dynamic traffic assignment models

Dynamic Traffic Assignment

Early Experiences

Current Practices

Research Needs

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Activity Based Models

Network Assignment

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(opens new window) is a hot topic in travel forecasting.

# Background

Traditional user equilibrium highway assignment models predict the effects of congestion and the routing changes of traffic as a result of that congestion. They neglect, however, many of the details of real-world traffic operations, such as queuing, shock waves, and signalization. Currently, it is common practice to feed the results of user equilibrium traffic assignments into dynamic network models as a mechanism for evaluating these policies. The simulation models themselves, however, do not predict the routing of traffic, and therefore are unable to account for re-routing owing to changes in congestion levels or policy, and can be inconsistent with the routes determined by the assignment. Dynamic network models overcome this dichotomy by combining a time-dependent shortest path algorithm with some type of simulation (often meso or macroscopic) of link travel times and delay. In doing so it allows added reality and consistency in the assignment step, as well as the ability to evaluate policies designed to improve traffic operations. These are some of the main benefits of dynamic network models .

DTA models can generally be classified by how they model link or intersection delay. Analytical DTA models treat it in the same manner as static equilibrium assignment models, with no explicit representation of signals. Link capacity functions, often similar or identical to those used in static assignment, are used to calculate link travel times. Analytical models have been widely used in research and for real-time control system applications. Simulation-based DTA models include explicit representation of traffic control devices. Such models require detailed signal parameters to include phasing, cycle length, and offsets for each signal in the network. Delay is calculated for each approach, with vehicles moving from one link to the next only if available downstream capacity is available. The underlying traffic model is often different, but at the network level such models behave in a similar fashion.

Demand is specified in the form of origin–destination matrices for short time intervals, typically 15 minutes each. Trips are typically randomly loaded onto the network during each time interval. As with traffic microsimulation models, adequate downstream capacity must be present to load the trips onto the network. The shortest paths through time and space are found for each origin–destination pair, and flows loaded to these paths. A generalized flowchart of the process is shown below.

As with static assignment models, the process shown above is iteratively solved until a stable solution is reached. The memory and computing requirements of DTA, however, are orders of magnitude larger than for static assignment, reducing the number of iterations and paths that can be kept in memory. Instead of a single time period, as with static assignment, DTA models must store data for each time interval as well. A three-hour static assignment would involve only one time interval. A DTA model of the same period, however, might require 12 intervals, each 15 minutes in duration. These are all in addition to the memory requirements imposed by the number of user classes and zones.

# Early Experiences

Research into DTA dates back several decades, but was largely limited to academics working on its formulation and theoretical aspects. DTA overcomes the limitations of static assignment models, although at the cost of increased data requirements and computational burden. Moreover, software platforms capable of solving the DTA problem for large urban systems and experience in their use are recent developments.

(opens new window) has been successfully applied to a large subarea of Calgary and to analyses of the Rue Notre-Dame in Montreal. Although user group presentations of both applications have been made, and reported very encouraging results, the work is currently unpublished and inaccessible except through contact with the developers.

(opens new window) . The network from the Atlanta Regional Commission (ARC) regional travel model formed the starting point for the DTA network. Intersections were coded, centroid connectors were re-defined, and network coding errors were corrected. A signal synthesizer derived locally optimal timing parameters for more than 2,200 signalized intersections in the network. Trip matrices from the ARC model were divided into 15-minute intervals for the specification of demand. Approximately 40 runs of the model were required to diagnose coding and software errors. Unfortunately, the execution time for the model was approximately one week per run. The resulting model eventually validated well to observed conditions; however, the length of time required to render it operational and the run time required prevented it from being used in studies as originally intended. Subsequent work by the developer has resulted in substantial reductions in run time, but this remains a significant issue that must be overcome before such models can be more widely used.

# Current Practices

# research needs.

A number of cities are currently testing DTA models, but are not far enough along in their work to share even preliminary results. At least a dozen such cases are known to be in varying stages of planning or execution, suggesting that the use of DTA models in planning applications is about to expand dramatically. However, in addition to the issue of long run times, a number of other issues must be addressed before such models are likely to be widely adopted:

• Criteria for the validation of such models have not been widely accepted. The paucity of traffic counts in most urban areas, and especially at 15, 30, or 60 minute intervals, is a significant barrier to definitive assessment of these models.

