scholarly journals Applying Ollivier-Ricci curvature to indicate the mismatch of travel demand and supply in urban transit network

2022 ◽  
Vol 106 ◽  
pp. 102666
Author(s):  
Yaoli Wang ◽  
Zhou Huang ◽  
Ganmin Yin ◽  
Haifeng Li ◽  
Liu Yang ◽  
...  
Keyword(s):  
Ricci Curvature ◽  
Travel Demand ◽  
Urban Transit ◽  
Demand And Supply ◽  
Transit Network

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

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.

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 How Does Travel Demand Follow the Change in Infrastructure? Multiple-Year Eigenvector Centrality Analysis

2021 ◽  
Vol 13 (23) ◽  
pp. 13366
Author(s):  
Hiroe Ando ◽  
Fumitaka Kurauchi
Keyword(s):  
Road Network ◽  
Travel Demand ◽  
Time Lag ◽  
Physical Structure ◽  
Eigenvector Centrality ◽  
Demand And Supply ◽  
The Road ◽  
Centrality Analysis ◽  
The Impact

The road network is one of the most permanent elements of the physical structure of cities, and the long-term impacts should be considered for effective and efficient road network improvement. It is therefore important to catch up on how the road will be used after construction. However, we do not have much knowledge on the pattern and time lag in the change process of travel demand and supply in the real situation. To explore such changes, this study proposes to evaluate a network with eigenvector centrality (EC) measures that can evaluate the importance of nodes in a network. We believe the analysis based on topological properties by the graph theory is suitable to verify the evolution of road networks. This study analysed long-term changes over 20 years in an actual city to understand the impact of road network improvements. The EC analysis with the weights of traffic indices obtained from survey data evaluates the connectivity of road services on the supply side, and traffic concentration on the demand side.

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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