Such a mode has been widely conducted in the shared mobilities, like the taxi sharing and carpooling. Moreover, to provide more flexible service, many-to-many service is raised that clients are allowed to share a bus if they have an overlapping route. It can be seen as two independent many-to-one trips when optimizing the bus route. When considering the return trip, a concept named as many-to-one-to-many is proposed by Tarantilis et al. Many existing studies have explored the many-to-one CB service. Taking the passengers whose destination is the airport as an example, they can share a CB by publishing request online days or hours earlier.
The former can meet the demand of clients who have the same trip purpose. For the CB companies, named CB operators, they can dynamically change the route and schedule based on the real-time travel request.ĬB system can be divided into two main categories based on clients’ OD distribution: many-to-one mode, that is, clients have different origins but the same destination many-to-many mode services, that is, clients have different origins and destinations. Clients can publish their desired pick-up/drop-off location and time windows. CB provides the ride-hailing service that clients can make travel requests by using mobility apps before departure.
CB plays the role of a bridge between the traditional buses and taxi/private car. It has a flexible route to pick-up waiting clients in their origins. Hence, the customized bus (CB) is proposed and becomes popular in many cities around the world. Bus clients have to walk for a long distance to the fixed station from their desired origins, home, or workplace, which would largely harm their enthusiasm. However, the disadvantage of taking the bus is the low level of service (LOS) caused by the long travel time. The travel fee is obviously lower than the taxi and private car. Multiple clients can share a vehicle, which only occupy very limited traffic resources. Encouraging public transport, like buses, is one of the main methods to address such a problem.
In recent years, a series of traffic problems happen due to traffic congestion in an urban area, such as increasing on-road travel time of citizens during daily commuting. The results indicate that the real-time route optimization can be achieved within the computation time of 0.17–0.38 seconds. Finally, a numerical study based on Sioux Falls network reveals the effectiveness of the proposed methodology. To improve computation efficiency, a real-time search algorithm is proposed that the neighboring buses are tested one by one. A concept of profit difference is introduced to decide the served demand. The second phase optimizes the bus route by establishing three nonlinear programming models under the given data from phase 1.
In phase 1, the vehicle-related data including existing route and schedule, client-related data involving pick-up/drop-off location, and time windows are collected once receiving a new CB request. The on-road bus has a flexible route, which can be updated based on the real-time data and route optimization solutions. This paper investigates the real-time customized bus (CB) route optimization problem, which aims to maximize the service rate for clients and profits for operators.