Wenliang Zhou

1. 1.1. High-speed sleeper trains typically operate from 18:00–24:00 to 06:00–09:00 the next day, with trip durations of 6–15 hours. With a unique longitudinal sleeper layout, high-speed performanc...

1. Light Rail Transit (LRT) systems are pivotal components of sustainable urban mobility, offering substantial environmental, social, and operational advantages. As global cities grapple with incre...

This study quantifies the relative influence of economic drivers (fuel price, ticket price) versus environmental factors (weather) on urban rail ridership in a developing city, interpreting findings for transport emissions modeling and sustainable mobility policy. A seven-year multivariate dataset (2016–2023) from Addis Ababa's Light Rail Transit (LRT) system is analyzed using deep learning models (LSTM-Attention, Lite Informer, and a hybrid ensemble). Fuel price exhibits a substantially stronger association with ridership (r = -0.56) than ticket price (r = -0.27) or any weather variable (|r| ≤ 0.04). Lite Informer achieves the lowest prediction error (RMSE = 13,512.88), confirming that attention-based architectures capture delayed, non-linear economic effects. The inverse fuel price–ridership relationship challenges conventional assumptions: rising fuel costs correlate with decreased rail ridership, not mode shift to transit. In cost-sensitive developing cities, fuel taxes may have ambiguous emissions effects without complementary measures. Transport authorities should integrate fuel price indicators into forecasting systems.

Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study addresses that gap by proposing an integrated optimization framework that simultaneously considers all three planning layers under time-dependent passenger demand conditions. The problem is formulated as a bi-objective Integer Nonlinear Programming (INLP) model, aiming to jointly minimize passenger waiting time and total operational cost. To solve this large-scale, combinatorial problem, a tailored Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed. The algorithm incorporates discrete variable handling, constraint-preserving mechanisms, and a customized encoding scheme that aligns with the structural characteristics of URT operations. The proposed framework is applied to real-world data from the Addis Ababa Light Rail Transit (AALRT) system. The results demonstrate that the MOPSO-based approach offers a more diverse and operationally feasible set of trade-off solutions compared to a widely used benchmark algorithm, NSGA-II. Specifically, it provides transit planners with a flexible decision-support tool capable of identifying schedules that balance service quality and cost, based on varying strategic or budgetary priorities. By integrating interdependent planning decisions into a unified model and leveraging the strengths of a customized metaheuristic algorithm, this study contributes a scalable, adaptable, and practically relevant methodology for improving the performance of urban rail systems.

This paper addresses a line planning problem (LPP) that simultaneously optimizes both train and passenger times in passenger railway systems, considering time-dependent origin-destination-period demand and passenger train choice. The problem is clearly and flexibly modeled in a physical infrastructure-based directed graph, which efficiently integrates the train operation choice and the passenger train choice. The problem is first formulated as a mixed-integer, non-concave, and non-linear programming model aimed at minimizing both the total operating cost of trains and the total travel cost of passengers. To solve the problem, an extended time-dimension method is proposed to transform the non-concave and non-linear model into a mixed-integer linear programming (MILP) model that can be solved using a commercial solver. Additionally, a set of simplification strategies is introduced to reduce the computational complexity while ensuring the global optimality of the linear model. A case study of a busy Chinese railway line demonstrates that the optimized time-dependent line plan enhances operational efficiency and accommodates the diversified travel preferences driven by time-dependent demand.

In the transportation organization optimization of high-speed railway (HSR), optimizations such as line planning, ticket pricing, and seat allocation are generally studied separately. However, in reality, when passengers choose trains, they need to consider multiple factors such as train routes, stop plans, seat prices, seat availability, and departure times. Therefore, there is an urgent need for an integrated optimization method to simultaneously make decisions regarding these multiple factors. This study constructs a nonlinear optimization model of line planning integrating differentiated pricing and seat allocation decisions for HSR under elastic demand. To efficiently solve the model, an improved heuristic algorithm based on the simulated annealing framework combined with a linear passenger flow allocation method is proposed. Finally, case analysis proves that the improved algorithm can effectively solve the model under the input conditions of an actual Y-shaped HSR network composed of 13 stations, with a potential for a 106.54% improvement from the initial solution to the final solution. The uniqueness of our study lies in the joint optimization of three critical HSR operations, which has not been comprehensively explored in prior studies and is of great significance for improving the level of HSR train operations and passenger services.