Evaluation of Operating Schemes on Municipal Rail Transit with Express/Local Mode

The express/local train mode has made municipal rail more competitive in connecting the city center with its suburbs. Therefore, how to design operating schemes is of concern to decision makers. Unlike earlier line planning optimization, this paper aims to provide a quick and comprehensive evaluation of operating performance in all feasible options through reasonable reduction of the scope of the operating schemes, rather than optimization for a single objective. To achieve this, the paper constructs a travel topology network structure based on the separation of trains, which regards express and local trains running on the common line as independent competitors. Focusing on the problems of uneven departure intervals and inconsistent dwell times caused by overtaking, a generalized cost model suitable for the express/local train mode is established. To solve the express/local line problems of multi-routes and high-overlapping, the improved Multinominal Logit (MNL) Model is used to construct a passenger flow assignment model, with the solution achieved by the Method of Successive Averages algorithm. Taking a municipal rail line in Guangzhou as a case study, this paper analyzes the advantages and disadvantages of feasible operation schemes with different characteristics in the express/local train ratio, the location and number of overtaking stations based on passenger flow assignment results and also the impacts of crowdedness on passenger flow dynamics. The results verified the effectiveness of our approach in the comprehensive evaluation of the express/local train mode and shed light on potential practical applications in determining a reasonable operating scheme.

Research on Passenger Flow Assignment of High-Speed Trains Based on Personalized Itinerary Choice

Under the condition that ticket fare for high-speed train could fluctuate around a benchmark price in China, aimed at predicting how the passengers will distribute among different trains given a ticket fare, the passenger flow assignment method for high-speed trains is studied. Different from the classical researches on the passenger flow assignment, by introducing a variable that represents the value of time, this research allows passengers to make their personalized choice between the principles of time minimization and expense minimization, so as to demonstrate how the passengers holding different time values respond to each ticket fare scheme. An equilibrium passenger flow assignment model based on personalized choice is built and an improved Monte-Carlo random simulation algorithm is designed for solving the model. The actual ticket sale data for Beijing-Shanghai high-speed railway are used to verify the feasibility of the proposed model and algorithm. The passenger flow assignment results under various fare schemes show how the distribution of passenger flow changes with the adjustment of ticket fare.

A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules

Precise estimation of passenger spatial-temporal trajectory is the basis for urban rail transit (URT) passenger flow assignment and ticket fare clearing. Inspired by the correlation between passenger tap-in/out time and train schedules, we present a method to estimate URT passenger spatial-temporal trajectory. First, we classify passengers into four types according to the number of their routes and transfers. Subsequently, based on the characteristic that passengers tap-out in batches at each station, the K-means algorithm is used to assign passengers to trains. Then, we acquire passenger access, egress, and transfer time distribution, which are used to give a probability estimation of passenger trajectories. Finally, in a multi-route case of the Beijing Subway, this method presents an estimation result with 91.2% of the passengers choosing the same route in two consecutive days, and the difference of route choice ratio in these two days is 3.8%. Our method has high accuracy and provides a new method for passenger microcosmic behavior research.

Passenger Flow Pushing Assignment Method for an Urban Rail Network Based on Hierarchical Path and Line Decomposition

: For urban rail transit, an environmentally-friendly transportation mode, reasonable passenger flow assignment is the basis of train planning and passenger control, which is conducive to the sustainability of finance, operation and production. With the continuous expansion of the scale of urban rail networks, passenger travel path decision-making tends to be complex, which puts forward higher requirements of networked transportation organization. Based on undirected graphs and the idea of the recursive divide-and-conquer algorithm, this paper proposes a hierarchical effective path search method made up of a three-layer path generation strategy, which consists of deep search line paths, key station paths composed of origin–destination (O-D) nodes and transfer stations, and the station sequence path between the key stations. It can effectively simplify the path search and eliminate obvious unreasonable paths. Comparing the existing research results based on the classical polynomial Logit model, a practical Improved C-Logit multi-path passenger flow assignment model is proposed to calculate the selection ratio of each path in the set of effective paths. Combining the hierarchical path search strategy, the O-D pairs of passenger flow are divided into local-line and cross-line situations. The time-varying cross-line passenger flow is decomposed into a series of passenger sections along the key station paths. A passenger flow pushing assignment algorithm based on line decomposition is designed, which satisfies the dynamic, time-varying and continuous characteristics. The validation of Guangzhou Metro’s actual line network and time-varying O-D passenger demand in 2019 shows that the spatio-temporal distribution results of the passenger pushing assignment have a high degree of coincidence with the actual statistical data.

An Enhanced Dynamic User Optimal Passenger Flow Assignment Model for Metro Networks

By considering the difference between a car driver’s route choice behavior on the road and a passenger’s route choice behavior in urban rail transit (URT), this paper proposes an enhanced Dynamic User Optimal (DUO) passenger flow assignment model for metro networks. To capture realistic URT phenomena, the model has integrated the train operation disturbance constraint. Real passenger and train data are used to verify the proposed model and algorithm. The results indicate that the DUO-based model is more suitable for describing passenger route choice behavior under uncertain conditions compared to a static model. Moreover, this paper found that passengers under oversaturated conditions are more sensitive to train operation disturbances compared to undersaturated passengers.