Analysis and Prediction of Traffic Conditions Using Machine Learning Models on Ikorodu Road in Lagos State, Nigeria
Abstract
1. Introduction
2. Literature Review
2.1. Traffic Flow and Congestion
2.2. Traditional Traffic Data Collection Methods
2.3. Machine Learning in Traffic Prediction
2.4. Applications on Machine Learning in Urban Transportation Systems
2.5. Challenges in Traffic Prediction and Model Validation
3. Materials and Methods
3.1. Study Area
3.2. Methods
3.3. Traffic Count Survey
3.4. Machine Learning Models (ML)
3.4.1. Random Forest Model (RFC)
3.4.2. Decision Tree Model (DT)
3.4.3. Gradient Boosting Classifier (GBC)
3.5. Reliability Analysis
3.5.1. Descriptive Statistics
3.5.2. Correlation Analysis
3.5.3. Model Development
3.5.4. Data Preprocessing and Partitioning
3.5.5. Feature Selection
3.5.6. Model Evaluation and Validation
3.5.7. Predicting Traffic Conditions
4. Results and Discussion
4.1. Daily Traffic Volume
4.2. Time-Based Traffic Volume
4.3. Descriptive Statistics Results
4.4. Correlation Analysis Results
4.5. Model Performance Cross-Validation Results
4.6. Model Validation
4.7. Traffic Condition Predictions
5. Conclusions
- Implementation of dynamic traffic management: The Lagos Traffic Management Authority should consider using a random forest classifier model or similar predictive tools to facilitate dynamic traffic management strategies. This could involve taking proactive measures during predicted peak times to mitigate congestion.
- Consideration of additional factors: Future studies and traffic prediction models should incorporate additional external factors, such as weather conditions and special events, for more accurate and comprehensive traffic predictions.
- Exploration of advanced modeling techniques: Further research could explore the use of more advanced machine learning techniques or deep learning models that might better capture complex patterns in the data and provide more accurate predictions.
- Development of real-time traffic prediction systems: As technology and data collection techniques evolve, there is potential for developing real-time traffic prediction systems. Such systems can provide even more dynamic and responsive traffic management solutions.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
ITS | Intelligent transportation systems |
ADT | Average daily traffic |
AADT | Annual average daily traffic |
AI | Artificial intelligence |
RFC | Random forest classifier |
DT | Decision tree |
GBC | Gradient boosting classifier |
ML | Machine learning |
Xi | Features |
F | Trained model |
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Day | Total Flow (Veh/day) | Average Flow (Veh/day) |
---|---|---|
Monday | 5582 | 465.17 |
Tuesday | 5372 | 447.67 |
Wednesday | 4739 | 394.92 |
Thursday | 4996 | 416.33 |
Friday | 5239 | 436.58 |
Saturday | 3083 | 256.92 |
Sunday | 3832 | 319.33 |
Time | Car Total | Jeeps Total | Trucks Total | Buses Total | Tricycles Total | Bicycles Total |
---|---|---|---|---|---|---|
6:00 | 2258 | 1141 | 34 | 270 | 211 | 12 |
7:00 | 2271 | 590 | 23 | 455 | 174 | 7 |
8:00 | 1145 | 268 | 17 | 266 | 187 | 12 |
9:00 | 1173 | 276 | 15 | 313 | 269 | 6 |
10:00 | 733 | 154 | 15 | 186 | 294 | 10 |
11:00 | 438 | 175 | 12 | 309 | 187 | 15 |
12:00 | 471 | 234 | 24 | 262 | 195 | 9 |
13:00 | 459 | 241 | 9 | 160 | 168 | 6 |
14:00 | 1245 | 295 | 10 | 197 | 166 | 13 |
15:00 | 1863 | 450 | 23 | 232 | 170 | 5 |
16:00 | 2730 | 721 | 19 | 431 | 195 | 9 |
17:00 | 3445 | 555 | 14 | 321 | 167 | 10 |
18:00 | 2258 | 1141 | 34 | 270 | 211 | 12 |
19:00 | 2271 | 590 | 23 | 455 | 174 | 7 |
20:00 | 1145 | 268 | 17 | 266 | 187 | 12 |
21:00 | 1173 | 276 | 15 | 313 | 269 | 6 |
22:00 | 733 | 154 | 15 | 186 | 294 | 10 |
23:00 | 438 | 175 | 12 | 309 | 187 | 15 |
0:00 | 471 | 234 | 24 | 262 | 195 | 9 |
1:00 | 459 | 241 | 9 | 160 | 168 | 6 |
2:00 | 1245 | 295 | 10 | 197 | 166 | 13 |
3:00 | 1863 | 450 | 23 | 232 | 170 | 5 |
4:00 | 2730 | 721 | 19 | 431 | 195 | 9 |
5:00 | 3445 | 555 | 14 | 321 | 167 | 10 |
