This repository contains code to replicate experiments in: Real-Time Approximate Routing for Smart Transit Systems. URL: https://arxiv.org/abs/2103.06212.Usage instructions: to replicate experiments for the Manhattan network (table 2 in the paper):Download all files and unzip files manhattan_dist_1.txt, manhattan_dist_2.txt and manhattan_dist_3.txtRun line_planning.py.The experiments run by default using trip request from April 3rd 2018. To run the experiments for the fhv data from Feb 3 or March 6, uncomment the parameter 'month' line #407 in the file line_instance.py.The code allows to test

This is the software code for the rolling horizon framework suggested in the paper below: Offline Pickup and Delivery Problem with Time Windows via Rolling Horizon Trip-Vehicle Assignment, Y Kim, D Edirimanna, M Wilbur, P Pugliese, A Laszka, A Dubey, S Samaranayake, accepted in The 37th AAAI Conference on Artificial Intelligence.Data includes:RollingHorizon└── data   ├── map   │   ├── edges.csv   │   ├── nodes.csv   │   └── times.csv   ├── requests   │   └── request

Extract the datasets to the data folder and into their respective folders. apc: cleaned-wego-daily.apc.parquet weather: darksky_nashville_20220406.csv and weatherbit_weather_2010_2022.parquet gtfs: alltrips_mta_wego.parquet traffic: inrix data, can download separatelyAlso, you could refer the code repository at: https://github.com/smarttransit-ai/mta_occupancy_prediction. The related paper is: J. P. Talusan, A. Mukhopadhyay, D. Freudberg and A. Dubey, "On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC

This dataset was named by a subset of the authors as "The I-24 Trajectory Dataset" and is given a subsequent date in the title of this node to distinguish it from ongoing trajectory data gathered by I-24 Motion and other experiments.The dataset was created by recording CAN and GPS data from a single vehicle driving on I-24. The dataset includes Time, Velocity, Acceleration, Space Gap, Lateral Distance, Relative Velocity, Longitude GPS, Latitude GPS, Score, TrackID, L_Approach, R_Approach, L_Adjacent, and R_Adjacent. The time feature was taken from recorded GPS data recorded at 10 Hz. Data feat

Validation of the PID controller used by the custom cruise control system. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section VII (Test 6), and is referenced elsewhere in CIRCLES research as Minitest 12. M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/DI-CPS56137.2022.00013.This da

Data from applying step inputs to acceleration control of a Toyota for system characterization. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section VII (Test 5), and is referenced elsewhere in CIRCLES research as Minitest 11. M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/DI-CPS561

This data corresponds to a vehicle driving on mainly long flat roads to determine energy consumption. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section VI (Test 4), and is referenced elsewhere in CIRCLES research as Minitest 7. M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/DI-CP

This data corresponds to CAN-data for testing cruise controller state changes from user-interaction. The experiment involved modifications by the user on a Toyota Rav4 vehicle. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section V (Test 3).M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/

CAN and GPS data from a controlled platoon experiment with 3 vehicles, to verify radar data as part of overhead camera information. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section IV (Test 2), and is referenced elsewhere in CIRCLES research as Minitest 4. M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022,

CAN and GPS data from a controlled platoon experiment to verify radar data. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section III (Test 1). M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/DI-CPS56137.2022.00013.