Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method

Zhu, Huaizhong; Yang, Xiaoguang; Wang, Yizhe

doi:10.1155/2018/6142724

Cited by 19 publications

(13 citation statements)

References 9 publications

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“…Saadi et al (2017) have investigated the performance of several ML techniques and a fully connected DL model with only two hidden layers and have shown that their very simple DL model outperforms almost all other techniques except a boosted decision tree. Besides the simple DNN models in Dominguez-Sanchez et al (2017), Jung and Sohn (2017), and Zhu et al (2018b), a hybrid model containing a stacked AE and a DNN has been implemented by Liu and Chen (2017) to predict hourly passenger flow.…”

Section: Ride Sharing and Public Transportationmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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Section: Ride Sharing and Public Transportationmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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“…Regarding the Bayesian network model, Roos et al [24] proposed a method based on a dynamic Bayesian network to predict the short-term passenger flow of the Paris Metro, 2 Scientific Programming which can work normally even when the data are incomplete. For the neural network model [25,26], Zhu et al [27] constructed a three-layer neural network to predict the outbound and inbound passenger flow of a metro station by analyzing the main dynamic factors that affect passenger flow in a rail transit station. e prediction accuracy was higher than the traditional linear regression method.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Passenger Flow Forecast of Rail Transit Station Based on MIC Feature Selection and ST-LightGBM considering Transfer Passenger Flow

Zhang

Wang

Gao

et al. 2020

Scientific Programming

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To solve the problems of current short-term forecasting methods for metro passenger flow, such as unclear influencing factors, low accuracy, and high time-space complexity, a method for metro passenger flow based on ST-LightGBM after considering transfer passenger flow is proposed. Firstly, using historical data as the training set to transform the problem into a data-driven multi-input single-output regression prediction problem, the problem of the short-term prediction of metro passenger flow is formalized and the difficulties of the problem are identified. Secondly, we extract the candidate temporal and spatial features that may affect passenger flow at a metro station from passenger travel data based on the spatial transfer and spatial similarity of passenger flow. Thirdly, we use a maximal information coefficient (MIC) feature selection algorithm to select the significant impact features as the input. Finally, a short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established. Taking transfer passenger flow into account, this method has a low space-time cost and high accuracy. The experimental results on the dataset of Lianban metro station in Xiamen city show that the proposed method obtains higher prediction accuracy than SARIMA, SVR, and BP network.

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“…With the continuous expansion of urban traffic demand, the urban rail transit plays an increasingly important role in the urban public transport system because of its effective mobility, sufficient punctuality, strong security, and environment-friendliness. [1][2][3] As one of the most important stages in operation management, the timetable has a significant effect on the quality and efficiency of urban rail transit systems. Therefore, the issue of timetable synchronization and optimization in urban metro networks is attracting considerable interests.…”

Section: Motivation and Incitementmentioning

confidence: 99%

“…The direct decision variables of the upper level model can be stated as follows: the arrival time ft Arr 1l g of the last train on each line l, the running time ft Run sl g of the last train between adjacent on each line l, and the stopping time ft Stop sl g of the last train at each station s on line l. The other decision variables can be calculated based on these three types of direct decision variables. In specific, the arrival time t Arr sl and departure time t Dep sl at any station s on each line l can be calculated by equations (2) and (3). The transfer binary variable fx sl, s 0 l 0 g and the number of trains before the last connecting train fn sl, s 0 l 0 g can be determined by equations 7and (8).…”

Section: Genetic Algorithm For the Upper Level Modelmentioning

confidence: 99%

Last train timetable optimization considering detour routing strategy in an urban rail transit network

Yao

Zhu

Shi

et al. 2019

Measurement and Control

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As an important means of transportation, urban rail transit provides effective mobility, sufficient punctuality, strong security, and environment-friendliness in large cities. However, this transportation mode cannot offer a 24-h service to passengers with the consideration of operation cost and the necessity of maintenance, that is, a final time should be set. Therefore, operators need to design a last train timetable in consideration of the number of successful travel passengers and the total passenger transfer waiting time. This paper proposes a bi-level last train timetable optimization model. Its upper level model aims to maximize the number of passengers who travel by the last train service successful and minimize their transfer waiting time, and its lower level model aims to determine passenger route choice considering the detour routing strategy based on the last train timetable. A genetic algorithm is proposed to solve the upper level model, and the lower level model is solved by a semi-assignment algorithm. The implementation of the proposed model in the Beijing urban rail transit network proves that the model can optimize not only the number of successful transfer directions and successful travel passengers but also the passenger transfer waiting time of successful transfer directions. The optimization results can provide operators detailed information about the stations inaccessible to passengers from all origin stations and uncommon path guides for passengers of all origin–destination pairs. These types of information facilitate the operation of real-world urban rail transit systems.

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Prediction of Daily Entrance and Exit Passenger Flow of Rail Transit Stations by Deep Learning Method

Cited by 19 publications

References 9 publications

Applications of Deep Learning in Intelligent Transportation Systems

Applications of Deep Learning in Intelligent Transportation Systems

Short-Term Passenger Flow Forecast of Rail Transit Station Based on MIC Feature Selection and ST-LightGBM considering Transfer Passenger Flow

Last train timetable optimization considering detour routing strategy in an urban rail transit network

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