Do e-scooter speed governance policies yield behavioral safety gains beyond the mechanical cap they impose? A firmware ceiling mechanically prevents speeding, but whether the same riders also generate fewer harsh accelerations and harsh decelerations when the ungoverned mode is withdrawn remains open. We analyze 19.5 million GPS-instrumented trips from 52 South Korean cities (February to November 2023). Our two-stage predict-then-validate design targets two trip-level binary outcomes, any harsh-acceleration event and any harsh-deceleration event. In Phase~I, we predict each outcome's within-user reduction under an ungoverned-to-governed substitution, using a rider-heterogeneous random-parameters binary logit on the pre-ban period. In Phase~II, we validate these predictions using a difference-in-differences specification that exploits the operator's system-wide December~2023 removal of the ungoverned mode. The causal estimates confirm the Phase~I predictions in sign and order of magnitude on both outcomes, are Bonferroni-significant, and satisfy a 3-month pre-ban parallel-trends test. A within-user composition check finds no behavioral offsetting, indicating that firmware removal of an ungoverned mode lowers both harsh-event margins through a purely mechanical channel. These results imply that speed governance policies can deliver measurable safety gains on unconstrained behavioral margins.

翻译：电动滑板车速度管控政策能否在机械限速之外带来行为安全增益？固件限速有效防止超速，但当移除自由模式后，同一批骑行者是否也会减少急加速与急减速行为仍属未知。本研究分析了来自韩国52座城市的1950万次GPS记录出行数据（2023年2月至11月）。我们采用两阶段预测-验证设计，针对两个行程级二元结果变量——是否发生急加速事件与是否发生急减速事件。第一阶段，利用禁令实施前的数据，通过骑行者异质性随机参数二元Logit模型，预测在自由模式向管控模式转换情境下各结果的用户内降幅。第二阶段，采用双重差分模型验证上述预测，该模型利用运营商于2023年12月在全系统范围内移除自由模式带来的准自然实验。因果估计证实了第一阶段预测在两种结果变量的符号与量级上的准确性，通过Bonferroni显著性检验，并满足禁令前3个月的平行趋势检验。用户内构成检验未发现行为抵消效应，表明通过固件移除自由模式仅通过纯机械通道降低两类急事件发生率。这些结果表明，速度管控政策能在未受约束的行为层面产生可测度的安全增益。