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.

[1]  E. Cascetta,et al.  STOCHASTIC USER EQUILIBRIUM ASSIGNMENT WITH EXPLICIT PATH ENUMERATION: COMPARISON OF MODELS AND ALGORITHMS , 1997 .

[2]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[3]  Andrea Nicolini,et al.  Towards Zero Energy Stadiums: The Case Study of the Dacia Arena in Udine, Italy , 2018, Energies.

[4]  Dingyou Lei,et al.  The model and algorithm of distributing cooperation profits among operators of urban rail transit under PPP pattern , 2017, Cluster Computing.

[5]  Anthony Chen,et al.  C-logit stochastic user equilibrium model: formulations and solution algorithm , 2012 .

[6]  Feng Zhou,et al.  Estimation Method of Path-Selecting Proportion for Urban Rail Transit Based on AFC Data , 2015 .

[7]  Zhi-Hua Hu,et al.  Urban Transit Network Properties Evaluation and Optimization Based on Complex Network Theory , 2019, Sustainability.

[8]  Marc Barthelemy,et al.  Optimal geometry of transportation networks. , 2019, Physical review. E.

[9]  Rafael Rodríguez-Puente,et al.  Algorithm for shortest path search in Geographic Information Systems by using reduced graphs , 2013, SpringerPlus.

[10]  Carlo Maria Medaglia,et al.  The dynamic role of Italian energy strategies in the worldwide scenario , 2019, Kybernetes.

[11]  Shing Chung Josh Wong,et al.  A stochastic transit assignment model using a dynamic schedule-based network , 1999 .

[12]  Timothy M. Chan All-Pairs Shortest Paths with Real Weights in O(n3/log n) Time , 2008, Algorithmica.

[13]  E. Cascetta,et al.  A DAY-TO-DAY AND WITHIN-DAY DYNAMIC STOCHASTIC ASSIGNMENT MODEL , 1991 .

[14]  Andrea Papola,et al.  Random utility models with implicit availability/perception of choice alternatives for the simulation of travel demand , 2001 .

[15]  Arman Sajedinejad,et al.  Urban rail transit planning using a two-stage simulation-based optimization approach , 2014, Simul. Model. Pract. Theory.

[16]  D. Sanchez,et al.  Evaluating relative benefits of different types of R&D for clean energy technologies , 2017 .