Deep learning based energy efficient optimal timetable rescheduling model for intelligent metro transportation systems

Kuppusamy, P.; Venkatraman, S.; Rishikeshan, C. A.; Reddy, Y. C. A. Padmanabha

doi:10.1016/j.phycom.2020.101131

Cited by 27 publications

(6 citation statements)

References 14 publications

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“…Automated extraction of roads from remotely sensed images has been an active research subject for over a decade due to its substantial role in several application areas like urban planning, transportation navigation, traffic management, emergency handling, etc. A new TTR model to optimize the energy of metro transport systems by the incorporation of improved genetic algorithm (IGA) and long short term memory (LSTM) based recurrent neural network (RNN) (Kuppusamy et al, 2020). The concept of road network extraction is relatively simple, but reliability remains a major challenge.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated Extraction of Road Networks From Remotely Sensed Images Using Morphology Based Approach

Rishikeshan

Venkatraman

et al. 2023

Preprint

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Road networks are one of the major features that influence urban planning and development. Thus, road maps of urban areas have to be updated periodically. With advances in remote sensing technology and platforms more and more high quality and fine spatial resolution satellite images are available. Manual method of feature extraction from remote sensing imagery is a tedious and time-consuming process. Automated feature and road network extraction can drastically minimize the time and cost of data acquisition and database update. Thus, automated and replicable techniques play vital role in updating road network to evaluate the spatial and temporal evolution of urban sprawl especially for vastly growing urban areas. This research work presents a mathematical morphology (MM) based approach which is effective and useful for the extraction of road networks from satellite and aerial imageries with better accuracy and minimal turnaround time. Maintenance of actual size and shape of the road networks, runtime control over structuring elements, automation, faster processing and single band adaptability are features of this work. Accuracy of developed methodology has been assessed with ground truths of the area of interest.

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Section: Introductionmentioning

confidence: 99%

Semi-automated Extraction of Road Networks From Remotely Sensed Images Using Morphology Based Approach

Rishikeshan

Venkatraman

et al. 2023

Preprint

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“…The constraints about the dwelling time reduce the state space of variables [16]. Kuppusamy et al [17] proposed a solution method combining GA and neural network. Further, he verified that the timetable can be re-optimized under the condition of random disturbance.…”

Section: Introductionmentioning

confidence: 99%

“…After targeted timetable optimization, Case 2-2 only uses 96 kwh of capacity, which is 11.9% less than Case 2-1. Benefiting from joint optimization, Case 2-3 uses the least capacity (88.9 kWh), which is 18.4% and 7.3% less than Case 2-1 and Case 2-2, respectively.Figures 16,17…”

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confidence: 99%

Joint optimization combining the capacity of subway on‐board energy storage device and timetable

Wang

Sun

Wang

et al. 2022

IET Intelligent Trans Sys

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On‐board energy storage devices (OESD) and energy‐efficient train timetabling (EETT) are considered two effective ways to improve the usage rate of regenerative braking energy (RBE) of subway trains. EETT is less costly but has lower ceilings, whereas OESD, although expensive, maximizes the reuse of RBE. To make the RBE usage rate of the train group reach the required target, a typical problem is how to use the OESD with the minimum capacity to reduce the cost. With this consideration, this paper proposes an optimization method that combines EETT and OESD. In this method, the joint optimization of timetable and OESD capacity allocation in case OESDs of different capacities can be carried between trains is considered to further improve the optimization. A non‐linear programming model is built to minimize the total OESD capacity of the train group under the constraint of reaching target RBE usage. An efficient heuristic algorithm is designed to simultaneously obtain the optimal timetable and the matching capacity allocation scheme of OESDs. Finally, some simulation cases based on real‐line data are designed to verify the effectiveness of the method.

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“…On this basis, to reschedule the system under featured circumstances become a crucial solution. The concept of rescheduling comes from the scheduling theory and rescheduling methods have been widely adopted in many engineering fields such as manufacturing [44,45,46,47] and transportation industry [48,49,50]. Carlos et al [44] proposed a rescheduling mechanism that satisfied distributed constraints and contract network protocols to improve the adaptability of production systems to unpredictable order requirements.…”

mentioning

confidence: 99%

“…Li et al [49] designed an integrated rescheduling model of production and delivery to deal with the unexpected situation that may occur in the process of cargo transportation. Kuppusamy et al [50] designed a new train timetable rescheduling model to reduce the impact of accidents on subway operation efficiency. Taking into account the randomness of truck arrival time, Mohammad et al [51] set up a rescheduling optimization mechanism for the terminal, which effectively improved the efficiency of unloading and loading at the terminal.…”

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confidence: 99%

A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies

Zhao

Yang

et al. 2022

Applied Energy

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Deep learning based energy efficient optimal timetable rescheduling model for intelligent metro transportation systems

Cited by 27 publications

References 14 publications

Semi-automated Extraction of Road Networks From Remotely Sensed Images Using Morphology Based Approach

Semi-automated Extraction of Road Networks From Remotely Sensed Images Using Morphology Based Approach

Joint optimization combining the capacity of subway on‐board energy storage device and timetable

A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies

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