Global Positioning System Reference
In-Depth Information
Shortest Path
Distance
(ft)
Average
Recorded Speed
(mi/h)
Calculated
Speed (mi/h)
Is Path
Feasible?
Data Points
S0 → S2
392.6
26.8
31.5
YES
5125.9
699
33
NO
S2 → S3
S3 → S4
213
29
35
YES
S0 → alt2
392.8
26.8
31.5
YES
alt2 → S3
215.7
29.4
33
YES
Table 1. Speed Comparison for Determining Feasibility of Shortest Paths
4. Performance analysis of the decision-rule map-matching algorithm
Success in solving spatial ambiguities depends on the values assigned to each variable of the
map-matching algorithm. The analysis in this chapter examines the performance of the
map-matching algorithm as values of the following parameters vary: 1) buffer size, 2) speed
range, 3) number of consecutive data points, 4) temporal resolution, and 5) DGPS positional
error.
4.1 Spatial data description
The data employed in this study were collected by winter maintenance vehicles in Columbia
and Portage Counties, Wisconsin, and Polk County, Iowa. These counties have different
accuracy roadway centerline maps with 1:2,400, 1:12,000, and 1:100,000 nominal scales,
respectively, and employ different AVL/DGPS systems for data collection. Selected data
sets with sampling intervals of 2 and 10 seconds were collected for different storm events
and vehicle operators driving through various routes over the 2000-2001, 2001-2002, and
2002-2003 winter seasons. These routes include federal, state, and interstate highways, and
local roads. Figures 7, 8, and 9 display examples of data collected in Columbia, Portage, and
Polk counties every 2, 10, and 10 seconds, respectively. Notice that none of the counties
employed an integrated dead reckoning system and heading information was not available
during the data collection process.
Fig. 7. DGPS Data Points Collected in Portage County Every 10 seconds
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