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Cities worldwide are deploying electric buses rapidly to enhance eco-friendly public transportation. Most existing electric transit systems rely solely on stationary charging at terminals and have to equip buses with large-sized batteries to maintain operation in networks. Consequently, the stationary-charging electric bus (SE-Bus) systems are subject to high battery costs, limited range per charge, long charging time (layover time), etc. These limitations may be addressed by integrating the new wireless power transfer technology, which provides contactless charging to the operating buses without extra delays. To guide wireless-charging electric bus (WE-Bus) designs in corridors, this paper proposes an optimization model to jointly optimize (i) the electric supply system and (ii) the transit service that are interdependent. Specifically, we construct parsimonious models for system metrics using five design variables/functions, i.e., battery size, the number of wireless power transfer (WPT) pads, stationary charging rate, service headways, and stop densities. Accordingly, we formulate the optimal design problem to minimize the generalized system cost as the sum of the transit agency's and patrons' costs. The problem is attacked by first decomposing into two sub-problems regarding (i) electric supply system design and (ii) transit service design and then solving them iteratively. The (i) sub-problem is converted to a linear programming problem by linearization, and the (ii) sub-problem is analyzed with optimal conditions. Numerical tests show the proposed solution approach produces accurate results. Extensive numerical experiments unveil several findings. First, the WE-Bus systems outperform the SE-Bus in various scenarios except when wireless charging facilities are too costly. Second, the optimized WE-Bus designs can reduce costs not only to the agency (with smaller-sized bus battery and reduced fleet cost) but also to patrons (who enjoy improved services with shorter headways). Additional parameter analyses also investigate the impacts of demand levels, wireless charging rates, depths of discharge, etc. Lastly, a case study in Chengdu (China) illustrates the application of the proposed model. • Models are built for wireless-charging electric bus (WE-Bus) designs. • The electric system and the transit service are jointly optimized. • WE-Bus systems show a wide range of application. • Optimized WE-Bus designs may save costs for the agency and transit patrons. [ABSTRACT FROM AUTHOR]

• A zonal-based flexible bus service (ZBFBS) is scheduled under dynamic stochastic demand and time-dependent travel time. • Zonal-based time-space network is proposed for flexible bus scheduling with flexibility in arrival and departure times. • A reliability-based gradient solution method with relaxations is developed to solve this inherently non-convex problem. This paper schedules the zonal-based flexible bus service (ZBFBS) considering elastic stochastic demand, stochastic location, time-dependent travel time, and passenger time window constraints based on a scheduled-based formulation. Unlike a traditional time–space network that stipulates the precise arrival and departure times on specific nodes, a zonal-based time–space network is proposed to define the routes in terms of zonal visits of the flexible buses while allowing for flexibility in their arrival and departure times to cater for randomness. The ZBFBS scheduling problem is formulated as a two-stage decision-dependent stochastic problem with recourse. The first stage schedules the zonal visits of flexible buses and the second stage matches each passenger with either flexible bus or ad hoc service, with the latter incurring extra cost to carry the unmatched passengers. To effectively solve the problem, a state-augmented network, that integrates time and zone, is proposed to reduce the number of variables. Moreover, relaxation formulations based on vehicle types and routes are introduced, with an insertion heuristic implemented for vehicle scheduling. The problem is solved by a gradient-based solution approach. Numerical studies demonstrate the efficiency and quality of the solution methods under a variety of ride requests, as well as its advantage over the frequency-based approach in substantially reducing the ad hoc service cost. The applicability of the model is validated by solving an instance of Chengdu, China, with real data. [ABSTRACT FROM AUTHOR]

• Propose a transfer-based customized bus network design with passenger-route assignment and a modular fleet. • Formulate the problem into a mixed-integer non-linear programming model. • Propose an exact solution method by reformulating the problem into a linear model. • Present a customized Particle Swarm Optimization (PSO) algorithm for large-scale problems. • Verify the hybrid PSO algorithm can produce high quality near-optimal solutions efficiently. • Show that the incorporation of passenger-route assignment optimization and transfer operation benefits customized bus network design. Customized bus (CB) is an increasingly popular mode of transportation in many cities around the world. However, studies on CB network design have mostly overlooked three options that may further improve system performance: passenger-route assignment, passenger transfer, and modular vehicles. To bridge this gap, this paper proposes to design a transfer-based CB network with a modular fleet while simultaneously optimizing the passenger-route assignment. To solve the optimal network structure with this new design paradigm, we formulate the network design problem into a nonlinear mixed integer optimization model. A linearization approach and a particle swarm optimization (PSO) algorithm are proposed to solve the exact and near-optimal solution(s) to the model, respectively. Numerical experiments are conducted on the Sioux Falls network and a large-scale network in Chengdu, China. Results show that the customized PSO algorithm efficiently provides high quality near-optimal solutions compared with CPLEX, the genetic algorithm, and the simulated annealing algorithm. Results also show that incorporating passenger-route assignment optimization and the transfer operation produces a more cost-effective CB operational network with less operational costs and higher service quality. The benefit increases as the passenger demand grows. [ABSTRACT FROM AUTHOR]