城市轨道交通站点周边无桩共享单车需求演变分析与预测——以深圳为例

IF 1.7 4区 工程技术 Q4 TRANSPORTATION
Yingping Zhao, Yiling Wu, Xinfeng Zhang, Yaowei Wang, Zhenduo Zhang, Hongyu Lu, Dongfang Ma
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引用次数: 0

摘要

无桩共享单车(DSB)的出现使其成为城市轨道交通(URT)车站的重要换乘方式。然而,在高峰时段的地铁站等人口密集地区,人们越来越担心共享单车的供需不平衡。为了促进共享单车的地铁换乘更加顺畅,对DSB需求进行估算是非常重要的,尤其是在地铁站附近的自行车上下车需求的差异。本研究首先利用深圳地铁使用数据和共享单车使用数据,对地铁和共享单车使用数据进行分析,探讨其潜在的换乘用途,发现不同地铁站对共享单车的需求存在较大差异。使用集水区法来估计自行车作为潜在的地铁换乘方式的使用情况,其中集水区被定义为距离地铁站中心150 m的半径。DSB行程需求分为两种类型:上车和下车。最新的深度学习方法,自适应图卷积递归网络(AGCRN),由于其能够在自适应图中实现实体之间关系的建模,因此可以更准确地预测DSB需求,并将其预测与长短期记忆(LSTM),时空神经网络(STNN),扩散卷积递归神经网络(DCRNN)和图WaveNet进行比较。结果表明,基于图的方法(STNN、DCRNN、Graph WaveNet和AGCRN)优于LSTM,基于自适应图的方法(Graph WaveNet和AGCRN)在平均绝对误差(MAE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)方面优于静态图方法。DSB预测结果表明,AGCRN在本研究中表现最好。更多的数据,特别是土地利用数据和城市轨道交通站点体量数据,有望提高该方法的预测精度,因为它可能改善站点特征和地铁站体量相关性的图形表示。有了更准确的预测结果,就可以更好地实现自行车运营优化的平衡策略,从而更好地利用自行车,从而提高DSB到地铁的转换率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analysis and Prediction of Dockless Shared Bike Demand Evolving Around Urban Rail Transit Stations: Case Study in Shenzhen, China

Analysis and Prediction of Dockless Shared Bike Demand Evolving Around Urban Rail Transit Stations: Case Study in Shenzhen, China

The emergence of dockless shared bikes (DSB) has led to their use as an important transfer mode to urban rail transit (URT) stations. However, in highly populated areas such as subway stations in peak hours, there is increasing concern about the imbalance between the demand and supply of shared bikes. To promote smoother subway transfer trips using shared bikes, it is very important to estimate the DSB demand, especially the disparity in the volume of bike pick-up and drop-off demand around subway stations. This research first utilizes the Shenzhen metro usage data and DSB usage data, analyzes data regarding subway and shared bike usage, discusses their potential transfer uses, and finds great disparity in DSB demand between different subway stations. The catchment area method is used to estimate bike usage as a potential transfer mode to the subway, where the catchment area is defined as a radius of 150 m from the subway station center. The DSB trip demand is categorized into two types: pick-up and drop-off. The most recent deep learning method, adaptive graph convolutional recurrent network (AGCRN), is used to predict the DSB demand more accurately because of its ability in enabling the modeling of relationships between entities in a self-adapted graph, and the prediction is compared with long short-term memory (LSTM), spatiotemporal neural network (STNN), diffusion convolutional recurrent neural network (DCRNN), and Graph WaveNet. Results show that methods with graphs (STNN, DCRNN, Graph WaveNet, and AGCRN) perform better than LSTM, and methods with adaptive graphs (Graph WaveNet and AGCRN) outperform methods with static graphs in terms of mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). DSB prediction results show that AGCRN performs the best in this study. More data, particularly land use data and URT station volume data, are expected to improve the predictive accuracy of the method due to potentially improved graph representation of station characteristics and subway station volume correlations. And with more accurate prediction results, it will be possible to achieve a better balancing strategy for bike operation optimization for better bike usage, and thus for a higher transfer rate of DSB to subway.

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来源期刊
Urban Rail Transit
Urban Rail Transit Multiple-
CiteScore
3.10
自引率
6.70%
发文量
20
审稿时长
5 weeks
期刊介绍: Urban Rail Transit is a peer-reviewed, international, interdisciplinary and open-access journal published under the SpringerOpen brand that provides a platform for scientists, researchers and engineers of urban rail transit to publish their original, significant articles on topics in urban rail transportation operation and management, design and planning, civil engineering, equipment and systems and other related topics to urban rail transit. It is to promote the academic discussions and technical exchanges among peers in the field. The journal also reports important news on the development and operating experience of urban rail transit and related government policies, laws, guidelines, and regulations. It could serve as an important reference for decision¬makers and technologists in urban rail research and construction field. Specific topics cover: Column I: Urban Rail Transportation Operation and Management • urban rail transit flow theory, operation, planning, control and management • traffic and transport safety • traffic polices and economics • urban rail management • traffic information management • urban rail scheduling • train scheduling and management • strategies of ticket price • traffic information engineering & control • intelligent transportation system (ITS) and information technology • economics, finance, business & industry • train operation, control • transport Industries • transportation engineering Column II: Urban Rail Transportation Design and Planning • urban rail planning • pedestrian studies • sustainable transport engineering • rail electrification • rail signaling and communication • Intelligent & Automated Transport System Technology ? • rolling stock design theory and structural reliability • urban rail transit electrification and automation technologies • transport Industries • transportation engineering Column III: Civil Engineering • civil engineering technologies • maintenance of rail infrastructure • transportation infrastructure systems • roads, bridges, tunnels, and underground engineering ? • subgrade and pavement maintenance and performance Column IV: Equipments and Systems • mechanical-electronic technologies • manufacturing engineering • inspection for trains and rail • vehicle-track coupling system dynamics, simulation and control • superconductivity and levitation technology • magnetic suspension and evacuated tube transport • railway technology & engineering • Railway Transport Industries • transport & vehicle engineering Column V: other topics of interest • modern tram • interdisciplinary transportation research • environmental impacts such as vibration, noise and pollution Article types: • Papers. Reports of original research work. • Design notes. Brief contributions on current design, development and application work; not normally more than 2500 words (3 journal pages), including descriptions of apparatus or techniques developed for a specific purpose, important experimental or theoretical points and novel technical solutions to commonly encountered problems. • Rapid communications. Brief, urgent announcements of significant advances or preliminary accounts of new work, not more than 3500 words (4 journal pages). The most important criteria for acceptance of a rapid communication are novel and significant. For these articles authors must state briefly, in a covering letter, exactly why their works merit rapid publication. • Review articles. These are intended to summarize accepted practice and report on recent progress in selected areas. Such articles are generally commissioned from experts in various field s by the Editorial Board, but others wishing to write a review article may submit an outline for preliminary consideration.
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