Package Eco-tour as Special Interest Tourism Product-Bangladesh Perspective

In terms of visitation by tourists and issues of sustainability, the Natural World Heritage Sites are getting wider attention in recent times. Taking into consideration as a case, Bangladesh in general, with the Sundarbans in particular, this study has been aimed to outline the visit to this forest as a special interest tourism activity with detailed conceptual framework and marketing approaches. This has also attempted to develop the grounds of marketing of a Package Eco-tour that is capable of minimising the negative impacts of tourism on such sites authenticity, ecological set up and biodiversity. With the unique application of the Participant Observation approach in tourism research, the study has been based on the explanatory case study method. Results of this study have showed that the tourists visit in an all inclusive package format can be practised within a delicate and fragile natural set up that can potentially reduce the harmful negative consequences.  Again, as a form of special interest tourism, such kind of package tour represents better marketing prospects through creating appeal to both domestic and international tourists.  Key Words: Eco-tourism, Special Interest Tourism, the Natural WHS, tourism package tour, sustainabilit

Rebalancing shared mobility systems by user incentive scheme via reinforcement learning

Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, leading to users being unable to receive service. If such imbalance problems are not mitigated some users will not be serviced. There is an increasing interest in the use of reinforcement learning (RL) techniques for improving the resource supply balance and service level of systems. The goal of these techniques is to produce an effective user incentivization policy scheme to encourage users of a shared mobility system to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior are intended to over time increase the service level of the shared mobility system and improve user experience. In this thesis, two important questions are explored: (1) What state-action representation should be used to produce an effective user incentive scheme for a shared mobility system? (2) How effective are reinforcement learning-based solutions on the rebalancing problem under varying levels of resource supply, user demand, and budget? Our extensive empirical results based on data-driven simulation show that: 1. A state space with predicted user behavior coupled with a simple action mechanism produces an effective incentive scheme under varying environment scenarios. 2. The reinforcement learning-based incentive mechanisms perform at varying degrees of effectiveness under different environmental scenarios in terms of service level