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Published on: 2023-12-27

Multi-Level Objectives Control of AVs at A Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach

Wenfeng Lin

Code and experimental data for "Multi-Level Objectives Control of AVs at A Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach."Codes are written in Python and developed on an open-source tool “Flow”.( https://flow.readthedocs.io/en/latest/index.html)

 

Mixed trafficAutonomous vehicles
Category: Road transport data (passenger), Others
DOI: 10.26599/ETSD.2023.9190025
CSTR: 32009.11.ETSD.2023.9190025
POSITIN Global
Published on: 2023-08-01Updated on: 2023-12-17

Vehicle and charging scheduling of electric bus fleets: a comprehensive review

Le Zhang, Yu Han, Jiankun Peng, Yadong Wang

Purpose — Transit electrification has emerged as an unstoppable force, driven by the considerable environmental benefits if offers. Nevertheless, the adoption of battery electric bus is still impeded by its limited flexibility. The constraint necessitates adjustments to current bus scheduling plans. To this end, this paper aspires to offer a thorough review of articles focused on battery electric bus scheduling.

Design/methodology/approach — We provide a comprehensive review of 42 papers on electric bus scheduling and related studies, with a focus on the most recent developments and trends in this research domain.

 

Charging scheduleElectric bus
Category: Road transport data (passenger), Traffic flow data
DOI: 10.26599/ETSD.2023.9190017
CSTR: 32009.11.ETSD.2023.9190017.V3
POSITIN Asia, China
Published on: 2023-11-20

VTOL sites location considering obstacle clearance during approach and departure

Yanjun Wang

This dataset constains data and codes for determining the VTOL sites.

UamVtol sites
Category: Road transport data (passenger), Travel demand data
DOI: 10.26599/ETSD.2023.9190024
CSTR: 32009.11.ETSD.2023.9190024
POSITIN Asia, China, Shenzhen
Published on: 2023-10-31

Empowering highway network: optimal deployment and strategy for dynamic wireless charging lanes

Lingshu Zhong

The OD data in this paper are from the real road network data of Guangdong province. The initial SoC in the paper is randomly generated and follows a normal distribution.

OdSoc
Category: Road transport data (passenger), Travel demand data
DOI: 10.26599/ETSD.2023.9190023
CSTR: 32009.11.ETSD.2023.9190023
POSITIN Asia, China, Guangdong
Published on: 2023-10-25

Real-Time Intersection Vehicle Turning Movement Counts from Live UAV Video Stream using Multiple Object Tracking

yuhao wang, Ivan Wang-Hei Ho, Yuhong Wang

There are two folders containing the replication files.

The first folder contains the Voc-Poc tool for transmitting the captured video stream from the Goggles to the local server.

The second folder contains the main code and main results of this manuscript. The main code is for the YOLO + StrongSORT + TMC collection algorithm. The main code is in the track.py file.

Traffic flow controlDrone
Category: Road transport data (passenger), Computer vision data
DOI: 10.26599/ETSD.2023.9190022
CSTR: 32009.11.ETSD.2023.9190022
POSITIN Asia, China, Hong Kong
Published on: 2023-09-13

Less is more: Lightweight reinforcement learning method for traffic signal control with less observation

Qiang Wu

Data and code for "Less is more: Lightweight reinforcement learning method for traffic signal control with less observation"

Signal timing designTraffic flow control
Category: Road transport data (passenger), Traffic flow data
DOI: 10.26599/ETSD.2023.9190021
CSTR: 32009.11.ETSD.2023.9190021
POSITIN Asia, China, Hangzhou and Jinan
Published on: 2023-08-10

Electric Vehicle Trip Energy Consumption Data

Yang Liu

The data consists of normal driving records for dozens of private cars over several months (from June 5, 2015 to June 30, 2016), with a sampling frequency of one minute. The basic specifications of the vehicles are as follows: Roewe E50 is a pure electric vehicle, weighing 1080 kilograms. It is equipped with a 22.4 kWh battery pack, and is reported to have a driving range of 170 kilometers. The raw data has been preprocessed and denoised, resulting in a final dataset containing 10,000 trips. This dataset offers substantial potential for reuse in research and analysis focused on electric vehicle energy consumption. Researchers, engineers, and policy makers can leverage this data to understand patterns, develop optimization algorithms, and inform energy-efficient practices. The dataset adheres to all applicable legal requirements. All sensitive information has been removed, and the data has been preprocessed to ensure confidentiality. There are no known legal or ethical obstacles to its use.

Trip energy consumptionElectric vehicle
Category: Road transport data (passenger), Others
DOI: 10.26599/ETSD.2023.9190020
CSTR: 32009.11.ETSD.2023.9190020
POSITIN Asia, China, Shanghai
Published on: 2023-08-08

Codes and simulation results for evaluation of platooning configurations

Junfan Zhuo, Feng Zhu

The package includes the codes (Codes.rar) and simulation results (simulation results.rar) related to the manuscript 'Evaluation of platooning configurations for connected and automated vehicles at an isolated roundabout in a mixed traffic environment'. Main codes are written in Python and Matlab, and the simulation results are stored in ‘.xlsx’ format.

