Industrial internet applications for efficient road winter maintenance

Purpose For the expected increase in the capacity of existing transportation systems and efficient energy utilisation, smart maintenance solutions that are supported by online and integrated condition monitoring systems are required. Industrial internet is one of the smart maintenance solutions which enables real-time acquisition and analysis of asset condition by linking intelligent devices with different stakeholders’ applications and databases. The purpose of this paper is to present some aspects of industrial internet application as required for integrating weather information and floating road condition data from vehicle mounted sensors to enhance effective and efficient winter maintenance. Design/methodology/approach The concept of real-time road condition assessment using in-vehicle sensors is demonstrated in a case study of a 3.5 km road section located in Northern Sweden. The main floating data sources were acceleration and position sensors from a smartphone positioned on the dash board of a truck. Features extracted from the acceleration signal were two road roughness estimations. To extract targeted information and knowledge, the floating data were further processed to produce time series data of the road condition using Kalman filtering. The time series data were thereafter combined with weather data to assess the condition of the road. Findings In the case study, examples of visualisation and analytics to support winter maintenance planning, execution and resource allocation were presented. Reasonable correlation was shown between estimated road roughness and annual road survey data to validate and prove the presented results wider applicability. Originality/value The paper describes a concept of floating data for an industrial internet application for efficient road maintenance. The resulting improvement in winter maintenance will promote dependable, safe and sustainable transportation of goods and people, especially in Northern Nordic region with harsh and sometimes unpredictable weather conditions.

[1]  Chih-Wei Yi,et al.  Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies , 2015, IEEE Transactions on Intelligent Transportation Systems.

[2]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[3]  Xiaomeng Wang,et al.  A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data , 2015, PloS one.

[4]  Shahram Azadi,et al.  Road profile estimation using neural network algorithm , 2010 .

[5]  Pertti Nurmi,et al.  Statistical modelling of wintertime road surface friction , 2013 .

[6]  Ioannis Brilakis,et al.  Improving Road Asset Condition Monitoring , 2016 .

[7]  P. S. Heyns,et al.  Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation , 2010 .

[8]  Jens Eliasson,et al.  Road surface information system , 2012 .

[9]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[10]  Clifford F. Mass,et al.  Nowcasting: The Promise of New Technologies of Communication, Modeling, and Observation , 2012 .

[11]  Patrik Jonsson,et al.  Road Surface Status Classification Using Spectral Analysis of NIR Camera Images , 2015, IEEE Sensors Journal.

[12]  Ulf Sandberg,et al.  Tyre/road noise reference book , 2002 .

[13]  Eugene J. O'Brien,et al.  Characterisation of pavement profile heights using accelerometer readings and a combinatorial optimisation technique , 2010 .

[14]  H Jones,et al.  Roadroid continuous road condition monitoring with smart phones , 2014 .

[15]  Michael W Gillespie Thomas D Queiroz Cesar A.V Sayers,et al.  The International Road Roughness Experiment (IRRE) : establishing correlation and a calibration standard for measurements , 1986 .

[16]  Roger Johnsson,et al.  Methods for road texture estimation using vehicle measurements , 2012 .

[17]  Eugene J. O'Brien,et al.  The use of vehicle acceleration measurements to estimate road roughness , 2008 .

[18]  Patrice Aknin,et al.  Pattern recognition approach for the prediction of infrequent target events in floating train data sequences within a predictive maintenance framework , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[19]  Jie Wu,et al.  CRSM: Crowdsourcing Based Road Surface Monitoring , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[21]  Atul Negi,et al.  Principle application and vision in Internet of Things (IoT) , 2015, International Conference on Computing, Communication & Automation.

[22]  B.S. Kerner,et al.  Traffic state detection with floating car data in road networks , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..