction to GIS
• Online Geocoding Methods
• Web-based GIS solutions
• GIS in Logistics

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Introduction to GIS
An Information System For Maintaining and Using
Spatial Information
Views
Products
Updates
Analysis
Mobile / LBS
Mission Critical Applications
A Generic Platform for Working With Geographic
Information
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Online Geocoding Processing

• Data cleansing from multiple data sources
• Various errors might exist in the input data

dirty input data
clean output data
State DOT
Traffic Information Service Provider (ISP)
Local Agencies
State Patrol
Clean Reference DB
Radio
Data collection
Data cleansing
Data disseminating
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An Example Online Incident Locator

• Match the similarity between an input record and
  reference ones

Input
Reference
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Data Matching

• Various input data errors including spelling
  mistakes, truncations, inconsistent conventions
  and missing fields

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Two-Stage Matching

• 1. Use inexpensive metric to quickly find a
  relatively small candidate set
• 2. Identify the best matches for the input within
  the candidate set in terms of the similarity
  score.
• Use the offline pre-built similarity index to
  improve the performance for online operations.
• Three ways to build similarity index in the first
  stage
• Build on the whole words in every important
  column
• Build on every token in every important column
  (token based)
• Build on every q-gram in every important column
  (q-gram based)

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Token and Q-gram Based Matching Methods

• Token based
• Build tree or hash based similarity index upon
  all tokens in important columns of all reference
  records.
• Candidates should share at least one common token
  for each of the columns main_base and
  cross_base with that of the input record (e.g.,
  Mountain Viw/ Mountain View).
• Q-gram based
• Divides a token (word) into character groups
  (grams) with equal length q. E.g., for
  Redlands, if q3, six q-grams Red, edl,
  dla, lan, and, and nds.
• Build tree or hash based similarity index upon
  all q-grams.
• Candidates should share at least one common
  q-gram for each of the columns main_base and
  cross_base with that of the input record (e.g.,
  Moutain Viw / Mountain View).

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Second Stage Measuring Similarity Score

• Edit distance ed(s1,s2) minimum number of
  character edit operations (delete, insert,
  replace) required to transform s1 to s2, divided
  by the maximum length of s1 and s2.
• IDF weight more frequent, less weight.
• The record similarity function
• is the cost to transfer record u to v,
  proportional to ed(u,v).

ed(s1,s2) 2/8
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Experimental Results

• The road network (reference table) - the
  processed TIGER database in Los Angeles Area.
• Based on 500 geocoded (correct) incident records
  in downtown LA, we randomly generated dirty
  input data

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Matching Accuracy
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Online Performance
With the pre-built index, only a small portion of
reference data is retrieved to match an input
record, therefore, significantly improving the
online performance.

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The Size of the Candidate Set

• The smaller q value means finer granularity, and
  may catch more candidates which might be missed
  for larger q values.
• The size of the candidate set increases as q
  value becomes smaller.

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Remarks

• Proposed two efficient approximated matching
  methods for online incident data cleansing.
• A two-stage matching procedure is developed to
  significantly improve the online performance.
• The q-gram based method outperforms the token
  based one in terms of match accuracy. Suggest
  q3.
• This study can be applied to ITS online data
  management such as loop detector data and
  construction data. More geographical information
  can be accommodated.

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Outline

• Introduction to GIS
• Online Geocoding Methods
• Web-based GIS solutions
• GIS in Logistics

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Why Web-based GIS Solutions

• Consume fewer licenses and require thinner
  client.
• Provide rich spatial analysis and editing
  functionalities.
• Satisfy service Oriented Architecture (SOA)
• Provide SOAP (Simple Object Access Protocol),WMS
  (Web Map Service), KML(Keyhole Markup Language)
  based services.

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NYSDMV Application Accident Location Information
System (ALIS)Location Editing, Query and
Reporting

• Integration with NYSDMV and NYSDOT Legacy systems
  

• Multi-Agency Effort

• Web Application Host
• NYSOFT
• Data Management
• NYSCSCIC
• Application Users
• NYSDMV
• NYSDOT
• NYSCSCIC
• GIS Data Co-op (Local Government Agencies)

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ALIS Web-based GIS Application

• Automatically verifies the location information
  against the GIS basemap
• Allows users to edit or update accident locations
  based on the availability of improved map data in
  a region or the availability of more information
  pertaining to the accident case.
• Allows users to monitor and record changes made
  to the geospatial database.
• Provides users the ability to select street
  segments for editing using either spatial
  queries, attribute queries, or network tracing.

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Outline

• Introduction to GIS
• Online Geocoding Methods
• Web-based GIS solutions
• GIS in Logistics

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What is GIS Logistics?

• Using advanced Geographic Information Systems
  (GIS) tools and methods in conjunction with
  existing infrastructure and procedures in order
  to solve logistics problems

• Main Applications
• Site Selection Analysis
• Asset and Property Management
• Territory Optimization
• Real-time Dynamic Routing and Scheduling
• Supply Chain Management

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Why use GIS Logistics?
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What is Territory Optimization?

• A periodic vehicle routing solution in a big
  territory
• Distribute periodic orders among available
  trucks/drivers
• Input service requests, truck schedules, and
  business rules
• Output truck daily schedule
• Large-scale problem size and complicated business
  rules
• The goal
• Balance workloads among employees
• Minimize total travel time (by all trucks over
  the entire planning period)
• Minimize time window violation
• Minimize overtime

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Territory Optimization
Tool Bar
Map View
List View
Explorer Tree View
Gantt Chart View
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System Architecture
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What is Real-time Dynamic Routing and Scheduling?

• Customers call for periodic service requests
• Used to determine optimal truck schedule
• candidates
• Need to be served in a real-time fashion
• Might change the existing daily schedule

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Real-time Dynamic Routing and Scheduling

• Efficiently handles recurring service requests
• Dynamically constructs the service tree
  structure
• Considers combinations of feasible employees and
  date ranges
• Re-sequences a daily route by solving a Vehicle
  Routing
• Problem with Time Windows (VRPTW)

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• Thank you!
• Questions?