scholarly journals A Combined Modal Split and Traffic Assignment Model With Capacity Constraints for Siting Remote Park-and-Ride Facilities

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 80502-80517 ◽  
Author(s):  
Bowen Hou ◽  
Shuzhi Zhao ◽  
Huasheng Liu
Keyword(s):  
Traffic Assignment ◽  
Capacity Constraints ◽  
Modal Split ◽  
Park And Ride ◽  
Assignment Model

Combined multinomial logit modal split and paired combinatorial logit traffic assignment model

2018 ◽  
Vol 14 (9) ◽  
pp. 737-760 ◽  
Author(s):  
Jian Wang ◽  
Srinivas Peeta ◽  
Xiaozheng He ◽  
Jinbao Zhao
Keyword(s):  
Traffic Assignment ◽  
Multinomial Logit ◽  
Modal Split ◽  
Assignment Model

On the probabilistic origin of dial's multipath traffic assignment model

1976 ◽  
Vol 10 (5) ◽  
pp. 339-341 ◽  
Author(s):  
Michael Florian ◽  
Bennett Fox
Keyword(s):  
Traffic Assignment ◽  
Assignment Model

Calibrating Combined Model of Trip Distribution, Modal Split, and Traffic Assignment

1997 ◽  
Vol 1607 (1) ◽  
pp. 1-5 ◽  
Author(s):  
David E. Boyce ◽  
Yu-Fang Zhang
Keyword(s):  
Traffic Assignment ◽  
Calibration Method ◽  
Operational Method ◽  
Combined Model ◽  
Trip Distribution ◽  
Modal Split ◽  
Travel Forecasting

A method for calibrating a combined model of travel choices for the Chicago, Illinois, region is described and illustrated. This model and its simultaneous calibration method seek to ensure that travel choices are represented consistently, thereby offering an operational method for implementing and solving the four-step travel forecasting procedure with feedback.

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 Quantifying road traffic emissions embedded in a multi-objective traffic assignment model

2020 ◽  
Vol 47 ◽  
pp. 648-655 ◽  
Author(s):  
Eloísa Macedo ◽  
Ricardo Tomás ◽  
Paulo Fernandes ◽  
Margarida C. Coelho ◽  
Jorge M. Bandeira
Keyword(s):  
Traffic Assignment ◽  
Road Traffic ◽  
Traffic Emissions ◽  
Multi Objective ◽  
Road Traffic Emissions ◽  
Assignment Model

Integrated simulation-based dynamic traffic and transit assignment model for large-scale network

2020 ◽  
Vol 47 (8) ◽  
pp. 898-907 ◽  
Author(s):  
Islam Kamel ◽  
Amer Shalaby ◽  
Baher Abdulhai
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic ◽  
Transit Assignment ◽  
Large Scale Network ◽  
Assignment Model ◽  
Morning Peak ◽  
Simulation Based ◽  
Scale Network ◽  
Assignment Models

Although the traffic and transit assignment processes are intertwined, the interactions between them are usually ignored in practice, especially for large-scale networks. In this paper, we build a simulation-based traffic and transit assignment model that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area during the morning peak. This traffic assignment model is dynamic, user-equilibrium seeking, and includes surface transit routes. It utilizes the congested travel times, determined by the dynamic traffic assignment, rather than using predefined timetables. Unlike the static transit assignment models, the proposed transit model distinguishes between different intervals within the morning peak by using the accurate demand, transit schedule, and time-based road level-of-service. The traffic and transit assignment models are calibrated against actual field observations. The resulting dynamic model is suitable for testing different demand management strategies that impose dynamic changes on multiple modes simultaneously.

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