Establish an open-sourced network-based simulation platform for shared mobility operations. The simulation explicitly characterizes drivers’ movements on road networks for trip delivery, idle cruising, and en-route pick-up.
- Download the code.
git clone git@github.com:HKU-Smart-Mobility-Lab/Transpotation_Simulator.git
- Download the dependencies and libraries.
pip install -r requirements.txt
-simulator
--input
---graph.graphml
---order.pickle
---driver_info.pickle
--output
--- some output files
--driver_generation.py
--dispatch_alg.py
--handle_raw_data.py
--find_closest_point.py
--simulator_pattern.py
--simulator_env.py
--A2C.py
--sarsa.py
--main.py
--config.py
--LICENSE.md
--readme.md
There are three files in 'input' directory. You can use the driver data and order data provided by us. Also, you can run python handle_raw_data.py to generate orders' information, run python driver_generation.py to generate drviers' information.
In config.py, you can set the parameters of the simulator.
't_initial' # start time of the simulation (s)
't_end' # end time of the simulation (s)
'delta_t' # interval of the simulation (s)
'vehicle_speed' # speed of vehicle (km / h)
'repo_speed' # speed of reposition
'order_sample_ratio' # ratio of order sampling
'order_generation_mode' # the mode of order generation
'driver_sample_ratio' : 1, # ratio of driver sampling
'maximum_wait_time_mean' : 300, # mean value of maximum waiting time
'maximum_wait_time_std' : 0, # variance of maximum waiting time
"maximum_pickup_time_passenger_can_tolerate_mean":float('inf'), # s
"maximum_pickup_time_passenger_can_tolerate_std"
"maximum_price_passenger_can_tolerate_mean"
"maximum_price_passenger_can_tolerate_std"
'maximal_pickup_distance' # km
'request_interval': 5, #
'cruise_flag' :False, #
'delivery_mode':'rg',
'pickup_mode':'rg',
'max_idle_time' : 1,
'cruise_mode': 'random',
'reposition_flag': False,
'eligible_time_for_reposition' : 10, # s
'reposition_mode': '',
'track_recording_flag' : True,
'driver_far_matching_cancel_prob_file' : 'driver_far_matching_cancel_prob',
'input_file_path':'input/dataset.csv',
'request_file_name' : 'input/order', #'toy_requests',
'driver_file_name' : 'input/driver_info',
'road_network_file_name' : 'road_network_information.pickle',
'dispatch_method': 'LD', #LD: lagarange decomposition method designed by Peibo Duan
# 'method': 'instant_reward_no_subway',
'simulator_mode' : 'toy_mode',
'experiment_mode' : 'train',
'driver_num':500,
'side':4, # grid side length
'price_per_km':5, # ￥ / km
'road_information_mode':'load',
'north_lat': 40.8845,
'south_lat': 40.6968,
'east_lng': -74.0831,
'west_lng': -73.8414,
'rl_mode': 'reposition', # reposition and matching
'method': 'sarsa_no_subway', # 'sarsa_no_subway' / 'pickup_distance' / 'instant_reward_no_subway'
'reposition_method' #2C_global_aware', # A2C, A2C_global_aware, random_cruise, stay We use osmnx to acquire the shortest path from the real world. You can set 'north_lat', 'south_lat', 'east_lng' and 'west_lng' in config.py , and you can get road network for the specified region.
You can set the maximum order price that is normally distributed and acceptable to passengers by modifing maximum_price_passenger_can_tolerate_mean' and maximum_price_passenger_can_tolerate_std.
In dispatch_alg.py, we implement the function LD, we use binary map matching algorithm to dispatch orders.
You can modify the parameters in config.py, and then excute python main.py. The records will be recorded in the directory named output.
We welcome your contributions.
- Please report bugs and improvements by submitting GitHub issue.
- Submit your contributions using pull requests.
If you use this simulator for academic research, you are highly encouraged to cite our paper:
An Open-Sourced Network-Based Large-Scale Simulation Platform for Shared Mobility Operations
This simulator is supported by the Smart Mobility Lab at The Univerisity of Hong Kong.