TTEkits 0.2.3

This is a travel time estimation Python Library!

Background

Twenty-first century urban planner have identified the understanding of Complex city traffic pattern as a major priority, leading to a sharp increase in the amount and the diversity of traffic data being collected. For instance, taxi companies in an increasing number of major cities have started recording metadata for every individual car ride, such as its origin, destination, and travel time. In this paper, we show that we can leverage network optimization insights to extract accurate travel time estimations from such origin-destination data, using information from a large number of taxi trips to reconstruct the traffic patterns in an entire city.

This Python Library TTEkits used the algorithm proposed by Dimitris Bertsimas et al. in the paper published in Operation Research for travel time estimation.

Install

pip install TTEkits
Before you can install TTEkits, you'll need to install some dependency libraries.

Usage

• step 1: import library

from TTEkits import model

• step 2: instantiation

graph_model = model.World(type=1,num=1000,sigma=0.1,reg=1000,time_limit=0.6)

• step 3: model train

graph_model.train()

• step 4: model test

graph_model.test()

• step 5: visualization

G = ox.graph_from_place('Manhattan, New York City, New York, USA', network_type='drive')  
picture = Visualization(G,type=2,manual=True)
picture.plot_real_path(-73.98215485,40.76793671,-73.96463013,40.76560211)

License

@Elon Lau

This reposity is licensed under the MIT license. See LICENSE for details.