1. ESRI TechnologyESRI TechnologyESRI TechnologyESRI Technology
Routing Technology FocusRouting Technology Focus
Don WeigelDon Weigel - Program Manager- Program Manager
Transportation/Logistics Products and ServicesTransportation/Logistics Products and Services
dweigel@esri.comdweigel@esri.com (909) 793-2853 ext. 1766(909) 793-2853 ext. 1766
2. OutlineOutlineOutlineOutline
• OverviewOverview
– Why GIS?Why GIS?
– A few applicationsA few applications
– Integration of GIS and OR for RoutingIntegration of GIS and OR for Routing
and Site Locationand Site Location
– ApplicationsApplications
– GIS Data ManagementGIS Data Management
– MethodologyMethodology
– Object ModelObject Model
3. Customer LocationsCustomer Locations
Trade AreasTrade Areas
Street NetworksStreet Networks
Post CodesPost Codes
Market PotentialMarket Potential
DemographicsDemographics
Why Use Maps?Why Use Maps?Why Use Maps?Why Use Maps?
BecauseBecause
Everything isEverything is
SomewhereSomewhere......
... and... and
somehowsomehow
connected.connected. A way toA way to
model andmodel and
betterbetter
understandunderstand
the worldthe world
4. How Does GISHow Does GIS
Help in Routing?Help in Routing?
How Does GISHow Does GIS
Help in Routing?Help in Routing? Customer
Database
- Street Topology/Speed- Street Topology/Speed
- Address Ranges- Address Ranges
- Graphics- Graphics
Dispatcher
Driver
Geocode
Solver
Manifest,
Directions,
Maps
5. GIS is… Spatial DataGIS is… Spatial Data
Creation and EditingCreation and Editing
GIS is… Spatial DataGIS is… Spatial Data
Creation and EditingCreation and Editing
Radius = 40
Radius = 55
6. • Multiple editMultiple edit
sessions andsessions and
long transactionslong transactions
• One databaseOne database
A
CB
Version A Version B Version C
GIS is... Data ManagementGIS is... Data ManagementGIS is... Data ManagementGIS is... Data Management
7. GIS is … AnalysisGIS is … AnalysisGIS is … AnalysisGIS is … Analysis
13. Map “Overlay” =Map “Overlay” =
Relational “Join”Relational “Join”
Coordinates are theCoordinates are the
““Common Keys”Common Keys”
Map “Overlay” =Map “Overlay” =
Relational “Join”Relational “Join”
Coordinates are theCoordinates are the
““Common Keys”Common Keys”
Spatial Data ManipulationSpatial Data ManipulationSpatial Data ManipulationSpatial Data Manipulation
e.g. Customerse.g. Customers
e.g. Districtse.g. Districts
Spatial DatabaseSpatial Database
LayersLayers
GIS Creates “Spatial Relationships”GIS Creates “Spatial Relationships”
Between Otherwise Unrelated DataBetween Otherwise Unrelated Data
14. SharedShared
GeoDataGeoData
Organisation XOrganisation X
Organisation YOrganisation Y
Department ADepartment A
Department CDepartment C
Department BDepartment B
The PublicThe Public
GIS is... OrganizationalGIS is... Organizational
IntegrationIntegration
GIS is... OrganizationalGIS is... Organizational
IntegrationIntegration
16. The “Value-add” of GIS isThe “Value-add” of GIS is
Accurate DistanceAccurate Distance
(and Time) Computation(and Time) Computation
The “Value-add” of GIS isThe “Value-add” of GIS is
Accurate DistanceAccurate Distance
(and Time) Computation(and Time) Computation
17. Many Companies OfferMany Companies Offer
Logistics Solutions but theyLogistics Solutions but they
Fake the DistancesFake the Distances
Many Companies OfferMany Companies Offer
Logistics Solutions but theyLogistics Solutions but they
Fake the DistancesFake the Distances
Euclidean Distance or if they’reEuclidean Distance or if they’re
“fancy”, they correct for Earth“fancy”, they correct for Earth
curvaturecurvature
18. This may work in Kansas...This may work in Kansas...This may work in Kansas...This may work in Kansas...
But not anywhere that hasBut not anywhere that has
interesting geography e.g.interesting geography e.g.
lakes, rivers, mountains,lakes, rivers, mountains,
canyons, etc.canyons, etc.
