Prediction of Surface Subsidence Caused by Rectangular Pipe Jacking Construction
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A Hybrid Method for Predicting Traffic Congestion During Peak Hours In
sensors Article A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen Zhenwei Luo 1, Yu Zhang 1, Lin Li 1,2,* , Biao He 3, Chengming Li 4, Haihong Zhu 1,2,*, Wei Wang 1, Shen Ying 1,2 and Yuliang Xi 1 1 School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China; [email protected] (Z.L.); [email protected] (Y.Z.); [email protected] (W.W.); [email protected] (S.Y.); [email protected] (Y.X.) 2 RE-Institute of Smart Perception and Intelligent Computing, Wuhan University, Wuhan 430079, China 3 School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518000, China; [email protected] 4 Chinese Academy of Surveying and Mapping, 28 Lianghuachi West Road, Haidian Qu, Beijing 100830, China; [email protected] * Correspondence: [email protected] (L.L.); [email protected] (H.Z.); Tel.: +86-27-6877-8879 (L.L. & H.Z.) Received: 11 October 2019; Accepted: 23 December 2019; Published: 25 December 2019 Abstract: Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion; however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. -
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TRANSPORT ISSN 1648-4142 / eISSN 1648-3480 2020 Volume 35 Issue 1: 26–36 https://doi.org/10.3846/transport.2020.12106 A COMPUTATION METHOD ON TIME-DEPENDENT ACCESSIBILITY OF URBAN RAIL TRANSIT NETWORKS FOR THE LAST SERVICE Yao CHEN, Baohua MAO*, Yun BAI, Zhujun LI, Jimeng TANG Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing, China Received 18 December 2016; revised 24 April 2017; accepted 16 May 2017 Abstract. Urban rail transit networks seldom provide 24-hour service. The last train is the latest chance for passengers. If passengers arrive too late to catch the last train, the path becomes inaccessible. The network accessibility thus varies de- pending on the departure time of passenger trips. This paper focuses on the computation method on the time-dependent accessibility of urban rail transit networks in order to facilitate the itinerary planning of passengers. A label setting algo- rithm is first designed to calculate the latest possible times for Origin–Destination (O–D) pairs, which is the latest depar- ture times of passengers from the origins such that the destinations can be reach successfully. A searching approach is then developed to find the shortest accessible path at any possible departure times. The method is applied in a real-world metro network. The results show that the method is a powerful tool in solving the service accessibility problem. It has the abil- ity to allow passengers to plan an optimal itinerary. Comparison analysis indicates that the proposed method can provide exact solutions in much shorter time, compared with a path enumeration method. -
The Dynamic Optimization of the Departure Times of Metro Users During Rush Hour in an Agent-Based Simulation: a Case Study in Shenzhen, China
applied sciences Article The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China Yuliang Xi 1,2, Qingyun Du 1,2,*, Biao He 3, Fu Ren 1,2, Yu Zhang 1,2 and Xinyue Ye 4 ID 1 School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China; [email protected] (Y.X.); [email protected] (F.R.); [email protected] (Y.Z.) 2 Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Luoyu Road 129, Wuhan 430079, China 3 College of Architecture and Urban Planning, Shenzhen University, Nanhai Street 3688, Nanshan District, Shenzhen 518061, China; [email protected] 4 Department of Geography, Kent State University, Kent, OH 44242, USA; [email protected] * Correspondence: [email protected]; Tel.: +86-27-6877-8842; Fax: +86-27-6877-8893 Received: 13 September 2017; Accepted: 18 October 2017; Published: 25 October 2017 Abstract: As serious traffic problems have increased throughout the world, various types of studies, especially traffic simulations, have been conducted to investigate this issue. Activity-based traffic simulation models, such as MATSim (Multi-Agent Transport Simulation), are intended to identify optimal combinations of activities in time and space. It is also necessary to examine commuting-based traffic simulations. Such simulations focus on optimizing travel times by adjusting departure times, travel modes or travel routes to present travel suggestions to the public. This paper examines the optimal departure times of metro users during rush hour using a newly developed simulation tool. A strategy for identifying relatively optimal departure times is identified. -
