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Record W4220997794 · doi:10.1155/2022/1785199

Pricing Method of the Flexible Bus Service Based on Cumulative Prospect Theory

2022· article· en· W4220997794 on OpenAlexvenueno aff
Wanjing Ma, Yuhang Guo, Kun An, Lei Wang

Bibliographic record

VenueJournal of Advanced Transportation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsProfit maximizationMaximizationProbabilistic logicProfit (economics)Social WelfareComputer scienceService (business)Operations researchTransport engineeringMicroeconomicsBusinessEconomicsEngineeringMarketing

Abstract

fetched live from OpenAlex

Pricing directly affects the sustainable development of the flexible bus service. This study proposes a profit maximization model and a social welfare maximization model for the flexible bus operator based on the cumulative prospect theory. Fares and uncertain travel time due to unforeseen detours in serving passengers jointly affect passengers’ mode choice. On the other hand, fares and passengers’ probabilistic choices over the flexible bus jointly determine the profits of the flexible bus company and social welfare. This study explores the relationship between the probability of passengers choosing the flexible bus, trip fares, and uncertain travel time. Serving more passengers indicates more profits, which also results in longer detour time thus decreasing the probability of passengers choosing the flexible bus. Considering the interactive influence among passengers, we further calculate the detour time distribution. Finally, a pricing model is established to compensate for the side effects of the detour. The results show that heterogeneous fares can help the flexible bus company to obtain higher profits but have negligible influence on social welfare. In addition, the development of long-distance services and regulations over the detour time can also help to obtain more profits.

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 score0.947
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.319
Teacher spread0.302 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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