Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization
1. Introduction and Background
2.1. Overview of the Stop Penalty Derivation
2.2. Factors Impacting Stop Penalty
2.3. Collection of Field Data
2.4. Data Preparation
2.4.1. Vehicle Classification
2.4.2. Instantaneous Fuel Consumption Rates
2.4.3. Cruising Speeds and CSSPs
2.4.4. Road Gradient
2.5. Machine Learning (ML) Models
2.5.1. Multigene Genetic Programming
2.5.2. Development of MGGP Models
3. Results and Discussion
3.1. Models Training, Testing, and Validation
3.2. Parametric Analysis
3.3. Comparison of Stop Penalties from Various Studies
- FC measurements were collected in the field, unlike Alshayeb et al. , whose stop penalties were simulation-based.
- Large number of LDVs and LDTs were included, whereas most previous studies used less than three vehicles.
- The tested fleet consisted of modern vehicles, whereas tested vehicles in the previous studies, except for Stevanovic et al. , are old for contemporary standards.
- Tested vehicles covered long distances, resulting in a significantly larger dataset than those used in the previous studies.
- The models cover multiple factors impacting the stop penalty (vehicle type, cruising speed, road gradient, FC idling rate, driving behavior, and decelerating duration), whereas most of the previous studies investigated only the impact of the cruising speed.
4. Conclusions and Future Research
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Total cost function of the k-prototype algorithm|
|Number of clusters|
|Cost of assigning numerical objects in cluster i|
|Cost of assigning categorical objects in cluster i|
|Within-cluster sum of squares|
|Numerical object number j in cluster i|
|Mean point of the centroid of cluster (i)|
|Number of numerical objects in each cluster i|
|Categorical prototype number j in cluster i|
|Number of categorical objects in cluster i|
|Set of all unique values in the categorical attribute j|
|Input Parameter||LDV1 Model||LDV2 Model||LDV3 Model||LDV4 Model||LDT1 Model||LDT2 Model||LDT3 Model|
|Function set||+, −, x, /, log, sqrt, square|
|Number of generations||500|
|Maximum number of genes allowed in an individual||6|
|Maximum tree depth||4|
|Tournament type||Pareto (probability = 1)|
|Number of inputs||8|
|Constants range||[−10, 10]|
|Complexity measure||Node count|
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Alshayeb, S.; Stevanovic, A.; Park, B.B. Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization. Energies 2021, 14, 7431. https://doi.org/10.3390/en14217431
Alshayeb S, Stevanovic A, Park BB. Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization. Energies. 2021; 14(21):7431. https://doi.org/10.3390/en14217431Chicago/Turabian Style
Alshayeb, Suhaib, Aleksandar Stevanovic, and B. Brian Park. 2021. "Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization" Energies 14, no. 21: 7431. https://doi.org/10.3390/en14217431