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

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

This study investigates an integrated train stop planning and timetabling problem considering the uncertainty of passenger demand in a railway network. With the scenario-based representation of demand distribution, an integer linear programming (ILP) model is formulated to simultaneously optimise the train timetable and stop plan, in which the time-space network representation method is adopted to characterise the movements of trains and passengers. The objective is to jointly optimise the total travel time of trains and passengers, and the expected number of unsatisfied or over-satisfied passengers over all demand scenarios. By analyzing the characteristics of the model, we decompose it into two subproblems based on trains (timetabling) and passengers (assignment), respectively. Then, an iterative algorithm based on variable neighbourhood search (VNS) is designed to solve the two subproblems iteratively. Additionally, a series of numerical experiments based on the real-world instance in a high-speed railway (HSR) network are implemented to verify the effectiveness of the proposed stochastic programming (SP) model and the iterative algorithm. The results demonstrate that (i) the proposed SP model is easy to implement in practice, and (ii) the developed iterative algorithm exhibits much higher computational efficiency than the optimisation solver.

Understanding the nature of human movement is important in many fields, such as transportation management, urban planning, and epidemiology. In this study, we focus on travelers who are grouped by their travel modes in the Tokyo Metropolitan Area. Notably, for each group of travelers, the distribution of daily travel time follows a lognormal distribution. For any travel mode, the distributions of normalized travel time for different years (1968–2008) and distances to the city center converge to a curve, although the average travel time and share of travel modes vary. By employing the concept of utility, we developed a disaggregate model wherein an individual maximizes utility from traveling (=benefit - cost) by modifying daily travel time. This model theoretically explained the observed distributions of travel time. The model was further used to demonstrate travel mode choices. The model parameters were estimated using the distribution of travel time, travel mode share, and manageable assumptions. These results provided interesting interpretations of the perceptual utility of the daily travel time of a population. Moreover, the obtained curves of utility versus time are similar to the shapes of the overall benefits assumed in previous studies. Overall benefit is a concept that has been developed and examined since the 1980s, and our study provides a mathematical foundation for this idea.

The high-speed railways have made rapid developments in recent years. Fulfilling passenger demand and providing precise train services are the core problems to be solved in railway operation. This paper proposes an optimization strategy for demand-responsive transport to integrate train-stop planning and timetabling in high-speed railways. Passenger travel information, including their origins, destinations and expected departure times is taken as input. A mixed integer linear programming model is established to obtain an effective service plan, which consists of train stop pattern, passenger ride plan and train arrival/departure times at all stations. The optimization objective is to minimize the remaining passenger demand and train travel time. Finally, the proposed method is applied to a real-world case, and a series of several experiments are conducted to prove the efficiency and validity of the proposed model. The results suggest that the proposed approach could generate efficient service plans which are responsive to passenger demand.

Riverbank filtration (RBF) is a surface water treatment technology that uses the aquifer adjacent to the river as a natural filter. A production well in the aquifer near the river draws water from both the aquifer and the river. As river water travels through the aquifer to the well, contaminants in the water undergo biologic and chemical reactions while traveling through the subsurface materials, leading to higher quality water at the well as compared with the river. A key parameter in siting the RBF well is the travel time of the contaminants from the river to the well, which must be sufficiently long to allow adequate time for biologic and chemical reactions to occur. Because the well induces flow into the aquifer from all along the river channel, multiple travel paths exist between the river and the well, each with a different contaminant travel time. The water produced at the well contains a collection of contaminant particles from these multiple travel paths and can be characterized by a distribution of travel times. The travel time of the contaminant depends on the groundwater velocity along the flow path, the sorption properties of the contaminant and the aquifer matrix, and the chemical reactions that occur during transport. In this work, we demonstrate the use of an adjoint-based modeling approach to characterize the distribution of travel times at the RBF well. In adjoint modeling, transport is modeled backward in space and time, from the point of view of the RBF well. The output of an adjoint simulation is the distribution of solute travel times at the RBF well at any time of interest. We consider different sorption models and sorption parameters, and we consider different chemical reactions and reaction rates. We simulate the travel time distributions using either MT3DMS or RT3D, depending on the type of chemical reaction, using a flow field generated using MODFLOW.

