The Layered Bidirectional Q-Learning (LBQ) algorithm is designed for path planning, tackling the complexities inherent in multilayer path planning during the recycling process. This approach incorporates a bidirectional update mechanism that minimizes the unpredictability associated with initial exploration phases. Additionally, the algorithm employs a hierarchical reinforcement learning strategy, which breaks down intricate tasks into more manageable subtasks. Through the strategic design of reward functions that address various constraints, the LBQ algorithm successfully optimizes paths under multiple conditions.

The replication file contains data used in the paper "Collaborative electric vehicle routing with meet points" published in COMMTR. In this paper, we use real-world locations of grocery stores in Gothenburg, Sweden. The original data is the real addresses. Additionally, we use some test location data to evaluate large-scale instance performance, with customer locations randomly generated within a 25 km × 25 km region. The x and y coordinates range from 0 to 25 and the unit is kilometer. In addition to customer locations, we have included meet point and depot locations in the file. The type of each location and its associated company are noted. This data has been prepared for replication purposes. 
The code for the proposed algorithms is currently being used in another paper, which is under review. We will upload the code once the other paper is published. 

This repository provides the system dynamics simulation model to the paper titled "Bridging the gap: towards a holistic understanding of shared micromobility fleet development dynamics", submitted to the Journal Communications in Transportation Research. The simulation model is made available as a Vensim Packaged Model in the file format vpmx. The free software Vensim Model Reader is necessary to view and simulate the model (https://vensim.com/free-downloads/#Model_Reader). The Model Reader allows read-only access to models created with Vensim. Policies and policy combinations can be defined and simulated using the slider controls. All equations and parameter values can be inspected using the “Document” and “Document all” tools. It furthermore includes the complete simulation results for all parameters and variables for ten different policy packages in the file format csv (Comma Separates Values). 

During the eye-tracking measurement—using a Tobii Pro eye camera—the participants were asked to watch a video shot inside an autonomous vehicle. The video was compiled from publicly available sequences according to two aspects:

1) Different situations are shown from different positions,

2) The eye movements observed at the beginning and at the end of the video can be compared to some extent; therefore, we selected the first and last sections of the video in such a way that the subjects can watch the ride from the same angle and have relatively more time to become involved in the given situation.

The recording consisted of 5 different sequences, within which we defined 29 areas of interest (AOI) 

In terms of the data collected via eye cameras, four important measures can be defined:

−      Average fixation duration: how long a fixation lasted on average within the given AOI.

−      Fixation count: number of fixations within the given AOI.

−      Total fixation duration: total length of fixations within the given AOI.

−      Pupil diameter (right/left): the change in right and left pupil diameter within the given AOI.

1. This dataset contains all the RAAML files for the architecture design of the urban intersection information physical system proposed in this studyTo open this file, the MagicDraw software mentioned in this article needs to be used.
2. The architecture design file utilizes the RAAML database released by OMG by extending the functionality of UML and SysML. This database includes basic models related to reliability analysis, which have now been imported into the provided dataset. Please refer to OMG Risk Analysis and Assessment Modeling Language, Risk Analysis and Assessment Modeling Language (RAAML) Libraries and profiles for the original file, Version 1.0, OMG Document Number: ptc/21-12-02, http://www.omg.org/spec/RAAML.
3. The data involved in Section 4.6 of this article are all the results obtained in Section 4.5, and the response surface method built-in in Design Expert is used for relevant calculations.