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Keywords = intercity car-hailing travel

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12 pages, 3056 KiB  
Article
Intercity Online Car-Hailing Travel Demand Prediction via a Spatiotemporal Transformer Method
by Hongbo Li, Jincheng Wang, Yilong Ren and Feng Mao
Appl. Sci. 2021, 11(24), 11750; https://doi.org/10.3390/app112411750 - 10 Dec 2021
Cited by 9 | Viewed by 3057
Abstract
Traffic prediction is a critical aspect of many real-world scenarios that requires accurate traffic status predictions, such as travel demand prediction. The emergence of online car-hailing activities has given people greater mobility and makes intercity travel more frequent. The increase in online car-hailing [...] Read more.
Traffic prediction is a critical aspect of many real-world scenarios that requires accurate traffic status predictions, such as travel demand prediction. The emergence of online car-hailing activities has given people greater mobility and makes intercity travel more frequent. The increase in online car-hailing demand has often led to a supply–demand imbalance where there is a mismatch between the immediate availability of car-hailing services and the number of passengers in certain areas. Accurate prediction of online car-hailing demand promotes efficiencies and minimizes resources and time waste. However, many prior related studies often fail to fully utilize spatiotemporal characteristics. With the development of newer deep-learning models, this paper aims to solve online car-hailing problems with an ST-transformer model. The spatiotemporal characteristics of online car-hailing data are analyzed and extracted. The study region is divided into subareas, and the demand for each subarea is summed at a specific time interval. Historical demand of the areas is used to predict future demand. The results of the ST-transformer outperformed other baseline models, namely, VAR, SVR, LSTM, LSTNet, and transformers. The validated results suggest that the ST-transformer is more capable of capturing spatiotemporal characteristics compared to the other models. Additionally, compared to others, the model is less affected by data sparsity. Full article
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19 pages, 4684 KiB  
Article
Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel
by Jincheng Wang, Qunqi Wu, Feng Mao, Yilong Ren, Zilin Chen and Yaqun Gao
Sustainability 2021, 13(13), 7419; https://doi.org/10.3390/su13137419 - 2 Jul 2021
Cited by 5 | Viewed by 3258
Abstract
Online car-hailing travel has become an important part of the urban transportation system and is gradually changing the mode of intercity travel. Analyzing and understanding the influencing factors of intercity online car-hailing travel hold great significance for planning and designing intercity transportation and [...] Read more.
Online car-hailing travel has become an important part of the urban transportation system and is gradually changing the mode of intercity travel. Analyzing and understanding the influencing factors of intercity online car-hailing travel hold great significance for planning and designing intercity transportation and transfer systems. However, few studies have analyzed the influencing factors of intercity car-hailing travel or forecast travel demand. This paper takes trips between Yinchuan and Shizuishan, China, as the research case and analyzes the influence of time, space, passengers, and the environment on intercity online car-hailing travel. The relationship between the urban built environment and intercity online car-hailing travel demand is also investigated through a geographically weighted regression (GWR) model. We find that the peak hours for intercity car-hailing trips are between 9:00 and 10:00 and between 16:00 and 18:00, which are significantly different from those for intracity trips. Weather conditions strongly affect the mobility of intercity trips. The urban built environment also has a significant impact on intercity car-hailing ridership, and residential districts and transportation facilities are the factors with the greatest influence on intercity online car-hailing travel. These results can provide practical help to city managers improve the management of intercity traffic and develop better transportation policies. Full article
(This article belongs to the Section Sustainable Transportation)
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