The COVID pandemic severely impacted mobility during lockdown as well as the period after that observing different phases of unlocking process. Lockdown has resulted in drastic mobility reduction and subsequent unlocking process brought back mobility gradually to a certain level, substantially lower than the Pre-COVID periods. However, this is expected to be back gradually to same level of Pre-COVID after certain period of time. While this change is planned to be gradual, it creates an opportunity to improve mobility towards non-motorized transport (NMT) which can influence travel behavior for present as well for the future covering Pre and Post-COVID periods. Apart from studying the influencing travel behavior during Pre and Post-COVID conditions, change in quantum of commuting population is required to be studied for various estimations related to transport planning. This quantum of commuting population is largely affected by the adopted locking/unlocking strategies by Government of India. Commuting population and travel behavior got influenced by the large migration from metro cities to smaller towns, adopted work from home (WFH) styles, reduced commute of elderly and children and other restrictions on gatherings, etc. Considering the range of allowed activities, influenced by the phases of locking/unlocking strategies, twelve different scenarios have been developed for various estimations. The developed scenarios have been named with alpha numeric style ranging from A0 to E5. A0 presenting the Pre-COVID scenario. Alphabet “A” represent Pre-COVID scenario, “B”, “C”, “D” and “E” Scenarios represent the unlocking phases, while number 1, 2, 3, 4 and 5 represents the different policy aspects related to the change on capacity of transport services (i.e. public transport capacity and change due to infrastructural improvement for NMT). Scenario A0 represents the Pre-COVID situation, while other ranges (viz., B1, B2, B3, C2, C3, C4, D2, D3, D5) represents various locking/unlocking levels. The Scenario E5 represents the situation without any movement restrictions, where public transport systems are assumed to be functioning with full capacity and infrastructure has been improved for NMT trips. Estimations for all the 12 scenarios include commuting population, their modal split, vehicle kilometers travelled by these vehicles and corresponding vehicular emission. Estimated modal split also includes the influence of improved NMT friendly infrastructure and reduced capacity of public transport systems. The results of developed scenarios provide a handy information for the policy makers to choose right policy to promote sustainable transportation with adequate emphasis on NMT. It is evident that as compared to the Pre-COVID scenario (A0), Post-COVID scenario with improved NMT infrastructure (E5) has VKT reduction of 19% in MTWs, 5% in Cars and 49% in Buses. Increase in bicycle trips have been estimated to be 5.88 million for Post-COVID scenario as compared to 1.1 million trips estimated for the Pre-COVID Scenario. Similar trend has also been observed for fuel consumption (reduction of 4.7%–11.8%) and corresponding vehicular emission (reduction of 14%). This study estimated the potential benefits of providing NMT friendly infrastructure considering the gradual shift influenced by locking/unlocking phases of COVID pandemic.

COVID 大流行严重影响了锁定期间以及之后观察解锁过程不同阶段的移动性。锁定导致流动性急剧减少，随后的解锁过程使流动性逐渐恢复到一定水平，大大低于 COVID 之前的时期。但是，预计在一段时间后，这将逐渐恢复到与 COVID 之前相同的水平。虽然这种变化计划是渐进的，但它创造了一个机会，以改善向非机动交通 (NMT) 的流动性，这可能会影响当前和未来的旅行行为，涵盖前和后 COVID 时期。除了研究 COVID 之前和之后的影响旅行行为之外，需要研究通勤人口数量的变化，以进行与交通规划相关的各种估计。这一数量的通勤人口在很大程度上受到印度政府采用的锁定/解锁策略的影响。通勤人口和出行行为受到从地铁城市向小城镇的大规模迁移、采用在家工作（WFH）方式、减少老人和儿童通勤以及其他聚会限制等因素的影响。在锁定/解锁策略的各个阶段，已经为各种估计开发了十二种不同的场景。已开发的场景以字母数字样式命名，范围从 A0 到 E5。A0 展示了 COVID 之前的情景。字母“A”代表Pre-COVID情景，“B”，“C”，“D”和“E”情景代表解锁阶段，而数字 1、2、3、4 和 5 代表与交通服务能力变化（即公共交通能力和基础设施改善导致的变化）相关的不同政策方面。 NMT）。场景 A0 代表 COVID 前的情况，而其他范围（即 B1、B2、B3、C2、C3、C4、D2、D3、D5）代表各种锁定/解锁级别。情景 E5 代表了没有任何行动限制的情况，其中假设公共交通系统满负荷运行，并且基础设施已针对 NMT 旅行进行了改进。对所有 12 种情景的估计包括通勤人口、他们的模式划分、这些车辆行驶的车辆公里数和相应的车辆排放。估计的模式分裂还包括改善 NMT 友好基础设施和公共交通系统容量减少的影响。已开发情景的结果为决策者选择正确的政策以促进可持续交通并充分重视 NMT 提供了方便的信息。很明显，与前 COVID 情景（A0）相比，具有改进 NMT 基础设施（E5）的后 COVID 情景的 MTW 减少了 19%，汽车减少了 5%，公共汽车减少了 49%。估计后 COVID 情景的自行车出行增加了 588 万人次，而前 COVID 情景估计的自行车出行次数增加了 110 万人次。在燃料消耗（减少 4.7%–11.8%）和相应的车辆排放（减少 14%）方面也观察到了类似的趋势。