Stop Planning Research Articles

Train Planning for Through Operation Between Intercity and High-Speed Railways: Enhancing Sustainability Through Integrated Transport Solutions

In order to advocate for green and environmentally friendly travel modes, enhance the attractiveness of rail transit, and promote the sustainable development of rail transport, we focus on the transportation organization problem under limited-resource conditions. This paper studies the formulation of a train plan under the condition of through operation between intercity and high-speed railway, constructing a multi-objective nonlinear optimization model with train frequency, a stop plan, and turn-back station locations as decision variables. Given the high dimensionality of model variables and complex constraints, an improved multi-population genetic algorithm (IMGA) is designed. Through an actual case study of the through operation between the Chengdu–Mianyang–Leshan Intercity Railway and the Chengdu–Chongqing High-Speed Railway, a staged solution method is adopted for analysis. The results indicate that the through-operation mode can save operational costs for enterprises and travel costs for passengers, while also better adapting to changes in passenger flow. Additionally, the IMGA demonstrates better solution quality and higher efficiency compared to the classical genetic algorithm. The main contribution of this paper is to propose a novel approach to solve the train plan problem. It also contributes to creating a high-quality, high-efficiency, and high-comfort integrated transportation service network, promoting the sustainable development of rail transit.

Stop plan optimisation for three-pattern skip-stop schemes for urban rail transit systems

Mass rapid transit systems around the world are typically designed for all-stop operation schemes, in which train overtaking is not possible. To accelerate transit operation, the conventional A/B skip-stop scheme may be planned. This research explores alternative skip-stop schemes with three stop patterns, aiming to better match transit services with the spatial distribution of travel demand. The proposed generalised skip-stop operation model considers both the total cost of passengers and operator. A genetic algorithm is employed to solve the stop-plan optimisation problem, and a heuristic is tailored to determine an optimal dispatch headway for the respective stop plan. Based on computational experiments using synthetic data, the results suggest that skip-stop schemes have the potential to reduce total time costs by about 10%, particularly when there are structured demand concentrations, transit systems can operate safely with low time headway and short-distance demand is low. Although the total-cost saving of the best A/B skip-stop plan found is generally superior to those of other three-pattern skip-stop schemes, a three-pattern skip-stop scheme was found to offer a better total-cost saving in a scenario without short-distance travel demands. Overall, this research offers valuable insights into the potential benefits and limitations of different skip-stop schemes, contributing to a better understanding of their impact on passengers and operators.

Integrated optimization of flexible train timetable, stop plan and exogenous passenger assignment under demand responsive condition

Demand responsive bus has been put into practice widely in recent decades. However, implementing this concept in railway transportation poses considerable challenges. One of the main obstacles is how to synchronize train timetable, stop plan, and passenger assignment to guarantee the interests of the operators and passengers. Additionally, it is crucial to fulfill various operational requirements under resource constraints and address the issue of supply-demand mismatch. This paper introduces an integrated optimization model incorporating a flexible train timetable, stop plan, and exogenous passenger assignment based on demand responsiveness. Realistic situations, such as overtaking, no preset train departure time window, and allowing passengers’ requests to be rejected, are considered. Leveraging data from the trip reservation system (TRS), we aim to maximize enterprise operating profit while minimizing passenger travel costs. To achieve this, we embed the train stop plans and passenger assignment constraints into the train timetable, introduce the reservation rolling horizon, and establish a multi-objective mixed-integer nonlinear programming (MINLP) model. We employ the GUROBI optimization solver to obtain optimal or near-optimal solutions for our proposed model. Numerical and real-world cases are conducted to demonstrate the performance of the proposed method. The experimental results show that, even for the real-world Xiamen-Shenzhen railway corridor, the GUROBI solver efficiently generates integrated solutions within acceptable computational times, highlighting the effectiveness of our proposed model.