Arxiv:1605.08390V1 [Cs.AI] 19 Apr 2016 Corresponding Author: Fan Zhang (Email: [email protected])
1 Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems Juanjuan Zhao, Fan Zhang, Member, IEEE, Lai Tu, Chengzhong Xu, Fellow, IEEE, Dayong Shen, Chen Tian, Xiang-Yang Li, Fellow, IEEE, and Zhengxi Li Nowadays, metro systems play an important role in meeting the urban transportation demand in large cities. The understanding of passenger route choice is critical for public transit management. The wide deployment of Automated Fare Collection(AFC) systems opens up a new opportunity. However, only each trip’s tap-in and tap-out timestamp and stations can be directly obtained from AFC system records; the train and route chosen by a passenger are unknown, which are necessary to solve our problem. While existing methods work well in some specific situations, they don’t work for complicated situations. In this paper, we propose a solution that needs no additional equipment or human involvement than the AFC systems. We develop a probabilistic model that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains. We validate our approach using a large scale data set collected from the Shenzhen metro system. The measured results provide us with useful inputs when building the passenger path choice model. Index Terms—Metro systems, Smart card, Data mining, Intelligent transportation systems. I. INTRODUCTION HengGang g OWADAYS, metro systems play an important role in n QingHu TangKeng o L HeAo LiuYue g AiLian DaYun LongHua n JiXiang YongHu NanLian a meeting the urban transportation demand in large cities. LongSheng DanZhuTou u h N DaFen S ShangTang Due to its fast speed, high efficiency, large volume and HongLangBei XingDong LiuXianDong XiLi DaXueCheng TangLang ZhangLingPo HongShan MuMianWan JiChangDong BuJi HouRui BaiShiLong CaoPu punctuality, the urban metro has become the first choice of ShuiBei LongChengGuangChang GuShu LingZhi MinLe WuHe MinZhi BanTian TianBei XiXiang ShangMeiLin YangMei ZhangLong CuiZhu many people. -
Address List of Special Warehousing Service Note: the Address Marked in Red Are Newly Added Address
Address List of Special Warehousing Service Note: The address marked in red are newly added address. (Effective date:September 1, 2021) Province / Directly- controlled City District/county Town, Sub-district and House Number Municipality / Autonomous Region/SAR B4-25, Gate 1, ProLogis Logistics Park, No.1 Tiedi Road, Anhui Province Hefei Shushan District High-tech Zone No.18 Tianzhushan Road, Longshan Sub-district, Wuhu Anhui Province Wuhu Jiujiang District Economic and Technological Development Zone Anhui Province Chuzhou Langya District Longji Leye Photovoltaic Co., Ltd., No.19 Huai'an Road 3/F, No.8 Building, South Area, Lixiang Innovation Park, Anhui Province Chuzhou Nanqiao District Chuzhou, 018 Township Road Anhui Province Chuzhou Nanqiao District No.19 Huai'an Road Yuanrong New Material Holding Co., Ltd., 50 Meters Anhui Province Hefei Shushan District Westward of Bridge of Intersection of Changning Avenue and Ningxi Road Anhui Province Hefei Yaohai District No.88 Dayu Road Anhui Province Hefei Yaohai District No.2177 Dongfang Avenue Beijing BOE Vision-Electronic Technology Co., Ltd., No. Anhui Province Hefei Yaohai District 2177 Dongfang Avenue Anhui Province Hefei Yaohai District No.668 Longzihu Road Anhui Province Hefei Yaohai District No. 668 Longzihu Road Anhui Province Hefei Yaohai District No.2177 Tongling North Road Anhui Province Hefei Yaohai District No.3166 Tongling North Road Anhui Province Hefei Yaohai District No.8 Xiangwang Road Anhui Province Wuhu Jiujiang District No. 8 Anshan Road Anhui Province Wuhu Jiujiang District