The present work is an extension of [1] where local outdoor excursion was analysed and optimized in order to limit the exposure to virus during city lock-downs. The present work is applicable for analysis of situations after lock-down where outdoor excursions have a aim (commuting to work or shopping) and may not be strictly local. In particular we investigate the biking path optimization. We use drifted random walks for this optimization which under the condition of uniform distribution of initial starting points and destination locations gives a perfect or quasi perfect load balancing of streets. We compare this with the case when biking lanes are just following the main commuting lanes by bus or subway, thus minimizing the exposure to virus by a factor between 3 and 9 on various models. Interestingly the path selection is immediately beneficial to the first user even if the other users stay on preferential paths.