Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data
Abstract
:1. Introduction
- We proposed a VR-XGBoost model for recognizing vehicles entering expressway service areas based on ETC data, which not only achieves an effective estimation of the pause rate but also accurately recognizes individual vehicles driving into ESA.
- Taking into full consideration the driving pattern of vehicles entering/exiting the ESA, we proposed a K-VDTE model for vehicle dwell time estimation.
- The validity of the proposed method is verified by using real ETC data, which can provide a more scientific and reasonable reference basis for ESA reconstruction and extension.
2. Related Work
2.1. Pause Rate Estimation
2.2. Vehicle Dwell Time Estimation
3. Methodology
3.1. Framework
3.2. Data Overview and Preprocessing
3.2.1. Data Overview
3.2.2. Data Preprocessing
- (1)
- Data Redundancy
- (2)
- Data Missing
- (3)
- Data Abnormality
Algorithm 1: ETC data cleaning algorithm |
Input: ETC data eData, Topology data TP, Opposite topology data TP′ Output: ETC trajectory dataset 1: G_eData = eData.Groupby([,,]); # Grouping 2: For ∈ G_eData do: # Traversal operation for each set of data 3: # Sorted by transaction time 4: # Data deduplication 5: While (i=1, i < len()): 6: 7: IF : 8: 9: continue; 10: Else IF : 11: 12: 13: delete # Delete opposite gantry transaction data 14: i+= 2; 15: 16: , 17: IF && : 18: # Replacement of opposite gantry ID 19: i+= 2; 20: Else: 21: break; 22: End IF 23: End IF 24: Else: 25: break; 26: End IF 27: IF i = len()-1: 28: ; 29: End IF 30: End While 31: End For |
Algorithm 2: Fusion of ETC trajectory and ESA data |
Input: ETC trajectory dataset eTRAJ, ESA dataset sData, time difference ∆t Output: final trajectory data 1: 2: For do: 3: 4: 5: If in 6: s 7: For row in sdTmp.iterrows(): 8: IF < row.CapTime < : 9: ; 10: IF row.ExEn = 0: 11: 12: Else: 13: 14: End IF 15: Else: 16: continue; 17: End IF 18: End For 19: Else: 20: continue; 21: End IF 22: 23: End For |
3.3. XGBoost-Based VeESA Recognition
3.3.1. Feature Vector Modeling
- (1)
- Speed Features
- (2)
- Spatiotemporal Features
- (3)
- External Features
3.3.2. Modeling of Recognition of VeESA
3.4. Kinematics-Based Dwell Time Estimation
- Stage 1: smooth driving upstream
- Stage 2: decelerating into the ESA
- Stage 4: accelerating out of the ESA
- Stage 5: smooth driving downstream
4. Experiments
4.1. VR-XGBoost Evaluation
4.1.1. Construction of Feature Vector
4.1.2. Parameters Selection
- (1)
- General Parameters: booster, silent, nthread, etc.
- (2)
- Booster Parameters: the number of decision trees (n_estimators), learning rate (learn_rate), maximum depth of the tree (max_depth), minimum weight in leaf nodes (min_child_weight), parameter that controls the number of leaves (gamma), proportion of sample sampling (subsample), scale of feature sampling (colsample_bytree), etc.
- (3)
- Learning Task Parameters: objective and evaluative (eval_metric).
4.1.3. Comparative Analysis of Classification Models
4.2. K-VDTE Evaluation
5. Conclusions
- (1)
- Experiments were conducted using real ETC data with a user penetration rate of over 80%. It not only solves the issue of insufficient data volume but also solves the geographical differences existing in different service areas in vehicle dwell time estimation. It can provide a more scientific and reasonable reference basis for the evaluation of the service capacity of ESA.
- (2)
- Considering multidimensional information such as speed features, spatiotemporal features and external features, we constructed a VR-XGBoost model. This model can achieve not only the estimation of the overall pause rate of ESA but also the accurate recognition of vehicles entering the service area.
