Scheduling urban and trans-urban transportation is an important issue for industrial societies. The Urban Transit Crew Scheduling Problem is one of the most important optimization problem related to this issue. It mainly relies on scheduling bus drivers' workday respecting both collective agreements and the bus schedule needs. If this problem has been intensively studied from a tactical point of view, its operational aspect has been neglected while the problem becomes more and more complex and more and more prone to disruptions. In this way, this paper presents how the constraint programming technologies are able to recover the tactical plans at the operational level in order to efficiently help in answering regulation needs after disruptions. 1 Context and opportunities Scheduling urban and trans-urban transportation is an important issue for industrial societies. Several aspects are considered by territorial collectivities and transportation operators: human and material resource for, environmental and social constraint enforcement , user need requirements. Basically, there exists two central problems: transit scheduling (vehicles-buses, trains, tramways-planning on routes) and driver duty planning (assigning crew to those routes). These problems become more and more complex (regulation, network expansion) and more and more prone to disruptions (city events, accidents, resource failures) in the operational phase. This is the context in which the Urban Transit Crew Scheduling Problem (UTCSP) has been introduced [5]. In our proposal, we have to schedule bus drivers' workdays according to several constraints mainly related to: (1) collective agreements, e.g. breaking rules (basically, how long a bus driver can work before a break) ; (2) the bus schedule itself, e.g. chaining rules (geographic position, schedule compatibility between two tasks, etc.). From a tactical point of view, the UTCSP has been thoroughly studied both at the academic and industrial levels. Primarily, the technologies derived from mathematical programming (from integer linear programming to column generation and dynamic programming) dominate the literature and offer satisfying results. The problem is mainly solved using a set covering approach that represents bus schedule as a set of tasks, linked together by chaining rules and respecting the breaking rules. From an operational point of view, these technologies become useless due to their time consumption. Such a pitfall leads the operators to manually repair the tactical solutions after a