DRSP-Sim: A Simulator for Dynamic Ride-Sharing with Pooling: Joint Matching,Pricing, Route Planning, and Dispatching

Marina Haliem, Vaneet Aggarwal, Bharat Bhargava, "DRSP-Sim: A Simulator for Dynamic Ride-Sharing with Pooling: Joint Matching,Pricing, Route Planning, and Dispatching", Under Review at Journal of Open Source Software (JOSS).

Simulator Documentation and Flexibilities

This project is in python 3.7 and Tesnorflow 1.15.0

Simulator Setup and Flexibilities

First step is set some variables the path to the sqlite database in config/settings as db_dir = "data/db.sqlite3". Also, set the path to the directory to which logs should be stored as DEFAULT_LOG_DIR = "logs/tmp".

For the routing service, the user can choose to either use the OSRM server for real-time routing, or use our FastRouting service using pre-computed routes over the city. If the user chooses to use OSRM, this can be set in simulator/settings using: flags.DEFINE_boolean('use_osrm', False, "whether to use OSRM"). In this case, the host port for connecting needs to be set at config/settings.py as OSRM_HOSTPORT = os.getenv("OSRM_HOSTPORT", "localhost:5000").

After that, there is a number of variables in simulator/settings.py that provide wide flexibilities for the user to conduct a wide-range of experiments such as:

1. Whether to enable pooling:

flags.DEFINE_boolean('enable_pooling', True, "Enable RideSharing/CarPooling")

2. Whether to use our pricing benchmark:

flags.DEFINE_boolean('enable_pricing', True, "Enable Pricing Novelty")

If this is set to False, the simulator will default to the Pooling Pricing explained in our manuscript.

3. Set the number of vehicles to be involved in the simulation:

flags.DEFINE_integer('vehicles', 8000, "number of vehicles")

4. Among these vehicles, the user can set how many of them to adopt the DQN dispatching policy and how many of them to just go the destination-driven dispatching using these 2 variables:

flags.DEFINE_integer('dummy_vehicles', 0, "number of vehicles using dummy agent")
flags.DEFINE_integer('dqn_vehicles', 8000, "number of vehicles using dqn agent")

5. The user can also choose to log the events associated with vehicles or not using:

flags.DEFINE_boolean('log_vehicle', False, "whether to log vehicle states").

The customer-related events are being logged by default.

6. There is also multiple variables that are related to the DQN policy as well as the training hyper-parameters such as:

MAX_MEMORY_SIZE 	  		# Number of replay memory the dummy_agent uses for training.
SAVE_INTERVAL 		  		# The frequency with which the network is saved.
TARGET_UPDATE_INTERVAL 	# The frequency with which the target network is updated.

7. In addition to variables involved in calculating the reward function:

WORKING_COST = 0.2
DRIVING_COST = 0.2
STATE_REWARD_TABLE = {
		status_codes.V_IDLE : -WORKING_COST,
		status_codes.V_CRUISING : -(WORKING_COST + DRIVING_COST),
		status_codes.V_ASSIGNED : -(WORKING_COST + DRIVING_COST),
		status_codes.V_OCCUPIED : -(WORKING_COST + DRIVING_COST),
		status_codes.V_OFF_DUTY : 0.0}

8. There is also various variables available in config/settings.py that related to the construction of the region graph relying on the New York city map, obtained from Open-StreetMap such as:

1. The minimum and maximum time allowed between dispatching:
 	MIN_DISPATCH_CYCLE
	MAX_DISPATCH_CYCLE
2. Map-related variables such as:
	CENTER_LATITUDE
	CENTER_LONGITUDE
	LAT_WIDTH
	LON_WIDTH
	MAP_WIDTH = int(LON_WIDTH / DELTA_LON) + 1
	MAP_HEIGHT = int(LAT_WIDTH / DELTA_LAT) + 1

9. Switching between training and testing modes:

flags.DEFINE_boolean('train', True, "run training dqn_agent network.").

This variable should be set to False in the testing mode.

10. Finally, after setting all releveant paths, add the full path to the repo directory in simulator_driver.py:

import sys
sys.path.insert(0, '..../Dynamic-RideSharing-Pooling-Simulator/')

Then, run this file:

python simulator_driver.py

Data Generation

The user can choose to either go through the pre-processing steps mentioned below to generate the data, or just fetch the pre-processed files directly from: https://purr.purdue.edu/publications/3843/1, load them into a directory, and set the DATA_DIR variable in config/settings.py

Below you will find step-by-step instructions to set up the NYC taxi simulation using 2016-05 trips for training and 2016-06 trips for evaluation.

