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.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Melody J. Dickinson, MLOG 2010
Jillian Leifer, MLOG 2010
Advisor: Jarrod Goentzel
Sponsor: Pepsi Bottling Group (PBG)
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?
OCTOBER 2009 AT-A-GLANCE:
Given this information, why would drivers follow their route plans?
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
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.
Melody J. Dickinson
MIT Computer Science & Artificial Intelligence Laboratory