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• This paper examines the integrated optimization of train stopping plan and seat allocation. • The integrated optimization problem is formulated as a mixed-integer mathematical model. • The integrated optimization approach improves efficiency against separate optimization of stopping plan and seat allocation. • Numerical studies were conducted on Zhengzhou-Xi'an and Shanghai-Beijing high-speed railway corridors. This paper examines the integrated optimization of train stopping plan and seat allocation scheme in railway systems, where equilibrium passenger travel choice under elastic demand is considered. The integrated optimization problem is formulated as a non-concave and non-linear mixed-integer mathematical model, where the objective is to maximize the system net benefit considering ticket revenue, consumer surplus, and cost associated with train stoppings. The integrated optimization model can be reformulated into a mixed-integer linear programming (MILP) model based on a series of linearization, relaxation, and outer-approximation techniques, which can then be solved by commercial MILP solvers (e.g., GUROBI). We also compare the integrated optimization approach with that when the train stopping plan and seat allocation are optimized separately and identify the potential benefits. Numerical studies have been conducted on a small-scale example, the Zhengzhou-Xi'an and Shanghai-Beijing high-speed railway corridors to illustrate the proposed model and solution approach. [ABSTRACT FROM AUTHOR]

• The net CO2 emissions of the Beijing-Shanghai high-speed rail are assessed. • The framework incorporates modal substitution and traffic generation effects. • Avoided infrastructure expansion and vehicle manufacturing are considered. • Large share of traffic diverted from ordinary-speed trains may raise emissions. This paper provides an ex-post assessment of net carbon dioxide (CO2) emissions of the Beijing-Shanghai high-speed rail (HSR) by including both mode substitution and traffic generation effects with a life-cycle approach. We are among the first authors to examine the emissions effects of a long-haul HSR and to consider the effects of avoided infrastructure expansion and vehicle manufacturing due to traffic diversion. We find that avoided infrastructure expansion plays a limited role in offsetting CO2 emissions from the construction of an HSR infrastructure, but the reduced demand for passenger vehicles can offset a larger share of emissions from the manufacturing of HSR rolling stock. Initially, too much traffic was diverted from ordinary-speed rail (OSR) to HSR, increasing emissions at the operation stage. As traffic diverted from road and air increased, the net emissions at the operation stage turned negative, offsetting emissions from infrastructure construction and vehicle manufacturing. [ABSTRACT FROM AUTHOR]

The capacity allocation is a practically significant factor to influence the quality of the train timetables in the rail operations, especially under the fluctuation of passenger demands. This paper aims to investigate a detailed description for the structure and characteristics of the capacity allocation problem under random demand in the high-speed rail network. A two-stage stochastic integer programming model is provided to get the capacity allocation solutions to meet random fluctuations of passenger demands in the daily operations, which incorporates demand uncertainty and makes no assumptions for the rail network structure and distribution of passenger demands. Given the inherent complexity for solving this problem, we provide a solution framework including a heuristic algorithm based on tabu search in order to obtain a near-optimal solution and strategies to obtain an efficient timetable and train formation adjustment. Finally, two sets of examples, in which a sample rail network with 5 stations and the Beijing-Shanghai high-speed rail network data are adopted as the experimental environments to illustrate the performance and effectiveness of the proposed methods. • A two-stage stochastic program model for train capacity allocation is presented. • A heuristic algorithm based on tabu search is provided to solve the problem. • A strategy for an efficient timetable and train formation adjustment is provided. • Numerical experiments are given to illustrate the proposed approaches. [ABSTRACT FROM AUTHOR]