1. Download OSM Data

wget https://download.bbbike.org/osm/bbbike/NewYork/NewYork.osm.pbf -P osrm

2. Preprocess OSM Data

cd osrm
docker run -t -v $(pwd):/data osrm/osrm-backend osrm-extract -p /opt/car.lua /data/NewYork.osm.pbf
docker run -t -v $(pwd):/data osrm/osrm-backend osrm-partition /data/NewYork.osrm
docker run -t -v $(pwd):/data osrm/osrm-backend osrm-customize /data/NewYork.osrm

3. Download Trip Data

mkdir data
wget https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2016-05.csv -P data/trip_records
wget https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2016-05.csv -P data/trip_records
wget https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2016-06.csv -P data/trip_records
wget https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2016-06.csv -P data/trip_records

4. Build Docker image

docker-compose build sim

5. Preprocess Trip Records

docker-compose run --no-deps sim python src/preprocessing/preprocess_nyc_dataset.py ./data/trip_records/ --month 2016-05
docker-compose run --no-deps sim python src/preprocessing/preprocess_nyc_dataset.py ./data/trip_records/ --month 2016-06

6. Snap origins and destinations of all trips to OSM

docker-compose run sim python src/preprocessing/snap_to_road.py ./data/trip_records/trips_2016-05.csv ./data/trip_records/mm_trips_2016-05.csv
docker-compose run sim python src/preprocessing/snap_to_road.py ./data/trip_records/trips_2016-06.csv ./data/trip_records/mm_trips_2016-06.csv

7. Create trip database for Simulation

docker-compose run --no-deps sim python src/preprocessing/create_db.py ./data/trip_records/mm_trips_2016-06.csv

8. Prepare statistical demand profile using training dataset

docker-compose run --no-deps sim python src/preprocessing/create_profile.py ./data/trip_records/mm_trips_2016-05.csv

9. Precompute trip time and trajectories by OSRM

docker-compose run sim python src/preprocessing/create_tt_map.py ./data

The tt_map needs to be recreated when you change simulation settings such as MAX_MOVE.

10. Change simulation settings

You can find simulation setting files in src/config/settings and src/simulator/settings.

© CLAN Labs, Purdue.

Please cite the following papers if using any part of the code:

Marina Haliem, Vaneet Aggarwal, and Bharat K. Bhargava, "A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching Using Deep Reinforcement Learning", EEE Transactions on Intelligent Transportation Systems. @ARTICLE{9507388, author={Haliem, Marina and Mani, Ganapathy and Aggarwal, Vaneet and Bhargava, Bharat}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching Using Deep Reinforcement Learning}, year={2021}, pages={1-12}, doi={10.1109/TITS.2021.3096537}}

Marina Haliem, Vaneet Aggarwal, and Bharat K. Bhargava, "Adapool: An adaptive model-free ride-sharing approach for dispatching using deep reinforcement learning", In BuildSys’20. @inproceedings{HaliemAB20, author = {Marina Haliem andvVaneet Aggarwal andvBharat K. Bhargava}, title = {AdaPool: An Adaptive Model-Free Ride-Sharing Approach for Dispatching using Deep Reinforcement Learning}, booktitle = {BuildSys '20: The 7th {ACM} International Conference on Systems for Energy Efficient Buildings, Cities, and Transportation, Virtual Event, Japan, November 18-20, 2020}, pages = {304--305}, publisher = {{ACM}}, year = {2020}, url = {https://doi.org/10.1145/3408308.3431114}, doi = {10.1145/3408308.3431114}}

Marina Haliem, Ganapathy Mani, Vaneet Aggarwal, Bharat Bhargava, "A Distributed Model-Free Ride-Sharing Algorithm with Pricing using Deep Reinforcement Learning", Computer Science in Cars Symposium, CSCS 2020. @inproceedings{10.1145/3385958.3430484, author = {Haliem, Marina and Mani, Ganapathy and Aggarwal, Vaneet and Bhargava, Bharat}, title = {A Distributed Model-Free Ride-Sharing Algorithm with Pricing Using Deep Reinforcement Learning}, year = {2020}, isbn = {9781450376211}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3385958.3430484}, booktitle = {Computer Science in Cars Symposium}, articleno = {5}, numpages = {10} }

Since this code uses codes developed in the papers below, please cite those too.

Abubakr Al-Abbasi, Arnob Ghosh, and Vaneet Aggarwal, "DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 2, pp. 4714-4727, Dec 2019. @article{al2019deeppool, title={Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning}, author={Al-Abbasi, Abubakr O and Ghosh, Arnob and Aggarwal, Vaneet}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={20}, number={12}, pages={4714--4727}, year={2019}, publisher={IEEE} }

A. Singh, A. Alabbasi, and V. Aggarwal, "A distributed model-free algorithm for multi-hop ride-sharing using deep reinforcement learning," IEEE Transactions on Intelligent Transportation Systems, Oct 2019 (also in NeurIPS Workshop 2019). @ARTICLE{9477304, author={Singh, Ashutosh and Al-Abbasi, Abubakr O. and Aggarwal, Vaneet}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={A Distributed Model-Free Algorithm for Multi-Hop Ride-Sharing Using Deep Reinforcement Learning}, year={2021}, pages={1-11},doi={10.1109/TITS.2021.3083740}}

T. Oda and C. Joe-Wong, "Movi: A model-free approach to dynamic fleet management," IEEE INFOCOM 2018. (Their code is available at https://github.com/misteroda/FleetAI ) @inproceedings{oda2018movi, title={MOVI: A model-free approach to dynamic fleet management}, author={Oda, Takuma and Joe-Wong, Carlee}, booktitle={IEEE INFOCOM 2018-IEEE Conference on Computer Communications}, pages={2708--2716}, year={2018}, organization={IEEE} }

Clan-labs/Dynamic-RideSharing-Pooling-Simulator

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