Spatiotemporal synchronous coupling algorithm for urban rail transit timetables design under dynamic passenger demand

Train timetabling for a double-track urban rail transit line under dynamic passenger demand

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.

Both passenger demand and service supply are among the most important factors that determine the performance of urban rail transit system. It is not easy to find out optimal solution for the match between the passenger demand and service supply with traditional methods, due to the complexity of the combinatorial intelligent supply - demand matching problem. In order to get the comprehensively optimal matching degree, this paper transforms the multi-criteria problem into the distributed artificial intelligence optimization by using multi-agent dynamic interaction technique. On the demand side, the dynamic passenger traffic demand with agents is modelled from perspective of boundedly rational travel decision. On the supply side, the dynamic service supply of train traffic with agent is modelled. The headway time is designated as the main decision variable, for the key link between the passenger demand and service supply is the headway time in different time-of-day intervals. To make the passenger demand more closely matched with service supply in urban rail transit network system at the reasonable travel cost and operational cost, the calculation formula for matching degree is proposed, along with the distributed system architecture for agent-based matching mechanism, and the negotiation-based iterative mechanisms for balancing. The proposed methods are validated on the simulation platform NetLogo. The simulation results emphasize the importance of representing the supply side and the demand side jointly/interactively. These findings are meaningful for policies on both development of efficient capacity usage strategies of urban rail transit network and provision of high level of service for passengers.

Optimization of Urban Single-line Metro Timetable for Total Passenger Travel Time under Dynamic Passenger Demand

Urban rail transit is developing rapidly, and new lines are constantly being opened and connected to the existing road network. Accurately grasping the passenger flow after the opening of the new line can provide a reference for the operation and management of urban rail transit stations. In order to predict the daily passenger flow after the opening of the new line, based on the passenger flow of the existing line stations, combined with the distribution of job-resident population, the new line stations and the existing line stations are matched by station classification, and a neural network model is established to predict the daily passenger flow after the new line is opened. The passenger flow provides a reference for the passenger flow management for the opening of new rail transit lines in the future.

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.

In order to reduce the impact of normal large passenger flow on rail transit stations, trains and operations, a qualitative and quantitative approach is adopted to analyze the process of propagation and dissipation of large passenger flow on rail transit lines. Based on the timetable of trains and the AFC swiping card data of passengers entering and leaving the station, the connection between passengers and trains in space and time is analyzed. The formation and spread of large passenger flow are analyzed from several aspects: the number of arriving passenger flow, the number of boarding passengers and alighting passengers, retention rate, train capacity and train full load rate, so as to study the mechanism of large passenger flow propagation based on train capacity constraints. Finally, combined with the specific route, it can be concluded that the spread of large passenger flow in time and space is continuous, and the large passenger flow at a single station will affect the travel of passengers at subsequent stations.

The imbalances between supply and demand in an urban rail transit system have received extensive attention. Addressing these imbalances by controlling the movements of passengers in the system is a real challenge. To complete the peak passenger flow control scheme, a novel discrete event simulation (DES) optimization model based on queuing theory is proposed to minimize the urban rail transit company losses and the passenger time delays considering effects of congestion propagation among facilities at busy stations. The proposed approach can generate a control scheme that consists of the controlling number of entering passengers and the controlling parameters of facilities inside the station. The first stage of the method models the subway network using queuing theory and then builds a DES model that is based on an urban rail transit queuing network. In the second stage, a service-security-economic-oriented optimization model is established and combined with a queuing network simulation model to implement the simulation optimization. We present three numerical experiments to compare the optimization results in different passenger flow scenarios. The results demonstrate that the DES optimization method in this paper can provide a reasonable and reliable control scheme in daily operations.

The contradiction between transport capacity and passenger demand in urban rail transit is usually prominent during peak hours in some megacities of China, and some passenger flow control measures have been adopted to alleviate passenger congestion. To better save passengers’ travel time when taking passenger flow control measures, this paper proposes an integrated optimization method of bus route adjustment with network-level passenger flow control for urban rail transit, in which the controlled passengers can freely choose to shift to bus or to retain in urban rail transit for pursuing a lower travel cost. With the objectives of minimizing average additional travel time for all affected passengers and maximizing the operating revenue of urban rail transit, an integer non-linear programming model is formulated to determine the inbound passenger volumes and bus adjustment schemes. To solve this proposed model effectively, a multi-objective particle swarm optimization based on dual-population co-evolution is designed. Finally, three sets of numerical experiments, including an integrated optimization experiment and two independent optimization experiments of passenger flow control, are implemented to demonstrate the feasibility and benefits of the proposed method.

