xzhou99 / space-time-network_optimization_4_transportation_logistics

Learning-transportation https://github.com/xzhou99/learning-transportation

####Contact: Dr. Xuesong Zhou Email: [email protected]

Learning-transportation documents are are contained in the lessons. These lessons help the new users to quickly learn about the software DTALite and walk them through different tools and processes to analyze a network.

These learning documents can be found in the lessons folder on the homepage of learning-transportation. Different lessons contained in the folder are;

Lesson 1 --> Learning Transportation Network Analysis This lesson is divided into following three parts:

Lesson 1.1 --> Introduction with the West Jordan Network

Link: https://github.com/xzhou99/learning-transportation/tree/master/Lessons/Lesson%201/Lesson%201.1

Learning Objectives:

1. Understand how to view/edit network attributes in NeXTA and GIS.

2. Run a basic simulation, comparing two different scenarios.

3. Understand how basic network attributes affect traffic simulation results.

Lesson 1.2 --> Create a Network from Scratch and Run Dynamic Traffic Assignment.

Link: https://github.com/xzhou99/learning-transportation/tree/master/Lessons/Lesson%201/Lesson%201.2

Learning Objectives:

1. Path travel time is the sum of link travel time along routes

2. Signal timing could lead to time-dependent traffic delay

3. A large number of MOEs available for comparing scenarios

Lesson 1.3 --> GIS Importing Document

Link: https://github.com/xzhou99/learning-transportation/tree/master/Lessons/Lesson%201/Lesson%201.3

Learning Objective: Importing GIS Network data into NeXTA.

Lesson 2 --> Traffic Congestion Propogation: Understanding Bheoretical Basics of Dynamic Traffic Network Assignment and Simulation.

Link: https://github.com/xzhou99/learning-transportation/tree/master/Lessons/Lesson%202

Learning Objectives:

1. Understand major input and output data for a dynamic network loading program

2. Identify bottlenecks and model congestion propagation

3. Calculate traffic states through different computational approaches.

Lesson 3 --> Network Modeling This lesson is further divided into following two parts:

Lesson 3.1 --> Understand Network Equilibrium Model: Braess' Paradox.

Link: https://github.com/xzhou99/learning-transportation/tree/master/Lessons/Lesson%203/Lesson%203.1

Learning Objectives:

1. How to create a network by importing an Excel file in NeXTA

2. Understand modeling principles of user equilibrium

3. Know how to setup BPR function parameters for special link types

4. Understand the impact of adding a link and analyze the performance at link, path and network levels

5. The impact of different levels of demand on Braess’ paradox

6. Understand the impact of road pricing on Braess paradox and how to resolve Braess’ Paradox

Lesson 3.2 --> Route Choice Behavior: Modeling Considering Traveler Information Provision.

Link: https://github.com/xzhou99/learning-transportation/tree/master/Lessons/Lesson%203/Lesson%203.2

Learning Objectives:

1. Understand how different types/sources of travel time information are used in the route choice process in DTALite

2. Understand how to change incident, VMS, and travel time information settings in DTALite

3. Understand how to evaluate the route choice effects of VMS & traveler information in NeXTA

Other online github resources: https://github.com/xchChen/CACSP_ADMM The C# code of multi-robot coordination and scheduling framework using ADMM solution method, a specific case for the crane and AGV coordination and scheudling problem (CACSP) in automated container hub.

Open-source space-time diagram visualization tool for General Modeling Network Specification (GMNS) https://github.com/YXZhangSWJTU/space-time-diagram_gmns

Matlab functions used to perform analyses for DTW vehicle trajectory analysis paper and Master's Thesis Taylor, J., Zhou, X., Rouphail, N.M. and Porter, R.J., 2015. Method for investigating intradriver heterogeneity using vehicle trajectory data: A dynamic time warping approach. Transportation Research Part B: Methodological, 73, pp.59-80. https://www.sciencedirect.com/science/article/pii/S0191261514002264

https://github.com/jeff-d-taylor/DTW_Paper_Code

Input files for DTW code, derived from NGSIM data set https://github.com/jeff-d-taylor/DTW_Vehicle_Trajectory_Data https://github.com/jeff-d-taylor/Basic_DTW_Matlab

https://github.com/jiawei92/CAVLite