- (3)
- After an in-depth study of the driving pattern of vehicles in the process of driving in/out of the ESA, we proposed a K-VDTE to realize vehicle dwell time estimation. The estimation accuracy of vehicle dwell time can be further improved by considering vehicle kinematics.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Field Name | Description | Example | |
---|---|---|---|
1 | VehID | vehicle ID | A000001 |
2 | VehClass | vehicle type | 1 |
3 | EnWeight | entrance gross axle weight | 1500 |
4 | EnStation | entrance ID | 1002 |
5 | EnTime | entrance time | 2020/9/5 00:00:00 |
6 | GantryID | gantry ID | G000335001000120020 |
7 | TradeTime | transaction time | 2020/9/5 01:00:00 |
8 | Workday | workday | 0 |
Field Name | Description | Example | |
---|---|---|---|
1 | SAID | service area ID | Yangli Part A |
2 | EnEx | entrance/exit | 0/1 |
3 | VehID | vehicle ID | A000001 |
4 | CapTime | capture time | 2020/9/5 00:00:00 |
Part A | A0000001 | 2020-09-05 08:06:03 | 2020-09-05 08:08:20 | 2020-09-05 08:14:12 | 2020-09-05 08:23:02 | 23 | 6101 | 2020-09-05 06:29:55 | 18.8 | 1 | 2020-09-05 08:01:59 | 2020-09-05 08:04:08 | 1 |
A0000002 | 2020-09-03 06:28:34 | 2020-09-03 06:30:46 | 2020-09-03 06:43:52 | 2020-09-03 06:52:07 | 22 | 6103 | 2020-09-03 04:24:28 | 11.4 | 0 | 2020-09-03 06:24:42 | 2020-09-03 06:33:32 | 1 | |
A0000003 | 2020-09-10 23:38:27 | 2020-09-10 23:40:24 | 2020-09-10 23:43:03 | 2020-09-10 23:50:57 | 1 | 2202 | 2020-09-10 23:19:23 | 0 | 0 | 0 | |||
A0000004 | 2020-09-07 03:51:13 | 2020-09-07 03:54:27 | 2020-09-07 03:59:52 | 2020-09-07 04:11:46 | 11 | 6101 | 2020-09-06 22:46:11 | 14.3 | 0 | 0 | |||
A0000005 | 2020-09-03 21:14:13 | 2020-09-03 21:17:24 | 2020-09-04 04:56:05 | 2020-09-04 05:06:41 | 16 | 6307 | 2020-09-03 19:33:43 | 45.1 | 0 | 2020-09-03 21:12:24 | 1 | ||
Part B | A0000006 | 2020-09-04 17:17:52 | 2020-09-04 17:32:36 | 2020-09-04 17:48:00 | 2020-09-04 17:50:17 | 16 | 6707 | 2020-09-04 16:48:35 | 50.1 | 0 | 2020-09-04 17:36:41 | 1 | |
A0000007 | 2020-09-08 13:42:53 | 2020-09-08 13:54:00 | 2020-09-08 14:05:48 | 2020-09-08 14:07:57 | 2 | 6707 | 2020-09-08 13:23:20 | 0 | 0 | 0 | |||
A0000008 | 2020-09-06 10:47:19 | 2020-09-06 10:55:17 | 2020-09-06 11:19:16 | 2020-09-06 11:21:21 | 3 | 2903 | 2020-09-06 09:52:21 | 0 | 1 | 2020-09-06 10:47:21 | 2020-09-06 11:08:32 | 1 | |
A0000009 | 2020-09-06 16:58:22 | 2020-09-06 17:07:12 | 2020-09-06 17:09:52 | 2020-09-06 17:12:13 | 12 | 6707 | 2020-09-06 16:37:20 | 7.6 | 1 | 0 | |||
A0000010 | 2020-09-10 21:51:28 | 2020-09-10 22:01:59 | 2020-09-10 22:21:59 | 2020-09-10 22:24:20 | 14 | 6707 | 2020-09-10 21:25:11 | 17.9 | 0 | 2020-09-10 21:53:26 | 2020-09-10 22:09:52 | 1 |
Part A | 114.6 | 21.4 | 109.2 | 85.7 | 0.88 | 14 | 0 | 2 | 0 | 4 | 1 |
92.0 | 93.4 | 84.9 | 92.1 | 1.01 | 10 | 0 | 2 | 0 | 3 | 0 | |
68.0 | 7.2 | 66.6 | 54.1 | 12.18 | 21 | 1 | 13 | 13.54 | 10 | 1 | |
75.4 | 69.6 | 69.3 | 48.6 | 1.86 | 21 | 0 | 14 | 15.9 | 11 | 0 | |
77.4 | 60.5 | 64.8 | 44.9 | 18.55 | 22 | 1 | 15 | 30.28 | 8 | 0 | |
