Smart ITS Sensor for the Transportation Planning Based on IoT Approaches Using Serverless and Microservices Architecture

Currently, there are many challenges in the transportation scope that researchers are attempting to resolve, and one of them is transportation planning. The main contribution of this paper is the design and implementation of an ITS (Intelligent Transportation Systems) smart sensor prototype that incorporates and combines the Internet of Things (IoT) approaches using the Serverless and Microservice Architecture, to help the transportation planning for Bus Rapid Transit (BRT) systems. The ITS smart sensor prototype can detect several Bluetooth signals of several devices (e.g., from mobile phones) that people use while travelling by the BRT system (e.g., in Bogota city). From that information, the ITS smart-sensor prototype can create an O/D (origin/destiny) matrix for several BRT routes, and this information can be used by the Administrator Authorities (AA) to produce a suitable transportation planning for the BRT systems. In addition, this information can be used by the center of traffic management and the AA from ITS cloud services using the Serverless and Microservice architecture.

[1]  Hossein Jula,et al.  Vehicle Route Guidance Systems: Classification and Comparison , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[2]  Romain Billot,et al.  Spatiotemporal Analysis of Bluetooth Data: Application to a Large Urban Network , 2015, IEEE Transactions on Intelligent Transportation Systems.

[3]  Luis Felipe Herrera-Quintero,et al.  IoT approach applied in the context of ITS: monitoring highways through instant messaging , 2015, 2015 14th International Conference on ITS Telecommunications (ITST).

[4]  Oriol Serch,et al.  Dynamic OD matrix estimation exploiting bluetooth data in Urban networks , 2012 .

[5]  Luis Felipe Herrera-Quintero,et al.  Smart ITS sensor for the transportation planning using the IoT and Bigdata approaches to produce ITS cloud services , 2016, 2016 8th Euro American Conference on Telematics and Information Systems (EATIS).

[6]  Linux Containers : Why They ’ re in Your Future and What Has to Happen First , 2014 .

[7]  RattenVanessa Cloud Computing Services , 2012 .

[8]  Maria Nadia Postorino,et al.  Fixed Point Approaches to the Estimation of O/D Matrices Using Traffic Counts on Congested Networks , 2001, Transp. Sci..

[9]  Arshdeep Bahga,et al.  Cloud-Based Information Technology Framework for Data Driven Intelligent Transportation Systems , 2013 .

[10]  Luis Felipe Herrera-Quintero,et al.  Web service platform for automatic generation of O/D matrix for mass transportation systems , 2013, 2013 13th International Conference on ITS Telecommunications (ITST).

[11]  Richard J. La,et al.  Estimation of Average Vehicle Speeds Traveling on Heterogeneous Lanes Using Bluetooth Sensors , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[12]  Luis Felipe Herrera-Quintero,et al.  Wireless Sensor Networks and Service-Oriented Architecture, as suitable approaches to be applied into ITS , 2012, 2012 6th Euro American Conference on Telematics and Information Systems (EATIS).

[13]  Daniel Benhammou,et al.  Data Collection: Affordable Real-Time Traffic Adaptive Control , 2013 .

[14]  Chen-Wei Yang,et al.  An NTCIP-based Semantic ITS Middleware for Emergency Vehicle Preemption , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[15]  Stephen Ennis,et al.  Cloud Event Programming Paradigms: Applications and Analysis , 2016, 2016 IEEE 9th International Conference on Cloud Computing (CLOUD).

[16]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[17]  Varun Singh,et al.  Advanced traveler information system for Hyderabad City , 2005, IEEE Transactions on Intelligent Transportation Systems.

[18]  Nicholas A. Kraft,et al.  Patrol Routing Expression, Execution, Evaluation, and Engagement , 2011, IEEE Transactions on Intelligent Transportation Systems.

[19]  Edward Chung,et al.  Estimating link-dependent Origin-Destination matrices from sample trajectories and traffic counts , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Danny Weyns,et al.  A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems , 2011, IEEE Transactions on Intelligent Transportation Systems.

[21]  Y. Canon-Lozano,et al.  Automatic generation of O/D matrix for mass transportation systems using an ITS approach , 2012, 2012 IEEE Colombian Intelligent Transportation Systems Symposium (CITSS).

[22]  Alfred Zimmermann,et al.  Towards Integrating Microservices with Adaptable Enterprise Architecture , 2016, 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW).

[23]  Changjun Jiang,et al.  Applying SOA to intelligent transportation system , 2005, 2005 IEEE International Conference on Services Computing (SCC'05) Vol-1.