Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network

Huang, Zihao; Huang, Gang; Chen, Zhijun; Wu, Chaozhong; Ma, Xiaofeng; Wang, Haobo

doi:10.3390/info10060193

Cited by 6 publications

(4 citation statements)

References 21 publications

(26 reference statements)

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“…Here, we use the GPS trajectory data set collected from Chengdu, China in 2016, which has been extensively used by other researchers in previous years. These studies involve different topics including data processing and outlier detection ( 23 ), demand prediction ( 24 – 29 ), order dispatching ( 30 , 31 ), ride-splitting ( 32 ), traffic flow prediction ( 33 , 34 ), and also travel time prediction ( 35 , 36 ).…”

Section: Data Set and Study Areamentioning

confidence: 99%

Temporal Aggregated Analysis of GPS Trajectory Data Using Two-Fluid Model

Zhang

Loder

Rempe

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The market for on-demand mobility services is growing worldwide. These services include, for example, ride-hailing, ride-sharing, and car-sharing. Large-scale fleets of such services collect GPS trajectory (probe vehicle) data constantly everywhere in the network. At a certain penetration rate, this data becomes representative of the entire road network. It can give valuable insights into traffic dynamics and the evolution of congestion. In this paper, we use such GPS trajectory data from Chengdu, China, to investigate the stability and recurrence of macroscopic traffic patterns. Using the two-fluid theory, we find that the two-fluid coefficients are robust on between-day variation, not only supporting the theory itself but also emphasizing that the general evolution of traffic is a robust pattern. We investigate the deviations from the model using time series analysis of the residuals of the two-fluid model. Here, we find evidence for daily and weekly seasonality in the residuals, indicating that congestion patterns are convincingly recurring. These patterns can be used for network-wide traffic state prediction. We conclude that GPS trajectory data from large on-demand mobility fleets is a promising data source for observing traffic patterns in urban road networks once the data becomes representative.

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Section: Data Set and Study Areamentioning

confidence: 99%

Temporal Aggregated Analysis of GPS Trajectory Data Using Two-Fluid Model

Zhang

Loder

Rempe

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…BP neural network is a multilayer feed-forward neural network, which has become an important method for research on tra c ow prediction due to its good self-learning ability, generalization ability, and nonlinear mapping ability [4]. Due to the randomness of the initial weights and thresholds selected by BP neural network, it has poor global search ability and is easy to fall into the local optimal solution and slow convergence rate [5].…”

Section: Introductionmentioning

confidence: 99%

Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network

Wang

2022

Mobile Information Systems

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In order to explore a mobile virtual reality railway traffic congestion prediction algorithm based on convolutional neural network, an expanded causal convolution neural network (DCFCN) was proposed, which introduced the expanded convolution to increase the size of the receptive field and obtain the long-term memory of the sequence. At the same time, causal convolution is introduced to solve the problem of information leakage. DCFCN is made up of 6 convolutional layers, each layer achieves causal convolution through padding, and the expansion coefficient increases exponentially layer by layer. Experimental results show that LSTM and GRU can obtain the time sequence relationship of mobile virtual reality traffic flow sequence, and the prediction effect is better than simple method and traditional ARIMA model, but still inferior to DCFCN. The RMSE of DCFCN decreased by 0.38 compared with single-layer LSTM, 0.52 compared with double-layer LSTM, and 0.38 compared with single-layer and double-layer GRU. It shows that TCN model can indeed do better than RNN in sequence modeling. It is proved that the proposed DCFCN is superior to other comparison models in mobile virtual reality traffic flow prediction, and the computational efficiency on GPU is significantly improved.

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“…Big data analysis, starting from the pain point of users to the hotel, prompted the hotel to launch a time-sharing refund reform, innovative "ladder cancellation" mode, delayed the time that cannot be cancelled as far as possible, and cancelled free of charge due to flight changes [11][12][13]. is initiative benefited millions of tourists in one year [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Tourism Consumer Demand Forecasting under the Background of Big Data

2022

Mathematical Problems in Engineering

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In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world’s leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country’s total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists’ willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans.

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Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network

Cited by 6 publications

References 21 publications

Temporal Aggregated Analysis of GPS Trajectory Data Using Two-Fluid Model

Temporal Aggregated Analysis of GPS Trajectory Data Using Two-Fluid Model

Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network

Tourism Consumer Demand Forecasting under the Background of Big Data

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