Regulation strategies of ride-hailing market in China: an evolutionary game theoretic perspective

Lei, Licai; Gao, Shang; Zeng, En-yu

doi:10.1007/s10660-020-09412-5

Cited by 29 publications

(14 citation statements)

References 54 publications

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“…In addition, there is a literature confirming the feasibility of applying evolutionary game theory to the problem of ORH supervision [40], and the literature also took the ORH platform, drivers, and passengers as the main players. However, there are still some differences between this paper and the literature.…”

Section: Discussionmentioning

confidence: 99%

Active Supervision Strategies of Online Ride-Hailing Based on the Tripartite Evolutionary Game Model

Xie

Yuan

2020

IEEE Access

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As a very important passenger transportation model in the era of sharing economy, the online ride-hailing (ORH) has also caused new traffic management issues while improving resource allocation. Although regulations and policies have imposed macro-level supervision on the ORH market, they have not prevented some drivers from cheating on platforms' subsidies and jeopardizing passengers' safeties at the source. In order to realize the voluntary and sustainable ORH supervision, and enable relevant participants to actively supervise, report and comply with rules, this paper constructs an evolutionary game model among the platform, passengers and drivers. Based on the bounded rationality and expected benefits of the participants, the main factors determining the optimal strategies are analyzed. At the same time, the evolution path and the equilibrium state of the three game groups are studied by numerical simulation. The results show that important factors of realizing the benign supervision of ORH include minimizing the reporting costs of passengers, making penalties for drivers who violate the rules far greater than the illicit incomes, realizing the platform supervision costs less than the sum of penalty incomes and positive social effects. In addition, improving rewards for reporting can promote the continuity of passengers' participation but increase the possibility of false reports. Therefore, the platform needs to consider the cost of identifying false information when designing the reward amount.INDEX TERMS Tripartite evolutionary game, online ride-hailing platform, active supervision, passengers and drivers, numerical simulation.

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Section: Discussionmentioning

confidence: 99%

Active Supervision Strategies of Online Ride-Hailing Based on the Tripartite Evolutionary Game Model

Xie

Yuan

2020

IEEE Access

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“…This paper proposes an ensemble learning model to forecast the online car-hailing demand with multi-hierarchical datasets considering both temporal and spatial features. The basic idea of stacking ensemble learning is to integrate several different types of classifiers to generate a strong classifier to improve the generalization ability of strong classifiers [15] . The training process is shown in Fig.…”

Section: A Stacking Ensemble Learningmentioning

confidence: 99%

Demand Forecasting of Online Car-Hailing With Stacking Ensemble Learning Approach and Large-Scale Datasets

Jin

et al. 2020

IEEE Access

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With the rapid development and convenient service of online car-hailing, it has gradually become the preferred choice for people to travel. Accurate forecasting of car-hailing trip demand not only enables the drivers and companies to dispatch the vehicles and increase the mileage utilization, but also reduces the passengers' waiting-time. The rebalance of spatiotemporal demand and supply could mitigate traffic congestion, reduce traffic emission, and guide people's travel patterns. This study aimed to develop a short-term demand forecasting model for car-hailing services using stacking ensemble learning approach. The spatial-temporal characteristics of online car-hailing demand were analyzed and extracted through data analysis. The region-level spatial characteristics, time features, and weather conditions were added into the forecasting model. Then the stacking ensemble learning model was developed to predict the car-hailing demand at region-level for different time intervals, including 10 min, 15 min, and 30 min. The validation results suggested that the proposed stacking ensemble learning model has reasonable good prediction accuracy for different time intervals. The comparison results show that the short-term demand forecasting model based on stacking ensemble learning is better than single LSTM, SVR, lightGBM and Random Forest models. MAE and RMSE increased by 6.0% and 5.2% respectively at 30 min time interval, which further verifies the effectiveness and feasibility of the proposed model. INDEX TERMS stacking ensemble learning approach, online car-hailing, feature selection.

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“…However, the potential safety hazards of the online car-hailing industry also frequently occur 4 – 7 . From the customer perspective, drivers and passengers may face moral hazard, opportunism and other issues due to asymmetric information and incomplete contracts 8 , 9 . From a platform perspective, a centralised online car-hailing platform may have potential unreliable issues, such as illegally tampering with data and illegally collecting users’ privacy 10 .…”

Section: Introductionmentioning

confidence: 99%

“…Many scholars have carried out research on the regulation and governance of online car-hailing and have presented multiple paths of online car-hailing governance from the perspectives of legal norms, public management, and governance model design. Specific governance modes include policy and regulation formulation 11 , 12 , experimental regulation 13 , third-party regulation 14 , government-led regulation 6 , platform self-regulation 15 , 16 , and multi-subject collaborative governance 8 , 9 , 15 . These governance modes have effectively regulated the online car-hailing industry to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary game study on the governance and development of online car-hailing based on blockchain technology

Wang

Liu

Zhao

2022

Sci Rep

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Changes in the online car-hailing industry have brought new challenges to government governance. Effectively enhancing governance efficiency has become the focus of academic research. Based on the technical governance perspective, this paper introduces the consortium blockchain to construct an evolutionary game model between the online car-hailing platform and the government under blockchain technology. By solving the replicated dynamic equations and the Jacobian matrix, the influences of the change in initial conditions and decision parameters on the evolutionary stability results are revealed, and numerical experiments are carried out by using the Python programming language. This paper claims that the system presents three evolutionary stable results and a periodic stochastic state when the key parameters are located in different thresholds. The additional cost of the platform’s negative regulation and the government’s punishment intensity have a positive effect on the evolution of the system to the ideal state (active regulation, active governance). Platform technology R&D cost and government innovation input have a negative effect on the evolution of the system to the ideal state. Therefore, using blockchain to increase the additional cost of the platform’s negative regulation, appropriately increasing the government’s punishment intensity, reasonably controlling the government’s innovation input to the platform, and reducing the technology R&D cost of the platform will help the system evolve into an ideal state. This paper provides useful references to implement effective governance and the innovative and healthy development of the online car-hailing industry.

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Regulation strategies of ride-hailing market in China: an evolutionary game theoretic perspective

Cited by 29 publications

References 54 publications

Active Supervision Strategies of Online Ride-Hailing Based on the Tripartite Evolutionary Game Model

Active Supervision Strategies of Online Ride-Hailing Based on the Tripartite Evolutionary Game Model

Demand Forecasting of Online Car-Hailing With Stacking Ensemble Learning Approach and Large-Scale Datasets

Evolutionary game study on the governance and development of online car-hailing based on blockchain technology

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