Train rescheduling for minimizing passenger travel time under disruption for metro lines

The authors of this paper present the results of the numerical optimization of departure times of trains at an interchange station when services are disrupted. The evaluation function is based on accumulated, or aggregated, passenger delays and the dynamic model of the interchange accounts for variations in passenger walking times. The authors have developed a passenger flow model using petri nets, which enables the representation of passenger flows inside a station to be made with sufficient accuracy and short calculation times. The evaluation function of aggregated passenger times is defined, and the optimization is carried out using the multi-dimensional downhill simplex method. The results show that the optimized train departure times essentially represent compromises between the benefit generated by the extended holding of trains to allow more passengers to catch an earlier service and the disbenefit generated by longer waiting times of passengers already on the train being held. This paper concludes with recommendations for combining these techniques with other models and evaluation criteria in order to move towards a practical train controller. Examples of refinements are a model of passenger behaviour when guidance information is provided, variations in dwell times when different numbers of passengers get on or off a train, congestion inside trains, and the impact of holding a train on the overall operation of trains on the line.

Urban rail transit passenger flow distribution has a very important reference value for urban rail transit operation and management. Most of the current research focuses on static passenger flow distribution, and there are few studies on passenger flow allocation considering dynamic passenger flow demand changes. Therefore, this paper focuses on considering dynamic passenger flow. The problem of passenger flow distribution under the change of passenger flow demand. This paper first analyzes the time and space distribution characteristics of passenger travel demand, uses the 4-parameter Logistic function to fit the cumulative passenger flow curve segmentally, deduces the calculation formula of passenger flow demand at each station in any time period, and divides passenger travel into smooth state and the generalized travel cost of passengers is calculated based on the congestion state, combined with the logit passenger flow distribution model, the travel cost is dynamically revised and a solution algorithm is designed to achieve dynamic passenger flow distribution. Finally, the feasibility and effectiveness of the model and algorithm are analyzed and verified by constructing the urban rail transit network, and the passenger flow distribution under the dynamic passenger flow demand is realized.

Train rescheduling and platforming in large high-speed railway stations

Based on the analysis of the influencing factors of urban rail transit passenger flow transfer path selection behavior and the components of travel time, a method of estimating the proportion of urban rail transit passenger flow transfer path selection based on IC card data is proposed. Firstly, the research determines the shortest interchange path algorithm as Dijkstra algorithm and the graph-based depth-first search algorithm as the effective interchange path search algorithm; analyzes the factors influencing the passenger flow interchange path selection behavior as three types of travel time, interchange cost and road network familiarity; constructs the generalized cost function of effective interchange path selection by combining travel time and interchange cost; establishes the passenger flow probability selection model by combining road network familiarity. To eliminate the influence of absolute difference of utility on the probability of path selection in the polynomial logit model, the path probability selection model is improved with the help of minimum travel cost; Bayesian estimation is used to compare the passenger travel time with the theoretical travel time to identify the closest effective interchange path, and the joint distribution probability is used to calibrate the probability selection model in terms of travel time and interchange cost. The coefficient, coefficient of interchange passenger flow, penalty coefficient of interchange number, familiarity of road network and other related parameters are combined with the joint distribution probability to calibrate the probability of selection of different effective interchange paths.

Real‐time train rescheduling for high‐speed railway (HSR) is a pivotal technique in HSR transportation to efficiently recover train operation under disturbance scenarios. This paper aims to put forward an integrated resolving and rescheduling method considering network delay propagation. A tree‐based conflict resolution mechanism is first established, with delicate considerations on the strategy’s adaptability under different conflict scenarios. By inputting the scheduled arrival and departure time under different conflict resolution strategies, the timetable optimizing model aims to look for an optimal solution with minimal weighted train delay and average train adjustments under necessary technical and empirical constraints solved by a combined algorithm of Pareto optimality and Nash equilibrium, where the feasible solution space is narrowed in advance by a depth‐first pruning algorithm. The performance of this coordinated train rescheduling approach is validated by a typical section disturbance in a regional HSR network administrated by the Shanghai Bureau. The results show that the proposed method can well utilizes timetable buffers and organizes train avoidance. The delay propagation characteristics are also simultaneously estimated based on the indicators of cumulative delay and instantaneous delay, which are established considering the spatio‐temporal difference between the scheduled and planned timetables, in order to verify the coordination between resolution strategies and train running delays.

