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Fang Liu
School of Transportation Engineering, Changsha University of Science and Technology, Changsha 410205, China

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Journal article
Published: 06 January 2021 in Transportation Research Part C: Emerging Technologies
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Region-level passenger demand prediction plays an important role in the coordination of travel demand and supply in the urban public transportation system. The complex urban road network structure leads to irregular shapes and arrangements of regions, which poses a challenge for capturing the spatio-temporal correlation of demand generated in different regions. In this study, we propose a multi-community spatio-temporal graph convolutional network (MC_STGCN) framework to predict passenger demand at a multi-region level by exploring spatio-temporal correlations among regions. Specifically, the gated recurrent unit (GRU) is applied to encode the temporal correlation in regions into a vector. On the other hand, the spatial correlations among regions are encoded into two graphs through the graph convolutional network (GCN): geographically adjacent graph and functional similarity graph. Then, a prediction module based on the Louvain algorithm is used to accomplish the passenger demand prediction of multi-regions. The two real-world taxi order data collected in Shenzhen City and New York City are used in model validation and comparison. The numerical results show that the MC_STGCN model outperforms both classical time-series prediction methods and deep learning approaches. Moreover, in order to better illustrate the superiority of the proposed model, we further discuss the improvement of prediction performance though spatio-temporal correlation modeling and analyzing, the effectiveness of community detection compared with random classification of regions, and the advantages of regional level prediction compared with grid-based prediction models.

ACS Style

Jinjun Tang; Jian Liang; Fang Liu; Jingjing Hao; Yinhai Wang. Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network. Transportation Research Part C: Emerging Technologies 2021, 124, 102951 .

AMA Style

Jinjun Tang, Jian Liang, Fang Liu, Jingjing Hao, Yinhai Wang. Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network. Transportation Research Part C: Emerging Technologies. 2021; 124 ():102951.

Chicago/Turabian Style

Jinjun Tang; Jian Liang; Fang Liu; Jingjing Hao; Yinhai Wang. 2021. "Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network." Transportation Research Part C: Emerging Technologies 124, no. : 102951.

Journal article
Published: 24 December 2020 in Sustainability
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Accessibility has attracted wide interest from urban planners and transportation engineers. It is an important indicator to support the development of sustainable policies for transportation systems in major events, such as the COVID-19 pandemic. Taxis are a vital travel mode in urban areas that provide door-to-door services for individuals to perform urban activities. This study, with taxi trajectory data, proposes an improved method to evaluate dynamic accessibility depending on traditional location-based measures. A new impedance function is introduced by taking characteristics of the taxi system into account, such as passenger waiting time and the taxi fare rule. An improved attraction function is formulated by considering dynamic availability intensity. Besides, we generate five accessibility scenarios containing different indicators to compare the variation of accessibility. A case study is conducted with the data from Shenzhen, China. The results show that the proposed method found reduced urban accessibility, but with a higher value in southern center areas during the evening peak period due to short passenger waiting time and high destination attractiveness. Each spatio-temporal indicator has an influence on the variation in accessibility.

ACS Style

Helai Huang; Jialing Wu; Fang Liu; Yiwei Wang. Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data. Sustainability 2020, 13, 112 .

AMA Style

Helai Huang, Jialing Wu, Fang Liu, Yiwei Wang. Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data. Sustainability. 2020; 13 (1):112.

Chicago/Turabian Style

Helai Huang; Jialing Wu; Fang Liu; Yiwei Wang. 2020. "Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data." Sustainability 13, no. 1: 112.

Journal article
Published: 17 September 2020 in Physica A: Statistical Mechanics and its Applications
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Extracting travel patterns from large-scaled vehicle trajectories is the key step to analyze urban travel characteristics, which can also provide effective strategies for urban traffic planning, construction, management and policy decision. In this study, we adopt the DBSCAN (Density-Based Spatial Clustering of Application with Noise) algorithm by fusing spatial, temporal and directional attributes extracting from vehicle trajectories Furthermore, LCS (Longest Common Sequences) is adopted to estimate spatial similarity, and two measurements are also designed to evaluate the temporal and directional similarity between trajectories. Accordingly, a statistical feature-based parameter optimization method is proposed in the clustering process to achieve reasonable clustering results. Finally, trajectory data collected from Harbin city, China are used to validate the effectiveness of clustering method. A comparison of clustering results considering different combination of attributes is conducted to further demonstrate the advantage of the proposed model.

ACS Style

Jinjun Tang; Wei Bi; Fang Liu; Wenhui Zhang. Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories. Physica A: Statistical Mechanics and its Applications 2020, 561, 125301 .

AMA Style

Jinjun Tang, Wei Bi, Fang Liu, Wenhui Zhang. Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories. Physica A: Statistical Mechanics and its Applications. 2020; 561 ():125301.

Chicago/Turabian Style

Jinjun Tang; Wei Bi; Fang Liu; Wenhui Zhang. 2020. "Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories." Physica A: Statistical Mechanics and its Applications 561, no. : 125301.

Journal article
Published: 07 October 2019 in Sustainability
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Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

ACS Style

Jinjun Tang; Fan Gao; Fang Liu; Wenhui Zhang; Yong Qi. Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM. Sustainability 2019, 11, 5525 .

AMA Style

Jinjun Tang, Fan Gao, Fang Liu, Wenhui Zhang, Yong Qi. Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM. Sustainability. 2019; 11 (19):5525.

Chicago/Turabian Style

Jinjun Tang; Fan Gao; Fang Liu; Wenhui Zhang; Yong Qi. 2019. "Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM." Sustainability 11, no. 19: 5525.

Journal article
Published: 17 April 2019 in Expert Systems with Applications
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Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions. High precision prediction of lane changing intent is helpful to enhance proactivity in driving safety protection. This study proposed a lane-changing prediction model based on Fuzzy C-means clustering algorithm and adaptive Neural Network (FCMNN), which introduced a new prediction process: (1) Unsupervised learning method: categorize original dataset into different clusters according to their distribution features; (2) Supervised learning method: optimize sub-Neural Network structures and weighting parameters for each cluster or pattern. Through comparing with several traditional methods under different simulation scenarios, the proposed model effectively improve the prediction performance and stability. The results obtained in this study will be helpful to deeply analyze the intent recognition of driving behavior, improve the safety of lane-changing behavior, and provide key technology in driving prediction of Advanced Driver Assistance System (ADAS).

ACS Style

Jinjun Tang; ShaoWei Yu; Fang Liu; Xinqiang Chen; Helai Huang. A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network. Expert Systems with Applications 2019, 130, 265 -275.

AMA Style

Jinjun Tang, ShaoWei Yu, Fang Liu, Xinqiang Chen, Helai Huang. A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network. Expert Systems with Applications. 2019; 130 ():265-275.

Chicago/Turabian Style

Jinjun Tang; ShaoWei Yu; Fang Liu; Xinqiang Chen; Helai Huang. 2019. "A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network." Expert Systems with Applications 130, no. : 265-275.