8 Papers
17 Citations
Li Xin is an academic researcher from Dalian Maritime University. The author has contributed to research in topics: Flow (mathematics) & Node (networking). The author has an hindex of 3, co-authored 8 publications.
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Papers
Impacts of free-floating bikesharing system on public transit ridership
TL;DR: Wang et al. as discussed by the authors used a propensity score matching-based difference-in-difference method to evaluate the impact of free-floating BSS on bus ridership in Chengdu, China.
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Presenting a Multi-Start Hybrid Heuristic for Solving the Problem of Two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD)
TL;DR: This study proposes a three-index flow-based mixed integer formulation to solve a two-echelon location routing problem with simultaneous pickup and delivery by developing a multistart hybrid heuristic with path relinking (MHH-PR), which is composed of local search and a variable neighbourhood descent algorithm.
Measuring the Spatial Spillover Effects of Multimodel Transit System in Beijing: A Structural Spatial Vector Autoregressive Approach
TL;DR: Wang et al. as discussed by the authors developed an enhanced spatial vector autoregressive (SpVAR) model to investigate relations in public transport systems in the case of sudden large passenger flow impact.
Patent
Complex weighted traffic network key node identification method based on semi-local centrality
Liu Weiyan,Li Xin,Liu Tao,Liu Bin,Gou Rong +4 more
- 16 Aug 2019
TL;DR: In this paper, a complex weighted traffic network key node identification method based on semi-local centrality is proposed, where the road grade is used as the weight, and the semi local centrality algorithm is adopted, so that the problems that the key nodes identification calculation complexity of the existing traffic network is high and the traffic network characteristics are not considered are solved.
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Patent
Converse traffic behavior identification method based on bicycle track data
Ma Xiaolei,Luan Sen,Meng Li,Li Xin +3 more
- 31 Dec 2019
TL;DR: In this paper, a converse traffic behavior identification method based on bicycle track data is proposed, which comprises the following steps: 1) bicycle driving track data are acquired and cleaned, wherein to-be-cleaned tracks comprise a track with a lower sampling rate and a track having an abnormal speed, and map matching is carried out: each track is projected to a corresponding road section to realize map matching.
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