amazon-last-mile-challenges.yaml

Name: 2021 Amazon Last Mile Routing Research Challenge Dataset
Description: "The 2021 Amazon Last Mile Routing Research Challenge was an innovative research initiative led by Amazon.com and supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics. Over a period of 4 months, participants were challenged to develop innovative machine learning-based methods to enhance classic optimization-based approaches to solve the travelling salesperson problem, by learning from historical routes executed by Amazon delivery drivers. The primary goal of the Amazon Last Mile Routing Research Challenge was to foster innovative applied research in route planning, building on recent advances in predictive modeling, and using a real-world problem and data. The dataset released for the research challenge includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world dataset excludes any personally identifiable information (all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity). Although multiple synthetic benchmark datasets are available in the literature, the dataset of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available dataset to include instances based on real-world operational routing data. The dataset is fully described and formally introduced in the following Transportation Science article: https://pubsonline.informs.org/doi/10.1287/trsc.2022.1173" 


Documentation: https://github.com/MIT-CAVE/rc-cli/blob/main/templates/data_structures.md
Contact: lastmile-research-challenge@amazon.com
ManagedBy: "[Amazon](https://www.amazon.com/)"
UpdateFrequency: None

Tags:
  - transportation
  - machine learning
  - deep learning
  - amazon.science
  - urban
  - analytics
  - geospatial
  - logistics
  - last mile
  - optimization
  - routing

License: "Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The material for the Amazon Last Mile Routing Research Challenge is provided under a Creative Commons Attribution-NonCommercial 4.0 International Public License (the “License”). You may not use this material except in compliance with the License. You may obtain a copy of the License at: https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt"

Citation: "Merchan, Daniel; Arora, Jatin; Pachon, Julian; Konduri, Karthik; Winkenbach, Matthias; Parks, Steven; Noszek, Joseph (2022). Transportation Science."

Resources:
  - Description: "Dataset including training and testing data. Folder almrrc2021_data_training includes 6,112 historical routes used for model traning; folder almrrc2021_data_evaluation includes 3072 historical routes used for model evaluation. All routes correspond to year 2018. Please refer to the Tranportation Science article  (https://pubsonline.informs.org/doi/10.1287/trsc.2022.1173) for a comprehensive description of the dataset, and to this documentation page (https://github.com/MIT-CAVE/rc-cli/blob/main/templates/data_structures.md) for details on data structure and format."
    ARN: arn:aws:s3:::amazon-last-mile-challenges
    Region: us-west-2
    Type: S3 Bucket
DataAtWork:
  Tools & Applications:
    - Title: Code repository used for the 2021 Amazon Routing Research Challenge (this repository is included for reference and documentation purposes only, you do not need to install it to access the data)
      URL: https://github.com/MIT-CAVE/rc-cli
      AuthorName: CAVE Lab, MIT Center for Transportation and Logistics
      AuthorURL: https://cave.mit.edu/
    - Title: AWS Last Mile Route Sequence Optimization
      URL: https://github.com/aws-samples/amazon-sagemaker-amazon-routing-challenge-sol
      AuthorName: Chen Wu, Yin Song, Verdi March, Eden Duthi
  Publications:
    - Title: "2021 Amazon Last Mile Routing Research Challenge: Data Set"
      URL: https://pubsonline.informs.org/doi/pdf/10.1287/trsc.2022.1173
      AuthorName: Daniel Merchán, Jatin Arora, Julian Pachon, Karthik Konduri, Matthias Winkenbach, Steven Parks, Joseph Noszek