Transfer Coordination for Metro Networks during the Start- or End-of-Service Period

Ning, Liqiao; Zhao, Peng; Xu, Wenkai; Ke, Qiao

doi:10.1155/2018/3835270

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…Thus, the authors implement metaheuristics, such as Multi-start Iterated Local Searches and Variable Neighborhood Searches. Indeed, the synchronization events between trips belonging to different planning periods are relevant as it is stated by the study of Ning et al [15] which addresses an optimization problem to reduce transfer waiting time and transfer availability with respect to the first or last train. Finally, Wu et al [16] enhance the problem of [1] to a biobjective version that optimizes the number of passengers benefited with synchronization and the deviation from an initial timetable.…”

Section: Related Literaturementioning

confidence: 99%

“…the rest of variables non-negative (18) Constraints (14) guarantee that the even headway for each line is bounded by the maximum headway time. Inequalities (15) and (16) bound the first departure time and the holding time at each stop by the even headway for each line, respectively. Finally, constraints (17) and (18) define the domain of the decision variables.…”

Section: Flmentioning

confidence: 99%

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Integrating Frequency Setting, Timetabling, and Route Assignment to Synchronize Transit Lines

Ibarra-Rojas

Muñoz

Giesen

et al. 2019

Journal of Advanced Transportation

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Synchronization of different transit lines is an important activity to increase the level of service in transportation systems. In particular, for passengers, transferring from one line to another, there may be low-frequency periods and transfer zones where walking is needed, or passengers are exposed to adverse weather conditions and uncomfortable infrastructure. In this study, we define the Bus Lines Synchronization Problem that determines the frequency for each line (regarding the even headway), the timetable (including holding times for buses at transfer stops), and passenger-route assignments to minimize the sum of passenger and operational costs. We propose a nonlinear mixed integer formulation with time-indexed variables which allow representing the route choice for passengers and different types of costs. We implement an iterative heuristic algorithm based on fixing variables and solving a simplified formulation with a commercial solver. We implement our proposed heuristic on the transit network in Santiago, Chile. Numerical results indicate that our approach is capable of reducing operating costs and increasing the level of service for large scenarios.

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Section: Related Literaturementioning

confidence: 99%

Section: Flmentioning

confidence: 99%

Integrating Frequency Setting, Timetabling, and Route Assignment to Synchronize Transit Lines

Ibarra-Rojas

Muñoz

Giesen

et al. 2019

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques

Rajasenbagam¹,

Jeyanthi

Pandian

2021

J Ambient Intell Human Comput

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In this research, A Deep Convolutional Neural Network was proposed to detect Pneumonia infection in the lung using Chest X-ray images. The proposed Deep CNN models were trained with a Pneumonia Chest X-ray Dataset containing 12,000 images of infected and not infected chest X-ray images. The dataset was preprocessed and developed from the Chest X-ray8 dataset. The Content-based image retrieval technique was used to annotate the images in the dataset using Metadata and further contents. The data augmentation techniques were used to increase the number of images in each of class. The basic manipulation techniques and Deep Convolutional Generative Adversarial Network (DCGAN) were used to create the augmented images. The VGG19 network was used to develop the proposed Deep CNN model. The classification accuracy of the proposed Deep CNN model was 99.34 percent in the unseen chest X-ray images. The performance of the proposed deep CNN was compared with state-of-the-art transfer learning techniques such as AlexNet, VGG16Net and InceptionNet. The comparison results show that the classification performance of the proposed Deep CNN model was greater than the other techniques.

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Integrating underground line design with existing public transportation systems to increase transit network connectivity: Case study in Greater Cairo

Owais

Ahmed

Moussa

et al. 2021

Expert Systems with Applications

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Transfer Coordination for Metro Networks during the Start- or End-of-Service Period

Cited by 11 publications

References 19 publications

Integrating Frequency Setting, Timetabling, and Route Assignment to Synchronize Transit Lines

Integrating Frequency Setting, Timetabling, and Route Assignment to Synchronize Transit Lines

Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques

Integrating underground line design with existing public transportation systems to increase transit network connectivity: Case study in Greater Cairo

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