Integrated optimization of train stop planning and seat allocation for high-speed railways based on active management for dynamic demand

ABSTRACT Proactive demand management methods can achieve a trade-off between supply and demand for high-speed railways. Focusing on multi-type demand configurations, four active demand management methods were designed, two single-scenario and two multi-scenario methods. An optimization model of stop planning and seat allocation was constructed to minimize the difference between demand and supply, number of trains, and train-stop costs. The number of train stops, transport capacity, and train occupancy rates were considered. Particle swarm optimization and CPLEX were combined to handle this significant linear programming problem. A real case study of different demand scenarios based on the Hohhot-Beijing high-speed railway in China verified the feasibility of active demand management methods. The results indicate that integrated optimization can be justified with mixed load patterns. Compared with the original scheme, the optimized scheme reduced the number of trains by 23.4% and increased train occupancy rate by more than 36.7%.

Analyzing the impact of timetable elements on railway line capacity

Train timetabling poses inherent challenges, prompting the need to enhance existing schedules by extracting valuable insights from the current timetable structure. By such philosophy, this paper studies the impact of timetable elements on the consumed capacity. These elements primarily encompass train operation parameters, including running times and stop plans. Their impact is defined as the deviation of consumed capacity relative to their variations. We initially establish a link between consumed capacity and timetable elements. This relationship is articulated as the signed sum of timetable elements along a designated ”critical path”. Then, the limited impact of any timetable element is clarified, namely changing an element can impact the consumed capacity with its neighbor trains in a combinatorial way. With this knowledge, we analyze the impact of a single element, using stop plans for example. This result is then generalized into analyzing the impact of several dependent and independent stop plans. The findings on capacity calculation and impact analysis of a single element are tested through real-world numerical computations and then extended to analyzing various capacity factors, such as average speed and heterogeneity.

Optimization for Customized Bus Stop Planning, Order Schedule, and Routing Design in On-Demand Urban Mobility

Optimization for Customized Bus Stop Planning, Order Schedule, and Routing Design in On-Demand Urban Mobility

A CALCULATION METHOD FOR HIGH-SPEED RAILWAY CAPACITY BASED ON IMPROVED TRAIN DEDUCTION METHOD

A reasonable calculation of railway capacity is very important for research. With the rapid development of high-speed railways, more and more differences between traditional and modern railways have emerged in the transportation organization and capacity calculation methods. In this article, the calculation methods used under different conditions with different train types are studied based on the train deduction method. Train deduction is a method that calculates the number of trains that cannot pass through the line when other trains change their stop plan or operation speed based on the coefficient of deduction method, which is widely used in China. Then, optimized models for trains with the same-speed and different-speeds are built to determine the maximum number of trains. These models are built based on the constraints of passenger service quality and overtaking times. In addition, the models are solved based on a train operation plan. Hence, the capacity calculated by these methods is more reasonable for the actual condition. Finally, an actual case of Beijing–Shanghai high-speed railway is implemented and tested with the model. The optimal capacity scheme is simulated and analysed, and the result agreed well with the railway transport enterprise.

Integrating stop planning, timetabling and rolling stock planning on high-speed railway lines: A multi-objective optimization approach

Designing railway operational planning is a complex and challenging task to cope with fluctuating passenger demand and satisfy actual operational requirements. Focusing on high-speed railway (HSR) lines, this paper proposes an integrated modeling framework for phase-oriented railway operational planning. The framework simultaneously optimizes the stop planning, train timetable problem (TTP) and rolling stock planning (RSP). A multi-objective optimization model, with macro passenger demand, two train speed levels, and overtaking operations taken into account, is formulated to minimize the total train running distance, train dwell time, and rolling stock resources. The constraints involve the train operation zones, train operation process, passenger demand, rolling stock circulation conditions, and actual operational requirements. The proposed model is addressed using a framework based on the ε-constraint method, from which an approximate Pareto-optimal solution set is obtained. A case study on the Beijing-Shanghai HSR corridor demonstrates the effectiveness of the proposed approach and reveals some trade-offs between the proposed objectives.

