In continuation of a set of types of problems in fleet management area, Urban public transport and dial-a-ride are more recent.

Urban public transport deserves to be called a class of problems by itself. It consists of determining ways to provide good quality of service to passengers with finite resources and operating costs. Their planning process often involves 1. Network route design, 2. Frequency setting and timetable development, 3. Vehicle scheduling and 4. Crew scheduling and rostering. Some state-of-the-art models involve tuning the routing and scheduling with minimization of passenger cost functions. Metaheuristics schemes that combine simulated annealing, tabu, and greedy search methods serve this purpose. One of the distinguishing features of this problem space is that customers often formulate two requests per day, specifying an outbound request from pick-up to drop-off and an inbound request for the round trip. Another feature is that the quality of service needs to be maximized while minimizing operating costs incurred to satisfy all the requests.

Dial-a-ride transport is a move toward more economical and greater flexibility of transport services. Demand responsive transportation systems require the planning of routes and customer pick-up and drop-off scheduling on the basis of received requests. It must deal with multiple vehicles with limited capacity and time-windows. The problem of working out optimal routes and times is referred to as the Dial-a-ride problem. As with many problem spaces in fleet management, this can be treated as NP-hard combinatorial optimization problem. Attempts to develop an optimal solution has been limited to simple and small-size problems.

Such a service may operate in a static or dynamic mode. In the static settings, all the customer requests are known beforehand, nd the system solves a tour each vehicle must make within the constraints of the pick-up and drop-off time window and minimizing the solution cost. In the dynamic mode, the customer requests arrive over time to a control station and the solution may change over time. Processing must also keep up with the incoming rate without interfering with the optimization cycle at the end of the service. The goal is two-fold: reduce overall costs and improve quality of service to customers. Several algorithms have been tried for this purpose. They include tabu search heuristics, dynamic programming, branch and cut, a heuristic two phase solution method, genetic algorithms and variable neighborhood search.

Even the users can be differentiated as well as the transportation modes. Some meta-heuristics such as vehicle waiting time with passenger onboard can also be used with branch-cut algorithms for this purpose.

Studying quality of service in this context has evolved into models which use various measurement scales. The quality of service provided by organizations also depend on the type of organization and the operational rules used.