A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning
Abstract
:1. Introduction
2. Data and Materials
3. Method
3.1. Definition of Pedestrian Trajectory Patterns in Mass Indoor Positioning Data
3.2. Stay Point Extraction
3.2.1. Distance Threshold Determination
3.2.2. Time Threshold Determination
3.2.3. The Algorithm for the Extraction of Stay Points from the Staying Trajectories
Algorithm 1: Stay Point_Detection (WP, distThreh, timeThreh,CE) |
Input: A Wi-Fi point log WP, a distance threshold distThreh and time span threshold timeThreh, an updated centroid CE, the initial CE was set as the starting point of a complete pedestrian trajectory. |
Output: A set of stay points SP = {WP} |
Orderby: Wi-Fi record time T |
1. Loop: All pedestrian positioning records |
2. While j < pointNum do |
3. j = j + 1 |
4. While j < pointNum do |
5. Calculate the distance between CE and Wi-Fi: Distance(CE, pj) |
6. If dist < distThreh, then |
7. Update CE (p0, p1, …, pj) |
8. Calculate the time span between two Wi-Fi points as diffTime = pj.T-pj-1. T |
9. If diffTime is Continuous-time then |
10. SP.insert(WPj) |
11. j = j + 1 |
12. Else |
13. SP.time = SP.Endtime − SP. Starttime |
14. If SP.time > timeThreh then |
15. Clear CE.value |
16. j = j + 1 |
17. Return SP |
3.3. Drift Point Extraction Based on Noncustomer Behavior Patterns
3.3.1. Drift Phenomenon in Stay Points
3.3.2. Extraction of Drift Points from Stay Points
Algorithm 2 Drift Point Detection (WP, distThreh, timeThreh, CE, driftdistThreh, drifttime, Threh) |
Input: A Wi-Fi point log WP, a distance threshold distThreh and time span threshold timeThreh, an updated centroid CE, a distance threshold of drift points and time span threshold of drift points |
Output: A set of stay points and drift points |
Orderby: Wi-Fi record time T |
1. Loop: All pedestrian positioning records |
2. While j < pointNum do |
3. J = j + 1 |
4. While j < pointNum do |
5. Calculate the distance between CE and Wi-Fi point: dist = Distance(CE,pj) |
6. Calculate the time span between two Wi-Fi points: diffTime = pj.T-pj-1.T |
7. Calculate the time span between drift point and stay point: driftdiffTime = pj.T-pj−i.T |
8. Calculate the time span between drift point and stay point: backstayPointdist = pj.T-pj−i.T |
9. If dist<distThreh then |
10. Update CE(p0,p1..pj) |
11. If diffTime is Continuous-time then |
12. DP.insert(WPj) |
13. j = j + 1 |
14. Elif dist > driftdistThreh and diffTime is Continuous-time |
15. and driftdiffTime < drifttimeThreh |
16. i = i + 1 |
17. Elif backstayPointdist< distThreh and diffTime is Continuous-time |
18. DP.insert(WPj,WPj−1,‘‘‘,WPj−i) |
19. DP.time = DP.Endtime − DP.Starttime |
20. i = 0 |
21. If DP.time > timeThreh then |
22. Clear CE.value |
23. j = j + 1 |
24. Return DP |
3.4. Accuracy Analysis Based on Drift Points
4. Experiments and Analysis
4.1. Error and Accuracy of the Indoor Wi-Fi Positioning System
4.2. Analysis of the Spatial Accuracy of the Indoor Wi-Fi Positioning System
4.2.1. Relationship between Crowd Density and Indoor Positioning Error
4.2.2. Relationship between the AP Sensors and Indoor Positioning Error
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Floor | Time | CM | CDP | AVE (m) | SD (m) | AoD (m) | SDoD (m) |
---|---|---|---|---|---|---|---|
Ground Floor | 1 day | 35,478 | 463 | 3.21 | 2.43 | 9.12 | 1.16 |
1 week | 210,562 | 3126 | 3.32 | 2.22 | 9.11 | 0.96 | |
1 month | 1,015,468 | 13,659 | 3.09 | 2.35 | 8.89 | 1.12 | |
Second Floor | 1 day | 32,156 | 421 | 3.11 | 2.54 | 9.12 | 0.96 |
1 week | 200,456 | 2965 | 3.89 | 2.23 | 9.56 | 0.89 | |
1 month | 1,008,632 | 12,654 | 3.81 | 2.41 | 8.95 | 1.07 | |
Third Floor | 1 day | 38,456 | 481 | 3.65 | 2.33 | 9.66 | 1.13 |
1 week | 226,544 | 3248 | 3.65 | 2.47 | 9.43 | 0.98 | |
1 month | 1,125,986 | 14,025 | 3.42 | 2.69 | 9.13 | 1.03 |
Model Summary | Coefficients | ||||
---|---|---|---|---|---|
Adjusted R2 | Std. | Model | T | Sig. | |
1st Floor | 0.977 | 33.698 | Crowd Density | 182.658 | 0.03 |
2nd Floor | 0.931 | 38.365 | Crowd Density | 188.634 | 0.04 |
3rd Floor | 0.948 | 35.291 | Crowd Density | 186.889 | 0.01 |
Model Summary | Coefficients | ||||
---|---|---|---|---|---|
Adjusted R2 | Std. | Model | T | Sig. | |
1st Floor | 0 | 99.312 | Crowd Density | 0.461 | 0.791 |
2nd Floor | 0 | 96.567 | Crowd Density | 0.369 | 0.801 |
3rd Floor | 0 | 91.226 | Crowd Density | 0.335 | 0.737 |
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Share and Cite
Yu, D.; Hu, Q.; Wang, S. A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning. ISPRS Int. J. Geo-Inf. 2019, 8, 468. https://doi.org/10.3390/ijgi8110468
Yu D, Hu Q, Wang S. A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning. ISPRS International Journal of Geo-Information. 2019; 8(11):468. https://doi.org/10.3390/ijgi8110468
Chicago/Turabian StyleYu, Dengbo, Qingwu Hu, and Shaohua Wang. 2019. "A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning" ISPRS International Journal of Geo-Information 8, no. 11: 468. https://doi.org/10.3390/ijgi8110468