Dyna-Validator: A Model-based Reinforcement Learning Method with Validated Simulated Experiences

Dyna is a planning paradigm that naturally weaves learning and planning together through environmental models. Dyna-style reinforcement learning improves the sample efficiency using the simulation experience generated by the environment model to update the value function. However, the existing Dyna-style planning methods are usually based on tabular methods, only suitable for tasks with low-dimensional and small-scale space. In addition, the quality of the simulation experience generated by the existing methods cannot be guaranteed, which significantly limits its application in tasks such as continuous control of high-dimensional robots and autonomous driving. To this end, we propose a model-based approach that controls planning through a validator. The validator filters high-quality experiences for policy learning and decides whether to stop planning. To deal with the exploration and exploitation dilemma in reinforcement learning, a combination of ϵ-greedy strategy and simulated annealing (SA) cooling schedule control is designed as an action selection strategy. The excellent performance of the proposed method is demonstrated in a set of classical Atari games. Experimental results show that learning dynamic models in some games can improve sample efficiency. This benefit is maximized by choosing the proper planning steps. In the optimization planning phase, our method maintains a smaller gap with the current state-of-the-art model-based reinforcement learning (MuZero). In order to achieve a good compromise between model accuracy and optimal programming step size, it is necessary to control the programming reasonably. The practical application of this method in a physical robot system helps reduce the influence of an imprecise depth prediction model on the task. Without human supervision, it is easier to collect training data and learn complex skills (such as grabbing and carrying items) while being more effective at scaling tasks that have never been seen before.

Network periodic train timetabling with integrated stop planning and passenger routing: A periodic time–space network construct and ADMM algorithm

Designing a passenger-centric railway service is of great importance for railway companies to attract more passengers and increase their revenue. Focusing on the passenger-centric operational plans, this paper considers the problem of integrated periodic train timetabling and train stop planning in high speed railway networks. In view of the immediate dependency of passengers’ routes on the train timetable and stop plan, simultaneous passenger routing is included to directly shape the train stops and timetable in a passenger-friendly way. Besides travel time, ticket price is also considered in passenger utility to better depict passengers’ route choices in the railway network. A railway network-based time–space network with train stop-skip technique, periodic properties and detailed consideration of different train headways is designed, based on which a multi-commodity network flow model is developed. With a novel linearization technique of train capacity penalty term, the alternating direction method of multipliers (ADMM) is used to relax and decompose the track and train capacity constraints. Further, time deviation of regular train services is achieved by a new shortest path algorithm to design more flexible timetables, however its application is still limited to small instances due to computational efforts required. The algorithm is finally tested on the Shandong high-speed railway network in China, and demonstrates its capability of providing tighter lower bounds than Lagrangian relaxation method and generating good feasible solutions. Besides, the impacts of different cycle times on service quality and train cost are evaluated.

Joint train line planning and timetabling of intercity high-speed rail with actual time-dependent demand

Intercity high-speed rail (HSR) is a type of mass transit built in densely populated metropolitan areas and has become an essential part of sustainable transportation systems in some countries. One of the most challenging problems is how to jointly optimize the train line plan and timetable with simultaneous consideration of the system efficiency, cost, and level of service. In this paper, we focus on optimizing the train line plan and timetable of intercity HSR given actual time-dependent demand while appropriately considering the operational constraints including overtaking. First, we formulate the optimization problem as a mixed integer nonlinear programming (MINLP). Its objective is to minimize the total system cost comprised of the trains’ fixed and variable operation costs, passengers’ travel time cost, and penalties for passengers’ departure time deviation and passengers’ failing to board a train. We then design a double-layer simulated annealing (SA) algorithm, in which the inner layer algorithm optimizes the stop plan, the departure and arrival times at each station, while the outer layer algorithm optimizes the number of trains, their origin–destination stations, and their departure times at origin stations. Next a case study is conducted on Shanghai-Nanjing intercity HSR using actual time-dependent demand. The numerical results show that the proposed model can help significantly reduce the total number of trains used by increasing the average seat occupancy, while creating little impact on the average passenger travel time and departure time. The study provides valuable guidance for optimizing HSR train line plan and timetable based on real-world time-dependent demand.