Urban rail transit features much strength, such as large capacity, safety and environment-friendliness, and it becomes a preferred choice for most passengers. It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit system and achieve the goal of smart operations, this paper applies machine learning algorithms and completes the feature engineering of urban rail transit passenger flow data in terms of time, space and external factors. Based on passenger flow characteristics as collected, we build the short-term passenger flow forecast models, which include light gradient boosting machine (LightGBM) model, long short-term memory (LSTM) model, and LightGBM-LSTM fusion model. Besides, we construct the autoregressive integrated moving average (ARIMA) model and extreme gradient boosting (XGBoost) model for experiment comparison. Finally, we conduct the passenger flow forecasting experiments on the Hangzhou subway dataset based on the five prediction-models mentioned above. Then, five types of subway stations are selected (residential type, occupation type, residential-occupation type, business type, and transportation hubs type) and three accuracy evaluation indicators are chosen (mean absolute error, root mean square error and mean absolute percentage error) to evaluate the prediction accuracy of the five prediction models. The experimental results show that, the multi-feature machine learning model can effectively forecast urban rail transit short-term passenger flow which is difficult for the traditional time series model. However, the single model has poor adaptability to different types of subway stations. Compared with a single model, LightGBM-LSTM model equipped with merits of multiple models, fulfills a better function in forecasting and a better adaptability to different types of urban rail transit stations.

The passenger flow of urban rail transit (UTR) is influenced by many factors including gross domestic production, total length of urban rail lines, and the urban population scale of the city. A macro-factor passenger flow estimation model is needed for more and more cities in Chinato to build urban rail transit system. Before the urban rail transit becomes an operating network, a lot of government agencies usually build the first metro line in the center of city. Considering only one line services a limited number of people in the city, a modified multiple variables linear regression model is established based on the URT operating data in cities of China which can forecast the passenger flow of urban rail transit exactly. The results show that the model is very accurate in passenger flow forecasting.

In China, many cities are planning urban rail transit system, but a comprehensive passenger flow estimation model is still lacking. The total passenger flow of urban rail transit in a city depends on many factors, such as urban population, total length of rail lines, gross domestic production of the city etc. To estimate the total passenger flow of urban rail transit, a linear regression model with multiple variables is established in the paper, based on the real data collected in many cities with urban rail transit operating. The comparison of the estimated flow and the real flow in many cities shows that the model is very accurate in passenger flow forecasting.

In the complex system of urban rail transit, passenger flow forecast is an essential basis for rail transit network planning, rail station scale construction, and rail transit operation management. Urban rail transit passenger flow characteristics embody not only the periodicity and tendency but also the mutagenicity and particularity. Despite the time series analysis method is efficient and applicable, there are serious challenges associated with producing reliable and high-quality forecasts. When it comes to the mutagenicity and particularity of time series in passenger flow especially, it fails to live up to expectations. To tackle these challenges, a time series prediction model based on automatic machine learning is proposed, which can interpret the trend, periodicity, and holiday effects of subway passenger flow. In this paper, the Prophet model is constructed to verify the robustness and accuracy of the passenger flow time series forecast, drawing on the daily time series of passenger flow in the Beijing metro Guomao Station. Our founding indicates that the MAPE of the prediction reaches 5%. The comparison illustrates that the accuracy of the Prophet model is superior to that of the ARIMA and SARIMA models.

In recent years, cities have been increasingly confronted with frequent and severe extreme weather events, such as heavy rainfall, high temperatures, and strong winds. These extreme weather conditions not only impact urban transportation operations but also directly affect passengers' travel patterns and behaviors. In this context, gaining a deeper understanding of the mechanisms through which weather factors influence urban subway passenger flow becomes crucial. This study aims to investigate the relationship between passenger flow and weather factors, and provide valuable insights for urban rail transit management to enhance proactive decision-making. To achieve this research objective, we first establish a framework for studying the impact of weather on passenger flow. Based on this framework, an analysis is conducted using passenger flow data and weather data from Beijing Metro Line 4. The findings of this study reveal significant effects of different weather factors on subway passenger flow. Increasing severity of weather conditions and higher wind speeds have a negative influence on passenger flow, leading to a decrease in ridership. Conversely, a significant positive correlation is observed between the highest temperature and subway passenger flow, indicating that as the highest temperature rises, the passenger volume also increases.

Urban rail transit is operated with network scenario mostly. Train routing determines spatial service range on the network, which will affect whether passengers transfer during the trip. Timetable determines arrival and departure time of each train at each station. To achieve balance between demand and supply and enhance service quality, simultaneous optimization of train routing and timetable is of significant importance for rail companies with the network. This will reduce the waiting time of passengers effectively, and the stations where train serve will be convenient for most passengers. However, existing studies in train operation planning with time-varying passenger demand haven’t addressed this aspect adequately. To bridge this gap, we develop an integrated optimization model for the Train Routing and Timetable Problem (TRTP) within a rail transit network, considering the dynamic of passenger demand and passenger path choice. Our objective is to minimize the operating costs for companies and to reduce the total waiting time experienced by passengers. The constrains of TRTP include time constraint, path constraint and train constraint. What’s more, the compatibility of headways on different routings is the focus when modeling. To verify the effectiveness of our proposed model, we conduct a numerical experiment with CPLEX. After that, the results of integrated optimization and staged optimization are compared to show the beneficial of integration with train routing and timetable. Moreover, we devise an algorithm tailored to this problem to solve the model, and a network case study is presented. As a case study, we apply the model to Guangzhou Metro Line 3 to validate its performance further, which is a Y-type line, the most common example of operation with network of urban rail transit in China. The results of our study demonstrate that the proposed integrated optimization model can achieve a reduction in operating costs and total waiting time for passengers effectively, providing convenience for passenger travel.