19. Euclidean vs. Actual MilesEuclidean vs. Actual MilesEuclidean vs. Actual MilesEuclidean vs. Actual Miles
39.89 miles as the crow flies39.89 miles as the crow flies
52 miles actual52 miles actual
19.58 miles as the crow flies19.58 miles as the crow flies
39.98 miles actual39.98 miles actual
20. Euclidean vs. Actual MilesEuclidean vs. Actual MilesEuclidean vs. Actual MilesEuclidean vs. Actual Miles
3.9 miles as the crow flies3.9 miles as the crow flies
16.3 miles actual16.3 miles actual
10 miles as the crow flies10 miles as the crow flies
26 miles actual26 miles actual
21. RouteRoute
OptimizationOptimization
RouteRoute
OptimizationOptimization
““As the Crow Flies” SequenceAs the Crow Flies” Sequence How the Crow would have to DriveHow the Crow would have to Drive
Optimized Sequence using ArcLogisticsOptimized Sequence using ArcLogistics
Red route 8.7 milesRed route 8.7 miles
Green route 8.2 milesGreen route 8.2 miles
Total = 16.9 milesTotal = 16.9 miles
Red route 29.4 milesRed route 29.4 miles
Green route 27.0 milesGreen route 27.0 miles
Total = 56.4 milesTotal = 56.4 miles
Red route 23.0 milesRed route 23.0 miles
Green route 24.2 milesGreen route 24.2 miles
Total = 47.2 milesTotal = 47.2 miles
Mileage savings = 17%Mileage savings = 17%
22. Two Views of NetworksTwo Views of NetworksTwo Views of NetworksTwo Views of Networks
Geometry important or unimportant ...Geometry important or unimportant ...
N a v ig a b le r iv e r
R a ilr o a d
A ir lin e
r o u t e
H ig h w a y
N e t w o r k
v ie w
T r a in s t a t io n
A ir p o r t
A ir p o r t
F a c t o r y B r id g e
N a v ig a b le R iv e r
B r id g e
R a ilr o a d
Highway
A ir lin e r o u t e
4 3
6 6
G e o g r a p h ic
v ie w
23. Geometry and ConnectivityGeometry and ConnectivityGeometry and ConnectivityGeometry and Connectivity
j1 2 3
j 1 2 6
e1
e2
e3
j 1 2 4
j1 2 5
G e o m e t r i c
N e t w o r k
j1 2 3
j1 2 4
j1 2 5
j1 2 6
J u n c t io n
j 1 2 4 , e 1
A d ja c e n t J u n c t i o n a n d E d g e
j 1 2 4 , e 1 j 1 2 5 , e 2 j 1 2 6 , e 3
j 1 2 4 , e 2
j 1 2 4 , e 3
C o n n e c t iv it y T a b le
L o g i c a l N e t w o r k
Geometric networkGeometric network
contains geometrycontains geometry
Logical networkLogical network
contains connectivitycontains connectivity
ConnectivityConnectivity
establishedestablished
automatically viaautomatically via
geometricgeometric
coincidencecoincidence
24. 0 10 20 30 40 50 60 70
YCoordinateAxis
0
10
20
30
40
50
60
a2
N3
N4
N1
N2
N6
D, a6, N5
a4
a4
a5
a7
a7
a7
a7
a2
a2
a3
a3
a1
a1
a5
a5
a5
X Coordinate Axis
A
C
B
E
Arc Coordinate Data
Arc Start
X,Y
Intermediate
X,Y
End
X,Y
a1
a2
a3
a4
a5
a6
a7
40, 60
70, 50
10, 25
40, 60
10, 25
30, 20
55, 27
70, 60
70, 10; 10,10
10, 60
30, 50
20,27; 30,30; 50,32
55, 15; 40, 15; 45,27
70, 50
10, 25
40, 60
30, 40
70, 50
30, 20
55, 27
Arc Topology
Arc Start
Node
End
Node
Left
Poly
Right
Poly
a1
a2
a3
a4
a5
a6
a7
N1
N2
N3
N4
N3
N5
N6
N2
N3
N1
N1
N2
N5
N6
E
E
E
A
A
B
B
A
B
A
A
B
B
C
Node
Topology
Node Arcs
N1
N2
N3
N4
N5
N6
a1, a3, a4
a1, a2, a5
a2, a3, a5
a4
a6
a7
Street Network Becomes...Street Network Becomes...Street Network Becomes...Street Network Becomes...
26. What is a Route?What is a Route?What is a Route?What is a Route?
• Who is the user?Who is the user?
– Accounting, Operations, Drivers?Accounting, Operations, Drivers?
• What is the Objective?What is the Objective?
– Balancing, Dump fee minimization,Balancing, Dump fee minimization,
Resource maximization, FuelResource maximization, Fuel
minimization, O.T. minimization etc.minimization, O.T. minimization etc.
• What are the Constraints?What are the Constraints?
