An expert system to discover key congestion points for urban traffic

Abstract Discovering key congestion points periodically in traffic jams is a critical issue. It supports road managers to make sense of the situations, and rule out the congestion economically and efficiently. However, city-scale and synchronal traffic data bring hardships for such kind of analyses. With recent developments in data science, the availability of traffic conditions data generated by the rising digital map applications makes this issue feasible. Therefore, we firstly propose a digital map data-driven expert system to discover and measure the city-scale key congestion points. It is based on a state-of-the-art feature selection method, BSSReduce (Bijective soft set based feature selection). Data from Baidu Map for Chongqing and Beijing are collected as a case to conduct this study. The results indicate that our proposed method helps the road managers recognize 75 and 300 key congestion points from over 10,000 and 50,000 points of the urban roads each month. The visualized results, as well as the significance measurements, provide road managers an expert system to quickly rule out congestion and work out new solutions to future traffic management.

[1]  Feng Chen,et al.  From Twitter to detector: real-time traffic incident detection using social media data , 2016 .

[2]  A. R. Roy,et al.  Soft set theory , 2003 .

[3]  Chenxia Jin,et al.  Feature selection with partition differentiation entropy for large-scale data sets , 2016, Inf. Sci..

[4]  Javier J. Sanchez-Medina,et al.  Traffic Signal Optimization in “La Almozara” District in Saragossa Under Congestion Conditions, Using Genetic Algorithms, Traffic Microsimulation, and Cluster Computing , 2010, IEEE Transactions on Intelligent Transportation Systems.

[5]  Kevin Heaslip,et al.  Developing a Twitter-based traffic event detection model using deep learning architectures , 2019, Expert Syst. Appl..

[6]  Li Li,et al.  Regional Detection of Traffic Congestion Using in a Large-Scale Surveillance System via Deep Residual TrafficNet , 2018, IEEE Access.

[7]  Benjamin Coifman.  Identifying the Onset of Congestion Rapidly with Existing Traffic Detectors , 1999 .

[8]  Carlos Bento,et al.  Intelligent road traffic status detection system through cellular networks handover information: An exploratory study , 2013 .

[9]  Luca Calderoni,et al.  Deploying a network of smart cameras for traffic monitoring on a "city kernel" , 2014, Expert Syst. Appl..

[10]  Zhengbing He,et al.  Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data , 2017, Comput. Aided Civ. Infrastructure Eng..

[11]  D. Molodtsov.  Soft set theory—First results , 1999 .

[12]  Aboul Ella Hassanien,et al.  An improved moth flame optimization algorithm based on rough sets for tomato diseases detection , 2017, Comput. Electron. Agric..

[13]  Xizhao Wang,et al.  Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets , 2018, IEEE Transactions on Fuzzy Systems.

[14]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[15]  G. Poiani,et al.  Investigation on quantum electrodynamics at short distances by means of bremsstrahlung and pair production , 1959 .

[16]  Javier Gozálvez,et al.  Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications , 2013, J. Netw. Comput. Appl..

[17]  David González,et al.  A Review of Motion Planning Techniques for Automated Vehicles , 2016, IEEE Transactions on Intelligent Transportation Systems.

[18]  Keemin Sohn,et al.  Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network , 2018, IEEE Transactions on Intelligent Transportation Systems.

[19]  Kewen Xia,et al.  Strip Surface Defects Recognition Based on PSO-RS&SOCP-SVM Algorithm , 2017 .

[20]  Carlos Henggeler Antunes,et al.  A web spatial decision support system for vehicle routing using Google Maps , 2011, Decis. Support Syst..

[21]  Berk Anbaroglu,et al.  Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks , 2013 .

[22]  Qinghua Hu,et al.  Neighbor Inconsistent Pair Selection for Attribute Reduction by Rough Set Approach , 2018, IEEE Transactions on Fuzzy Systems.

[23]  Francesco Marcelloni,et al.  Detection of traffic congestion and incidents from GPS trace analysis , 2017, Expert Syst. Appl..

[24]  Usman Qamar,et al.  An incremental dependency calculation technique for feature selection using rough sets , 2016, Inf. Sci..

[25]  Hossam Faris,et al.  Binary grasshopper optimisation algorithm approaches for feature selection problems , 2019, Expert Syst. Appl..

[26]  Antonio F. Gómez-Skarmeta,et al.  A Cooperative Approach to Traffic Congestion Detection With Complex Event Processing and VANET , 2012, IEEE Transactions on Intelligent Transportation Systems.

[27]  Chao Wei,et al.  Efficient Traffic State Estimation for Large-Scale Urban Road Networks , 2013, IEEE Transactions on Intelligent Transportation Systems.

[28]  Yong Wang,et al.  BSSReduce an $O(\left|U\right|)$ Incremental Feature Selection Approach for Large-Scale and High-Dimensional Data , 2018, IEEE Transactions on Fuzzy Systems.

[29]  Guizhen Yu,et al.  Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns , 2017 .

[30]  Jukka Riekki,et al.  Personalised assistance for fuel-efficient driving , 2015 .

[31]  Fangchun Yang,et al.  A traffic congestion detection and information dissemination scheme for urban expressways using vehicular networks , 2014 .

[32]  Xia Zhang,et al.  The bijective soft set with its operations , 2010, Comput. Math. Appl..

[33]  Sartra Wongthanavasu,et al.  On reduction of attributes in inconsistent decision tables based on information entropies and stripped quotient sets , 2019, Expert Syst. Appl..

[34]  Zhigang Xu,et al.  V2V‐based method for the detection of road traffic congestion , 2019, IET Intelligent Transport Systems.

[35]  R. M. Fano,et al.  The statistical theory of information , 1959 .