拥挤公共交通系统的需求管理：基于智能卡数据的分析框架和案例

4.讨论

　　案例研究的结果表明，交通管理机构可以通过以下几种方式设计简单的票价折扣方案。下面列出的建议面向特定群体，或者放宽了用户的时间置换约束，以此说明用户的不同特征。交通管理机构可以选择是否面向某一群体，但也可以面向不同群体进行不同程度的干预。这些干预措施的可行性取决于乘客对复杂性、合作机会以及财务影响的态度。

　　定价结构:折扣结构可以模仿消费回扣，在一段时间内(例如每周)提供一次大额返还。这可能会吸引那些忽视小额以及定期折扣的用户，凸显节约总量。另一种选择是引入彩票政策，而不是保证返还。通过让注册用户有机会中彩，造成只有那些对价格更敏感或对参与感兴趣的用户才会参加，这意味着不改变行为的用户中彩的人数更少。注册要求可以让交通管理机构给用户更高的回报或更高的中彩几率，特别是如果它愿意支付与用户票价差价相同的金额。

　　定价基础:与其仅根据终点站提供折扣，不如针对起始站、特定线路或替代线路提供优惠。这些选项分别能更好地管理出行时间、车内拥挤或更改出发时间的不确定性。在不太拥挤的线路上打折可以平衡整个交通网络的需求，提高运力利用率。智能手机和位置服务与公共交通支付的一体化，使得采用这种基于线路的策略成为可能。

　　目标客户信息:提供更个性化的信息将帮助乘客做出更好的出行决策，显示最符合其需求的非高峰出行收益。可能的策略包括针对站点或用户的市场营销，或者让在线出行规划者看到特定时间的票价和拥挤数据。用户还可以通过交通管理机构网站上的个性化页面获得具体的出行建议(可能会根据用户所属群组的信息，或者用户特征与面板分析中的各因素之间的关系)。

　　相比于用新基础设施增加运力，需求干预见效更快、效率更高，但在公共交通中，需求干预通常被视为临时解决方案，直至运力得到增加。然而，即使在运力增加之后，交通管理机构仍可以将公共交通需求管理策略作为补充，为寻找解决其拥堵和业务需求的最佳方案提供更多工具。

5.结论

　　本文为公共交通系统需求管理策略的设计与评估提出了一个框架。了解与拥堵有关的需求模式和政策实施的背景，可以帮助决策者基于各种参数设计出更有效的政策干预策略。公共交通需求管理策略的评估和监测不仅要考虑需求的总体变化，同时需要详细分析乘客反应，通过反馈指导该策略的不断优化。AFC数据是持续动态评估的可靠数据来源，相较于过去的数据源，AFC数据可用于更精准地研究需求模式。相较于汇总数据分析，聚类分析和面板分析可以更好地洞察用户对所实施策略的反应，且不同于调研数据，它们可以运用在大规模的应用分析上。

　　本文利用一个公共交通系统的拥堵数据进行了实例研究，验证了本文提出的评估框架的有效性。结果表明，定价激励能有效地将用户转移出高峰时段，导致需求在时间上的再分配。我们对所有用户需求模式的变化进行分析后发现，早鸟优惠对错峰出行有影响，尽管可能不足以逆转外源客流的增长。此外，更多的分解分析（disaggregate analysis）表明，应关注通勤者和间歇用户，优惠活动的目标行程在他们的行程中占相当大的量，而且他们愿意接受票价差异。本研究中其他群组的出行次数太少，不是优惠活动的优先考虑对象，或者需要其他类型的激励措施鼓励他们在非高峰时段出行。面板分析进一步表明，考虑到用户强烈反对改变出行时间，部分设计可能需要调整。增加用户出行的灵活性，改进多式联运，更好地瞄准对价格敏感的用户，都是提高效率的有效方法，通过纵向分析进行监测可以为长期干预提供信息。

　　本文对行为改变者的识别分析依赖于智能卡数据，将行为改变与优惠活动的设计相关联。考虑在调查的同时进行面板分析的未来研究将使我们可以更精确地预测用户的反应，并更好地控制其他因素，如生活方式特征和社会人口特征。此外，我们还可以对用户行为改变的程度进行更详细的建模，例如序贯选择模型可首先用于对用户是否改变行为的决策建模，其次对有行为改变的行程建模(Ben-Akiva、Lerman and Lerman, 1985)。

　　（国务院发展研究中心资源与环境政策研究所李继峰译）

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