Time | Car Total | Cars Average | Jeeps Total | Jeeps Average | Trucks Total | Trucks Average | Buses Total | Buses Average | Tricycles Total | Tricycles Average | Bicycles Total | Bicycle Average |
---|---|---|---|---|---|---|---|---|---|---|---|---|
6:00 | 2258 | 322.57 | 1141 | 163 | 34 | 4.86 | 270 | 38.57 | 211 | 30.14 | 12 | 1.71 |
7:00 | 2271 | 324.43 | 590 | 84.29 | 23 | 3.29 | 455 | 65 | 174 | 24.86 | 7 | 1 |
8:00 | 1145 | 163.57 | 268 | 38.29 | 17 | 2.43 | 266 | 38 | 187 | 26.71 | 12 | 1.71 |
9:00 | 1173 | 167.57 | 276 | 39.43 | 15 | 2.14 | 313 | 44.71 | 269 | 38.43 | 6 | 0.86 |
10:00 | 733 | 104.71 | 154 | 22 | 15 | 2.14 | 186 | 26.57 | 294 | 42 | 10 | 1.43 |
11:00 | 438 | 62.57 | 175 | 25 | 12 | 1.71 | 309 | 44.14 | 187 | 26.71 | 15 | 2.14 |
12:00 | 471 | 67.29 | 234 | 33.43 | 24 | 3.43 | 262 | 37.43 | 195 | 27.86 | 9 | 1.29 |
13:00 | 459 | 65.57 | 241 | 34.43 | 9 | 1.29 | 160 | 22.86 | 168 | 24 | 6 | 0.86 |
14:00 | 1245 | 177.86 | 295 | 42.14 | 10 | 1.43 | 197 | 28.14 | 166 | 23.71 | 13 | 1.86 |
15:00 | 1863 | 266.14 | 450 | 64.29 | 23 | 3.29 | 232 | 33.14 | 170 | 24.29 | 5 | 0.71 |
16:00 | 2730 | 390 | 721 | 103 | 19 | 2.71 | 431 | 61.57 | 195 | 27.86 | 9 | 1.29 |
17:00 | 3445 | 492.14 | 555 | 79.29 | 14 | 2 | 321 | 45.86 | 167 | 23.86 | 10 | 1.43 |
18:00 | 2258 | 322.57 | 1141 | 163 | 34 | 4.86 | 270 | 38.57 | 211 | 30.14 | 12 | 1.71 |
19:00 | 2271 | 324.43 | 590 | 84.29 | 23 | 3.29 | 455 | 65 | 174 | 24.86 | 7 | 1 |
20:00 | 1145 | 163.57 | 268 | 38.29 | 17 | 2.43 | 266 | 38 | 187 | 26.71 | 12 | 1.71 |
21:00 | 1173 | 167.57 | 276 | 39.43 | 15 | 2.14 | 313 | 44.71 | 269 | 38.43 | 6 | 0.86 |
22:00 | 733 | 104.71 | 154 | 22 | 15 | 2.14 | 186 | 26.57 | 294 | 42 | 10 | 1.43 |
23:00 | 438 | 62.57 | 175 | 25 | 12 | 1.71 | 309 | 44.14 | 187 | 26.71 | 15 | 2.14 |
0:00 | 471 | 67.29 | 234 | 33.43 | 24 | 3.43 | 262 | 37.43 | 195 | 27.86 | 9 | 1.29 |
1:00 | 459 | 65.57 | 241 | 34.43 | 9 | 1.29 | 160 | 22.86 | 168 | 24 | 6 | 0.86 |
2:00 | 1245 | 177.86 | 295 | 42.14 | 10 | 1.43 | 197 | 28.14 | 166 | 23.71 | 13 | 1.86 |
3:00 | 1863 | 266.14 | 450 | 64.29 | 23 | 3.29 | 232 | 33.14 | 170 | 24.29 | 5 | 0.71 |
4:00 | 2730 | 390 | 721 | 103 | 19 | 2.71 | 431 | 61.57 | 195 | 27.86 | 9 | 1.29 |
5:00 | 3445 | 492.14 | 555 | 79.29 | 14 | 2 | 321 | 45.86 | 167 | 23.86 | 10 | 1.43 |
Variables | Correlation |
---|---|
Time of day and traffic volume | 0.1048 |
Model | Cross-Validation Score | Test Accuracy |
---|---|---|
Decision Tree Classifier | 0.881 | 0.88 |
Gradient Boosting Classifier | 0.925 | 0.88 |
Random Forest Classifier | 0.894 | 1.00 |
Class | Precision | Recall | F1-Score |
---|---|---|---|
High | 1.00 | 1.00 | 1.00 |
Low | 0.80 | 0.80 | 0.80 |
Medium | 0.67 | 0.67 | 0.67 |
Average/Total | 0.88 | 0.88 | 0.88 |
Class | Precision | Recall | F1-Score |
---|---|---|---|
High | 1.00 | 1.00 | 1.00 |
Low | 0.80 | 0.80 | 0.80 |
Medium | 0.67 | 0.67 | 0.67 |
Average/Total | 0.88 | 0.88 | 0.88 |
Class | Precision | Recall | F1-Score |
---|---|---|---|
High | 1.00 | 1.00 | 1.00 |
Low | 1.00 | 1.00 | 1.00 |
Medium | 1.00 | 1.00 | 1.00 |
Average/Total | 1.00 | 1.00 | 1.00 |
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Imoh, U.U.; Rad, M.M. Analysis and Prediction of Traffic Conditions Using Machine Learning Models on Ikorodu Road in Lagos State, Nigeria. Infrastructures 2025, 10, 122. https://doi.org/10.3390/infrastructures10050122
Imoh UU, Rad MM. Analysis and Prediction of Traffic Conditions Using Machine Learning Models on Ikorodu Road in Lagos State, Nigeria. Infrastructures. 2025; 10(5):122. https://doi.org/10.3390/infrastructures10050122
Chicago/Turabian StyleImoh, Udeme Udo, and Majid Movahedi Rad. 2025. "Analysis and Prediction of Traffic Conditions Using Machine Learning Models on Ikorodu Road in Lagos State, Nigeria" Infrastructures 10, no. 5: 122. https://doi.org/10.3390/infrastructures10050122
APA StyleImoh, U. U., & Rad, M. M. (2025). Analysis and Prediction of Traffic Conditions Using Machine Learning Models on Ikorodu Road in Lagos State, Nigeria. Infrastructures, 10(5), 122. https://doi.org/10.3390/infrastructures10050122