Safety-oriented train timetabling and stop planning with time-varying and elastic demand on overcrowded commuter metro lines

In some megacities, commuter metro lines that connect suburbs and downtown areas always suffer from serious non-equilibrium passenger congestion at stations during rush hours, bringing huge operation risks. To balance the non-equilibrium passenger congestion on overcrowded metro lines, this study aims to jointly optimize the train timetable and stop plan from the perspective of transportation safety in which some effective skip-stop patterns are adopted to allocate necessary train capacities to overcrowded stations with consideration to time-varying and elastic demand. We formulate the problem of interest as an integer linear programming model where the total passenger gathering risk, passenger waiting time, passenger riding time, and loss of ticket income are all included in the objective function. An efficient iterated local search (ILS) algorithm is designed for the convenience of solving large-scale models. Numerical experiments for a series of small-scale and real-world case studies are conducted to test the performance of the proposed methods. The computational results show that the designed algorithm can obtain high-quality train operation schemes in a much shorter time, which can greatly improve transportation safety by balancing the number of stranded passengers at any station.

Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand.

In recent years, with increasing passenger travel demand, high-speed railways have developed rapidly. The stop planning and timetabling problems are the core contents of high-speed railway transport planning and have important practical significance for improving efficiency of passenger travel and railway operation Dong et al. (2020). This study proposes a collaborative optimization approach that can be divided into two phases. In the first phase, a mixed-integer nonlinear programming model is constructed to obtain a stop plan by minimizing the total passenger travel time. The constraints of passenger origin-destination (OD) demand, train capacity, and stop frequency are considered in the first phase. In the second phase, the train timetable is optimized after the stop plan is obtained. A multiobjective mixed-integer linear optimization model is formulated by minimizing the total train travel time and the deviation between the expected and actual departure times from the origin station for all trains. Multiple types of trains and more refined headways are considered in the timetabling model. Finally, the approach is applied to China's high-speed railway, and the GUROBI optimizer is used to solve the models in the above two stages. By analyzing the results, the total passenger travel time and train travel time decreased by 2.81% and 3.34% respectively. The proposed method generates a more efficient solution for the railway system.

Conception of an itinerary planning assistant for EV drivers

Electric vehicles (EVs) require charge stops during long trips. Appropriate digital services can ease the usage of EVs and thus could accelerate their adoption. We propose a planning system that interacts with the user's calendar for deriving upcoming journeys. The core of the system is a newly developed itinerary planning algorithm that integrates charge stop planning. The algorithm uses dynamic information distributed between different stakeholders, namely information regarding charging infrastructure and navigational information. A simulation of distinct EV scenarios validates the efficacy of the proposed work.

Integrated optimization of train stop planning and ticket pricing with equity concerns of passengers for high-speed railways

Ticket pricing, which is critical to increasing the revenue for high-speed railways, is typically optimized with a given train stop plan. However, the passenger distributions are restricted by fixed train stop plans, and the optimal ticket price scheme may not be achieved. Therefore, a mixed-integer linear programming model is proposed to optimize collaboratively train stop planning and ticket pricing in this study. The objective is to maximize the total ticket revenue, while the total number of train stops is limited to guarantee service quality. Furthermore, equity concerns of passengers are addressed by ensuring that passengers have equitable travel costs and opportunities to access high-speed railways. Finally, numerical experiments are conducted on a hypothetical railway line and the Zhengzhou-Xi’an high-speed railway corridor to demonstrate the application of the proposed approach. The computational results indicate that the proposed method can increase the total ticket revenue by 27% through adding 14 train stops and formulating a new ticket price scheme compared to the traditional method. In addition, the equity performance can be kept at a level of 1.5.

