Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment
Abstract
:1. Introduction
2. Related Work
3. Methodology
3.1. Descriptions of the Proposed Model
3.2. Details of the Proposed Model
Algorithm 1. Proposed Travel Time Prediction Process. | |
1: | Initialize prediction horizon using Algorithm 3 |
2: | If at time point n, there is an observed travel time then |
3: | For i = 1:100 do |
4: | Generate possible candidate using with error term ; |
5: | Calculate similarity for each candidate using (3); |
6: | End For |
7: | For l = 1: do |
8: | For i = 1:100 do |
9: | For each possible travel time , calculate probability |
10: | at time point (n + l) using (1); |
11: | Calculate travel time candidate using (2); |
12: | End For |
13: | If object vehicle is observed at time point (n + l) then |
14: | ; |
15: | Begin the resampling process to modify the candidates. |
16: | Else if a crossing vehicle is observed at time point (n + l) then |
17: | If there is an observed crossing vehicle at time point (n + l − p) then |
18: | (l-p < , p < l); |
19: | Else |
20: | ; |
21: | End If |
22: | ; |
23: | Else, ; |
24: | End If |
25: | End For |
26: | End If |
Algorithm 2 Resampling. | |
1: | If at time point m, an object vehicle data is observed then |
2: | Sort candidates according to their weight in decreasing order, |
3: | and remove the later 50 candidates; |
4: | If at time point m then |
5: | For j = 1:100 do |
6: | Select according to , calculate weight using (3); |
7: | If then |
8: | (k = 1…K); |
9: | End If |
10: | End For |
11: | Combine with |
12: | and sort candidates according to weight in decreasing order; |
13: | Else K = 0; |
14: | End If |
15: | If 50 + K > 100 then |
16: | Remove the later (K − 50) candidates; |
17: | End If |
18: | If 50 + K < 100 then |
19: | Select (50−K) candidates randomly from |
20: | according to their weight and add them to ; |
21: | End If |
22: | End If |
4. Data Process
4.1. Data Descriptions
4.2. Relationships between Individual Vehicles
5. Experiments
5.1. Models for Comparison
5.2. Experiment Settings
5.3. Results and Discussion
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
Appendix A
Algorithm 3: Signal timing estimation | |
1: | For continuous data streams consists of object vehicle data and crossing vehicle data do |
2: | If the object vehicle data appear continuously during a period then |
3: | Record this period as the green phase |
4: | Intervals between each green phase are recorded as the red phase |
5: | This record is denoted as {S1} |
6: | End If |
7: | For all red phases in {S1} do |
8: | If the length of a red phase is shorter than 40s then |
9: | Change this red phase into the green phase |
10: | End If |
11: | End For |
12: | Remove the first and last phases in {S1} |
13: | If the crossing vehicle data appear continuously during a period then |
14: | Record this period as a the red phase |
15: | Intervals between each red phase are recorded as the green phase |
16: | This record is denoted as {S2} |
17: | End If |
18: | For all green phases in {S2} do |
19: | If the length of a green phase is shorter than 40s then |
20: | Change this green phase into the red phase |
21: | End If |
22: | End For |
23: | Remove the first and last phases in {S2} |
24: | For all red phases in {S2} do |
25: | If the length of a red phase is longer than 80 s then |
26: | Replace this red phase in {S2} with the corresponding phase in {S1} |
27: | End If |
28: | End For |
29: | Return samples of the green phase and the red phase in {S2} |
30: | End For |
31: | Calculate and with samples of the green phase between 40 s and 80 s long |
32: | Calculate the length of the green phase by A(1) and A(2). |
33: | Calculate and with samples of the red phase between 40 s and 80 s long |
34: | Calculate the length of the red phase by A(3) and A(4). |
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Term | Definition |
---|---|
Object vehicle | Probe vehicle that travels straight through the downstream signalized intersection |
Normal vehicle | Vehicle that cannot send probe data |
Crossing vehicle | Probe vehicle traveling in the crossing direction that goes through the same downstream signalized intersection |
Penetration rate | The ratio of probe vehicles to all vehicles |
Coverage rate | The proportion of travel time that can be predicted |
Penetration Rate (%) | 100 | 50 | 25 | 10 | 5 |
---|---|---|---|---|---|
Proposed model MAPE (%) | 19.3 | 25.6 | 26.2 | 26.5 | 33.8 |
Proposed model RMSE | 19.7 | 24.4 | 29.2 | 27.3 | 30.7 |
Average value MAPE (%) | 71.9 | 65.5 | 57.5 | 58.9 | 54.0 |
Average value RMSE | 43.1 | 42.6 | 44.3 | 39.8 | 34.2 |
Penetration Rate (%) | 100 | 50 | 25 | 10 | 5 |
---|---|---|---|---|---|
kNN-diff.MAPE (%) | 3.2 | −1.0 | −8.0 | − | − |
kNN-diff.RMSE | 2.0 | −1.0 | −5.0 | − | − |
PF-diff.MAPE (%) | −12 | −12 | −27 | − | − |
PF-diff.RMSE | −9.0 | −9.0 | −15 | − | − |
PM_-diff.MAPE (%) | 0.0 | 2.0 | 0.0 | −1.0 | −1.0 |
PM_-diff.RMSE | −3.0 | 1.0 | 2.0 | 0.0 | −6.0 |
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Tang, R.; Kanamori, R.; Yamamoto, T. Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment. Sensors 2020, 20, 265. https://doi.org/10.3390/s20010265
Tang R, Kanamori R, Yamamoto T. Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment. Sensors. 2020; 20(1):265. https://doi.org/10.3390/s20010265
Chicago/Turabian StyleTang, Ruotian, Ryo Kanamori, and Toshiyuki Yamamoto. 2020. "Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment" Sensors 20, no. 1: 265. https://doi.org/10.3390/s20010265
APA StyleTang, R., Kanamori, R., & Yamamoto, T. (2020). Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment. Sensors, 20(1), 265. https://doi.org/10.3390/s20010265