This paper presents a comparative analysis of demand-responsive and fixed-schedule, fixed route operations for a simplified station-based feeder to mass transit scenario. Traffic dynamics, demand-responsive fleet coordination, and the behaviour of individual transit users are represented using a public transit simulation framework. Each operational strategy is simulated for varying levels of demand and two fleet compositions with respect to vehicle capacities and fleet size are compared. The services are evaluated based on resulting passenger waiting times, in-vehicles times and additional waiting time if one is denied boarding a fully occupied vehicle. Results indicate that dividing planned service capacity into larger fleets of smaller vehicles can provide a higher level-of-service to passengers. On an aggregate level, utilizing a fixed operational policy results in shorter and more reliable waiting times for levels of demand where there is slack in service capacity. In scenarios where planned service capacity is sometimes exceeded, the on-demand service provides a more even spatial distribution of passenger waiting times, relative to a fixed service.@en

The paper develops a simulation model and evaluates fixed versus on-demand operational designs of a station-based automated feeder service. The evaluation considers the operational cost and average passenger level-of-service trade-offs as well as distributional differences in waiting times. Two case studies are used to evaluate such trade-offs under different fleet compositions; (1) a simple circular network feeder service; (2) a case based on a real-world coordinated branched service in Stockholm, combining fixed-line services on the trunk portion with a flexible feeder service on the branches. Results for the circular network indicate that there are benefits in utilizing an on-demand operational policy for the lowest and highest demand levels tested. When fixed service capacity is exceeded, it is found that there are potential benefits in on-demand operations with respect to average level-of-service, as well as delivering a more even distribution of passenger waiting times. Results for the real-world case show that combining DRT on branches with fixed services on the trunk improves the overall median waiting times for all DRT scenarios and provides substantial improvements for passengers on the trunk, at the cost of more variable, and less equitable waiting times on the branches. For larger fleet sizes, generalized travel costs are reduced with and without rebalancing and level-of-service provided to branch-to-branch passengers is improved considerably by rebalancing idling vehicles to branch end-stops. The case studies demonstrate the usefulness of the simulation framework in evaluating trade-offs between fixed and on-demand service design variables and their effects on disaggregate level-of-service provided for stop-based feeder services.@en

Over the past decade, there has been a surge of interest in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modelling decision in the development is how one chooses to represent the mode- and route-choices of travellers. The real-time adaptive behaviour of travellers is important to model in the presence of a flexible transit service, where the routing and scheduling of vehicles is highly dependent on supply-demand dynamics at a near real-time temporal resolution. We propose a utility-based transit route-choice model with representation of within-day adaptive travel behaviour and between-day learning where station-based fixed-transit, flexible-transit, and active-mode alternatives may be dynamically combined in a single path. To enable experimentation, this route-choice model is implemented within an agent-based dynamic public transit simulation framework. We first explore model properties in a choice between fixed- and flexible-transit modes for a toy network. The adaptive route choice framework is then applied to a case study based on a real-life branched transit service in Stockholm, Sweden. This case study illustrates level-of-service trade-offs, in terms of waiting times and in-vehicle times, between passenger groups and analyzes traveller mode choices within a mixed fixed- and flexible transit system. Results show that the proposed framework is capable of capturing dynamic route choices in mixed flexible and fixed transit systems and that the day-to-day learning model leads to stable fixed-flexible mode choices.@en

In this paper, we deal with the problem of determining when and where a bus should short-turn on a single bi-directional line in real-time. We formulate a decision rule for when to short-turn among candidate short-turning locations that is based on the objective of minimizing total generalized passenger travel cost including waiting times and forced transfer. Computational results and analysis are provided via a simulation study in BusMezzo, a dynamic, agent-based transit operations and assignment model that represents both vehicle as well as passenger progression. The simulation framework allows us to evaluate the resulting trade-off between passenger costs and transit performance that occur when a decision to short-turn is made. The proposed short-turning strategy is applied to a real-world high-frequency transit line in Stockholm, Sweden.@en