Evaluation of Bus Lane Layouts Based on a Bi-Level Programming Model—Using Part of the Qingshan Lake District of Nanchang City, China, as an Example

Xue, Yunqiang; Cheng, Lin; Zhong, Meng Chun; Sun, Xiaokang

doi:10.3390/su15118866

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…However, as busses share the road with private vehicles, they often encounter heavy traffic congestion and delays. To address this problem, bus priority solutions via bus lanes have evolved into an essential component of urban public transportation networks' growth and enhancement [40]. Russo et al [41] describe two implementations for DBL systems.…”

Section: Traffic Control Preference Via Dedicated Lanesmentioning

confidence: 99%

Reinforcement Learning for Traffic Control using social preferences

Barzilai

2024

Civil Engineering

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Traffic congestion arises from all directions, particularly during peak hours, and requires the implementation of a preference mechanism—designated lanes are set up as fast lanes for prioritizing public transportation and ride sharing. Defining a rigid criterion for using the fast lanes can be ineffective if the criterion for using these lanes is unrelated to traffic volume. In situations where fast lanes become overloaded, the rigid criteria do not ensure efficient travel. A social preference criterion, similar to those utilized in priority queues found in various service sectors such as government, travel, and cultural events, could be adapted for use in managing traffic flow and lane prioritization. The social preference criteria will be based on the driver’s characteristics (e.g., a handicraft driver) or not its travel purpose (e.g., a doctor traveling for emergency surgery). To facilitate efficient travel for vehicles utilizing the fast lanes, the implementation of a reinforcement learning (RL) algorithm, specifically the Q-learning algorithm, is proposed. The results indicated that individuals exhibit social preference for various categories of vehicle passenger characteristics. The Q-learning algorithm regulated traffic flow in a junction simulation, distinguishing between fast lanes and regular lanes based on both social preference and traffic volume. This approach ensured efficient prioritization and allocation of resources.

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Section: Traffic Control Preference Via Dedicated Lanesmentioning

confidence: 99%

Reinforcement Learning for Traffic Control using social preferences

Barzilai

2024

Civil Engineering

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Integrated Evaluation Method of Bus Lane Traffic Benefit Based on Multi-Source Data

Qiao,

Yang,

Peng

et al. 2024

Mathematics

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Bus lanes are an important measure for improving the quality of bus service and the efficiency of transportation systems. A scientific and reasonable evaluation of the overall traffic operation efficiency of the bus priority road section is helpful to fully understand the improvement effect of the introduction of bus lanes on traffic operation. To comprehensively and objectively evaluate the traffic benefits of bus lanes, the Delphi and grey correlation methods were used to construct a comprehensive weight calculation model of the indicators. The weights of eight traffic benefit evaluation indicators at the two levels of buses and general traffic were calculated, and the weights were then optimized using the target optimization model. Combined with different weight indexes, the evaluation of the traffic benefit level of the bus lane was realized using the matter-element extension model based on the improvement in the sticking progress. The bus lanes of the Daping-Yangjiaping, Huanghuayuan interchange-Luneng turntable, and Dashiba-Hongqihegou routes in the main urban area of Chongqing were used for verification. The results show that the traffic benefits of the three case areas have been improved to a certain extent after the construction of bus lanes, but the benefit level has not changed. Through the analysis of various operating indicators, the weaknesses that affect the traffic efficiency can be obtained, and then the decision-making basis for the implementation and improvement of the bus lane optimization scheme can be provided.

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Evaluation of Bus Lane Layouts Based on a Bi-Level Programming Model—Using Part of the Qingshan Lake District of Nanchang City, China, as an Example

Cited by 2 publications

References 23 publications

Reinforcement Learning for Traffic Control using social preferences

Reinforcement Learning for Traffic Control using social preferences

Integrated Evaluation Method of Bus Lane Traffic Benefit Based on Multi-Source Data

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