An Intelligence-Based Optimization Model of Passenger Flow in a Transportation Station

Yuen, Jason K.K.; Lee, Eric Wai Ming; Lo, Siuming; Yuen, Richard K.K.

doi:10.1109/tits.2013.2259482

Cited by 24 publications

(16 citation statements)

References 28 publications

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“…. , m) and the operating parameters n i zai and γ zai one by one; λ 1 1 is introduced into the matrix A 1 to A 1 1 , n 1 zai , γ 1 zai , and so on; and A 1,1 n , n n zai , γ n zai are calculated in the similar manner, until the calculation for all the stations m (1, 2, 3, . . .…”

Section: B Dynamic Video Passenger Flow Recognitionmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Jesus Felez . comfort has achieved great success in China, it has also brought about certain subsequent problems [1]: With the continuous increase of the living costs, the spatial and temporal distribution of the railway passenger transport lines is not balanced, and the difference in the passenger flow from the station to the departure is obvious. The passengers are stranded at the station during the peak hours, which has imposed pressure on the passenger transport organization and the operating safety of the railway lines [2]- [3].…”

Section: Introductionmentioning

confidence: 99%

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Railway Passenger Flow Recognition Algorithm for Terminal Station Based on Cost Theory and Automatic Frequency Control

Guo

Xie

et al. 2020

IEEE Access

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Passenger flow recognition is the basis of railway passenger terminal station and railway operation department for daily management, it has an important significance for the adjustment of the operation plan, the choice of the passenger travel mode, the estimation of the travel time and so on. It is especially important for the optimization of the operating plan for the railway route under the railway transportation mode. Therefore, considering the instability and complexity of passenger flow change in the railway passenger terminal station, this paper combines the cost theory(CT) and automatic frequency control(AFC) into the passenger flow prediction. Firstly, in order to obtain the volume of passenger loss due to the detained passenger flow reaching the threshold value, the angle cost formed by the railway travel of the passengers is analyzed. On this basis, an angle cost model is constructed to calculate the passenger loss rate. Then, with the volume of passenger lost calculated, combined with the passenger flow data obtained by the AFC, a passenger flow recognition algorithm for the railway passenger terminal station based on the dynamic video technology is put forward. Subsequently, the passenger flow recognition law between the flow of passenger waiting at the station and the actual passengers carried by the train that has passed by is analyzed, and the passenger flow impact dynamic exchange model is put forward. In addition, the algorithm for solving the model is analyzed and studied. Finally, a railway line is taken as an example for case study. The recognition results can provide a theoretical and methodological support for the optimization of the railway passenger transportation operating plan and serve as a reference for the temporary adjustment of the operational planning, the passenger flow recognition, the supplementation of the guidance model and so on.

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Section: B Dynamic Video Passenger Flow Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Railway Passenger Flow Recognition Algorithm for Terminal Station Based on Cost Theory and Automatic Frequency Control

Guo

Xie

et al. 2020

IEEE Access

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“…a∈ [0. 01,12], b∈ [-12,12], and W∈ [-12,12]. To determine a proper a, b and W, the following simple scheme are proposed on the basis above induced hybrid GA: 1) Pre-processing the input data series through fuzzy reasoning theory; 2) Generating the original population within a logical interval; 3) Utilizing crossover and mutation operations; 4) Creating new offspring via optimum reservation and proportion selection; 5) Sequencing the individuals by fitness and computing the distances between chromosomes; 6) Finishing the search process, if satisfies the stopping rule, or going back to step III; 7) Real-time and dynamic forecast with identical dimension recurrence [12].…”

Section: Experiments and Implementationmentioning

confidence: 99%

Passenger Flow Forecast Using Wavelet Neural Network Model

Chen¹,

Xin²

2015

Proceedings of the 2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering

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Abstract. Wavelet neural network (WNN), combining with wavelet analysis and neural network, brings forth a high-accuracy performance in identification and approximation. Passenger flow forecast plays an important role in transit scheduling and an improved WNN model is constructed to actualize dynamic forecast, in which Morlet wavelet is selected as the activation function. Input data series surveyed from No.609 line in Xi'an, China, is pre-processed via a fuzzy operator before transferred to train and test the constructed network. A hybrid genetic algorithm and identical dimension recurrence idea are performed to optimize the structure and shape of WNN dynamically to enhance its forecast accuracy. The result indicates the proposed WNN model can accelerate the convergence speed, improve the global generalization ability and possess the practicality in dynamic transit scheduling.

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“…It can be stated that travellers take into account a lot of factors (e.g. personal impressions, road geometry, environmental impacts) to choose the appropriate walking route to the public transportation stop or the route between the changing points (Yuen et al 2013). Travellers usually select their route based on the shortest distance, time or a combination of the two (Ortúzar, Willumsen 2011).…”

Section: Introductionmentioning

confidence: 99%

Route Plan Evaluation Method for Personalised Passenger Information Service

Földes

Csiszár

2015

Transport

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Due to changing expectations of characteristics of mobility demands, public transportation users increasingly require a reduction of both the preparation and travel time, an easier and more pleasant travelling experience as well as route plans based on reliable data. Both international and domestic research is widely concerned with route planning optimization. Exemplary assistance applications are already in operation, but they are only semi-occasionally and slightly personalized. Consequently, there is potential for significant research and development in this area. Our developed method and algorithm evaluates the routes based on the personalised user settings and in this way, the ideal routes can be determined. User preferences are represented in evaluation criteria. The algorithm also manages network modifications and often-changing user preferences. The novelty of our algorithm lies in the more realistic evaluation of the routes appreciably considering both the exact physical properties of the infrastructure and the users' detailed personal preferences.

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An Intelligence-Based Optimization Model of Passenger Flow in a Transportation Station

Cited by 24 publications

References 28 publications

Railway Passenger Flow Recognition Algorithm for Terminal Station Based on Cost Theory and Automatic Frequency Control

Railway Passenger Flow Recognition Algorithm for Terminal Station Based on Cost Theory and Automatic Frequency Control

Passenger Flow Forecast Using Wavelet Neural Network Model

Route Plan Evaluation Method for Personalised Passenger Information Service

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