Cairo's Bus Rapid Transit system (BRT) is a mode of transport recently proposed to provide a different approach to solving the city's transportation issues. The passenger's choice of transportation mode is thought to be significantly influenced by the cost of the trip. Willingness to pay (WTP) can guide authorities in structuring new public transportation and setting an appropriate fare structure policy. Data for this study was gathered through interviews with 250 passengers who were surveyed. The data collected is in the form of respondents’ characteristics, travel characteristics, and pay perception. This study focuses on a progressive willingness to pay system for different levels of improvement in comfort and travel time. According to the findings of the study, the average value of willingness to pay is 30 Egyptian pounds per passenger, which has the potential to increase the ministry of transportation's revenue on the Ring Road by 40%. The trip characteristics show that passengers prefer reducing trip time, the presence of private car parking (park and ride facilities), reducing the waiting time, then the ticket price, and the availability of complementary transportation services. When comparing countries with BRT systems, Cairo's BRT system is among the longest in the world. The proposed ticket price is considered the average for the BRT systems.

Travel time distribution studies are fundamental for supporting transportation system reliability studies, particularly for urban road networks. However, such studies are generally based on travel time data sets with limited sample sizes, which provide inconsistent findings. In this paper, a large amount of travel time data collected from the emerging radio frequency identification (RFID) technique are used to conduct empirical investigations and estimations of travel time distributions, and three major findings are determined. First, travel time data are shown to have a complex statistical structure: the travel time distribution is in general peaky, multi-modal, and skewed to the right, which cross validates findings shown in previous publications. Second, unimodal distribution models are shown to be unable to capture the complex statistical dynamics embedded in the travel time data; therefore, a multistate distribution model is more appropriate for modeling travel time distributions. In this respect, a three-component gaussian mixture model (GMM) is tested and results consistently outperform those of unimodal distribution models. Finally, the aggregation time interval is shown to have a trivial effect on the shape of travel time distributions: the travel time distribution is stable under different aggregation time intervals. Future work is recommended to investigate further travel time variabilities and travel time distribution estimations.

A novel generative adversarial network for estimation of trip travel time distribution with trajectory data

Valuing travel time variability: Characteristics of the travel time distribution on an urban road

The stop plan of high-speed railway passenger trains is the core content of the high-speed train operation plan. It has the key role on passenger service quality and railway transportation efficiency. The content of the high-speed railway passenger train stop plan is proposed by analyzing the train stop set. The collaborative optimization theory of the stop plan of high-speed railway passenger trains combined with the train working diagram is studied. Combined with the analysis of the factors affecting the high-speed train stop plan, and based on the target of the train stop plan, the stop plan is revealed, which provides theoretical support and technical means for the operation organization optimization of high-speed railway passenger trains.

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.

A constant sound-speed (isospeed) channel with horizontal boundaries and a current of constant direction and linear depth-dependent magnitude is considered. Ray paths are shown to be closely approximated by connected circular helical arcs. Expressions for the travel time and spreading loss of individual arrivals are found when necessary and sufficient conditions for the ray paths to be plane curves are satisfied. For a bottom-mounted source and receiver, it is shown that a current gradient can significantly affect the ray geometry, travel time, and spreading loss of each arrival. The total field for a 33-km × 40-m channel is calculated. It is found that small changes in the current gradient can produce large variations in the amplitude and phase of the field including the amplitude fade-phase offset phenomenon.

Vehicle travel time on arterial roads is a crucial parameter for traffic management and traveler information systems. A travel time distribution is an effective way to represent the essential properties of this parameter. This paper proposes a modified Gaussian Mixture Model for representing travel time distributions on arterial roads with signalized intersections. The proposed model is applicable to travel time data from both fixed and mobile sensors. The performance of the model was evaluated using real travel time measurements from fixed sensors in the field and virtual mobile sensor data generated from those real-world measurements. The evaluation results show very good performance of the model in representing the traffic state on arterial roads. The model can be applied to historical datasets to estimate the amount of stop time and non-stop time for vehicles on arterial links during a specific time period, which is useful information for a variety of traffic applications, such as arterial travel time prediction and arterial traffic energy/emission estimation.

A copula-based approach for estimating the travel time reliability of urban arterial

Indonesian experience on travel time use on-board of commuter rail services