GPS for Route Data Collection. Lisa Aultman-Hall Dept. of Civil & Environmental Engineering University of Connecticut
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1 GPS for Route Data Collection Lisa Aultman-Hall Dept. of Civil & Environmental Engineering University of Connecticut
2 Acknowledgements Reema Kundu and Eric Jackson University of Kentucky Wael ElDessouki and Jianhe Du University of Connecticut with helpful advice from Jean Wolf, Sean Doherty and Martin Lee Gosselin Aultman-Hall - August 2001
3 Research Interest Route Choice Behavior NOT how people should route (generating optimal routing) what motivations (particularly beyond travel time) affect routing scenery, traffic control, road type, congestion, turns population segmentation Aultman-Hall - August 2001
4 Who Cares? measure benefits of ITS such as route guidance and traveler info systems dissaggregate exposure for crash and safety analysis improve traffic assignment models Aultman-Hall - August 2001
5 Underlying Assumption all drivers seek to minimize their own travel time stochastic algorithms account for variation Aultman-Hall - August 2001
6 Aultman-Hall - August 2001 Travel Route Data
7 Wide-spread route data is now within reach computer power and memory Geographic Information Systems (GIS) Global Positioning Systems (GPS) Aultman-Hall - August 2001
8
9
10 Satellites and receivers are synchronized so they generate the same code at the same time. Receivers know the satellite orbits. Product of time difference and speed of light is distance.
11
12 But We have more than one satellite overhead
13
14 Typical Errors satellite clock ephemeris receiver atmosphere multipath S/A 2 feet 2 feet 4 feet 12 feet? up to 25 feet Multiply these values by PDOP to get the real time position accuracy. Good PDOP s range from 4 to 6. Aultman-Hall - August 2001
15 Vehicle + GPS Receiver fleet location and management in-vehicle navigation route data!!! Aultman-Hall - August 2001
16 A Model to Map GPS Data to Networks Aultman-Hall - August 2001
17 A Model to Map GPS Data to Networks Aultman-Hall - August 2001
18 Representation of Networks network accuracy center line representation
19 Urban Canyons / Moving Vehicle Aultman-Hall - August 2001
20 Algorithm Development GPS points link route data route choice models traffic line network Optimal GPS Settings PDOP filter noise to signal ratio frequency
21 Aultman-Hall - August 2001 Lexington, KY Population 250, Square Miles 1350 miles road
22
23 Route Development all road types turns downtown rural and treed areas some aimed to trick do NOT start and end at nodes Aultman-Hall - August 2001
24
25 Sample Routes
26 Sample Routes
27 Route Dataset 674 routes (18 driven multiple times) average 11.9 miles long maximum PDOP (6,8 & 10) NSR (2,4 & 6) log frequency (1 s, 2 s, 25 ft 50 ft) Aultman-Hall - August 2001
28 Research Procedure TRIMBLE GPS Receivers Office Pathfinder UNIX ArcInfo - find start and end nodes - use GPS points to adjust link impedances - use MPA to generate route PC ArcView for analysis
29 Start and End Nodes
30 Start and End Nodes A B
31 Start and End Nodes B
32 Start and End Nodes 35% start and end nodes both right 60% one or other at wrong end of link 2% wrong due to time lag in GPS starting to record points Aultman-Hall - August 2001
33 Start and End Nodes A B
34 Start and End Nodes PDOP, NSR and logging did not affect success rate still to come - change buffer size and seek improvement GPS points themselves as stops Aultman-Hall - August 2001
35 Route Prediction algorithm gets all cases of 16 of 18 of the routes (when start and end nodes correct) approximately 90% correct Aultman-Hall - August 2001
36 Problematic Route 1
37 Problematic Route 1
38 Problematic Route 2
39 Outstanding Issues start / end missed travel buffer size use inverse of point density as impedance how low can we go on logging frequency
40 Routing Behavior
41 Conclusions disaggregate route data collection via GPS is feasible using a MPA to translate the points to network nomenclature is very successful a method to quantify route variation is needed Aultman-Hall - August 2001
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