An Urban Road-Traffic Commuting Dynamics Study Based on Hotspot Clustering and a New Proposed Urban Commuting Electrostatics Model

With the recent rapid development of cities, the dynamics of urban road-traffic commuting are becoming more and more complex. In this research, we study urban road-traffic commuting dynamics based on clustering analysis and a new proposed urban commuting electrostatics model. As a case study, we investigate the characteristics of urban road-traffic commuting dynamics during the morning rush hour in Beijing, China, using over 1.3 million Global Positioning System (GPS) data records of vehicle trajectories. The hotspot clusters are identified using clustering analysis, after which the urban commuting electric field is simulated based on an urban commuting electrostatics model. The results show that the areas with high electric field intensity tend to have slow traffic, and also that the vehicles in most areas tend to head in the same direction as the electric field. The results above verify the validity of the model, in that the electric field intensity can reflect the traffic pressure of an area, and that the direction of the electric field can reflect the traffic direction in that area. This new proposed urban commuting electrostatics model helps greatly in understanding urban road-traffic commuting dynamics and has broad applicability for the optimization of urban and traffic system planning.

[1]  Chuan Ding,et al.  A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership , 2018, Comput. Environ. Urban Syst..

[2]  Nan Zhang,et al.  A human behavior integrated hierarchical model of airborne disease transmission in a large city , 2017, Building and Environment.

[3]  Daniel A. Rodriguez,et al.  Objective correlates and determinants of bicycle commuting propensity in an urban environment , 2015 .

[4]  David Malakoff.  Use our infographics to explore the rise of the urban planet , 2016 .

[5]  Yong Gao,et al.  Metropolitan Congestion Performance Measures Based on Mass Floating Car Data , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[6]  Changjoo Kim,et al.  The Changes of Urban Structure and Commuting: An Application to Metropolitan Statistical Areas in the United States , 2019 .

[7]  Daniel J. Graham,et al.  Crowding cost estimation with large scale smart card and vehicle location data , 2017 .

[8]  Vincent D. Blondel,et al.  Scaling Behaviors in the Communication Network between Cities , 2009, 2009 International Conference on Computational Science and Engineering.

[9]  Yunpeng Wang,et al.  Understanding commuting patterns using transit smart card data , 2017 .

[10]  María Henar Salas-Olmedo,et al.  Analysis of commuting needs using graph theory and census data: A comparison between two medium-sized cities in the UK , 2012 .

[11]  Syed Fawad Hussain,et al.  A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data , 2019, Expert Syst. Appl..

[12]  Zhenhong Du,et al.  Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data , 2018, ISPRS Int. J. Geo Inf..

[13]  Leandro Tortosa,et al.  Measuring urban activities using Foursquare data and network analysis: a case study of Murcia (Spain) , 2017, Int. J. Geogr. Inf. Sci..

[14]  Lei Zhang,et al.  Multi-level urban form and commuting mode share in rail station areas across the United States; a seemingly unrelated regression approach , 2019, Transport Policy.

[15]  Leandro Tortosa,et al.  An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector , 2012, Appl. Math. Comput..

[16]  Shuai Han,et al.  An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset , 2017 .

[17]  Juan Carlos Barbosa Herrera,et al.  Real time traffic states estimation on arterials based on trajectory data , 2014 .

[18]  David Z. Pan,et al.  Accurate lithography hotspot detection based on PCA-SVM classifier with hierarchical data clustering , 2014, Advanced Lithography.

[19]  Feng Mao,et al.  Mining spatiotemporal patterns of urban dwellers from taxi trajectory data , 2016, Frontiers of Earth Science.

[20]  Shing Chung Josh Wong,et al.  Role of road network features in the evaluation of incident impacts on urban traffic mobility , 2018, Transportation Research Part B: Methodological.

[21]  Gabriella Schoier,et al.  Spatial Data Mining for Highlighting Hotspots in Personal Navigation Routes , 2012, Int. J. Data Warehous. Min..

[22]  Li Wan,et al.  Big data and urban system model - Substitutes or complements? A case study of modelling commuting patterns in Beijing , 2017, Comput. Environ. Urban Syst..

[23]  Juan de Dios Ortúzar,et al.  Trip Distribution Modelling , 2011 .

[24]  Kun Qin,et al.  HOTSPOTS DETECTION FROM TRAJECTORY DATA BASED ON SPATIOTEMPORAL DATA FIELD CLUSTERING , 2017 .

[25]  Liang Wang,et al.  Urban Mobility Dynamics Based on Flexible Discrete Region Partition , 2014, Int. J. Distributed Sens. Networks.

[26]  David S. Ebert,et al.  Public behavior response analysis in disaster events utilizing visual analytics of microblog data , 2014, Comput. Graph..

[27]  Hong Huang,et al.  Spatial data mining and O-D hotspots discovery in cities based on an O-D hotspots clustering model using vehicles' GPS data: a case study in the morning rush hours in Beijing, China , 2018, EM-GIS@SIGSPATIAL.

[28]  Hong Huang,et al.  Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data , 2017 .

[29]  Qing Li,et al.  Incorporating free-floating car-sharing into an activity-based dynamic user equilibrium model: A demand-side model , 2018 .

[30]  Yueyue Fan,et al.  Stochastic travel demand estimation: Improving network identifiability using multi-day observation sets , 2018 .

[31]  K. Qina,et al.  Hotspots Detection from Trajectory Data Based on Spatiotemporal Data Field Clustering , 2017 .

[32]  Yan Shi,et al.  Detecting hotspots of urban residents’ behaviours based on spatio-temporal clustering techniques , 2017 .

[33]  Hui Xiong,et al.  Discovering Urban Functional Zones Using Latent Activity Trajectories , 2015, IEEE Transactions on Knowledge and Data Engineering.

[34]  Martin Andersson,et al.  Rural to urban long-distance commuting in Sweden: trends, characteristics and pathways , 2018 .

[35]  Leandro Tortosa,et al.  Analyzing the commercial activities of a street network by ranking their nodes: a case study in Murcia, Spain , 2014, Int. J. Geogr. Inf. Sci..

[36]  Carlos Ramiro Marmolejo Duarte,et al.  The influence of urban structure on commuting: an analysis for the main metropolitan systems in Spain , 2017 .

[37]  Takatoshi Tabuchi,et al.  Urban Agglomeration and Dispersion: A Synthesis of Alonso and Krugman* , 1998 .

[38]  Yuanyuan Li,et al.  Feature selection based on sensitivity analysis of fuzzy ISODATA , 2012, Neurocomputing.