scholarly journals A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Seungkyu Ryu ◽  
Anthony Chen ◽  
Songyot Kitthamkesorn
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Transportation Network ◽  
User Equilibrium ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Nested Logit ◽  
Two Phase ◽  
Phase Gradient ◽  
Modal Split

This study provides a gradient projection (GP) algorithm to solve the combined modal split and traffic assignment (CMSTA) problem. The nested logit (NL) model is used to consider the mode correlation under the user equilibrium (UE) route choice condition. Specifically, a two-phase GP algorithm is developed to handle the hierarchical structure of the NL model in the CMSTA problem. The Seoul transportation network in Korea is adopted to demonstrate an applicability in a large-scale multimodal transportation network. The results show that the proposed GP solution algorithm outperforms the method of the successive averages (MSA) algorithm and the classical Evan’s algorithm.

scholarly journals Stochastic Electric Vehicle Network with Elastic Demand and Environmental Costs

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Ma ◽  
Hua Wang ◽  
Tianpei Tang
Keyword(s):  
Large Scale ◽  
Transportation Network ◽  
Urban Transportation ◽  
User Equilibrium ◽  
Environmental Costs ◽  
Elastic Demand ◽  
Stochastic User Equilibrium ◽  
Travel Costs ◽  
Large Scale Network ◽  
Scale Network

Along with the increasing number of the electric vehicles (EVs), an urban transportation network with a large number of EVs will come true in the near future. Since many countries encourage EVs due to their environmental-friendly benefits, the environmental costs of vehicles have attracted much attention in recent years. In this paper, besides the environmental costs, we take into account the issues of the stochastic user equilibrium (SUE), the elastic demand (ED), and the driving range of EVs in the network. We propose an SUE with ED (SUEED) problem to consider these issues in the urban transportation network with EVs. An SUEED model is developed. We also propose a method of successive average (MSA) to solve the SUEED problem. The computational feasibility of the algorithm is tested in a large-scale network. Through a comparison analysis, we show the benefits of introducing EVs into the urban transportation network in the SUEED circumstance. Moreover, a sensitivity analysis is conducted to reveal the potential values of EVs against the development of EVs. The results suggest that EVs may help to reduce both the travelers’ travel costs and the environmental costs of the entire network.

scholarly journals A Path-Based Gradient Projection Algorithm for the Cost-Based System Optimum Problem in Networks with Continuously Distributed Value of Time

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Wen-Xiang Wu ◽  
Hai-Jun Huang
Keyword(s):  
User Equilibrium ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Value Of Time ◽  
Large Network ◽  
System Optimum ◽  
Gradient Projection Algorithm ◽  
Optimum Model ◽  
Viable Approach ◽  
The Cost

The cost-based system optimum problem in networks with continuously distributed value of time is formulated as a path-based form, which cannot be solved by the Frank-Wolfe algorithm. In light of magnitude improvement in the availability of computer memory in recent years, path-based algorithms have been regarded as a viable approach for traffic assignment problems with reasonably large network sizes. We develop a path-based gradient projection algorithm for solving the cost-based system optimum model, based on Goldstein-Levitin-Polyak method which has been successfully applied to solve standard user equilibrium and system optimum problems. The Sioux Falls network tested is used to verify the effectiveness of the algorithm.

scholarly journals Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

2021 ◽  
pp. 036119812110315
Author(s):  
Kyoungho Ahn ◽  
Youssef Bichiou ◽  
Mohamed Farag ◽  
Hesham A. Rakha
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Electric Vehicles ◽  
Large Scale ◽  
Traffic Assignment ◽  
Combustion Engine ◽  
Model Development ◽  
User Equilibrium ◽  
Multi Objective ◽  
Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

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