– Weight, Volume, # Stops, Distance toWeight, Volume, # Stops, Distance to
dump, # Vehiclesdump, # Vehicles
29. Point ClusteringPoint Clustering DistrictingDistricting Network PartitioningNetwork Partitioning
Simple Pathfinding/VRPSimple Pathfinding/VRP Periodic Routing/SchedulingPeriodic Routing/Scheduling Chinese PostmanChinese Postman
Week 1 Day 1
Week 1 Day 2
Week 1 Day 3
Importance of Street Data: H M LImportance of Street Data: H M L
LL LL L-ML-M
M-HM-H MM HH
Objectives
Constraints
Complexity: H M LComplexity: H M L
LL LL MM
M-HM-H HH HH
32. ESRI StrategyESRI StrategyESRI StrategyESRI Strategy
• Core ToolsCore Tools
– Front EndFront End
– Back EndBack End
• Sample ApplicationsSample Applications
• Technology TransferTechnology Transfer
– Partners (integration)Partners (integration)
– Clients (solutions)Clients (solutions)
• Some out of the box applicationsSome out of the box applications
33. A Blending of DisciplinesA Blending of DisciplinesA Blending of DisciplinesA Blending of Disciplines
GISGIS LogisticsLogistics
““Better DecisionsBetter Decisions
Through Better SpatialThrough Better Spatial
Information”Information”
““Balance CostBalance Cost
and Customerand Customer
Objectives toObjectives to
MaximizeMaximize
Sales and Profit”Sales and Profit”
OperationsOperations
ResearchResearch
““Better Decisions throughBetter Decisions through
Mathematical Models”Mathematical Models”
GIS ToolsGIS Tools
OR ToolsOR Tools
34. Why GIS + OR ?Why GIS + OR ?Why GIS + OR ?Why GIS + OR ?
• Geography-based objectivesGeography-based objectives
– Travel timeTravel time
– Travel distanceTravel distance
– Work area balancingWork area balancing
• Accuracy of “Origin Destination Matrix”Accuracy of “Origin Destination Matrix”
– Considers natural barriersConsiders natural barriers
– Considers travel time for each street segmentConsiders travel time for each street segment
– More realistic routesMore realistic routes
• Geography-based solution heuristicsGeography-based solution heuristics
– Seed locationsSeed locations
– Cutoff criteriaCutoff criteria
– Fast (2.4 quintillion permutations on a route with 20 stops!)Fast (2.4 quintillion permutations on a route with 20 stops!)
• 15% improvement over manual methods15% improvement over manual methods
35. GIS + OR: Typical BenefitsGIS + OR: Typical BenefitsGIS + OR: Typical BenefitsGIS + OR: Typical Benefits
• Reduce CostsReduce Costs 10%-30%:10%-30%:
– Minimize MileageMinimize Mileage
– Minimize OvertimeMinimize Overtime
– Minimize VehiclesMinimize Vehicles
– Reduce Time Spent RoutingReduce Time Spent Routing
– Reduce Wait TimeReduce Wait Time
• Increase ProductivityIncrease Productivity 10%-15%:10%-15%:
– Service More Customers with the Same FleetService More Customers with the Same Fleet
– Respond to Same-Day RequestsRespond to Same-Day Requests
– Reduce Cycle TimeReduce Cycle Time
– Lower Driver:Dispatcher ratioLower Driver:Dispatcher ratio
• Improve Customer Service andImprove Customer Service and
SatisfactionSatisfaction
– Offer Tighter Time WindowsOffer Tighter Time Windows
– Keep the promise: On-time PerformanceKeep the promise: On-time Performance
– Establish ReputationEstablish Reputation
–
36. Case Study: SEARSCase Study: SEARSCase Study: SEARSCase Study: SEARS
• 16,000 Vehicles Every Day16,000 Vehicles Every Day
– All next day or same day routesAll next day or same day routes
– 25 million stops per year25 million stops per year
– Processed in one hour - little or no manual effortProcessed in one hour - little or no manual effort
• 1 hour customer time windows1 hour customer time windows
• Volume increase = 9% with same fleetVolume increase = 9% with same fleet
• On-time Performance = 95%On-time Performance = 95%
• Overtime Reduced = 15%Overtime Reduced = 15%
• Improved Customer Satisfaction ScoresImproved Customer Satisfaction Scores
• Savings = $51 Million AnnuallySavings = $51 Million Annually
37. Routing ProblemRouting Problem
ComponentsComponents
Routing ProblemRouting Problem
ComponentsComponents
• ObjectivesObjectives
– Minimize or maximize something -Minimize or maximize something -
usually involving cost, distance orusually involving cost, distance or
travel time.travel time.
