Graph Analytics and Optimization Methods for Insights from the Uber Movement Data

In this work, we leverage the Uber movement dataset for the Los Angeles (LA) area where partial TAZ to TAZ (Traffic Analysis Zone) trip time data is available, to predict travel time patterns on the full TAZ to TAZ network. We first create a TAZ-TAZ network based on nearest neighbors and propose a model that allows us to complete the (O - D) (Origin-Destination) travel time matrix, using optimization methods such as non-negative least squares. We apply these algorithms to several communities in the TAZ-TAZ network and present insights in the form of completed (O - D) matrices and associated temporal trends. We qualify the error performance and scalability of our flows. We conclude by pointing out the directions in our ongoing work to improve the quality and scale of travel time estimation.

[1]  Bin Jiang,et al.  Street hierarchies: a minority of streets account for a majority of traffic flow , 2008, Int. J. Geogr. Inf. Sci..

[2]  Ee-Peng Lim,et al.  Measuring Centralities for Transportation Networks Beyond Structures , 2015, Applications of Social Media and Social Network Analysis.

[3]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[4]  Geoff Boeing.  A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood , 2017, ArXiv.

[5]  Eleni I. Vlahogianni,et al.  Short-term traffic forecasting: Where we are and where we’re going , 2014 .

[6]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[7]  Chia-Ching Lin,et al.  Mining the Shortest Path within a Travel Time Constraint in Road Network Environments , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[8]  Shlomo Bekhor,et al.  Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks , 2013, J. Intell. Transp. Syst..

[9]  Cyrus Shahabi,et al.  Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting , 2017, ICLR.

[10]  Shuigeng Zhou,et al.  Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation , 2012, Proc. VLDB Endow..

[11]  Patrick Jaillet,et al.  Travel Time Estimation in the Age of Big Data , 2019, Oper. Res..

[12]  Alexandre M. Bayen,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Learning the Dynamics of Arterial Traffic From Probe , 2022 .

[13]  Geoff Boeing,et al.  OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks , 2016, Comput. Environ. Urban Syst..

[14]  Morgan State.  Use of Probe Data for Arterial Roadway Travel Time Estimation and Freeway Medium-term Travel Time Prediction , 2016 .

[15]  V. Latora,et al.  Centrality in networks of urban streets. , 2006, Chaos.

[16]  Wei Xu,et al.  DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting , 2017, 2018 International Joint Conference on Neural Networks (IJCNN).

[17]  V. Latora,et al.  The Network Analysis of Urban Streets: A Primal Approach , 2006 .

[18]  Ali Haghani,et al.  Arterial Travel Time Validation and Augmentation with Two Independent Data Sources , 2015 .

[19]  Alexandre M. Bayen,et al.  Estimating arterial traffic conditions using sparse probe data , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[20]  Yohan Dupuis,et al.  Spatial Modeling of Urban Road Traffic Using Graph Theory , 2017 .

[21]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[22]  Edward Beimborn,et al.  A Transportation Modeling Primer , 2006 .

[23]  S. L. Dhingra,et al.  Heterogeneous traffic flow modelling for an arterial using grid based approach , 2008 .

[24]  Limin Jia,et al.  Analysis of Urban Road Traffic Network Based on Complex Network , 2016 .