scholarly journals A Continuous Dynamic Traffic Assignment Model From Plate Scanning Technique

2016 ◽  
Author(s):  
LIDIA RUIZ-RIPOLL ◽  
ANA RIVAS ◽  
INMACULADA GALLEGO ◽  
SANTOS SÁNCHEZ-CAMBRONERO ◽  
ROSA M. BARBA
Keyword(s):  
Traffic Flow ◽  
Dynamic Traffic Assignment ◽  
A Priori ◽  
Cost Functions ◽  
Assignment Model ◽  
Scanning Technique ◽  
Dynamic Estimation ◽  
Continuous Dynamic ◽  
Observable Field ◽  
Parameter Values

This paper presents a methodology for the dynamic estimation of traffic flows on all links of a network from observable field data assuming the first-in-first-out (FIFO) hypothesis. The traffic flow intensities recorded at the exit of the scanned links are propagated to obtain the flow waves on unscanned links. For that, the model calculates the flow-cost functions through information registered with the plate scanning technique. The model also responds to the concern about the parameter quality of flow-cost functions to replicate the real traffic flow behaviour. It includes a new algorithm for the adjustment of the parameter values to link characteristics when its quality is questionable. For that, it is necessary the a priori study of the location of the scanning devices to identify all path flows and to measure travel times in all links. A synthetic network is used to illustrate the proposed method and to prove its usefulness and feasibility.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4215

scholarly journals A Continuous Dynamic Traffic Assignment Model from Plate Scanning Technique

2016 ◽  
Vol 18 ◽  
pp. 332-340 ◽  
Author(s):  
Ana Rivas ◽  
Inmaculada Gallego ◽  
Santos Sánchez-Cambronero ◽  
Rosa M. Barba ◽  
Lidia Ruiz-Ripoll
Keyword(s):  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Assignment Model ◽  
Scanning Technique ◽  
Continuous Dynamic

Based on Transcad of the Shortest Path Assignment Method

2011 ◽  
Vol 219-220 ◽  
pp. 1105-1108
Author(s):  
Qing Yin Li ◽  
Rui Tang ◽  
Zhang Lu Tan
Keyword(s):  
Traffic Flow ◽  
Shortest Path ