Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect
Abstract Aiming at the challenges of peak passenger congestion, user behavior heterogeneity and insufficient network synergy faced by public transportation systems in urbanization, this study proposed the Dynamic Incentive Strategy-Heterogeneous Response Synergy Model (DIS-HARM). The model integrate...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-16632-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849226325599977472 |
|---|---|
| author | Yifang Chen Shunlin Wang |
| author_facet | Yifang Chen Shunlin Wang |
| author_sort | Yifang Chen |
| collection | DOAJ |
| description | Abstract Aiming at the challenges of peak passenger congestion, user behavior heterogeneity and insufficient network synergy faced by public transportation systems in urbanization, this study proposed the Dynamic Incentive Strategy-Heterogeneous Response Synergy Model (DIS-HARM). The model integrated reinforcement learning, user heterogeneity modeling and small-world network synergy mechanism, adjusted the carbon credit intensity in real time by dynamic incentive generator, quantified the diminishing marginal utility effect of incentives for high-income groups by combining elastic user identifiers, and designed weather attenuation coefficients to optimize the spread of social influence. Simulation results showed that DIS-HARM significantly improves system efficiency and fairness: the peak hour passenger flow reduction rate reaches 72.2% (2.5% higher than the static strategy), the average peak hourly cost is reduced by 3.125%, and 36.5% of the incentive resources are tilted to the low-income group (83.1% coverage rate) at the same time. The model provided a theoretical tool for dynamic pricing and differentiated incentive strategies for urban transportation management, helping to achieve the dual goals of green travel and social equity. |
| format | Article |
| id | doaj-art-06c65a7463cc441cbeff8ecd6ef536a9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-06c65a7463cc441cbeff8ecd6ef536a92025-08-24T11:24:56ZengNature PortfolioScientific Reports2045-23222025-08-0115111710.1038/s41598-025-16632-yOptimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effectYifang Chen0Shunlin Wang1Office of Academic Affairs, Ningbo Polytechnic UniversitySchool of Supply-Chain Administration, Ningbo Polytechnic UniversityAbstract Aiming at the challenges of peak passenger congestion, user behavior heterogeneity and insufficient network synergy faced by public transportation systems in urbanization, this study proposed the Dynamic Incentive Strategy-Heterogeneous Response Synergy Model (DIS-HARM). The model integrated reinforcement learning, user heterogeneity modeling and small-world network synergy mechanism, adjusted the carbon credit intensity in real time by dynamic incentive generator, quantified the diminishing marginal utility effect of incentives for high-income groups by combining elastic user identifiers, and designed weather attenuation coefficients to optimize the spread of social influence. Simulation results showed that DIS-HARM significantly improves system efficiency and fairness: the peak hour passenger flow reduction rate reaches 72.2% (2.5% higher than the static strategy), the average peak hourly cost is reduced by 3.125%, and 36.5% of the incentive resources are tilted to the low-income group (83.1% coverage rate) at the same time. The model provided a theoretical tool for dynamic pricing and differentiated incentive strategies for urban transportation management, helping to achieve the dual goals of green travel and social equity.https://doi.org/10.1038/s41598-025-16632-yReinforcement learningNetwork synergyPublic transportationDynamic incentivesStrategiesUser heterogeneity |
| spellingShingle | Yifang Chen Shunlin Wang Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect Scientific Reports Reinforcement learning Network synergy Public transportation Dynamic incentives Strategies User heterogeneity |
| title | Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect |
| title_full | Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect |
| title_fullStr | Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect |
| title_full_unstemmed | Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect |
| title_short | Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect |
| title_sort | optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect |
| topic | Reinforcement learning Network synergy Public transportation Dynamic incentives Strategies User heterogeneity |
| url | https://doi.org/10.1038/s41598-025-16632-y |
| work_keys_str_mv | AT yifangchen optimizationofdynamicincentivestrategiesforpublictransportationbasedonreinforcementlearningandnetworksynergyeffect AT shunlinwang optimizationofdynamicincentivestrategiesforpublictransportationbasedonreinforcementlearningandnetworksynergyeffect |