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Dynamic traffic assignment: model classifications and recent advances in travel choice principles

Dynamic Traffic Assignment (DTA) has been studied for more than four decades and numerous reviews of this research area have been conducted. This review focuses on the travel choice principle and the classification of DTA models, and is supplementary to the existing reviews. The implications of the travel choice principle for the existence and uniqueness of DTA solutions are discussed, and the interrelation between the travel choice principle and the traffic flow component is explained using the nonlinear complementarity problem, the variational inequality problem, the mathematical programming problem, and the fixed point problem formulations. This paper also points out that all of the reviewed travel choice principles are extended from those used in static traffic assignment. There are also many classifications of DTA models, in which each classification addresses one aspect of DTA modeling. Finally, some future research directions are identified.

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International Journal of Traffic and Transportation Engineering

p-ISSN: 2325-0062    e-ISSN: 2325-0070

2017;  6(1): 1-7

doi:10.5923/j.ijtte.20170601.01

Advances in Dynamic Traffic Assignment Models

Azad Abdulhafedh

University of Missouri-Columbia, USA

Copyright © 2017 Scientific & Academic Publishing. All Rights Reserved.

Dynamic Traffic Assignment (DTA) models have evolved rapidly in the last two decades and a certain degree of maturity has been reached, so as to allow their use in a number of both transportation planning studies, and real-time applications. This paper presents the findings of three recent dynamic traffic assignment models, namely; 1) A dynamic traffic assignment model for highly congested urban networks; 2) System-optimal dynamic traffic assignment with and without queue spillback: Its path-based formulation and solution via approximate path marginal cost; and 3) A dynamic traffic assignment model for the assessment of moving bottlenecks. The first model offers an equilibrium dynamic traffic assignment to address and overcome the challenges of dealing with highly congested urban networks, the second model offers an innovative method to find the path marginal cost for the System-Optimal dynamic traffic assignments through determining the link marginal cost criteria, and the third model presents a model that can evaluate the effects of moving bottlenecks on network performance in terms of travel times and traveling paths based on a mesoscopic simulation network-loading procedure.

Keywords: Traffic Assignment, Congested urban networks, Queue spillback, Moving bottlenecks

Cite this paper: Azad Abdulhafedh, Advances in Dynamic Traffic Assignment Models, International Journal of Traffic and Transportation Engineering , Vol. 6 No. 1, 2017, pp. 1-7. doi: 10.5923/j.ijtte.20170601.01.

Article Outline

1. introduction, 2. literature review, 3. discussion of findings, 4. conclusions.

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Performance analysis of internet of vehicles mesh networks based on actual switch models.

1. Introduction

2. related works, 3. network architecture and system model, 3.1. network model, 3.2. task generation model, 3.3. task forwarding model, 4. network indicator system construction, 4.1. packet loss rate, 4.2. task arrival rate, 4.3. node load rate, 4.4. link load rate, 4.5. total network traffic, 5. simulation results, 5.1. network performance for different qos, 5.2. network performance for different caching capacities, 5.3. network performance for different vehicle densities, 6. conclusions, author contributions, data availability statement, conflicts of interest.

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Hu, J.; Ren, Z.; Cheng, W.; Shuai, Z.; Li, Z. Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models. Electronics 2024 , 13 , 2605. https://doi.org/10.3390/electronics13132605

Hu J, Ren Z, Cheng W, Shuai Z, Li Z. Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models. Electronics . 2024; 13(13):2605. https://doi.org/10.3390/electronics13132605

Hu, Jialin, Zhiyuan Ren, Wenchi Cheng, Zhiliang Shuai, and Zhao Li. 2024. "Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models" Electronics 13, no. 13: 2605. https://doi.org/10.3390/electronics13132605

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Fairness in Resource Allocation: Foundation and Applications

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• First Online: 23 February 2020
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• Hamoud S. Bin-Obaid 5 , 6 &
• Theodore B. Trafalis 5  

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 315))

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This paper presents a comprehensive review of fairness in resource allocation and its foundation. Fairness is applied when the resources divided on multiple demands are limited. Implementing fairness in resource allocation is a challenging task since fairness and efficiency are contradicting objectives. A variety of approaches to find fair resource allocation from the literature are discussed such as max-min fairness, lexicographic ordering, proportional fairness in addition to some fairness measures. Both strength points and drawbacks for each approach are illustrated, and some connections among the approaches are elaborated. Examples of applications where fairness is applied are reviewed.

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Acknowledgements

Dr. Theodore Trafalis was supported by RSF Grant 14-41-00039, and he conducted research at National Research University Higher School of Economics.

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Bin-Obaid, H.S., Trafalis, T.B. (2020). Fairness in Resource Allocation: Foundation and Applications. In: Bychkov, I., Kalyagin, V., Pardalos, P., Prokopyev, O. (eds) Network Algorithms, Data Mining, and Applications. NET 2018. Springer Proceedings in Mathematics & Statistics, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-030-37157-9_1

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