Tractor and Semitrailer Routing Problem of Highway Port Networks under Unbalanced Demand

In China, highway port networks are essential in carrying out tractor and semitrailer transportation operations. To analyze the characteristics of tractor and semitrailer routing in highway port networks, this study examined the situation in which the demands at both ends of the operation might be unbalanced and multiple requirements might be raised in the operation of tractor and semitrailer transportation. An optimal tractor and semitrailer routing model for an entire network was established to reduce the total transportation costs and the number of towing vehicles in the network. Moreover, a heuristic algorithm was designed to solve the model. The comparisons of Strategy 1 and Strategy 2 for a two-stage network swap trailer show that the number of pure network swaps trailer tractors decreases by 21.6% and 18.6%, respectively; and that the cost drops by 7.8% and 7.9%, respectively. In other words, swap trailer transport enterprises can abandon the original swap trailer transportation mode for a two-stage network and adopt a routing optimization strategy for an entire network to achieve superior operation performance, reduce costs, and enhance profits. The study provides a reference for optimizing tractor and semitrailer routing in highway port networks with balanced and multiple demands

A Soft Rough-Fuzzy Preference Set-Based Evaluation Method for High-Speed Train Operation Diagrams

This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams

High-Speed Train Stop-Schedule Optimization Based on Passenger Travel Convenience

The stop-schedules for passenger trains are important to the operation planning of high-speed trains, and they decide the quality of passenger service and the transportation efficiency. This paper analyzes the specific manifestation of passenger travel convenience and proposes the concepts of interstation accessibility and degree of accessibility. In consideration of both the economic benefits of railway corporations and the travel convenience of passengers, a multitarget optimization model is established. The model aims at minimizing stop cost and maximizing passenger travel convenience. Several constraints are applied to the model establishment, including the number of stops made by individual trains, the frequency of train service received by each station, the operation section, and the 0-1 variable. A hybrid genetic algorithm is designed to solve the model. Both the model and the algorithm are validated through case study

Train timetabling with the general learning environment and multi-agent deep reinforcement learning

This paper proposes a multi-agent deep reinforcement learning approach for the train timetabling problem of different railway systems. A general train timetabling learning environment is constructed to model the problem as a Markov decision process, in which the objectives and complex constraints of the problem can be distributed naturally and elegantly. Through subtle changes, the environment can be flexibly switched between the widely used double-track railway system and the more complex single-track railway system. To address the curse of dimensionality, a multi-agent actor–critic algorithm framework is proposed to decompose the large-size combinatorial decision space into multiple independent ones, which are parameterized by deep neural networks. The proposed approach was tested using a real-world instance and several test instances. Experimental results show that cooperative policies of the single-track train timetabling problem can be obtained by the proposed method within a reasonable computing time that outperforms several prevailing methods in terms of the optimality of solutions, and the proposed method can be easily generalized to the double-track train timetabling problem by changing the environment slightly

Train Scheduling Optimization for an Urban Rail Transit Line: A Simulated-Annealing Algorithm Using a Large Neighborhood Search Metaheuristic

This paper describes an optimization model for an irregular train schedule. The aim is to optimize both the maximum train loading rate and the average deviation of departure intervals under time-varying passenger transport demand for an urban rail transit line in consideration of practical train operation constraints, i.e., headway, running time between stations, dwell time, and capacity. A heuristic simulated-annealing algorithm is designed to solve the optimization model, and a case study of an urban rail transit line is performed to assess its efficacy. The results show that, compared with the current regular train schedule, the total train dwell time under the optimized irregular schedule is reduced from 900 s to 848 s, and the reduction ratio for the maximum train loading rate is from 1.2% to 3.6% for different stations. When the average train departure interval is allowed to vary from 120 to 170 s, the optimized irregular schedule decreases the maximum train loading rate of the collinear and noncollinear sections by 3.21%–4.82% and 2.52%–3.64%, respectively. Sensitivity analysis is performed for a nonnegative weight coefficient, average train departure interval, and proportion of full-length and short-turn routings. The proposed approach can be used to support capacity improvement and schedule optimization for urban rail transit lines

Computer‐Aided Design System of Operation Organization: Urban Rail Transit

The operation organization decided the system lines, design of station, reasonable configuration of various devices and rational use of equipments’ capacity, which was an important part of urban rail transit. While the design of urban rail transit operation organization involved multiple elements, and the relations of various elements was quite complex, a feasible program would take a large amount of time by artificial, which was difficult to attain overall optimal. So the paper proposed to combine operation organization theories with computer, and constructed the computer assistant design system of urban rail transit operation organization to realize information and intelligence of operation organization

An Optimization Method of Multiclass Price Railway Passenger Transport Ticket Allocation under High Passenger Demand

