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ETS-Data, a publicly accessible database, provides indispensable materials for result replications, including data, codes, scripts, simulation, experiment design and other essential files. These aim to allow other researchers to reproduce and extend excellent research in the transportation community, which enhances the visibility, reputation, credibility and sharing of research publications.

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  • 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
    DOI: 10.26599/ETSD.2023.9190021
    CSTR: 32009.11.ETSD.2023.9190021
    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
    DOI: 10.26599/ETSD.2023.9190020
    CSTR: 32009.11.ETSD.2023.9190020
    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
    DOI: 10.26599/ETSD.2023.9190019
    CSTR: 32009.11.ETSD.2023.9190019
    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
    DOI: 10.26599/ETSD.2023.9190018
    CSTR: 32009.11.ETSD.2023.9190018
    Asia, China, Beijing
  • Published on: 2023-08-01Updated on: 2023-08-07

    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
    DOI: 10.26599/ETSD.2023.9190017
    CSTR: 32009.11.ETSD.2023.9190017.V2
    Asia, China

Communications in Transportation Research

Communications in Transportation Research publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. The mission is to provide fair, fast, and expert peer review to authors and insightful theories, impactful advances, and interesting discoveries to readers. We welcome submissions of significant and general topics, of inter-disciplinary nature (transport, civil, control, artificial intelligence, social science, psychological science, medical services, etc.), of complex and inter-related system of systems, of strong evidence of data strength, of visionary analysis and forecasts towards the way forward, and of potentially implementable and utilizable policies/practices. It is indexed in Scopus and DOAJ.

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