A framework for agent based simulation of demand responsive transport systems

Demand responsive transport (DRT), such as shared mini busses, have become a viable form of public transport mainly in rural areas in recent years. In contrast to ordinary schedule-based services, DRT systems come in many different shapes and forms and are usually customised to the environment they operate in. E.g., they might be restricted to certain user groups or only operate in specific areas or with a specific fixed terminus. With advances in information and communications technology (ICT) and the possibility of driverless operations in the future, DRT systems may become an attractive additional mode also in urban and inter-urban transport. This brings the necessity to assess and evaluate DRT services and possible business models, with transport simulations being one possible way. This study introduces a framework for an extensible, open source shared minibus service simulation. Based on the agent based transport simulation MATSim and its existing DVRP extension, the extension provides DRT services as an additional mode to the synthetic population of a MATSim scenario. The module allows assigning vehicles to groups according to different dispatch algorithms shaped to the actual use case. In a first case study, a DRT system complements ordinary public transport and car infrastructure on the heavily used commuter relation between Braunschweig and Wolfsburg. During peak times, average travel times per traveler over all modes between the two cities can be reduced by 5 minutes using a fleet of 50 8-seat-vehicles. In a second case study, a DRT system is used to shuttle passengers in an area with non-optimal public transport access to the train station in Braunschweig. With given capacity constraints, the optimizer has to decide which customers to serve in order to achieve an on-time arrival for passenger

Adaptive State Space Partitioning for Dynamic Decision Processes

With the rise of newbusiness processes that require real-time decision making, anticipatory decision making becomes necessary to use the available resources wisely. Dynamic real-time problems occur in many business fields, for example in vehicle routing applications with stochastic customer service requests expecting a fast response. For anticipatory decision making, offline simulation-based optimization methods like value function approximation are promising solution approaches. However, these methods require a suitable approximation architecture to store the value information for the problem states. In this paper, an approach is proposed that finds and adapts this architecture iteratively during the approximation process. A computational proof of concept is presented for a dynamic vehicle routing problem. In comparison to conventional architectures, the proposed method is able to improve the solution quality and reduces the required architecture size significantly