Search

This paper proposed an eight-phase transit signal priority control method, CBQL-TSP, based on a hybrid decision-making approach combining cooperative game theory and reinforcement learning, aiming to address the low efficiency of transit signal priority control in intelligent transportation systems. The method abstracted the allocation of bus signal priority into a multi-objective decision-making problem with an eight-phase signal sequence, refining the signal phase sequence into priority and non-priority phases, and constructing a cooperative game model. The paper introduced a hybrid decision-making algorithm, CBQL, to solve the multi-objective decision-making problem of eight-phase signal timing. The algorithm calculated the marginal contributions of each game member by solving the Shapley value function and constructed the state transition probability equation based on the Shapley value ratio. Compared to conventional control methods, CBQL-TSP incorporated the fairness principle of cooperative game theory and the adaptive learning ability of Q-learning, enabling dynamic adjustment of signal timing based on real-time traffic conditions. This method offered higher flexibility and efficiency in priority signal control. This paper established a simulation test platform to compare the average waiting car count and average travel time under different demand levels, evaluating the stability and efficiency of the method. The experimental results demonstrate that the proposed method exhibits higher stability. Furthermore, the overall average travel time is reduced by approximately 24.57%, and the average travel time for buses is reduced by about 37.40%. CBQL-TSP demonstrates higher control efficiency at bus-priority intersections, significantly reducing both overall and bus average travel times. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

By using the bus GPS data and map matching algorithm, this paper depicts the actual bus running status, counts the one-way time consumption and arrival time interval of the bus, quantitatively analyzes the reliability of bus travel time, evaluates the running effect of bus lines, and puts forward a method of identifying road sections with high traffic congestion based on an increasing rate of travel time. With the bus track data in Harbin as an example, this paper analyzes the operation of a bus No. 84. The results show that the one-way punctuality of this line is 88. 33%, and the one-way punctuality stability is 61.55%. The congestion model based on an increasing rate of travel time can effectively identify frequently congested sections of the line, and spatial distribution of the congested sections can be seen on the heat map. Through the analysis of the track data, changing characteristics of the bus operation are discovered, which provides an important reference for solving urban traffic congestion and improving bus service level and reliability. [ABSTRACT FROM AUTHOR]

In the past, the research on urban public transport network mainly focused on the nature of network topology, but less on the optimization of network robustness. After getting the real-time bus routes from the bus route website, the topology model of urban public transport network is constructed based on Python software. The connectivity, the relative size of the largest connected subgraph and the network efficiency are used as the robustness evaluation indexes of the public transport network. The network is attacked by degree attack and random attack mode. The results show that the urban public transport network has good robustness under random attack mode, and the robustness of urban public transport network is poor under degree attack mode. After optimizing the network by using high degree, high betweenness, low degree, low betweenness and random addition, it is concluded that the low degree and low betweenness strategies have a great improvement on the network robustness, while the high betweenness and high degree strategies have no improvement on the network robustness. [ABSTRACT FROM AUTHOR]

Copyright of Railway Investigation & Surveying is the property of Railway Investigation & Surveying Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A visual analysis method for existing public transportation data is difficult to analyze the spatial and temporal distribution of passengers, the spatial and temporal distribution of passenger flow, and the inter-regional passenger flow timing changes under different spatial granularities. This paper proposed a multi-view fusion visual analysis system. The system combined smart card data, vehicle GPS data, subway and bus route information of urban public transportation, then used the travel link model the regression model based on travel time and space characteristics ' to complete the origin-destination (OD) inference. Then, this paper designed the hierarchical visualization method of map visualization, and combined the rose map and the dynamic contrast stacking line graph to analyze the passenger flow timing characteristics and correlation between regions. Finally, visual analysis' results of real public transportation data in Shenzhen verify the effectiveness of the system. [ABSTRACT FROM AUTHOR]

This paper analyzed the wireless network log data of a university to study the law of crowd aggregation and movement on campus. Firstly, the number of wireless network connections of each building on campus was obtained by distributed statistical algorithms. Secondly, it established the R-tree index of building central latitude-longitude coordinates, and grouped the R-tree leaf nodes to divide the campus into parts. Thirdly, it clustered the building central latitude-longitude coordinates of each part to get the campus area division by the density clustering algorithm. Finally, this paper got the law of crowd aggregation area and movement between areas from clustering results and statistical results. Result of this paper can be used as reference for school bus route planning, shared bicycle deployment and campus function area planning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Ritual, Theatre & Folklore / Mínsú Qǔyì is the property of Shih Ho-Cheng Folk Culture Foundation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming to solve the limitation of the evaluation method of traditional bus route service quality， a universal weight analysis method based on passengers perceptions is proposed. Firstly， a weight determination method is established based on passengers perceptions in order to determine the weight of experts and the comprehensive weight of each group. Secondly， the quality evaluation AHP-BP network model of urban bus route service can be acquired by training， testing and verifying with the above data as prior samples. Finally， an empirical study is launched on a certain bus route in Shenyang， and the results demonstrate that the model reflect passenger perceptions adequately as well as reduce the randomness of subjective evaluation which can provide an improved direction for a bus route service. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper researched the relation of road conditions and time on the basis of people-machine-environmental coupling, and proposed a prediction method of time recursive to confirm the shortest time of route. This method was as an accumulated experience basing on the idea of supervised learning in artificial neural network, colligating with the difference of road conditions during different time section, the human factors function, and the randomness of the accident in course of driving, thus realized the guidance of the traffic route. Comparing with the real-time road conditions and accumulated experience, the method of time guidance prediction could offer real-time and effective road information for drivers. This guidance technology assists drivers to judge correctly in time and reduces the time losses because of the lack of the experience and the accidents. The guidance technology can be applied to the vehicles, which is with GPS. The example indicates that the model is effective combined with the real data. [ABSTRACT FROM AUTHOR]