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algorithms

Minimal examples of data structures and algorithms in Python

awesome-optimal-transport

A list of awesome papers and cool resources on optimal transport and its applications in general! As you will notice, this list is currently mostly focused on optimal transport for machine learning topics.

awesome-traffic-related-dataset

Traffic related dataset collection

awesome-transit

Community list of transit APIs, apps, datasets, research, and software :bus::star2::train::star2::steam_locomotive:

awesome-transportation-network-data

A list of transportation network data

deep-learning-on-traffic-prediction

Repository for Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions<https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9352246>

deeprl_signal_control

multi-agent deep reinforcement learning for large-scale traffic signal control.

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

mplight

papers-we-love

Papers from the computer science community to read and discuss.

python

All Algorithms implemented in Python

rl-for-transportation

Paper list of Reinforcement Learning (RL) applied on transportation

sumo-traci_osm

This repository contains a Python Script and a SUMO configuration (.sumocfg) file. On running the Python Script, you are asked to input any location name (better keep it specific like - New York City, Carnegie Mellon University). This geographic location is converted into coordinates using geocoder library. Then using Selenium library, a map of the given location in .osm format is downloaded into your default downloads folder. This file is then moved into your working directory (for which you will have to change the variable 'destination' in the python script). After the map.osm file is moved to the working directory, the network from this map is extracted into .net.xml format. Using randomTrips.py, random routes are generated in the network. In the .sumocfg file, the network file, route file and output files are declared. Now, in the Python Script, TraCI is used to simulate the .sumocfg file and the output is stored in .out.xml format file.

sumo_latest

SUMO开发者版本，保持最新nightly

traffic_control

根据毫米波雷达和视频融合数据，基于决策树算法，计算交叉口的相序和配时参数

traffic_signal_optimization

Traffic Signal Timings Optimization Based on Genetic Algorithm and Gradient Descent

trafficassign

Frank-Wolf algorithm for solving traffic assignment problem