货拉拉司机郑凯基坦言，之前担心准点率影响抢单，遇到堵车或客户临时调整时间时压力很大，如今知 道准点率与中单无关，"不用再拼命赶路，和用户沟通清楚就好"。 随着这些举措落地，新就业形态劳动者的"时间体验"正悄然改变。外卖骑手不用再为几分钟超时焦虑， 遇到商家出餐慢可上报异常获得补时；货车司机跑单时心里更有底，新司机能参考建议到达时间规划路 线，老司机也不必因意外情况担心收入受影响。司机刘圣说，平台给出的时间很合理，还会提前考虑货 车限行、高峰期堵车等因素，"只考核装货准点率，不牵扯卸货时间，对我们很公平"。 货拉拉司机管理部负责人汪晓兰表示，货拉拉持续公开算法规则，核心是推动平台规则透明化与司机体 验优化。本次公开的建议到达时间，科学地在导航基础时间上，基于距离、车型为司机额外延长更多时 间。同时，明确仅考核快车/特快订单的"装货地准点率"，且不影响司机中单，根本目的是尊重现实路 况复杂性，避免司机因时间压力产生不必要的赶路风险，将安全放在首位。这些举措，都是为了构建更 公平、更安全、更值得信赖的司机工作环境。 在业内人士看来，这场"时间松绑"的本质，是货拉拉等平台型企业的算法从"效率优先"向"人性化协 同"转 ...

仅考核快车/特快单装货准点率，准点率不影响中单 放宽"建议到达时间"，特殊情况可再申请加时 "我们跑车经常会参考平台给出的时间点，大部分时候都可以在那个时间之前到达。尤其是对于刚加入平台的新司机来说，有一个时间作为参 考，跑起来心里更有底。"货拉拉司机刘圣表示，许多司机接单后都会看到订单内显示的建议到达时间，但之前并不知道时间是怎么计算出来 的。 货拉拉第五次算法公开的内容则解答了这个问题。据悉，货拉拉建议到达时间是系统根据订单距离、实时路况、司机车型等因素，计算出的司 机预计到达用户指定装货起点或卸货终点的时间点，其主要由三部分构成：导航基础时间、距离延长时间和车型延长时间，系统会在导航基础 时间之上，根据订单距离远近和货车车型大小，叠加相应的延长时长，距离越长、车型越大，叠加的延长时间也越长。 在实际货运过程中，订单会先显示抵达装货地的时间，完成装货后，再显示抵达卸货地的建议时间。为应对行驶途中可能出现的意外情况(如 车辆故障、严重拥堵、联系用户困难等)，平台还开设了异常报备渠道，司机可通过该渠道申请延长送达时间。在部分极端天气(如台风、暴雨) 或特殊节假日期间，平台也会采取豁免准点率考核的措施。 货拉拉 ...

【环球网科技综合报道】12月10日，货拉拉围绕建议到达时间及装货准点率算法规则进行了第五次算法公开。内容显示，货拉拉平台建议到达 时间由"导航基础时间""距离延长时间"和"车型延长时间"三部分组成，算法会自动在导航预估的基础时间上，根据距离和车型额外设置延长时 间，距离越远、车型越大则延长时间越长，若司机有突发情况，还可通过异常报备入口申请再延长时间。此外，货拉拉还公布了准点率的计算 方式及考核规则，平台仅考核快车、特快订单抵达装货地的准点率，准点率不受司机抵达卸货地的时间影响，也不会影响司机中单。 货拉拉司机管理部负责人汪晓兰表示，货拉拉提供的建议到达时间仅作为司机参考，并非硬性运输完成时限。为避免司机为了赶时间而选择冒 险，平台会预留充足的时间给司机运输进行"缓冲"。建议到达时间更宽松、更人性化，司机在运输过程中也能更安全、体验更好。 货拉拉第五次算法公开的内容则解答了这个问题。据悉，货拉拉建议到达时间是系统根据订单距离、实时路况、司机车型等因素，计算出的司 机预计到达用户指定装货起点或卸货终点的时间点，其主要由三部分构成：导航基础时间、距离延长时间和车型延长时间，系统会在导航基础 时间之上，根据订单距离 ...

