Tool Analysis: Policy Tools for Smart City Transportation Optimization

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Added on  2022/11/30

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This essay provides a tool analysis for developing policies aimed at optimizing bus and local train schedules in a Smart City environment, with a focus on minimizing energy consumption and waiting times. The primary tools examined are E-Participation and Big Data Analytics. E-Participation facilitates vehicle tracking and booking, reducing waiting times and energy expenditure. Big Data Analytics enables efficient information management, personalized services, and GPS tracking to identify routes with minimal traffic, further reducing waiting times. The analysis highlights the importance of these tools in enhancing the efficiency and sustainability of urban transportation systems. Desklib is a platform where you can find such solved assignments and study resources.
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Running head: TOOL ANALYSIS
TOOL ANALYSIS
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1TOOL ANALYSIS
Tools used for developing policies
The tools that have been chosen for developing policy for optimizing bus and local
train schedules for minimizing the energy and waiting times in a Smart City environment are
as follows: -
E- Participation
With the help of E Participation implementation, the major advantage that will be
received includes proper tracking of vehicles. This proper tracking of vehicles will include
the fact that location of the vehicle can be easily traced and hence waiting time will be
decreasing. Another aspect that can be considered is that booking of vehicles can be made.
This booking of vehicles will help in reducing the waiting time of the customers in Smart
City (Zheng 2017). Hence, energy that will be required during the waiting process will be
being decreased.
Big Data Analytics
With the help of Big Data Analytics the main advantage that will be received includes
proper management of information the main aspect that will be considered is that the vehicle
brands get access to the personal data of the clients. This will ensure that the booking process
will be getting benefitted. This is the main reason that the time that will be required for
reaching the client place will be reducing. Payment process will also get facilitated (Karau et
al, 2015). The main aspect that the details of the cards will be stored in the apps and hence
tracking of the vehicles also get easier. With the help of the Big Data analysis the main GPS
tracking method, the traffic present in the routes can also be detected and the route that has
least traffic is considered and hence reducing the waiting time.
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2TOOL ANALYSIS
References
Karau, H., Konwinski, A., Wendell, P., & Zaharia, M. (2015). Learning spark: lightning-fast
big data analysis. " O'Reilly Media, Inc.".
Zheng, Y. (2017). Explaining citizens’ E-participation usage: functionality of E-participation
applications. Administration & Society, 49(3), 423-442.
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