Page 239 - Big Data Analytics for Connected Vehicles and Smart Cities

220 Big Data Analytics for Connected Vehicles and Smart Cities Practical Applications and Concepts for Transportation Data Analytics 221

ence alongside the variation in toll revenue. The toll return index could also be
a useful tool in the analysis of variable tolling techniques. In such approaches,
the toll is varied dynamically to achieve a specific level of service objective. In
many cases, the level of service objective is an average speed of 45 mph over the
variable tolling section of the network. The toll return index could be used as
an objective function in the calculation of the toll to be charged any given time.
Current dynamic tolling implementations rely on a lookup table that compares
traffic volumes and speeds with different toll levels. This typically takes no ac-
count of factors such as trip purpose when determining the elasticity associated
with the variable tolling deployment (with elasticity being defined as the num-
ber of vehicles that are redirected from the toll road to alternative parallel rights
for each incremental increase in the toll level).

Arterial Performance Management
The incorporation of user perception using social media analysis is likely to
yield some interesting results. While traffic and transportation engineers op-
timize based on the total delay along the corridor, discussions with drivers in-
dicate that they might hold the number of stops along the corridor as more
important than the total delay. This seems to be because drivers won’t mind a
slightly longer journey time along the corridor provided it is a smoother jour-
ney with less stops. This would suggest that a lower average speed would be
acceptable to drivers if the number of stops were minimized.

Decision Support for Bus Acquisition
While this analytic has been described in terms of a tool to enable better deci-
sions regarding bus acquisition timing, it also forms part of a larger picture
involving the use of scientific investment planning, supported by data and ana-
lytics. The principles involved in decision support for bus acquisition could
equally be applied to other infrastructure elements that support transportation
service delivery within a smart city.

References

[1] www.inrix.com .The data set for the freeway speed variability study was provided by IN-
RIX Inc. and preprocessed by Appian Strategies Inc. before delivery to the data science
team.
[2] Workshop session with Albert Yee, Emergent Technologies, and founder of the Caltrans
Freeway Management Academy.
[3] Discussion with Randy Cole, executive director for Ohio Turnpike, and a toll analytics
group under the auspices of the International Bridge Tunnel and Turnpike Association.