The development of high-speed railways (HSR) in China has attracted a large number of passengers from highway and aviation to railways due to their comfort and high speed. In this case, HSR passenger transportation can improve the operating income by optimizing the ticket allocation. Here, we propose an optimization method of multiclass price railway passenger transport ticket allocation under high passenger demand. First, for the “censored data” problem in the railway passenger demand forecast, we constructed an unconstrained model of railway passenger demand and solved the unconstrained demand through an expectation-maximization algorithm. Then, on this basis, we use gray neural networks (GNNs) to predict the passenger demand of different origins and destinations (ODs), and according to the prediction results, we propose two ticket allocation methods based on operation and capacity control: accurate predivided model and fuzzy predivided model. And we solve this problem by constructing a particle swarm optimization algorithm. Lastly, we use examples to prove that the proposed ticket allocation method can meet the passengers’ needs and have good economic benefits

A Support Vector Regression Approach for Investigating Multianticipative Driving Behavior

This paper presents a Support Vector Regression (SVR) approach that can be applied to predict the multianticipative driving behavior using vehicle trajectory data. Building upon the SVR approach, a multianticipative car-following model is developed and enhanced in learning speed and predication accuracy. The model training and validation are conducted by using the field trajectory data extracted from the Next Generation Simulation (NGSIM) project. During the model training and validation tests, the estimation results show that the SVR model performs as well as IDM model with respect to the model prediction accuracy. In addition, this paper performs a relative importance analysis to quantify the multianticipation in terms of the different stimuli to which drivers react in platoon car following. The analysis results confirm that drivers respond to the behavior of not only the immediate leading vehicle in front but also the second, third, and even fourth leading vehicles. Specifically, in congested traffic conditions, drivers are observed to be more sensitive to the relative speed than to the gap. These findings provide insight into multianticipative driving behavior and illustrate the necessity of taking into account multianticipative car-following model in microscopic traffic simulation

Modelling and impact analysis of interdependent characteristics on cascading overload failure of syncretic railway networks.

To study the performance and mutual influence of a syncretic railway network (SRN) that comprises high-speed railway, regional railway, and urban rail transit under the condition of traffic overload during peak hours, we discuss the interdependent characteristics on cascading overload failure of SRNs under the cooperative organization from the perspective of an interdependent network. However, most existing research on cascading failure in interdependent network ignores the inconsistency between the physical structure and transportation organization of the subnetwork in an actual network, in addition to the restrictions on the load redistribution strategy of stations and sections in the load-capacity model of the interdependent network; especially, the influence of transfer behavior on the load redistribution inter subnetwork. In this study, we investigate the robustness of an interdependent SRN under overload and risk propagation. We propose a partially interdependent network model of a multimode rail transit, develop a novel cascading overload failure model with tunable parameters of load redistribution inter subnetwork, and analyze interdependent characteristics, cascade failure process, and robustness of an SRN under multiscene conditions, i.e., different attack and load distribution strategies, via simulations. A case study of an SRN in the metropolitan area of Chengdu, China is presented; the results indicate that, when the reserve coefficient of the metro subnetwork is 0.4 and the overload coefficient of the regional railway subnetwork is greater than 1.2, the station reserve capacity and overload capacity of the SRN is appropriately improved. When passenger load increases to a certain range, the reserve and overload capacities of stations in the regional railway subnetwork do not considerably contribute to robustness. Thus, a surplus load distribution strategy is recommended to improve robustness. The results of this paper have considerable significance for the planning, structural optimization, and operation safety of SRNs

Train timetabling with passenger data and heterogeneous rolling stocks circulation on urban rail transit line

The planning process in urban rail transportation can be split into several stages, including line planning, timetabling, rolling stock scheduling and so on. The outcome of a stage provides inputs or constraints to the subsequent ones. However, while the output can be good or optimal at each stage, it rarely considers the global quality for the overall planning process. Furthermore, problems tackled at planning phase often use a more aggregate representation of reality, to achieve a more general overview. An integrated approach, while more complex to solve, may mitigate the gap between solution of different stages and a validation of a plan would still be required before its implementation in practice. In this paper, we focus on integrated optimization of train timetabling and rolling stock circulation for urban rail transit line with time-based origin-destination-dependent passenger travel demand and heterogeneous rolling stocks. The aim is to generate a comfortable timetable for passengers and an efficient timetable for operators. The objective is to minimize the total waiting time for passengers and the costs for operators, while constraints regarding train movements, passenger boarding and alighting, available rolling stocks and their capacity are considered. A mixed integer linear programming model is formulated and solved by an iterative programming approach. Computational experiments are performed on the Chongqing Rail Transit Line 2 to verify the efficiency and effectiveness of the proposed model and solving method. With respect to CPLEX, results show the proposed iterative programming approach has advantages both on computation time and solution quality