70.0 | 64.4 | 77.8 | 68.4 | 2.72 | 15 | 0 | 21 | 0 | 4 | 0 | |
Part B | 67.6 | 21.2 | 79.6 | 84.1 | 0.81 | 17 | 0 | 12 | 9.3 | 6 | 1 |
80.4 | 88.3 | 81.3 | 83.8 | 0.73 | 18 | 1 | 12 | 7.5 | 8 | 0 | |
77.0 | 20.1 | 86.1 | 74.4 | 0.56 | 20 | 0 | 11 | 4.6 | 16 | 1 | |
67.1 | 76.2 | 72.2 | 66.3 | 0.81 | 21 | 0 | 11 | 0 | 22 | 0 | |
90.1 | 9.7 | 104.7 | 94.9 | 0.42 | 22 | 1 | 1 | 0 | 69 | 1 | |
91.6 | 102.5 | 99.3 | 96.7 | 2.35 | 23 | 0 | 1 | 0 | 20 | 0 |
Parameter | Search Range | Step Size | Optimal Value | |
---|---|---|---|---|
General Parameters | booster | gbtree/gblinear | gbtree | |
silent | 0/1 | 0 | ||
nthread | 4 | |||
Booster Parameters | n_estimators | [100, 1000] | 100 | 300 |
learn_rate | [0, 0.5] | 0.01 | 0.1 | |
max_depth | [1, 10] | 1 | 5 | |
min_child_weight | [1, 10] | 1 | 1 | |
gamma | [0, 0.5] | 0.1 | 0 | |
subsample | [0.6, 1] | 0.05 | 0.8 | |
colsample_bytree | [0.6, 1] | 0.05 | 0.8 | |
Learning Task Parameters | objective | reg:linear/reg:logistic/ binary:logistic/… | binary:logistic | |
eval_metric | error/auc/rmse/… | auc |
Model | Part A | Part B | ||||||
---|---|---|---|---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | Accuracy | Precision | Recall | F1-Score | |
GaussianNB | 0.937 | 0.94 | 0.937 | 0.937 | 0.962 | 0.962 | 0.962 | 0.962 |
SVM | 0.954 | 0.954 | 0.954 | 0.954 | 0.973 | 0.974 | 0.973 | 0.973 |
KNN | 0.955 | 0.956 | 0.955 | 0.955 | 0.973 | 0.974 | 0.973 | 0.973 |
DT | 0.913 | 0.914 | 0.914 | 0.914 | 0.947 | 0.947 | 0.947 | 0.947 |
AdaBoost | 0.941 | 0.942 | 0.941 | 0.941 | 0.969 | 0.97 | 0.969 | 0.969 |
LR | 0.947 | 0.947 | 0.947 | 0.947 | 0.966 | 0.966 | 0.966 | 0.966 |
RF | 0.958 | 0.96 | 0.958 | 0.958 | 0.973 | 0.974 | 0.973 | 0.973 |
GBDT | 0.958 | 0.959 | 0.958 | 0.958 | 0.973 | 0.974 | 0.973 | 0.973 |
VR-XGBoost | 0.959 | 0.96 | 0.959 | 0.959 | 0.974 | 0.974 | 0.974 | 0.974 |
Model | Part A | Part B | ||||
---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | |||
Lasso | 4046 | 2095 | 0.275 | 3831 | 1823 | 0.2 |
KNN | 3443 | 1148 | 0.475 | 3506 | 1073 | 0.33 |
AdaBoost | 536 | 431 | 0.987 | 486 | 400 | 0.987 |
DT | 318 | 90 | 0.995 | 263 | 65 | 0.996 |
ExtraTree | 365 | 92 | 0.994 | 1248 | 146 | 0.915 |
RF | 276 | 71 | 0.997 | 263 | 55 | 0.996 |
GBDT | 272 | 72 | 0.997 | 315 | 61 | 0.994 |
XGBoost | 242 | 70 | 0.997 | 263 | 62 | 0.996 |
AvgSpeed | 85 | 71 | 1.000 | 36 | 30 | 1.000 |
K-VDTE | 69 | 52 | 1.000 | 22 | 14 | 1.000 |
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Cai, Q.; Yi, D.; Zou, F.; Zhou, Z.; Li, N.; Guo, F. Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data. Entropy 2022, 24, 1208. https://doi.org/10.3390/e24091208
Cai Q, Yi D, Zou F, Zhou Z, Li N, Guo F. Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data. Entropy. 2022; 24(9):1208. https://doi.org/10.3390/e24091208
Chicago/Turabian StyleCai, Qiqin, Dingrong Yi, Fumin Zou, Zhaoyi Zhou, Nan Li, and Feng Guo. 2022. "Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data" Entropy 24, no. 9: 1208. https://doi.org/10.3390/e24091208