• ConstraintsConstraints
– Approach Objective while consideringApproach Objective while considering
other factors - often includingother factors - often including
geographic constraints.geographic constraints.
38. Typical ObjectivesTypical ObjectivesTypical ObjectivesTypical Objectives
• Objectives Typically Represented asObjectives Typically Represented as
“Costs”.“Costs”.
– Min Mileage = Min cost/mileMin Mileage = Min cost/mile
– Min O.T. = Min O.T. costMin O.T. = Min O.T. cost
– Min route length = min hourly costMin route length = min hourly cost
– Min Vehicles = Min fixed costMin Vehicles = Min fixed cost
– Min TW Violation = Min ViolationMin TW Violation = Min Violation
“Penalty” Cost“Penalty” Cost
39. Typical ConstraintsTypical ConstraintsTypical ConstraintsTypical Constraints
• ““Hard” ConstraintsHard” Constraints
– CapacityCapacity
• WeightWeight
• VolumeVolume
• etc.etc.
• Max OrdersMax Orders
• Max HoursMax Hours
– Start Time*Start Time*
– Service TimeService Time
– SpecialtySpecialty
– Locked OrdersLocked Orders
• ““Soft” ConstraintsSoft” Constraints
– Time Windows ofTime Windows of
OrdersOrders
– Start Time*Start Time*
– Time Windows ofTime Windows of
LocationsLocations
Limits you put on the objective function
40. ConstraintsConstraintsConstraintsConstraints
• Unconstrained routes “look”Unconstrained routes “look”
efficient/good. They are circular andefficient/good. They are circular and
minimize travel time/distance.minimize travel time/distance.
• Constrained routes don’t “look” efficient.Constrained routes don’t “look” efficient.
They may cross, have wait time, overtime,They may cross, have wait time, overtime,
and drive far due to customer serviceand drive far due to customer service
objectives.objectives.
• This is a classic tradeoff.This is a classic tradeoff.
• Dispatchers don’t like the “look” ofDispatchers don’t like the “look” of
constrained routes.constrained routes.
45. Time WindowsTime WindowsTime WindowsTime Windows
• Soft ConstraintSoft Constraint
• Minimize time window violations.Minimize time window violations.
• Tight Time Windows will result in moreTight Time Windows will result in more
Overtime, Wait Time, and Drive Time.Overtime, Wait Time, and Drive Time.
• The wider the window, the more circularThe wider the window, the more circular
the route, and lower cost.the route, and lower cost.
• Drivers often won’t run a sequence thatDrivers often won’t run a sequence that
doubles back because of time windows.doubles back because of time windows.
They think they can sequence the routeThey think they can sequence the route
better.better.
46. CapacityCapacityCapacityCapacity
• Hard ConstraintHard Constraint
• Checks capacity before solvingChecks capacity before solving
• WeightWeight
• VolumeVolume
• Maximum OrdersMaximum Orders
• Weight and volume can be anything:Weight and volume can be anything:
– Pallets, cases, kegs, pianos, pigs, gallons.Pallets, cases, kegs, pianos, pigs, gallons.
47. Start TimeStart TimeStart TimeStart Time
• Soft or Hard ConstraintSoft or Hard Constraint
• When expressed as a range, ArcLogisticsWhen expressed as a range, ArcLogistics
will find the best start time to minimizewill find the best start time to minimize
cost. Allows for schedule compression.cost. Allows for schedule compression.
• When start and end time are the same,When start and end time are the same,
ArcLogistics will use that time even ifArcLogistics will use that time even if
costs are higher.costs are higher.
• Used to reduce unproductive (wait) time.Used to reduce unproductive (wait) time.
49. Service TimeService TimeService TimeService Time
• Hard ConstraintHard Constraint
• ArcLogistics will always use the serviceArcLogistics will always use the service
time specifiedtime specified
50. SpecialtySpecialtySpecialtySpecialty
• Hard ConstraintHard Constraint
• An order with a specialty must be servicedAn order with a specialty must be serviced
by a vehicle with a correspondingby a vehicle with a corresponding
specialtyspecialty
• ArcLogistics will not assign the order if noArcLogistics will not assign the order if no
vehicle/driver exists with the specialtyvehicle/driver exists with the specialty
necessary to service the order. A warningnecessary to service the order. A warning
message is displayed.message is displayed.
