Incorporating Dynamic Traffic Distribution into Pavement Maintenance Optimization Model

Mao, Xinhua; Cao, Yuan; Gan, Jiahua

doi:10.3390/su11092488

Cited by 17 publications

(9 citation statements)

References 40 publications

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“…Hence, the evacuation traffic equilibrium is formulated as ∑k∈Ksghksg=Qsg, ∀s∈V1, ∀g∈V3 ∑k∈Ksg∑s∈V1∑g∈V3hksg·δijsg,k=qij,∀i, j∈V1∪V2∪V3 where Equation (12) is the evacuation traffic conservation which indicates the evacuation demands from origin s to destination g is the sum of evacuation traffic flows on all routes connecting origin s to destination g [36]. Equation (13) indicates that the evacuation traffic flow on every single link is the sum of evacuation traffic flows on all routes on which the link lies [37].…”

Section: Methodsmentioning

confidence: 99%

Optimal Evacuation Strategy for Parking Lots Considering the Dynamic Background Traffic Flows

Mao

Cao

Gan

et al. 2019

IJERPH

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An optimal evacuation strategy for parking lots can shorten evacuation times and reduce casualties and economic loss. However, the impact of dynamic background traffic flows in a road network on the evacuation plan is rarely taken into account in existing approaches. This research develops an optimal evacuation model with total evacuation time minimization by dividing the evacuation process in a parking lot into two periods. In the first period, a queuing theory is used to estimate the queuing time, and in the second period, a traffic flow equilibrium model and an intersection delay model are employed to simulate vehicles’ route choice. To deal with these models, a modified ant colony algorithm is developed. The results of a numerical example prove that the proposed method has an advantage in improving evacuation efficiency. The results also show that background traffic flows affect not only vehicles’ average queuing time in parking lots but also optimal evacuation route choice. Additionally, a sensitivity analysis indicates that the minimum threshold of headway time that allows vehicles out of a parking lot to merge into the background traffic flows on the roads connecting the exits has a great impact on average queuing time, average travel time, and total evacuation time.

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Section: Methodsmentioning

confidence: 99%

Optimal Evacuation Strategy for Parking Lots Considering the Dynamic Background Traffic Flows

Mao

Cao

Gan

et al. 2019

IJERPH

Self Cite

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“…For example, Bocchini and Frangopol define bridge network resilience as the integral in time of the network functionality and present a linear programming model with two conflicting objectives, i.e., maximum resilience and minimum maintenance cost to generate an optimal bridge maintenance schedule [15]. In general, two approaches exist to solve the single-stage maintenance problem: the top-down approach and the bottom-up approach [16,17]. The top-down approach assumes that all the facilities in the transportation network have homogeneous features, e.g., traffic patterns, properties of performance deterioration [18], while the bottom-up approach assumes that the facilities have different characteristics, which can capture facility-specific features [19,20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…We define e as a vector of ones so that the discrete variable x ist (∀i ∈ I, ∀s ∈ S, ∀t ∈ T) is equivalent to x T (e − x) = 0 when 0 ≤ x ≤ 1 [33]. Hence, Equation 7is equivalent to Equations (16) and (17).…”

Section: Single-level Modelmentioning

confidence: 99%

Modeling the Optimal Maintenance Scheduling Strategy for Bridge Networks

et al. 2020

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An optimal maintenance scheduling strategy for bridge networks can generate an efficient allocation of resources with budget limits and mitigate the perturbations caused by maintenance activities to the traffic flows. This research formulates the optimal maintenance scheduling problem as a bi-level programming model. The upper-level model is a multi-objective nonlinear programming model, which minimizes the total traffic delays during the maintenance period and maximizes the number of bridges to be maintained subject to the budget limit and the number of crews. In the lower-level, the users’ route choice following the upper-level decision is simulated using a modified user equilibrium model. Then, the proposed bi-level model is transformed into an equivalent single-level model that is solved by the simulated annealing algorithm. Finally, the model and algorithm are tested using a highway bridge network. The results show that the proposed method has an advantage in saving maintenance costs, reducing traffic delays, minimizing makespan compared with two empirical maintenance strategies. The sensitivity analysis reveals that traffic demand, number of crews, availability of budget, and decision maker’s preference all have significant effects on the optimal maintenance scheduling scheme for bridges including time sequence and job sequence.

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“…The emergence of these distresses can affect the aesthetics of the road surface, reduce the driving experience, shorten the service life, and in severe cases, induce traffic safety accidents, causing serious economic and property losses and casualties. Therefore, timely detection of road surface distresses is of great significance in maintaining road safety and ensuring smooth traffic 2 . Currently, the main materials used for road surface paving are ordinary concrete and asphalt concrete.…”

Section: Introductionmentioning

confidence: 99%

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

Zhang,

Bei,

Ling

et al. 2024

Preprint

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Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex backgrounds in the images. To enhance the accuracy of asphalt pavement distress identification across various scenarios, this paper introduces an improved model named SMG-YOLOv8, based on the YOLOv8s framework. This model integrates the space-to-depth module and the multi-scale convolutional attention mechanism, while optimizing the backbone's C2f structure with a more efficient G-GhostC2f structure. Experimental results demonstrate that SMG-YOLOv8 outperforms the YOLOv8s baseline model, achieving Pmacro and mAP@50 scores of 81.1% and 79.4% respectively, marking an increase of 8.2% and 12.5% over the baseline. Furthermore, SMG-YOLOv8 exhibits clear advantages in identifying various types of pavement distresses, including longitudinal cracks, transverse cracks, mesh cracks, and potholes, when compared to YOLOv5n, YOLOv5s, YOLOv6s, and YOLOv8n models. This enhancement optimizes the network structure, reducing the number of parameters while maintaining excellent detection performance. In real-world scenarios, the SMG-YOLOv8 model has demonstrated strong generalization capability and practical utility, providing crucial technical support for intelligent pavement distress detection.

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Incorporating Dynamic Traffic Distribution into Pavement Maintenance Optimization Model

Cited by 17 publications

References 40 publications

Optimal Evacuation Strategy for Parking Lots Considering the Dynamic Background Traffic Flows

Optimal Evacuation Strategy for Parking Lots Considering the Dynamic Background Traffic Flows

Modeling the Optimal Maintenance Scheduling Strategy for Bridge Networks

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

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