摘要: |
Transportation agencies in urban, sub-urban, and rural communities have plans or are amid developing initial Smart City projects. The major component of
these projects comprises the Internet of Things (IoT). IoT enables collecting data flows and exchange to enable the analytics needed to manage and achieve
the end goals of any smart city project. Many agencies find that IoT Platform (IP) selection is very challenging compounded by limited technical resources
and are struggling to implement vital concepts aimed at enabling more effective and sustainable mobility.
This will be a comparative study that evaluates different IP solutions currently available in terms of interoperability, functional capabilities, delivery models, and integration strategies toward achieving sustainable mobility.
Other factors that will be examined include platform security, user experience, scalability, and suitability for urban sub-urban, and rural areas. Ultimately, the research team plans to employ AI (i.e., machine learning) algorithms that can help predict and adapt traffic management strategies to better leverage such metrics as link travel time on a specific segment of large-scale traffic networks.
These capabilities will be further used to develop an advanced traffic simulator (i.e., high fidelity, efficient, reliable, and location sensitive) necessary for developing future optimization algorithms. |