51. Locked OrdersLocked OrdersLocked OrdersLocked Orders
• Hard ConstraintHard Constraint
• Sometimes called “Pre-Assignment”Sometimes called “Pre-Assignment”
• Forces an order to a specific vehicleForces an order to a specific vehicle
even though this may be sub-optimaleven though this may be sub-optimal
• Usually due to some businessUsually due to some business
requirementrequirement
– Please send driver BobPlease send driver Bob
– Send the technician who took it apartSend the technician who took it apart
yesterdayyesterday
52. Locked RoutesLocked RoutesLocked RoutesLocked Routes
• Hard ConstraintHard Constraint
• Locks all stops to a specific vehicleLocks all stops to a specific vehicle
• Does not allow additional stops to beDoes not allow additional stops to be
addedadded
• Does not allow stops to be removedDoes not allow stops to be removed
53. Evaluating RoutesEvaluating RoutesEvaluating RoutesEvaluating Routes
• Dispatchers typically look to see that driversDispatchers typically look to see that drivers
have a “balanced” work loadhave a “balanced” work load
– In terms of miles, and # stopsIn terms of miles, and # stops
• However, ArcLogistics takes into account ServiceHowever, ArcLogistics takes into account Service
Time, Drive TimeTime, Drive Time
• Routes that have minimized costs may not haveRoutes that have minimized costs may not have
balanced miles or # of stopsbalanced miles or # of stops
• Example:Example:
– Urban vs. Rural RoutesUrban vs. Rural Routes
– Long vs. Short Service TimesLong vs. Short Service Times
• Manually balancing miles or stops will increaseManually balancing miles or stops will increase
costs (usually O.T. or TW Violations)costs (usually O.T. or TW Violations)
56. Minimized Cost vs. Balanced MilesMinimized Cost vs. Balanced Miles
and Ordersand Orders
Minimized Cost vs. Balanced MilesMinimized Cost vs. Balanced Miles
and Ordersand Orders
MinimizedMinimized
CostCost
BalancedBalanced
Orders & MilesOrders & Miles
$1302.66$1302.66
$1176.96$1176.96
4 hrs4 hrs
3.3 hrs3.3 hrs
14.2 hrs14.2 hrs
7 hrs7 hrs
57. Solution MethodologySolution MethodologySolution MethodologySolution Methodology
• Build clusters of stopsBuild clusters of stops
• Sequence stopsSequence stops
• Swap stopsSwap stops
While honoringWhile honoring constraintsconstraints andand
minimizingminimizing costscosts
58. Customer
Info
System
Geographic Database
of all U.S. Streets
- Street Topology/Speed
- Address Ranges
- Graphics
Assignment, Sequence, Travel Time, Arrival Times, etc.
Customer Location
C
ustom
erAddress,
O
rderType
R
oute
Geocode Build OD
Matrix
Assignment
Sequence
Route
Improvement
Generate
Manifest,
Directions,
Maps
Dispatcher
Driver
C
oordinates
N
etw
ork
Location
N
etw
ork
TravelTim
e
FinalR
oute
InitialR
oute
Address
Ranges
Topology/
Speed
Graphics
M
anifest
D
irections
M
aps
User Parameters
e.g. weight TW
violation vs. cost
Routing ProcessRouting ProcessRouting ProcessRouting Process
59. Weighted Objective FunctionWeighted Objective FunctionWeighted Objective FunctionWeighted Objective Function
Factors included in the objectiveFactors included in the objective
function:function:
• CostCost
• Time Window Violation (costTime Window Violation (cost
penalty)penalty)
Define:Define:
Cost:Cost: total cost of the route (dollars);total cost of the route (dollars);
tvtv: time window violation (minutes);: time window violation (minutes);
ƒƒ = cost += cost + αα22 ** tvtv
62. ClusteringClusteringClusteringClustering
Network-based
A seed point approachA seed point approach
takes into account thetakes into account the
real-world travel timereal-world travel time
and distanceand distance
(geography matters)(geography matters)
• Less MilesLess Miles
• Less OvertimeLess Overtime
• More accurate timeMore accurate time
windowswindows
63. Seeds vs. Fixed Work AreasSeeds vs. Fixed Work AreasSeeds vs. Fixed Work AreasSeeds vs. Fixed Work Areas
• Many organizations use polygonal areasMany organizations use polygonal areas
to assign work to certain driversto assign work to certain drivers
– To gain familiarity with an areaTo gain familiarity with an area
– To establish relationships with customersTo establish relationships with customers
– They live near the areaThey live near the area
• However, this can lead to inefficiencies.However, this can lead to inefficiencies.
• Seeds can be user-defined, orSeeds can be user-defined, or
automatically generated.automatically generated.
