Transcript Slide 1

Melody J. Dickinson, MLOG 2010
Jillian Leifer, MLOG 2010
Advisor: Jarrod Goentzel
Sponsor: Pepsi Bottling Group (PBG)
Incorporating Traffic Patterns to
Improve On-Time Delivery
Why We Care
Traffic, construction and other road hazards affect all
vehicles on the road—including delivery fleets. If
historical data on traffic patterns could be incorporated
into route plans, could on-time delivery be improved?
Commercial vehicle routing systems use a
static, deterministic model to develop
“optimized” route plans for their fleets.
• What if traffic patterns could be considered?
Delivery fleet vehicles may increase driving
efficiency by using stochastic data.
• Potential exists for dramatically improved
routing systems, as well as the achievement of
efficiencies in the delivery process.
•
January 2010 Poster Session
Initial Results
OCTOBER 2009 AT-A-GLANCE:
• There were 360 unique route sequences.
• Of those, drivers made the first stop as
scheduled 49 times (13.6%).
• Of those 49, only 37% of delivery time stamps
fell within the expected window. (Denoted by the
red dot within a rectangle under Methodology)
Given this information, why would drivers follow
their route plans?
Sample Route
Next Step: Comparing these routes to CarTel
data will uncover whether the discrepancy is
due to vehicle routing or the stop time model.
Methodology
To evaluate on-time delivery, we will benchmark the
current routing system against CarTel’s traffic probability
projections and compare to actual travel time.
End
Start
What is CarTel?
CarTel is a distributed, mobile
sensor network and telematics
system. By installing data
collecting devices on a fleet of
taxi cabs (Cabernet) and through
an iPhone application, historical
data on traffic time probabilities
has been collected for the greater
Boston metropolitan area.
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Time
MIT Computer Science &
Artificial Intelligence
Laboratory
Stop Transaction
Three sets of data are being used:
1. Archived Manifests reflecting drivers’ actual routes
2. Route Plans created by PBG’s routing software
3. Projected Travel Times using historical CarTel data
Expected Contribution
The results of this thesis will be applicable to
any operator of a delivery fleet. We expect that
incorporating traffic patterns will improve the
objective function for minimizing time and
increasing accuracy.
This research will inform the means to improve
customer service. In some cases, routes with
longer distances may be accepted in order to
achieve a faster time overall.
Melody J. Dickinson
Jillian Leifer