Page 298 - Big Data Analytics for Connected Vehicles and Smart Cities

278 Big Data Analytics for Connected Vehicles and Smart Cities Summary 279

2. Building a bridge between transportation and data science is not suffi-
cient; it is necessary for both parties to begin walking across the bridge
and meet somewhere in the middle.
3. The opportunities in big data and analytics are matched with poten-
tial challenges. Access to data and analytics power could mean that
people outside of transportation and outside of one’s enterprise are
better informed about smart city transportation planning and opera-
tions than the professionals involved in delivery.
4. There is an ongoing challenge associated with working across disci-
plinary boundaries. As in the case of intelligent transportation sys-
tems, smart city transportation—if it is to be done effectively and effi-
ciently—will require the mobilization of multidisciplinary teams who
understand the objectives and have, at the least, an awareness of one
another’s specializations.
5. Defining the questions is a good starting point, because in a new sub-
ject area the definition of the questions can help to clarify goals and
objectives. It is also difficult to get answers if you do not know what
questions to ask.
6. An awareness of big data is fundamental to the successful application
of these techniques within a smart city transportation environment.
Other areas of industry and commerce may be ahead of transporta-
tion in the adoption of these techniques, but the application of les-
sons learned and practical experiences from these other areas can ac-
celerate transportation and deliver spectacular results. They also say in
Silicon Valley that, “The early bird catches the worm, but the second
mouse gets the cheese.” Being just behind the leaders and early adopt-
ers could be excellent positioning to take advantage of lessons learned
and prior experience.
7. We have an excellent opportunity to adapt and adopt big data and
analytics techniques from other industries and apply them in smart
city transportation.
8. There has been a sea change in data science approaches. While previ-
ously we were inclined to fragment and partition data to manage it
effectively and efficiently, it is now possible to consolidate data and
create enterprise-wide views across all data. This enables the maxi-
mum probability of data elements combining to create new insight
and understanding. Consider the new possibility of a consolidated
data repository or data lake for smart city transportation.