64. Seeds vs. Fixed Work AreasSeeds vs. Fixed Work AreasSeeds vs. Fixed Work AreasSeeds vs. Fixed Work Areas
• The area where the driver works is theThe area where the driver works is the
same, but can overlap with other driverssame, but can overlap with other drivers
“areas” based on the workload on any“areas” based on the workload on any
given day.given day.
• Routes “Grow” from the seed location, butRoutes “Grow” from the seed location, but
is not limited to that vicinity if there is nois not limited to that vicinity if there is no
work.work.
67. MethodologyMethodologyMethodologyMethodology
• Ways to sequence 20 customers on one route = n!Ways to sequence 20 customers on one route = n!
– 2,432,902,008,176,640,000 possible combinations. (2.42,432,902,008,176,640,000 possible combinations. (2.4
quintillion)quintillion)
• It would take aIt would take a longlong time to evaluate everytime to evaluate every
permutation. Also not necessary.permutation. Also not necessary.
• ArcLogistics uses heuristics. In order to overcome theArcLogistics uses heuristics. In order to overcome the
common shortcomings of heuristics (trapped in localcommon shortcomings of heuristics (trapped in local
optima), we’ve used a state of the art method calledoptima), we’ve used a state of the art method called
Tabu Search.Tabu Search.
• Tabu Search can achieve 10% improvement over theTabu Search can achieve 10% improvement over the
normal heuristicsnormal heuristics
70. Street data is theStreet data is the
cornerstonecornerstone
Street data is theStreet data is the
cornerstonecornerstone
• Locate customers and facilitiesLocate customers and facilities
• Find the shortest pathsFind the shortest paths
– to populate an Origin-Destinationto populate an Origin-Destination
matrixmatrix
• Display the resultsDisplay the results
– what’s the route?what’s the route?
– directionsdirections
71. Data issues...Data issues...Data issues...Data issues...
• Street data is vital to your logisticsStreet data is vital to your logistics
solutionssolutions
– geocodinggeocoding
– routingrouting
– site locationsite location
• Quality varies significantlyQuality varies significantly
• DynamicDynamic
• Big investment (time and money)Big investment (time and money)
• Huge volumeHuge volume
72. Street data...Street data...Street data...Street data...
• what everyone wants...what everyone wants...
– cheapcheap
– correctcorrect
– currentcurrent
• But, presents the most difficultiesBut, presents the most difficulties
– huge data volumeshuge data volumes
– difficult to quantify “goodness”difficult to quantify “goodness”
– difficult to separate data and algorithmsdifficult to separate data and algorithms
73. The Gory DetailsThe Gory DetailsThe Gory DetailsThe Gory Details
• What makes good quality data?What makes good quality data?
• What are some of the importantWhat are some of the important
issues?issues?
• How does data quality affect routingHow does data quality affect routing
solutions?solutions?
But first, some terminology...But first, some terminology...
75. Attribute record Geometry
Each street segment has a multitudeEach street segment has a multitude
of attribute informationof attribute information
more about that latermore about that later
76. ““over/underpass or “brunnel”over/underpass or “brunnel”
(bridge over tunnel)(bridge over tunnel)
101101 199199
100100 198198
92373
92374
Left and right address ranges,Left and right address ranges,
left and right zip codes.left and right zip codes.
77. The #1 problem with streetThe #1 problem with street
data is CONNECTIVITYdata is CONNECTIVITY
The #1 problem with streetThe #1 problem with street
data is CONNECTIVITYdata is CONNECTIVITY
The “terminator” problem -The “terminator” problem -
jumping off a bridge to roadjumping off a bridge to road
belowbelow
……because the data has a nodebecause the data has a node
connecting the two streets, whichconnecting the two streets, which
implies it’s legal to make the jumpimplies it’s legal to make the jump
78. The “Blues Brother”The “Blues Brother”
problem - jump fromproblem - jump from
freeway to unconnectedfreeway to unconnected
side streetside street
……because the data has abecause the data has a
node joining the segmentsnode joining the segments
79. These problems greatlyThese problems greatly
affect routing solutionsaffect routing solutions
These problems greatlyThese problems greatly
affect routing solutionsaffect routing solutions
• Laughable when the route jumps offLaughable when the route jumps off
a bridgea bridge
• Significant affect on travel time (?)Significant affect on travel time (?)
80. Traditionally...Traditionally...Traditionally...Traditionally...
• GIS’s and GIS data have been veryGIS’s and GIS data have been very
centered around the concept of planarcentered around the concept of planar
graphsgraphs
– where arcs cross each other, there must bewhere arcs cross each other, there must be
a node (all arcs are on the same plane)a node (all arcs are on the same plane)
• Every arc is connected in a planarEvery arc is connected in a planar
graphs.graphs.
