Traffic assignment model for combined mode with travel condition constraints

Combined-mode traffic assignment is one of the key links for multi-modal transportation planning. In order to quantitatively evaluate the implementation effect of combined-mode transportation and explore its assignment mechanism, a combined-mode traffic assignment model with travel constraints is proposed. First, the practical factors of combined mode in real world are discussed and integrated into the analysis of multi-modal network transformation as related constraints. Second, the physical transportation network is translated to equivalent super network (SN) and state-augmented network (SAN) based on the graph theory. Moreover, the network size constrained by the actual conditions is conducive to further analysis of combined-mode trips. Third, a tri-level combined-mode traffic assignment model is formulated based on the simulation platform of SAN. The first level of the model is to address the combined-mode choice, the second level is to transfer choice on SAN, and the third level is to characterize auto travelers’ route choice behavior. By analyzing the impedance in multi-level network, an MSA algorithm solving Nest-Logit model is proposed. Finally, numerical example is performed to validate the model. The method proposed in this paper considers the travel similarity, reduces the network scale, conforms to the travel logic and makes up for the shortcomings of traditional traffic allocation methods.

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The Price of Anarchy of Non-Uniform Altruism Traffic Assignment

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.475 ◽

2011 ◽

Vol 467-469 ◽

pp. 475-480 ◽

Cited By ~ 1

Author(s):

Xiao Jun Yu

Keyword(s):

Variational Inequality ◽

Linear Combination ◽

Choice Behavior ◽

Traffic Assignment ◽

Price Of Anarchy ◽

Transportation Network ◽

Route Choice ◽

Route Choice Behavior ◽

Special Cases

In this paper, assumed the users are partially altruistic，the altruism level of different user class has difference. The user’s perceived cost assumed to be a linear combination of selfish component and altruistic component. A variational inequality (VI) model is investigated to formulate the user’s route choice behavior in non-uniform altruism transportation network with fixed demand. The price of anarchy caused by this kind of behavior is analytically derived and this result takes some known results in the literature as its special cases.

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A Stochastic Traffic Assignment Model for Transportation Network

2011 7th International Conference on Wireless Communications, Networking and Mobile Computing ◽

10.1109/wicom.2011.6040614 ◽

2011 ◽

Cited By ~ 1

Author(s):

Koonlachat Meesublak

Keyword(s):

Traffic Assignment ◽

Transportation Network ◽

Assignment Model

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A simulation-based dynamic traffic assignment model with combined modes

PROMET - Traffic&Transportation ◽

10.7307/ptt.v26i1.1252 ◽

2014 ◽

Vol 26 (1) ◽

pp. 65-73 ◽

Cited By ~ 5

Author(s):

Meng Meng ◽

Chunfu Shao ◽

Jingjing Zeng ◽

Chunjiao Dong

Keyword(s):

Simulation Model ◽

Traffic Assignment ◽

Dynamic Traffic Assignment ◽

Transportation Network ◽

Time Dependent ◽

Dynamic Traffic ◽

Mesoscopic Simulation ◽

Assignment Model ◽

Management Measures ◽

Private Car

This paper presents a dynamic traffic assignment (DTA) model for urban multi-modal transportation network by constructing a mesoscopic simulation model. Several traffic means such as private car, subway, bus and bicycle are considered in the network. The mesoscopic simulator consists of a mesoscopic supply simulator based on MesoTS model and a time-dependent demand simulator. The mode choice is simultaneously considered with the route choice based on the improved C-Logit model. The traffic assignment procedure is implemented by a time-dependent shortest path (TDSP) algorithm in which travellers choose their modes and routes based on a range of choice criteria. The model is particularly suited for appraising a variety of transportation management measures, especially for the application of Intelligent Transport Systems (ITS). Five example cases including OD demand level, bus frequency, parking fee, information supply and car ownership rate are designed to test the proposed simulation model through a medium-scale case study in Beijing Chaoyang District in China. Computational results illustrate excellent performance and the application of the model to analysis of urban multi-modal transportation networks.

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A Practical Traffic Assignment Model for Multimodal Transport System Considering Low-Mobility Groups

Mathematics ◽

10.3390/math8030351 ◽

2020 ◽

Vol 8 (3) ◽

pp. 351 ◽

Cited By ~ 3

Author(s):

Tao Zhang ◽

Yang Yang ◽

Gang Cheng ◽

Minjie Jin

Keyword(s):

Transport System ◽

Traffic Assignment ◽

Transportation Network ◽

Travel Costs ◽

Assignment Model ◽

Proposed Model ◽

Multimodal Transport ◽

Logit Function ◽

Private Car

In this study, we created a practical traffic assignment model for a multimodal transport system considering low-mobility groups with the aim of providing the foundation of transportation network design for low-mobility individuals. First, the route choice equilibrium for walking, non-vehicle, and private car modes is described using the logit function, which is formulated as a variational inequality problem considering different low-mobility groups. Then, the practicalities related to travel times at intersections, traffic barricades between different lanes, and fuel fees of private cars are integrated to design a generalized travel cost function. Last, the method of successive weight averages is used to solve the proposed model. The model and its solution are verified based on a real case study of the city of Wenling in China. The sensitivity of adjustment parameters related to travel costs are analyzed, the practicality of the proposed model is explored, and the results of traffic assignment for different low-mobility groups are discussed.

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A robust algorithm to solve the signal setting problem considering different traffic assignment approaches

International Journal of Applied Mathematics and Computer Science ◽

10.1515/amcs-2017-0057 ◽

2017 ◽

Vol 27 (4) ◽

pp. 815-826 ◽

Cited By ~ 5

Author(s):

Ludovica Adacher ◽

Andrea Gemma

Keyword(s):

Optimization Algorithm ◽

Network Performance ◽

Traffic Assignment ◽

Transportation Network ◽

User Equilibrium ◽

Route Choice Behavior ◽

Road Congestion ◽

Stochastic Discrete Optimization ◽

Flow Propagation ◽

Real Test

AbstractIn this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers’ comfort, fuel consumption efficiency, pollution and safety. In a traffic network, the signal control strategy affects the travel time on the roads and influences drivers’ route choice behavior. The paper presents an algorithm for signal setting optimization of signalized junctions in a congested road network. The objective function used in this work is a weighted sum of delays caused by the signalized intersections. We propose an iterative procedure to solve the problem by alternately updating signal settings based on fixed flows and traffic assignment based on fixed signal settings. To show the robustness of our method, we consider two different assignment methods: one based on user equilibrium assignment, well established in the literature as well as in practice, and the other based on a platoon simulation model with vehicular flow propagation and spill-back. Our optimization algorithm is also compared with others well known in the literature for this problem. The surrogate method (SM), particle swarm optimization (PSO) and the genetic algorithm (GA) are compared for a combined problem of global optimization of signal settings and traffic assignment (GOSSTA). Numerical experiments on a real test network are reported.

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