Comparative analyses of through-running vs. dead-end tunnel to urban transit network efficiency and design of through-running tunnel in the New York Pennsylvania Station

2020 ◽  
pp. 5202-5212
Author(s):  
J.C. Venturi ◽  
E. Chao ◽  
C. Nicolescu
Keyword(s):  
New York ◽  
Network Efficiency ◽  
Urban Transit ◽  
Transit Network ◽  
Comparative Analyses ◽  
Dead End

URBAN TRANSIT SYSTEM AS A SCALE-FREE NETWORK

2004 ◽  
Vol 18 (19n20) ◽  
pp. 1043-1049 ◽  
Author(s):  
JIANJUN WU ◽  
ZIYOU GAO ◽  
HUIJUN SUN ◽  
HAIJUN HUANG
Keyword(s):  
Traffic Congestion ◽  
Traffic Accidents ◽  
Scale Free Network ◽  
Urban Transit ◽  
Transit System ◽  
Transit Network ◽  
Scale Free ◽  
Free Network ◽  
System Data ◽  
Random Failures

Many systems can be represented by networks as a set of nodes joined together by links indicating interaction. Recently studies have suggested that a lot of real networks are scale-free, such as the WWW, social networks, etc. In this paper, discoveries of scale-free characteristics are reported on the network constructed from the real urban transit system data in Beijing. It is shown that the connectivity distribution of the transit network decays as a power-law, and the exponent λ is about equal to 2.24 from the simulation graph. Based on the scale-free network topology structure of the transit network, if only transit "hub nodes" are controlled well, the transit network can resist random failures (such as traffic congestion, traffic accidents, etc.) successfully.

Survivability of public transit network based on network structure entropy

2015 ◽  
Vol 26 (09) ◽  
pp. 1550104 ◽  
Author(s):  
Bai-Bai Fu ◽  
Lin Zhang ◽  
Shu-Bin Li ◽  
Yun-Xuan Li
Keyword(s):  
Network Model ◽  
Network Structure ◽  
Public Transit ◽  
Small World ◽  
Clustering Coefficient ◽  
Statistical Characteristics ◽  
Network Efficiency ◽  
Transit Network ◽  
Scale Free ◽  
The Public

In this work, we have collected 195 bus routes and 1433 bus stations of Jinan city as sample date to build up the public transit geospatial network model by applying space L method, until May 2014. Then, by analyzing the topological properties of public transit geospatial network model, which include degree and degree distribution, average shortest path length, clustering coefficient and betweenness, we get the conclusion that public transit network is a typical complex network with scale-free and small-world characteristics. Furthermore, in order to analyze the survivability of public transit network, we define new network structure entropy based on betweenness importance, and prove its correctness by giving that the new network structure entropy has the same statistical characteristics with network efficiency. Finally, the "inflexion zone" is discovered, which can be taken as the momentous indicator to determine the public transit network failure.

The Memetic algorithm for the optimization of urban transit network

2015 ◽  
Vol 42 (7) ◽  
pp. 3760-3773 ◽  
Author(s):  
Hang Zhao ◽  
Wangtu (Ato) Xu ◽  
Rong Jiang
Keyword(s):  
Memetic Algorithm ◽  
Urban Transit ◽  
Transit Network

scholarly journals A Differential Evolution for Optimization of Multiobjective Urban Transit Routing Problem

2018 ◽  
Vol 7 (3.20) ◽  
pp. 140
Author(s):  
Ahmed Tarajo BUBA ◽  
Lai Soon LEE
Keyword(s):  
Differential Evolution ◽  
Road Network ◽  
Service Level ◽  
Repair Mechanism ◽  
Urban Transit ◽  
Routing Problem ◽  
Transit Network ◽  
The Road ◽  
Pareto Fronts ◽  
Evolution Approach

In this paper, the urban transit routing problem is addressed by using a real-world urban transit network. Given the road network infrastructure and the demand, the problem consists in designing routes such that the service level as well as the operator cost are optimized. The optimality of the service level is measured in terms of average journey time and the route set length. A differential evolution approach is proposed to solve the problem. An improved sub-route reversal repair mechanism is introduced to deal with the infeasibility of route sets. Computational results on a real network produce solutions that are close to the lower bound values of the passenger and the operator costs. In addition, the proposed algorithm produces approximate Pareto fronts that enable the transit operator to evaluate the trade-off between the passenger and passenger costs. 

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