Equity-oriented integrated optimization of train timetable and stop plans for suburban railways system

In the suburban railway system, when oversaturation occurs in the railway line or the train skips the same station continuously, passengers waiting at different stations may receive a varying share of train resources. As a result, some passengers have to endure a very long waiting time, causing issues of inequity. Thus, this paper focuses on improving the system-wide equity performance through integrated optimization of the train timetable and stop plan while ensuring travel efficiency and cost rationality. As a part of the study, a novel equation to estimate equity performance by considering the difference in passenger travel utility is developed. Then, an integrated optimization model combining the train timetable and stop plan is proposed to minimize total travel time, train running time, and equity performance. Unlike other studies in the literature, this paper does not define any initial set of stopping plans, and synchronously optimizes the train timetable and stops the plan. This facilitates obtaining better solutions by expanding the solution space. Furthermore, it overcomes the NP-hardness of the investigated problem by using the adaptive large-scale neighborhood search (ALNS) algorithm to solve the multi-objective mixed-integer linear programming model. Finally, this paper discusses the original and optimized scheduling indicators of a real-world case based on the Shanghai Jinshan railway and performs a sensitivity analysis on equity weights. The results show that the proposed approach can improve the overall benefits of efficiency, cost, and equity by 23%. Specifically, in terms of equity, the passenger travel utility variance was reduced by 42.8%. This analysis provides essential information to railway managers in formulating the train operation plan.

Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study

In this study, we examined ticket pricing and train stop planning for the high-speed railway (HSR), which integrates two key aspects of railway operation and organization. We considered that passenger demand is sensitive to the generalized travel cost (depending on the ticket price and the travel time) and that the train stop plan can affect the travel time and passenger distribution. Then, a mixed-integer non-linear optimization model was proposed for the joint problem of ticket pricing and train stop planning to maximize HSR’s transport revenue and minimize passengers’ travel time. Based on the high similarity between combinatorial optimization problems and the solid annealing principle, we designed a combined simulated annealing (CSA) algorithm to solve practical problems. The results of a numerical example in the real HSR network showed that the proposed method can improve transport revenue by 5.1% and reduce passengers’ travel time loss by 11.15% without increasing transport capacity.

Joint optimization of high-speed train timetables, speed levels and stop plans for increasing capacity based on a compressed multilayer space-time network.

With the steady increase in passenger volume of high-speed railways in China, some high-speed railway sections have faced a difficult situation. To provide more transport services, it is necessary to add as many trains as possible in a section to increase capacity. To solve this problem, a compressed multilayer space-time network model is constructed with the maximum number of trains that can be scheduled in the train timetable as the objective. The combination of the train stop plan and speed level is represented by the layer of network where the train is located, and constraints such as train selection, train safety, train overtake and cross-line trains are considered. A method based on timing-cycle iterative optimization is designed to decompose the original problem into multiple subproblems, and the solving order of the subproblems is determined by a heuristic greedy rule. Taking the Beijing-Jinan section of the Beijing-Shanghai high-speed railway as an example, the maximum number of trains was increased by 12.5% compared with the timetable before optimization. The saturated timetables provide detailed schedules, which helps decision-makers better adjust the timetable to run more trains.

A Scheduling Approach for the Train-Set Circulation Plan and Timetable for the Longer Distance High-Speed Railway in Transition Time

Nowadays, high-speed railway (HSR) has become one of the main choices for passengers. As the number of passengers increases, their travel demands become diverse and the fluctuation range of passenger travel demands will also increase. In order to adapt to the change of passenger travel demands, the switching frequency of timetables needs to be increased. When switching the timetable, the train-set circulation plan also needs to be considered. In this paper, a scheduling approach for quickly solving the timetable and the train-set circulation plan in the transition time is proposed. A section sequence is constructed in the integer programming model, and the primary train-set circulation plan is obtained. Then a stop plan is obtained on the basis of passenger travel demands. To obtain the final train-set circulation plan and the timetable, a genetic algorithm (GA) is designed, and a method that can ensure that the timetable meets the safety operation requirements is proposed. The scheduling approach is tested on the Beijing-Shanghai HSR. The results show that by extending the transition time, the scheduling approach can switch the train-set position from the old state to new state, without additional consumption of resources, on the premise of meeting the travel demands of passengers.

A Joint Decision Strategy for Ticket Pricing and Stop Plan of High-Speed Railway: A Case Study

A Joint Decision Strategy for Ticket Pricing and Stop Plan of High-Speed Railway: A Case Study