A line planning approach for high-speed railway network with time-varying demand

Zhao, Shuo; Wu, Runfa; Shi, Feng

doi:10.1016/j.cie.2021.107547

Cited by 29 publications

(31 citation statements)

References 29 publications

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“…(2018), and Zhao et al. (2021) developed aperiodic line planning models that irregularly distribute a greater number of trains to different periods within a day. The periodic train service offers train regularity, and the aperiodic service provides train flexibility.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(2021) and Zhao et al. (2021) optimized timetables and vehicle circulation plans for urban rail lines and simulated the travel choices of passengers. The supporting mechanism of routing passengers on trains is that passengers are reasonably assumed to choose trains with minimum in‐vehicle, transfer, and waiting times as their preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bussieck et al (1997), Claessens et al (1998), and Goossens et al (2001) formulated line planning models for the Netherlands Railways, Chang et al (2000) took the Taiwan HSR as the background to optimize train services, and Kaspi and Raviv (2013) proposed a line planning and timetabling model of Israel Railways; those scholars all considered cyclic timetables with a cycle of 1 h and presented mathematical models for the LPP in which the line plan is repeated every hour. For aperiodic application, Cacchiani et al (2010) presented an aperiodic timetabling model; Fu et al (2015), Yang et al (2018), andZhao et al (2021) developed aperiodic line planning models that irregularly distribute a greater number of trains to different periods within a day. The periodic train service offers train regularity, and the aperiodic service provides train flexibility.…”

Section: Related Workmentioning

confidence: 99%

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Weekly line planning to improve the match between rail supply and passenger demand

Nie

2022

Computer aided Civil Eng

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Traditional railway line planning approaches are used to determine the best train lines during specified periods (e.g., 1 day, 1 h, or several hours). However, mismatches occur relative to the spatiotemporal changes in travel demands over longer time periods (e.g., 1 week). To more effectively match rail supply with passenger demand, a weekly line planning (WLP) model considering both peak and nonpeak passenger demand over an entire week and during all periods of each day is proposed. This model maximizes the defined supply-demand matching utility and minimizes train operational cost. Important constraints to ensure proper daily and hourly train repetition over multiple days are included. A customized genetic algorithm (GA) is developed, which outperforms the CPLEX solver in real-world cases of high-speed railways in China. Compared with practical and single-day planning, WLP shows clear improvement in terms of operational cost, passenger travel speed, and supply-demand matching.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Weekly line planning to improve the match between rail supply and passenger demand

Nie

2022

Computer aided Civil Eng

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“…Some scholars have taken the average daily passenger flow as the basis and considered the elastic characteristics of demand to a certain extent [7,15,21]. Recently, some studies have gradually focused on the time variability of demand [6,[22][23][24][25] and they always apply the multi-period planning method to remedy any defects.…”

Section: Literature Reviewmentioning

confidence: 99%

Fluctuating Demand-Oriented Optimization of Train Line Planning Considering Carriage Resources Transfer under Flexible Compositions

et al. 2022

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The intercity railway is subject to variation and fluctuation in demand both in time and space over a day to a large extent. In that case, more advanced line planning techniques are practically needed to match the non-equilibrium passenger demand. We propose an integer linear programming model for adapting to the fluctuating demand and improving rail line profit, in which the multi-period planning approach and flexible train composition mode are taken into consideration. In particular, we also consider the limitations of the carriage and the dynamic transfer of resources during a finite period to ensure the better implementation of the line planning and subsequent operation plans. Our purpose is to improve on previous line planning models by integrating the multi-period strategic-level line planning decision with resource constraints. Since the problem is computationally intractable for realistic size instances, an improved round heuristic algorithm that is based on linear relaxation is proposed and we compare the round heuristic performance with the commercial solver Gurobi on artificial instances. The numerical experiments that are based on an intercity railway in China certify the effectiveness and applicability of the proposed model and algorithm. We evaluate the impact of different optimization parameters and reserved carriages and the computation results show that in comparison to the fixed composition mode, the proposed approach can improve the utilization efficiency of carriage resources and increase the line profit by 1.9% under the same carriage resource conditions.

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“…e large proportion of cross-line logistics is one of the most important characteristics of railway logistics transport, and this proportion will continue to increase with the development of high-speed railways [3]. In such a huge cross-line passenger transport demand, the operation of high-speed railway cross-line trains is of great signi cance and importance to the organization of highspeed railway transport, especially in meeting the demand for cross-line passenger travel [4]. Our paper used machine learning (ML) as its main part; i.e., it is the study of computer algorithm that can improve automatically through experience and by the use of data.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Approach to Railway Logistics Transport Path Optimization

Cao¹

2022

Mathematical Problems in Engineering

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With the rapid development of high-speed railways, the continuous improvement of the road network, and the leapfrog increase in demand for cross-line passenger travel, the connectivity between cities is also increasing. The network structure has gradually evolved from a single line structure to a complex network structure, and the routes connecting city nodes are no longer unique. At this stage, there are multiple effective routes between destinations on the network, and the choice of train routes has diversified. Different options for interline trains have different degrees of impact on line and station operations. Therefore, this study constructed a deep learning CNN-GRU combined model to predict the traffic speed of railway logistics. The model used a convolutional neural network (CNN) to extract the spatial characteristics of speed data and GRU to extract the time characteristics of speed data. The experiment proved that the prediction accuracy of the combined model was better than that of the single GRU and CNN models. Finally, a multi-objective optimization model with the shortest total train running distance, the shortest total train running time, and the least influence on road sections is constructed using the prediction results.

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A line planning approach for high-speed railway network with time-varying demand

Cited by 29 publications

References 29 publications

Weekly line planning to improve the match between rail supply and passenger demand

Weekly line planning to improve the match between rail supply and passenger demand

Fluctuating Demand-Oriented Optimization of Train Line Planning Considering Carriage Resources Transfer under Flexible Compositions

A Machine Learning-Based Approach to Railway Logistics Transport Path Optimization

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