The State Railway of Thailand provides passengers with train location information on their Web site, which includes the name of the last station that each train arrives at or departs from, along with the timestamps and the accumulative train delay (in minutes) from the train timetable. This information allows passengers to intuitively predict the arrival time at their station by adding the last known train delay to the scheduled arrival time. This paper aims at providing a more accurate prediction of passenger train's arrival times using the historical travel times between train stations. Two algorithms that use train location information and historical travel times are proposed and evaluated. The first algorithm uses the moving average of historical travel times. The second algorithm utilizes the travel times of the k-nearest neighbors (k-NN) of the last known arrival time. To evaluate the proposed algorithms, we collected six months of data for three different trains and calculated prediction errors using mean absolute error (MAE). The prediction errors of the proposed algorithms are compared to the prediction errors of the baseline algorithm that predicts the arrival time by adding the last known train delay to the scheduled train arrival time. Both algorithms outperform the baseline prediction. The algorithm based on moving average travel time improves the prediction error by 22.9 percent on average, and the algorithm based on k-NN improves the prediction error by 23.0 percent on average (k=16).

The subway average daily passengers in China has exceeded 80 million, with total energy consumption reaching 25 billion kWh in 2023, highlighting the critical importance of energy saving in subway stations. This study conducted on-site data collection at a subway station in Suzhou located in hot summer and cold winter climate zone in China in 2024, gathering data on outdoor meteorological parameters, indoor temperature and humidity, hourly passengers, and HVAC system energy consumption. EnergyPlus and the SARIMA are used to build the subway station energy simulation model and passenger flow model, respectively. The accuracy of these models is validated against measured data. The influencing factors of HVAC system energy consumption and their impacts are identified and quantified by correlation analysis. Results indicate that passenger flow exhibits a distinct dual-peak periodic pattern and has a highly significant impact on HVAC system energy consumption. Other influencing factors, in descending order of impact, include indoor air temperature, equipment power, humidity, ventilation volume, soil temperature, wind speed, and lighting power. The design capacity of the HVAC system is found approximately 30% larger than the actual demand according to real passenger flow. Optimizing the system design based on actual peak passenger data and dynamically operating the HVAC according to real-time passenger data can result in a 20% energy-saving rate. These findings provide valuable insights into the optimized design and energy-efficient operation of subway station HVAC systems.

The current urban rail transit faces the pressure of effective transportation management brought about by the large passenger flow. Through this research, it is proposed to construct a coordinated control model for urban rail transit passenger flow and optimize the urban rail transit operation organization. Using the Mixed Logit model to construct the urban rail transit network passenger flow collaborative control optimization model can accurately describe the distribution of the urban rail transit network passenger flow online, which is especially applicable to the network topology change scenario brought by the new line introduction. Empirical evidence shows the correctness and effectiveness of the model.

How should customer delay on a rail transit system be measured? Research into quantifying reliability and passenger delay often defines “delay” as when speeds or travel times fall above or below a fixed threshold. When applied to a rail transit system, however, this technique may not fully capture the impacts on all transit riders. This paper proposes a new way to assess passenger delay by leveraging the power and richness of transaction-level fare system data not always available in other contexts. This method enables a true understanding of the full spectrum of the impact on transit passengers by an analysis of the entire distribution of passenger travel times rather than by a simple tally of individuals with travel times above a threshold. This method—comparing the cumulative distribution of travel times—combines concepts from traffic queueing theory and travel time reliability research in both the transit and the highway arenas. The proposed method was applied to fare system data from the Washington, D.C., Metropolitan Area Transit Authority’s Metrorail system to evaluate the delays that arose from using different strategies for providing continued service during rail system rehabilitation.

