Data used in this study is based on questionnaire distribution in four Indonesian cities about Transport-SuperApps behaviour. 1,051 valid data are used for the analysis. The list of questions that were used in this study is described in the paper. The script and the packages for analysis were disclosed, however, due to funders’ terms and conditions, the data cannot be disclosed.

Total 312 participants in approximately equal proportions of males and females were invited to participate the decision-making experiments for the payoffs that were contingent on their performance. The participants were randomly assigned to 17 groups and they were required to make DTD route choices with no mutual communication. The following eight DTD scenarios with the same origin-destination (OD) pair were set.

• Scenario 1 was the baseline scenario containing a symmetric two-route network.
• Scenarios 2-5 extended Scenario 1 by using asymmetric two-route networks and different cost functions to investigate subject’s route choice behaviors under different cost feedback.
• Scenarios 6-7 employed asymmetric networks with three routes to observe more complicated route choice behavior.          •Scenario 8 extended the configuration to non-linear cost functions and different group sizes (24 subjects per group in Scenario 8, while 16 in others), which would demonstrate the robustness of the proposed theoretical model.

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)

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.

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

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.

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.

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

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.

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.

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."

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.

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).

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.

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

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.

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.

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.

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

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.