MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

[1]  Liping Fu,et al.  Predicting Bus Arrival Time on the Basis of Global Positioning System Data , 2007 .

[2]  Dewei Li,et al.  Trade-off between efficiency and fairness in timetabling on a single urban rail transit line under time-dependent demand condition , 2019, Transportmetrica B: Transport Dynamics.

[3]  Jesús Arias-Fisteus,et al.  Benchmarking real-time vehicle data streaming models for a smart city , 2017, Inf. Syst..

[4]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[5]  Steven I-Jy Chien,et al.  DYNAMIC TRAVEL TIME PREDICTION WITH REAL-TIME AND HISTORICAL DATA , 2003 .

[6]  Stephane Hess,et al.  Improving bus service reliability: The Singapore experience , 2016 .

[7]  Jimmy J. Lin,et al.  The Lambda and the Kappa , 2017, IEEE Internet Computing.

[8]  Daniel J. Graham,et al.  Development of Key Performance Indicator to Compare Regularity of Service between Urban Bus Operators , 2011 .

[9]  Yang Zhao,et al.  Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro , 2019, IEEE Transactions on Intelligent Transportation Systems.

[10]  Tao Tang,et al.  Big Data Analytics in Intelligent Transportation Systems: A Survey , 2019, IEEE Transactions on Intelligent Transportation Systems.

[11]  Ahmed Lbath,et al.  IoV distributed architecture for real-time traffic data analytics , 2018, ANT/SEIT.

[12]  Michele De Gennaro,et al.  Big Data for Supporting Low-Carbon Road Transport Policies in Europe: Applications, Challenges and Opportunities , 2016, Big Data Res..

[13]  Lien-Wu Chen,et al.  Mobility-Aware and Congestion-Relieved Dedicated Path Planning for Group-Based Emergency Guiding Based on Internet of Things Technologies , 2017, IEEE Transactions on Intelligent Transportation Systems.

[14]  Chao Chen,et al.  TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data , 2018, IEEE Transactions on Intelligent Transportation Systems.

[15]  S. Swamynathan,et al.  Process model-based atomic service discovery and composition of composite semantic web services using web ontology language for services (OWL-S) , 2012, Enterp. Inf. Syst..

[16]  Alberto Córdoba,et al.  SesToCross: Semantic Expert System to Manage Single-Lane Road Crossing , 2017, IEEE Transactions on Intelligent Transportation Systems.

[17]  Antonio Pescapè,et al.  Benchmarking big data architectures for social networks data processing using public cloud platforms , 2018, Future Gener. Comput. Syst..

[18]  Pankaj Verma,et al.  Design and Development of GPS-GSM Based Tracking System with Google Map Based Monitoring , 2013 .

[19]  D. Watling,et al.  Big data and understanding change in the context of planning transport systems , 2019, Journal of Transport Geography.

[20]  Zhu Han,et al.  Internet of Vehicles: Sensing-Aided Transportation Information Collection and Diffusion , 2018, IEEE Transactions on Vehicular Technology.

[21]  Shahid Mumtaz,et al.  Social Big-Data-Based Content Dissemination in Internet of Vehicles , 2018, IEEE Transactions on Industrial Informatics.

[22]  Takayuki Ito,et al.  Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network , 2016, Sensors.

[23]  Der-Horng Lee,et al.  Sustainable transport policy - An evaluation of Singapore's past, present and future , 2017 .

[24]  Helmut Krcmar,et al.  A Concept for the Architecture of an Open Platform for Modular Mobility Services in the Smart City , 2016 .

[25]  Lelitha Vanajakshi,et al.  Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses , 2009 .

[26]  Luis Felipe Herrera-Quintero,et al.  Smart ITS Sensor for the Transportation Planning Based on IoT Approaches Using Serverless and Microservices Architecture , 2018, IEEE Intelligent Transportation Systems Magazine.

[27]  Steven P. Haveman,et al.  State of the Art of Mobility as a Service (MaaS) Ecosystems and Architectures—An Overview of, and a Definition, Ecosystem and System Architecture for Electric Mobility as a Service (eMaaS) , 2019, World Electric Vehicle Journal.

[28]  Amer Shalaby,et al.  PREDICTION MODEL OF BUS ARRIVAL AND DEPARTURE TIMES USING AVL AND APC DATA , 2004 .

[29]  Safa Hachani,et al.  A service-oriented approach for flexible process support within enterprises: application on PLM systems , 2013, Enterp. Inf. Syst..

[30]  Lei Jia,et al.  Real-Time Bus Arrival Time Prediction: Case Study for Jinan, China , 2013 .

[31]  Diego Seco,et al.  Microservice-Oriented Platform for Internet of Big Data Analytics: A Proof of Concept , 2019, Sensors.

[32]  E. Gwee,et al.  Electric Vehicle Development in Singapore and Technical Considerations for Charging Infrastructure , 2017 .

[33]  Jianxin Li,et al.  Benchmark Data and Method for Real-Time People Counting in Cluttered Scenes Using Depth Sensors , 2018, IEEE Transactions on Intelligent Transportation Systems.