Abstract
Smart transportation network development with environmental issues into consideration has brought Industry 4.0 based solutions on priority. In this direction, battery-powered electric bus systems have been considered widely for ensuring flexibility, operation cost, and lesser pollutants emission. Industry 4.0 provides automation through a cyber-physical system (CPS), the interconnection of bus system entities with industrial internet-of-things (IIoT), remote information availability through cloud computing and scientific disciplines (human-computer interaction, artificial intelligence, machine learning etc.) integration. In this work, a discrete event-based simulation-optimization approach is integrated that take care of bus energy consumption according to real-time city's passenger needs and on-road friction levels. The proposed simulation optimization methodology utilizes multi-objective with dependent and independent variables for optimizing the overall system performance. In simulation optimization, objective functions are designed to tackle battery consumption, Internet-of-Thing (IoT) network performance, cloud operations efficiency and smart scientific discipline integration. Simulation parameters are based on a real-time bus system which is further analyzed, filtered and adapted as per the needs of the system. In another analysis, supercharger's capacities are varied to evaluate the performance of the proposed system and identify the low cost and efficient smart transportation system. Simulation results show different scenarios for variations in the number of buses, charging stations, bus-depots, mobile charging facilities, and bus-schedules. Simulation results show that the average passenger's waiting time in the waiting is (after ticket booking) varies between 0.2 minutes to 0.7 minutes in real-time traffic conditions. In similar traffic conditions, total passenger's time in system (ticket booking to travel) varies between 41.6 minutes (for 24 hours) to 45.5 minutes (for 1 year). In the simulation, priorities are given to those dependent and independent variables which save the battery consumption and elongate the utilization of buses. Lastly, it is also observed that the proposed system is suitable for resource-constraint devices because Gate Equivalent (GE) calculation shows that the proposed system can be implemented between 1986 GEs (communicational cost without confidentiality and authentication) and 7939 GEs (computational cost with HMAC for authentication in data storage). This ensures varies security primitivs such as confidentiality, availability and authentication.
Highlights
Electric energy-based smart transportation system is the need for a new generation smart city’s infrastructure.The associate editor coordinating the review of this manuscript and approving it for publication was Eklas Hossain .Electric energy has various advantages over fossil fuel energies
In this work, a simulation-optimization approach and Industry 4.0 is applied for modeling, analyzing and evaluating the feasibility of an electric-powered bus system in Dehradun smart city’s public transportation system
The proposed simulation-optimization based public transportation system is composed of multiple routes with 100 stops served by 15 buses as per the pre-defined schedule
Summary
Electric energy-based smart transportation system is the need for a new generation smart city’s infrastructure. For example (i) the environmental protection issues arise from fossil fuel-based public transportation systems should be taken-up diligently, (ii) there is a strong need to accelerate the process of fossil-fuel-based bus system replacement with advanced infrastructure such as Industry 4 integrated electrified public transportation systems especially in the world’s top polluted cities (presently, the top 22 out of 30 are in India [34]), (iii) there is need to apply simulation-optimization approach over various sub-systems including bus charging infrastructure, bus’s routes, passenger’s demands, special-route operations, battery capacities, and sizes etc., (iv) simulation optimization in hybrid (software and hardware) model should take network issues on priority because network operations only indicate on-road and off-road situations, and (v) in hybrid simulation-optimization approach with Industry 4 standards, on-road network issues involve traffic conditions, passengers demands, uncertainties, natural disasters etc Simulation optimization is fruitful for detailed analysis and analyze the results necessary to understand the real-life system performance
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