Reducing Traffic Incidents in Meal Deliveries: Penalize the Platform or Its Independent Drivers?

• 成果类型: Article; Early Access
• 署名作者: Zhang, Wenchang; Tang, Christopher S.; Ming, Liu; Cheng, Yue
• 署名单位: Indiana University System; IU Kelley School of Business; Indiana University Bloomington; University of California System; University of California Los Angeles; The Chinese University of Hong Kong, Shenzhen; emlyon business school
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2023.0259
• 发表日期: 2026
• 关键词: economy; uber

摘要

Problem definition: Some governments have toughened traffic penalties for meal-delivery drivers, yet the number of meal delivery-related traffic incidents has not abated. These observations prompted two questions: Under a given penalty scheme, how should a profit-maximizing platform determine the delivery fee to charge its customers and the commission to pay its drivers? How should a welfare-maximizing government determine its penalty scheme to curb traffic incidents and improve the social surplus of all stakeholders? Methodology/results: We represent the system dynamics using a three-stage game-theoretic model, from which our equilibrium analysis yields three key insights. First, penalizing the platform for delivery-related incidents is more effective at reducing risky driving than penalizing drivers. Higher platform penalties discourage unrealistic deliverytime promises, allowing drivers to lower their driving speeds. By contrast, increasing driver penalties does not consistently curb risky driving, because the platform may offset penalties by raising driver commissions, which further incentivizes speeding. Second, lowering driver penalties expands the platform's service area by encouraging drivers to accept longer-distance or lower-paying orders. Third, penalizing only the platform-rather than the drivers-maximizes total social surplus, provided the penalty is not set excessively high, which could undermine the balance between safety and market coverage. Our findings remain robust even when key modeling assumptions are relaxed separately. We illustrate our findings using data collected from a Chinese meal-delivery platform. Managerial implications: Governments should shift from penalizing drivers to targeting platforms, acknowledging their pivotal role in shaping driver behavior. This policy change would incentivize platforms to promote safe driving practices over speedy deliveries.