Incorporating Traffic Patterns to Improve On-Time Delivery
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Incorporating Traffic Patterns to Improve On-Time Delivery. Melody J. Dickinson, MLOG 2010 Jillian Leifer, MLOG 2010 Advisor: Jarrod Goentzel Sponsor: Pepsi Bottling Group (PBG). Why We Care. Initial Results.

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Jillian leifer

Incorporating Traffic Patterns to Improve On-Time Delivery

Melody J. Dickinson, MLOG 2010

Jillian Leifer, MLOG 2010

Advisor: Jarrod Goentzel

Sponsor: Pepsi Bottling Group (PBG)

Why We Care

Initial Results

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?


  • 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)

  • 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.

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.


To evaluate on-time delivery, we will benchmark the current routing system against CarTel’s traffic probability projections and compare to actual travel time.

Three sets of data are being used:

Archived Manifests reflecting drivers’ actual routes

Route Plans created by PBG’s routing software

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.



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.


Stop Transaction

Melody J. Dickinson

Jillian Leifer

MIT Computer Science & Artificial Intelligence Laboratory