Improved Manpower Planning Based on Traffic Flow Forecast Using a Historical Queuing Model

Jin, Yinli; Gao, Yiwen; Ping, Wang; Wang, Jun; Wang, Lei

doi:10.1109/access.2019.2933319

Cited by 13 publications

(5 citation statements)

References 47 publications

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“…In conclusion, traffic prediction methods can be divided into two types: model-driven methods and data-driven ones. The model-driven methods, such as cell transmission model [6], queuing theory [7], car-following model [13], threephase traffic theory [16], etc. This type of methods is generally relied on various assumptions and ideal conditions, which make it difficult to achieve good prediction performance in real scenarios.…”

Section: Related Workmentioning

confidence: 99%

Predicting traffic propagation flow in urban road network with multi-graph convolutional network

Yang

2023

Complex Intell. Syst.

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Traffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series model to forecast traffic flow by capturing the spatial–temporal features. However, accurately predicting traffic propagation flow (tpf) is challenging, since the classical GCN model only considers the influence of adjacent road link. In complex urban road network, specific traffic propagation flow (tpf) is affected by various spatial features, such as adjacent tpf, which influences from tpf with same upstream link and tpf with same downstream link. Thus, we proposed a multi-graph learning-based model named TPP-GCN (traffic propagation prediction-graph convolutional network) in this paper to predict the traffic propagation flow in urban road network. The TPP-GCN model captures not only the temporal features but also multi-spatial features based on multi-layer convolution. We validated the model using real-world traffic flow data derived from taxi GPS data in Shenzhen, China. Finally, we compare and evaluate the proposed model with the existing models across several prediction scales.

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Section: Related Workmentioning

confidence: 99%

Predicting traffic propagation flow in urban road network with multi-graph convolutional network

Yang

2023

Complex Intell. Syst.

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“…Jin et al [37] addressed a high-precision prediction of vehicle flow based on historical multisource traffic data. Based on the prediction results, an improved manpower planning strategy is proposed to schedule the work accordingly.…”

Section: Scenario II Improved Manpower Planning For Highway Toll Gatementioning

confidence: 99%

Prediction of Large Scale Spatio-temporal Traffic Flow Data with New Graph Convolution Model

Wang¹,

Shi²,

He³

et al. 2022

Intelligent Electronics and Circuits - Terahertz, ITS, and Beyond

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Prompt and accurate prediction of traffic flow is quite useful. It will help traffic administrator to analyze the road occupancy status and formulate dynamic and flexible traffic control in advance to improve the road capacity. It can also provide more precise navigation guidance for the road users in future. However, it is hard to predict spatiotemporal traffic flow data in large scale promptly with high accuracy caused by complex interrelation and nonlinear dynamic nature. With development of deep learning and other technologies, many prediction networks could predict traffic flow with accumulated historical data in time series. In consideration of the regional characteristics of traffic flow, the emerging Graph Convolutional Network (GCN) model is systematically introduced with representative applications. Those successful applications provide a possible way to contribute fast and proper traffic control strategies that could relieve traffic pressure, reduce potential conflict, fasten emergency response, etc.

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“…Lu and Meng [35] developed a two-stage stochastic programming model to analyze the optimal build-operatetransfer highway capacity under traffic demand uncertainty. Jin et al [36] established a prediction model based on historical multi-source traffic flow data. Based on the prediction results, they proposed an improved human resource planning strategy for toll plazas, so as to reasonably arrange the working times and improve the operational efficiency of the highway in peak periods.…”

Section: Toll Plaza Capacity Designmentioning

confidence: 99%

Capacity Design and Pareto Improvement of Highway Toll Plaza in a Competitive Transport System

Gui

Zhang

et al. 2021

IEEE Access

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As the traffic infrastructure for collecting vehicle tolls, the capacity of the toll plaza determines the service level of the entire highway. The capacity of a toll plaza is highly correlated with its operating costs, especially in peak periods. In this paper, it is assumed that in a competitive transportation system, the residential area and the workplace are connected by a highway with a toll plaza which forms a bottleneck, parallel to a mass transit line; commuters can choose to travel by car or by public transport. By establishing an equilibrium model and two mathematical programming models, the capacity designs of the toll plaza were studied during the traffic service period to achieve three objectives, namely, the toll plaza breaking even, profit maximization, and total social cost minimization of the transportation system. The travel modal splits were analyzed under travel equilibrium in three situations, respectively. In addition, a biobjective optimization model was developed to optimize total profit and total social cost, and a Pareto optimization scheme was analyzed. Finally, the theoretical analyses were also verified by numerical examples.INDEX TERMS Bi-modal traffic, bi-objective optimization model, capacity design, highway toll plaza.

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Improved Manpower Planning Based on Traffic Flow Forecast Using a Historical Queuing Model

Cited by 13 publications

References 47 publications

Predicting traffic propagation flow in urban road network with multi-graph convolutional network

Predicting traffic propagation flow in urban road network with multi-graph convolutional network

Prediction of Large Scale Spatio-temporal Traffic Flow Data with New Graph Convolution Model

Capacity Design and Pareto Improvement of Highway Toll Plaza in a Competitive Transport System

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