A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors
Abstract
:1. Introduction
1.1. Background
1.2. Literature Review
1.3. Research Gaps and Contributions
2. Problem Description and Preliminaries
2.1. Problem Description and Assumptions
2.2. Preliminary Representation and Traditional Region-Level BPM
3. Model Formulation and Methodology
3.1. Random-Walk Method
3.1.1. Transferring Probability Calculation
3.1.2. Simulation Process
Algorithm 1: The pseudo-code for the random walk | |
Input: The surveyed OD matrix, observed trajectory data of the vehicles Output: The path set | |
1: | Initialization of and in , the length threshold |
2: | Calculate the transferring probability between each pair of nodes; |
3: | fordo |
4: | initialize to null and add into ; walking agent begins at ; ; |
5: | while the total length of does not exceed and the walking agent is not at do |
6: | select one of the agent’s adjacent nodes according to the |
7: | if is not a repeated or circular path do |
8: | add the selected adjacent node into ; |
9: | else do |
10: | select another one of the agent’s adjacent nodes according to the , return to line 7; |
11: | end if |
12: | let walking agent move to the last added node; |
13: | return |
14: | end for |
3.2. Formulating the P-BPM of Each Simulated Path
3.2.1. Problem Restatement
3.2.2. Formulating the P-BPM
3.3. Calculating the Deployment Score
4. Experiments
4.1. Dataset
4.2. Evaluation Methodology
4.2.1. Implementation Procedure
Algorithm 2: The pseudo-code for inferring the whole detected path | |
Input: The candidate graph , , and of each candidate node Output: | |
1: | Initialize and from the road map and , respectively |
2: | |
3: | for do |
4: | initialize from , = |
5: | for each segment from to in do |
6: | if is not adjacent to do |
7: | remove the path to from |
8: | remove the path to from |
9: | calculate the total value of the nodes in except and |
10: | calculate the total value of the nodes in except and |
11: | if do |
12: | insert to between and as |
13: | else do |
14: | insert to between and as |
15: | end if |
16: | end if |
17: | end for |
18: | return |
4.2.2. Performance Metrics
4.3. Experimental Results
4.4. Comparative Experiments
5. Conclusions and Discussions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Disclosure Statement
Variables
Variables | Details |
Binary, equal to 1 if there is an AVI sensor established at node , and 0 otherwise | |
Binary, equal to 1 if the path from i to j within path r is misidentified, and 0 otherwise | |
Integer, the misidentified traffic flow from node i to node j of path r | |
Integer, the total true traffic flow of path r | |
Float, the misidentified length from node i to node j path r | |
Float, the total length ofpath r | |
C | The set of candidate nodes, C |
L | The set of links between the candidate nodes |
R | The set of the total actual vehicle paths; each path is represented as a sequence of candidate nodes that the vehicle passes |
The set of the nodes in path r, |
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Deployment Proportion of AVI Sensors | Link | Node | Observability | ||||
---|---|---|---|---|---|---|---|
10% | 0.9521 | 0.4747 | 0.6335 | 0.9781 | 0.3753 | 0.5425 | 0.2638 |
20% | 0.9421 | 0.6092 | 0.7399 | 0.9697 | 0.5807 | 0.7264 | 0.4558 |
30% | 0.9485 | 0.668 | 0.7839 | 0.9712 | 0.6836 | 0.8024 | 0.5644 |
40% | 0.9572 | 0.7652 | 0.8505 | 0.9771 | 0.7979 | 0.8785 | 0.6878 |
50% | 0.9543 | 0.8164 | 0.88 | 0.9763 | 0.8693 | 0.9197 | 0.7599 |
60% | 0.9614 | 0.8483 | 0.9013 | 0.9788 | 0.9086 | 0.9424 | 0.8358 |
70% | 0.9632 | 0.8647 | 0.9113 | 0.9791 | 0.9347 | 0.9564 | 0.8788 |
80% | 0.967 | 0.9238 | 0.9449 | 0.9813 | 0.9695 | 0.9754 | 0.929 |
90% | 0.9679 | 0.9569 | 0.9624 | 0.9814 | 0.9893 | 0.9853 | 0.9685 |
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Li, D.; Wang, W.; Zhao, D. A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors. Sustainability 2022, 14, 9474. https://doi.org/10.3390/su14159474
Li D, Wang W, Zhao D. A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors. Sustainability. 2022; 14(15):9474. https://doi.org/10.3390/su14159474
Chicago/Turabian StyleLi, Dongya, Wei Wang, and De Zhao. 2022. "A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors" Sustainability 14, no. 15: 9474. https://doi.org/10.3390/su14159474