Mixed trafficConnected and automated vehiclesCooperative connected and automated vehicles
Category: Road transport data (passenger), Others
DOI: 10.26599/ETSD.2023.9190019
CSTR: 32009.11.ETSD.2023.9190019
POSITIN Asia, Singapore
Published on: 2023-08-07 Associated article: https://doi.org/10.1108/JICV-01-2018-0003

Training and Application Code of the MDP Driving Policy in Highway

Yang Guan

The dataset comes from the paper "Markov probabilistic decision making of self-driving cars in a highway with random traffic flow: a simulation study" and mainly consists of three parts:

1. The "policy_training" folder, which includes the code used for training driving strategies in the highway scenario mentioned in the paper. The main function is "main_v3.cpp," and the functionality is implemented in the file "f_define_v3.cpp." Additionally, "to_strategy.m" exports the trained strategy as a state-action table for application, and "FIGURE.me" visualizes this table.

2. The "policy_application" folder contains "test.py" as the main file, which uses "MdpModel.py" to call the state-action table from the "Strategy" folder and visualizes it through "MdpModel.py." The "Map" and "Vehicle" folders store visualization elements.

3. The third part is a video of the visualization results named "simulation_demo.avi."

Automated vehicleConnected and automated vehicles
Category: Road transport data (passenger), Driving behavior data
DOI: 10.26599/ETSD.2023.9190018
CSTR: 32009.11.ETSD.2023.9190018
POSITIN Asia, China, Beijing
Published on: 2023-07-29

Replication Package for Multivariate Modeling of Autonomous Vehicle Interests

Sailesh Acharya

This is a publicly released replication package containing data and analysis scripts from the research study titled 'Private or on-demand autonomous vehicles? Modeling public interest using a multivariate model.' The package includes the 2019 California Vehicle Survey data and R scripts used to clean and analyze the data. The study identifies the key factors influencing public interest in different forms of autonomous vehicles and their implications for future transportation policies.

Autonomous vehiclesAdoption interestsMultivariate ordered probit model
Category: Road transport data (passenger), Travel behavior data
DOI: 10.26599/ETSD.2023.9190016
CSTR: 32009.11.ETSD.2023.9190016
POSITIN North America, United States
Published on: 2023-07-10 Associated article: https://doi.org/10.1016/j.trb.2017.07.003

USA GA400 dataset-vehicles speeds and densities

Xiaobo Qu, Jin Zhang, Shuaian Wang

The GA400 dataset includes a total of 47,815 observations of speeds and densities from 76 stations and was collected on the Georgia State Route 400 in 2003. The original data is aggregated every 5 minutes. Column 1 is density(veh/km) and Column 2 is speed(km/h).

Driver behaviourRoad transport
Category: Road transport data (passenger), Traffic flow data
DOI: 10.26599/ETSD.2023.9190015
CSTR: 32009.11.ETSD.2023.9190015
POSITIN North America, United States, Georgia State
Published on: 2022-12-12 Associated article: https://doi.org/10.1016/j.commtr.2023.100092

Formulation and Solution for Calibrating Boundedly Rational Activity-Travel Assignment: An Exploratory Study

Dong Wang

1. The SF and EMA transportation network data comes from http://www.bgu.ac.il/~bargera/tntp/. 

2. The codes are run by MATLAB.

3. Operation guidance can be found in the corresponding document.

Bounded rationalityParameter calibrationActivity-travel assignmentRoad transport
Category: Road transport data (passenger), Others
DOI: 10.26599/ETSD.2022.9190013
CSTR: 32009.11.ETSD.2022.9190013
POSITIN North America, United States, Massachusetts
Published on: 2023-07-10

Lane changing and congestion are mutually reinforcing?

Yang Gao

1. The data and codes involved in the paper are included here, including the selected original raw data and freeway data.

2. The Python code includes data cleaning and preprocessing code, as well as the code to achieve all the main results in the paper.

3. Data format: .csv

4. Code running environment: Python Jupyter

TrajectoryDriver behaviour
Category: Road transport data (passenger), Vehicle dynamics data
DOI: 10.26599/ETSD.2023.9190014
CSTR: 32009.11.ETSD.2023.9190014
POSITIN Oceania, Australia, Sydney
Published on: 2022-10-25 Associated article: https://doi.org/10.1016/j.commtr.2022.100083

Impacts to Maritime Shipping from Marine Chokepoint Closures

Lincoln Pratson

This replication package contains links to the primary datasets used in Pratson, L.F., Assessing impacts to maritime shipping from marine chokepoint closures, Communications in Transportation Research. It also contains the secondary data sets generated from the primary datasets and the MATLAB code developed to create the secondary data sets. The primary datasets are the ORNL 2000 Global Shipping Lane Network GIS shapefile, and HS92_202102 version of CEPII's 2019 BACI Trade Flow data. The secondary datasets consist of three Excel files: (1) MarineChokepointBlockagePaperShipLaneNetworkNodeEdgeTable.xlsx, (2) MarineChokepointBlockagePaperChokepointScenarioTables.xlsx, and (3) MarineChokepointBlockagePaperMasterTradeAndChokepointTables.xlsx. The code used to produce these consists of eleven MATLAB scripts. See included replication explanatory file for further details.