This paper addresses a railway scenario with variable composition trains, and focuses on the management and simulation of the passenger flows and trains at stations. In particular, it proposes a management system aimed at optimally routing the passengers from their entrance in the station to the correct platform segment according the train they have to board, and scheduling the train service in terms of convoy composition, capacity and destinations. The resulting system is then modelled as a discrete event system (DES) and simulated, to evaluate its performance on the basis of indicators like the queue length, the unsatisfied demand, the passenger travel time in the station, and the passenger density at the platform. The proposed station management system is then tested on a mixed real/synthetic numerical example aimed at proving the feasibility and functioning of the proposed approach.

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap.

Inequity averse optimization of railway traffic management considering passenger route choice and Gini Coefficient

Short-term metro passenger flow prediction plays a great role in traffic planning and management, and it is an important prerequisite for achieving intelligent transportation. So, a novel hybrid Support Vector Regression (SVR) model based on Twice Clustering (TC) is proposed for short-term metro passenger flow prediction. The training sets and test sets are generated by TC with respect to values of passenger flow in different time periods to improve the prediction accuracy. Furthermore, each obtained cluster is decomposed by using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm and the Ensemble Empirical Mode Decomposition (EEMD) algorithm, respectively. The volatility of each component obtained after decomposition is further reduced. Then, the SVR model optimized by the Grey Wolf Optimization (GWO) algorithm is used to predict the decomposed components. Moreover, forecast based on one-month data from Xi’an Metro Line 2 Library Station (China). By comparing the prediction results of the TC condition, the Once Clustering (OC) condition and the non-clustering condition, it shows that the TC approach can adequately model the volatility and effectively improve the prediction accuracy. At the same time, experimental results show that the novel hybrid TC–CEEMDAN–GWO–SVR model has superior performance than Genetic Algorithm (GA) optimized SVR (SVR–GA) model and hybrid Back Propagation Neural Network (BPNN) model.

Time-sensitive parcel deliveries—shipments requested for delivery in a day or less—are an increasingly important aspect of urban logistics. It is challenging to deal with these deliveries from a carrier perspective. These require additional planning constraints, preventing the efficient consolidation of deliveries that is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities, such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo hitching. While there are various system designs, it is crucial that such a solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of mobility-on-demand (MOD) services that perform same-day parcel deliveries. To test the MOD-based solutions, we utilize a high-resolution agent- and activity-based simulation platform of passenger and freight flows. E-commerce demand carrier data collected in Singapore are used to characterize simulated parcel delivery demand. We explore operational scenarios that aim to minimize the adverse effects of fulfilling deliveries with MOD service vehicles on passenger flows. Adverse effects are measured in fulfillment, wait, and travel times. A case study on Singapore indicates that the MOD services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle kilometers travelled without compromising the quality of MOD for passenger travel. Insights into the operational performance of the cargo-hitching service are also provided.

To fully take the advantages of conventional bus and subway, and to maximize the overall feeder efficiency of the public transport system, the topic of feeder bus route optimization is studied in this paper. Considering the origin destination demand of passenger flow between subway stations and bus stations and transfer characteristics, the objective function is established with the minimum sum of bus operation cost and passenger travel cost. Taking into account the integrity of the feeder bus route, the rationality of the route, the route capacity and the station transfer factors, the constraints of the optimization model are constructed. Based on the idea of the genetic algorithm, the solution algorithm of the optimization model is developed. The genetic algorithm and enumeration algorithm are used to solve the optimization of the feeder bus route in this case, and the accuracy and efficiency of the solution are analyzed. The influence of the number of feeder bus routes on the system in the case network is compared and discussed. We compare and analyze the differences between the original bus network and the feeder bus network in terms of bus operation cost, passenger flow demand and total passenger travel cost. The research shows that the model and algorithm can find the approximate optimal solution of the feeder bus network scheme related to the subway through fewer iterations. The number of routes in the model has little impact on the whole feeder system, and the optimization scheme using five routes is effective and reasonable in this paper. Compared with the existing bus network, the optimization scheme has obvious advantages in improving the passenger-carrying rate, reducing the per capita travel cost and improving the overall operation efficiency of the system.