Intelligent passenger demand prediction‐based rerouting for comfort perception in public bus transport systems

Nayak, Archana M.; Chaubey, Nirbhay

doi:10.1002/dac.5351

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…The traditional methods concerning the intelligent transportation system with dynamic routing based on user demand are elucidated here. A machine learning-based vehicle scheduling based on user demand was introduced by Rajkumar, S.C. and Deborah, L.J [19] for vehicle traffic optimization. In this, the instant reservation of seats in the vehicles was accomplished by real-time sensors through efficient communication.…”

Section: Related Workmentioning

confidence: 99%

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PAARGAMAN: Passenger Demand Provoked (On-The-Fly) Routing Of Intelligent Public Transport Vehicle with Dynamic Route Updation, Generation, and Suggestion

Ladha,

Chaubey

2023

IJRITCC

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Demand-based public bus service meets the need of passengers with less money, time, and resources by reducing the number of private vehicles on the road. In contrast, dynamic real-time demand-based routing faces challenges like elevated travel time due to the requested assignment based on the paths and vehicle availability. Hence, this research introduces a novel framework named Passenger Influence Bus Service-Intelligent Public Transport System (PIBS-IPTS) for efficient routing of available vehicles based on the demand of passengers. For this, optimal paths are elected from the known routes of the general vehicle through the Cuckoo Search (CS) optimization algorithm. Then efficient route prediction is employed by the Artificial Neural Network (ANN) for passenger flow. Here, the unavailability of the passenger request, such as source location or Destination locations, or the unavailability of both locations is updated while employing the path generation process. The path generation process ensures the reduction of request drops generated by the passenger, which elevates the usage of the general bus service. Here, for the optimal selection of routes from the identified routing paths, a multi-objective function based on traffic density, route condition, and route mobility is employed for the selection of a near-optimal global solution. The method’s performance is analyzed using MAE, RMSE, and MAPE and obtained the best values of 0.69, 0.72, and 0.74, respectively.

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Section: Related Workmentioning

confidence: 99%

“…• The non-optimal routing makes a trade-off between the user demand and the transporter, limiting the passenger's real-time travel demands [22]. • Selecting the right vehicle for travelling from one location to the other is a challenging task for users with minimal time and cost [19].…”

Section: A Problem Statementmentioning

confidence: 99%

PAARGAMAN: Passenger Demand Provoked (On-The-Fly) Routing Of Intelligent Public Transport Vehicle with Dynamic Route Updation, Generation, and Suggestion

Ladha,

Chaubey

2023

IJRITCC

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“…Predicting the number of passengers is the main demand for a modern public transportation system which has experienced a revolution in recent years [13] and becomes the focus for solving various problems of bus operating companies [14]. Accurate predictions of the number of bus passengers can help bus operating companies make reasonable plans and encourage the development of good bus public transportation [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators

Siswanto,

Manongga,

Sembiring

et al. 2024

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The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Transport (AT) New Zealand metro patronage buses (01/01/2019-07/31/2023). The best prediction model was obtained from the lowest evaluation value and relatively fast time at variations of epoch 60, batch size 16, and neurons 32. The prediction results on training and testing data improved with the suitability of the model tuning. The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. Strong negative correlation on New Zealand Bus-Pavlovich, strong positive correlation on Go Bus with Ritchies and Pavlovich. Predictions that are less closely related and dependent are New Zealand Bus against Go Bus, Pavlovich, and Ritchies. The proposed prediction modeling can be used as a basis for creating operator policies and strategies to deal with passenger fluctuations and for the development of new prediction models.

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Analysing the indoor temperature ranges for an efficient control of HVAC systems

Barbadilla-Martín,

Ragel-Bonilla,

Aparicio-Ruiz

et al. 2023

Energy and Buildings

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Intelligent passenger demand prediction‐based rerouting for comfort perception in public bus transport systems

Cited by 5 publications

References 37 publications

PAARGAMAN: Passenger Demand Provoked (On-The-Fly) Routing Of Intelligent Public Transport Vehicle with Dynamic Route Updation, Generation, and Suggestion

PAARGAMAN: Passenger Demand Provoked (On-The-Fly) Routing Of Intelligent Public Transport Vehicle with Dynamic Route Updation, Generation, and Suggestion

Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators

Analysing the indoor temperature ranges for an efficient control of HVAC systems

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