81. Why is this?Why is this?Why is this?Why is this?
• Most street data sets have servedMost street data sets have served
multiple purposesmultiple purposes
– a street is a boundary between zip codes,a street is a boundary between zip codes,
counties, census districts, cities, etc.counties, census districts, cities, etc.
Planarity (closure) needed toPlanarity (closure) needed to
shade or label polygons,shade or label polygons,
calculate area, overlay with othercalculate area, overlay with other
datadata
82. Why is this?Why is this?Why is this?Why is this?
• Relatively cheap to capture theRelatively cheap to capture the
linework and make it planar...linework and make it planar...
– scan a mapscan a map
• ……compared with establishingcompared with establishing
correct connectivitycorrect connectivity
– automated methods do exist, but areautomated methods do exist, but are
falliblefallible
– ground truth neededground truth needed
83. Solutions to the planaritySolutions to the planarity
problemproblem
Solutions to the planaritySolutions to the planarity
problemproblem
• Remove the node! Make it non-planar!Remove the node! Make it non-planar!
ProblemsProblems
node may be needed for other reasonsnode may be needed for other reasons
identifying nodes to removeidentifying nodes to remove
84. Street Name Road Class Address RangeID
Smith major 101-199, 100-198101
Wesson minor 5043-5097, 5042-5096102
101 102
Example - node can’t be removed because attributes areExample - node can’t be removed because attributes are
differentdifferent
85. Solution - turntableSolution - turntableSolution - turntableSolution - turntable
101
103
102
104
From To
101 104
101 102
102 101
102 103
103 102
103 104
104 101
104 103
A turntable lists all the illegal
turns
Massive amount of informationMassive amount of information
compared to removing thecompared to removing the
nodenode
Difficult to maintainDifficult to maintain
split arc 101split arc 101
86. 5 minute5 minute
delaydelay
At some point, turntables are neededAt some point, turntables are needed
Turntables used to for turning penaltiesTurntables used to for turning penalties
(delays)(delays)
FromFrom ToTo MinutesMinutes
-- -- 55
87. Node ElevationsNode ElevationsNode ElevationsNode Elevations
101
103
102
104
ID From Elev. To Elev.
101 0 0
102 1 1
103 0 0
104 1 1
When network connectivity is created, the from and to nodeWhen network connectivity is created, the from and to node
elevations are taken into account and two nodes are created atelevations are taken into account and two nodes are created at
the intersectionthe intersection
88. Node ElevationsNode ElevationsNode ElevationsNode Elevations
• Can provide “best of both worlds”Can provide “best of both worlds”
• Data set still planar if nodeData set still planar if node
elevations ignoredelevations ignored
• When finding network connectivity,When finding network connectivity,
node elevation honorednode elevation honored
• Behave well in split operationsBehave well in split operations
90. Road ClassRoad ClassRoad ClassRoad Class
• Road Class is used as surrogate forRoad Class is used as surrogate for
travel timetravel time
– ““Primary road” = 55mphPrimary road” = 55mph
– Length of arc is know or can beLength of arc is know or can be
calculatedcalculated
– Travel time dependent on urbanizationTravel time dependent on urbanization
• ‘‘primary road in urban area’ NE ‘primaryprimary road in urban area’ NE ‘primary
road in rural arearoad in rural area
• CFCC or FCC code for TIGER dataCFCC or FCC code for TIGER data
91. Road HierarchyRoad HierarchyRoad HierarchyRoad Hierarchy
• Road class is often used as aRoad class is often used as a
surrogate for network hierarchysurrogate for network hierarchy
• Every ‘real’ network has a hierarchyEvery ‘real’ network has a hierarchy
- Interstate
- Primary
- Collectors
- Surface Streets
Faster travel
Slower travel
Few arcs
many arcs
92. Hierarchies in pathfindingHierarchies in pathfindingHierarchies in pathfindingHierarchies in pathfinding
City
City30 miles
Origin
Destination
(dense street network
in between)
94. HierarchiesHierarchiesHierarchiesHierarchies
• Really great for finding distancesReally great for finding distances
• The hierarchy must be connectedThe hierarchy must be connected
• The high-order hierarchy may needThe high-order hierarchy may need
to have roads of lower class toto have roads of lower class to
connect the hierarchyconnect the hierarchy
95. Cartographic DisplayCartographic DisplayCartographic DisplayCartographic Display
• Road Class used for cartographicRoad Class used for cartographic
symbolizationsymbolization
• Highway in a tunnelHighway in a tunnel
– Displayed differently but still same roadDisplayed differently but still same road
classclass
• Drawing orderDrawing order
– brunnelsbrunnels
96. Road Class does triple dutyRoad Class does triple dutyRoad Class does triple dutyRoad Class does triple duty
• ...for speed limit...for speed limit
• ...for hierarchy...for hierarchy
• ...for display...for display
• Expensive to capture all threeExpensive to capture all three
97. OnewayOnewayOnewayOneway
• Difficult to obtainDifficult to obtain
• Easy to representEasy to represent
– Oneway with digitized direction or against (“FT” vs.Oneway with digitized direction or against (“FT” vs.
