Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints

Abstract

Recent advances in vehicle technology offer new opportunities for an electric, automated, modular bus (MB) unit with an adjustable capacity to be applied to transit systems, promising to tackle the resource allocation challenges of traditional buses in coping with uneven travel demand. Drawing on the concept of modular vehicles, this paper introduces a novel scheduling system in which MB units can be combined/separated from fulfilling imbalanced trip demands through capacity adjustments. We develop an optimization model for determining the optimal formation and trip sequence of MB units. In particular, given that the vehicles are electrically powered, battery range limits and charging plans are considered in the system scheduling process. A column-generation-based heuristic algorithm is designed to efficiently solve this model, with constraints related to travel demand and charging station capacity incorporated into the master problem and the trip sequence for modular units with limited energy solved by the subproblem. Taking real data from transit operations for numerical examples, the proposed model performs well in terms of both algorithmic performance and practical applications. The generated optimal MB dispatching scheme can significantly reduce the operating cost from $1534.31 to $1144.26, a decrease of approximately 25% compared to conventional electric buses. The sensitivity analysis on the MB dispatch cost and battery capacity provides some insights for both the scenario configuration and the battery selection for MB system implementation.

Process Model for the Introduction of Automated Buses

“…Since this can take until 2040 or later according to the literature, this procedure is important for the further use of automated buses [43,44]. Compared with Gao et al who include range and charging conditions in the selection of bus routes, the method developed here considers many more parameters (e.g., infrastructure, vehicle technology, acceptance) in network planning [52]. Hatzenbühler et al consider infrastructure costs in network planning in the same way as this approach [54].…”

“…The evaluation of variants in Section 4.3 is the central component of the public transport approach and the basis on which implementation is decided. With respect to automated buses, the operational capability or route suitability is determined based on Compared with Gao et al who include range and charging conditions in the selection of bus routes, the method developed here considers many more parameters (e.g., infrastructure, vehicle technology, acceptance) in network planning [52]. Hatzenbühler et al consider infrastructure costs in network planning in the same way as this approach [54].…”

“…There are only a few research contributions to date on the public transport planning of automated buses [44,[48][49][50][51]. Gao et al developed an algorithm for the bus scheduling problem, Weng et al investigated the Pareto optimal route search, and Hatzenbühler et al developed optimization methods for network design for automated buses [52][53][54]. Furthermore, a design of transit networks based on agent-based modelling was evaluated [55].…”

Simulation-Based Investigation of On-Demand Vehicle Deployment for Night Bus Routes Using the Monte Carlo Method

“…This paper presents an intermediate solution aimed at addressing the challenges faces by conventional public transportation systems. Building on ongoing research regarding modular buses [7], the FLAIT-trains, comprised of FLAIT vehicles, have the potential to replace traditional public transportation systems. If the application of such systems can enhance the attractiveness of the public transportation system, it will increase the utilization rate of public transportation compared to private cars.…”