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Record W3106806699 · doi:10.1155/2020/7981791

Assessing the Impacts of Autonomous Bus-on-Demand Based on Agent-Based Simulation: A Case Study of Fuyang, Zhejiang, China

2020· article· en· W3106806699 on OpenAlexvenueno aff
Zhikang Zhai, Ying Yang, Yu Shen, Yuxiong Ji, Yuchuan Du

Bibliographic record

VenueJournal of Advanced Transportation · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of ChinaScience and Technology Commission of Shanghai MunicipalityNatural Science Foundation of Shanghai
KeywordsScheduleComputer scienceTransport engineeringControl (management)Local busSimulationControl busEngineeringSystem bus

Abstract

fetched live from OpenAlex

This paper envisions and assesses the performance of an autonomous bus-on-demand (ABoD) system. We take Fuyang, Zhejiang, China, as the study area to investigate the spatiotemporal distribution of bus travel demand during workdays, and we propose replacing inefficient bus routes with the ABoD system. Agent-based models with various bus dispatching and operation control strategies are constructed to evaluate the performance of the ABoD system. The behaviors and interactions of the agents, passengers, autonomous buses, and a control center are designed. After the verification of the simulated bus travel demand with real-world demand, a series of scenarios with various ABoD operation strategies are simulated. The simulation results show that, in comparison with both current fixed-schedule bus services and the optimized bus dispatching strategies, the ABoD system occupies fewer road resources and utilizes bus vehicles more efficiently. Besides, the system is adaptive to the sudden surge in bus travel demand and is economically sustainable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.351
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2020
Admission routes1
Has abstractyes

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