“TF”)“TF”)
• For point-to-point problems, not a significantFor point-to-point problems, not a significant
impact on travel timeimpact on travel time
– Oneway streets usually parallel each otherOneway streets usually parallel each other
• Significant impact on directionsSignificant impact on directions
– Don’t send driver wrong way down oneway streetDon’t send driver wrong way down oneway street
98. OnewayOnewayOnewayOneway
• Very significant for arc routingVery significant for arc routing
problemsproblems
– garbage truckgarbage truck
– meter readingmeter reading
– newspaper deliverynewspaper delivery
99. PermissionsPermissionsPermissionsPermissions
• Not all vehicles are allowed to travelNot all vehicles are allowed to travel
on all roadson all roads
• Depends on vehicle, local lawsDepends on vehicle, local laws
• Difficult to captureDifficult to capture
100. U-turnsU-turnsU-turnsU-turns
• Usually never part of dataUsually never part of data
• Dependent on vehicle characteristicsDependent on vehicle characteristics
and street geometryand street geometry
– cul-de-sacscul-de-sacs
• In complicated problems, the pathfindingIn complicated problems, the pathfinding
code has to examine geometry, orcode has to examine geometry, or
provide vehicle-based turntablesprovide vehicle-based turntables
From To Angle
101. Positional AccuracyPositional AccuracyPositional AccuracyPositional Accuracy
• Who cares???Who cares???
• Non-differential GPSNon-differential GPS
• Giant digitizing pucksGiant digitizing pucks
• Sketch in new subdivisionsSketch in new subdivisions
103. DirectionsDirectionsDirectionsDirections
• Printed directions - “Turn rightPrinted directions - “Turn right
where the old church used to be”where the old church used to be”
– rarely usefulrarely useful
– rental carsrental cars
• Thumbnail maps of each turningThumbnail maps of each turning
movement greatmovement great
– quite a lot of programming involvedquite a lot of programming involved
104. Data VolumesData VolumesData VolumesData Volumes
• GIS must handle huge networksGIS must handle huge networks
– millions of arcs affect:millions of arcs affect:
• displaydisplay
• geocodinggeocoding
• pathfindingpathfinding
105. Data VolumesData VolumesData VolumesData Volumes
• This is an area where GIS has pushed theThis is an area where GIS has pushed the
envelopeenvelope
– memory managementmemory management
• memory is getting cheap and commonmemory is getting cheap and common
– algorithmsalgorithms
• many different strategiesmany different strategies
• Most often, an “origin-destination” matrix isMost often, an “origin-destination” matrix is
requiredrequired
– 1000 customers = 1000 x 1000 matrix = 4 million bytes = 41000 customers = 1000 x 1000 matrix = 4 million bytes = 4
MegabytesMegabytes
– Double if time and distance are neededDouble if time and distance are needed
– ……and what about a matrix for time of day routing, or vehicleand what about a matrix for time of day routing, or vehicle
characteristics?characteristics?
106. Street Data is a significantStreet Data is a significant
investmentinvestment
Street Data is a significantStreet Data is a significant
investmentinvestment
• Depends on the applicationDepends on the application
• Update frequencyUpdate frequency
110. ArcIMS
Server
Geocoding
ArcLogistics
RouteServer
Tracking
Stops
Routes
Tracks
Current Position; Running Late;
Arrived; Actual ETA; Off Route;
Reschedule; Cancel
Streets
Wireless
Synchronization
Web Server
Mobile Link
I n t e r n e t
SDE/Oracle
Consumer
Vehicle
Palm OS or
CE browser
Dispatcher
View/Edit
Routes
Place Orders
Check Status
Receive ETA
Location
and Status
Manifest, Maps
Directions
Wireless
Ardis, RAM, CDPD
ActiveX
JAVA
Order Assignment; Order Sequence;
Planned ETA; Maps & Reports
RouteMap
ArcLogistics RoutePortal ConceptArcLogistics RoutePortal ConceptArcLogistics RoutePortal ConceptArcLogistics RoutePortal Concept
HTML
Internet