DisruptionFreight
Category: Maritime transport data, Trip transaction data
DOI: 10.26599/ETSD.2022.9190012
CSTR: 32009.11.ETSD.2022.9190012
POSITIN Global
Published on: 2022-05-09 Associated article: https://doi.org/10.1108/JICV-03-2022-0006

Battery electric buses charging schedule optimization considering time-of-use electricity price

Jian Zhang

This dataset is the replication package of the paper entitled "Battery electric buses charging schedule optimization considering time-of-use electricity price"  in the Journal of Intelligent and Connected Vehicles. The dataset includes the inputs of the MILP model and the corresponding code of the numerical test.

Electricity busCharging schedule
Category: Road transport data (passenger), Travel behavior data
DOI: 10.26599/ETSD.2022.9190004
CSTR: 32009.11.ETSD.2022.9190004
POSITIN Asia, China, Beijing
Published on: 2022-05-22 Associated article: https://doi.org/10.1016/j.commtr.2022.100069

Trip energy consumption estimation for battery electric buses: a regression method

Jinhua Ji

This dataset is the replication package of the paper entitled "Trip energy consumption estimation for battery electric buses: a regression method" in Communications in Transportation Research. The dataset includes the partial data of electric bus operation at trip level.

Electric busTrip energy consumption
Category: Road transport data (passenger), Others
DOI: 10.26599/ETSD.2022.9190005
CSTR: 32009.11.ETSD.2022.9190005
POSITIN Asia, China, Meihekou City
Published on: 2022-07-08 Associated article: https://doi.org/10.1016/j.commtr.2022.100079

Transport Network Downsizing based on Optimal Sub-network

Matthieu Guillot

Data and code for replicating the results and figures in the article

Graph neural networkLocal coordination
Category: Road transport data (passenger), Others
DOI: 10.26599/ETSD.2022.9190008
CSTR: 32009.11.ETSD.2022.9190008
POSITIN Europe, France, Lyon
Published on: 2022-06-03 Associated article: https://doi.org/10.1016/j.commtr.2022.100071

Assessment of the variability in total cost of vehicle ownership across vehicle and user profiles

Yulu Guo

This is the replication package for the paper entitled "Informing environmental policy design through an assessment of the variability in total cost of vehicle ownership across vehicle and user profiles" in Communications in Transportation Research.

Total cost of ownershipElectric passenger cars
Category: Road transport data (passenger), Vehicle dynamics data
DOI: 10.26599/ETSD.2022.9190006
CSTR: 32009.11.ETSD.2022.9190006
POSITIN Europe, Ireland
Published on: 2022-08-08 Associated article: https://doi.org/10.1016/j.commtr.2022.100080

Replication package explanatory file for "Replacing urban trucks via ground-air cooperation"

Ziling Zeng

The data used in this work is accessible through order in the space geodetic coordinate system.xls (coordinates of 200 orders in the space geodetic coordinate system) and 1kmzone_wsg_xy.csv (the latitude, longitude, and geodetic coordinates of the center of each 1km * 1km area).

For each business mode, a python file is provided individually. For instance,  mode1_truck_trailer.py is for mode 1, mode2_truck_drone.py is for mode 2 and mode3_drone_socialvehicle.py is for mode 3. The time, cost and energy consumption can be derived directly from these files.

DroneFreight
Category: Road transport data (freight), Travel demand data
DOI: 10.26599/ETSD.2022.9190010
CSTR: 32009.11.ETSD.2022.9190010
POSITIN Asia, China, Beijing
Published on: 2022-07-26 Associated article: https://doi.org/10.1016/j.commtr.2022.100076

A topology-based bounded rationality day-to-day traffic assignment model

Enrico Siri

The MATLAB code is the implementation of the model presented in the article 'A topology-based bounded rationality day-to-day traffic assignment model. The software allows to represent the evolution of the road network state when subjected to a disruptive event and is composed of 3 fundamental elements. 

-The main_Equilibrium.m function implements the Frank-Wolf algorithm for the computation of the User Equilibrium. This makes it possible to know the traffic flow patterns on the original network.

-The Disruption.m and ShockAssignment.m functions take care of constructing the new network following the disruption and routing the flows directly affected by the network topology changes.

-The function DTD_TrafficAssignment.m implements the proportional switch traffic assignment model presented in the aforementioned article. For each period, at each iteration, the amount of flows moving from more expensive paths to less expensive paths is calculated.

Driver behaviourDisruptionBounded rationality
Category: Road transport data (passenger), Travel behavior data
DOI: 10.26599/ETSD.2022.9190009
CSTR: 32009.11.ETSD.2022.9190009
POSITIN Europe, Italy
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