Core Insights - The article highlights the long-standing "relationship dilemma" in platform economies, where laborers lack transparency and dialogue with algorithms, leading to misunderstandings and mistrust [1][2] - The need for open dialogue mechanisms and transparency in algorithm rules is emphasized as essential for improving labor relations and ensuring sustainable platform development [3][4] Group 1: Labor Relations and Trust - A significant 62.7% of drivers believe that "platform rules are not transparent," indicating a major concern regarding trust and understanding between drivers and platforms [2] - The complexity of algorithm rules and dynamic commission rates contribute to a lack of trust among laborers, which can negatively impact service quality and platform stability [1][2] Group 2: Initiatives for Improvement - The first national "Orange Heart Protection Algorithm Consultation Conference" was held by Huolala, expanding the discussion topics from 5 to 15, covering various aspects of driver rights and platform rules [3] - Major platforms like Meituan and Ele.me are also shifting towards "two-way negotiation" by implementing measures such as eliminating penalties for delays and optimizing order assignment mechanisms [3] Group 3: Policy Alignment and Industry Standards - The trend of enhancing labor rights aligns with national policies aimed at protecting new employment forms, emphasizing the importance of laborer participation in the negotiation process [4] - Huolala's practices serve as a benchmark for the industry, responding positively to policy requirements and promoting effective collaboration between platforms and laborers [4] Group 4: Practical Measures and Innovations - Huolala has introduced an "algorithm disclosure" feature on its website to make platform rules more accessible and understandable, increasing driver income efficiency by adjusting order allocation algorithms [5] - The company has implemented a dual settlement mechanism to address issues like payment delays and occupational injuries, investing 10 million yuan annually to support drivers [5] - The shift from a "black box" to a "glass house" in platform governance signifies a deeper transformation in the industry, fostering a collaborative environment between platforms and laborers [5]

Core Insights - The "Court Informationization Blue Book" released by the Chinese Academy of Social Sciences highlights significant advancements in online litigation processes, particularly in civil and administrative second-instance cases, with over 370,000 online filings by the end of 2024, reducing the average filing time by over 70% compared to pre-trial periods [1][2] Group 1: Online Litigation Developments - The pilot program for online second-instance filings has been implemented in 16 regions, allowing for same-day applications and filings [1] - Shandong High Court has processed 99,000 online second-instance applications, with an average filing time reduction of over two-thirds [2] Group 2: Cross-Domain Litigation Services - The Supreme People's Court has guided 13 pilot regions to provide cross-domain services, significantly lowering litigation costs and enabling local processing of legal matters [2] Group 3: Dispute Resolution Innovations - The court's informationization efforts have led to the establishment of a mediation platform that has resolved over 2.5 million disputes through local governance units [3] Group 4: Technological Integration and Recommendations - The blue book emphasizes the need for developing specialized models for court litigation to reduce reliance on external technologies and suggests regular assessments of algorithm fairness and reliability [4]

Core Viewpoint - The digital freight platform Huolala has announced its second batch of algorithm disclosure measures, aimed at enhancing fairness and reducing costs for drivers [1] Group 1: Algorithm Disclosure - The disclosed content includes dynamic evaluation algorithms and rules to improve the fairness of order distribution [1] - The optimization of the automatic commission reduction algorithm is part of ongoing efforts to alleviate financial burdens on drivers [1] Group 2: Driver Support Initiatives - Huolala has improved its "driver autonomy, proximity priority" order distribution model, increasing the proportion of orders prioritized by distance from 90% to 93% [1] - The platform plans to launch additional driver commission reduction products, expecting to lower commissions by a total of 230 million yuan by 2025 [1] - The company will increase order subsidies, with an anticipated annual investment of nearly 70 million yuan to enhance driver income [1]

Group 1 - The core issue of "big data killing familiarity" involves platforms using personal data to implement differential pricing, leading to consumer dissatisfaction [1] - Consumers report being charged more for the same products in live-streaming sessions, highlighting a lack of transparency in pricing [1] - The collection of personal information and the subsequent analysis of consumer habits are the preliminary steps that enable differential pricing strategies [1] Group 2 - Algorithms should not operate outside of governance; their design and application must align with human-centered principles to protect consumer rights [2] - There is a call for platforms to adopt an open approach, enhancing algorithm transparency to combat "big data killing familiarity" [2] - Recommendations include returning control of information recommendations to users and establishing industry standards to improve algorithm transparency [2]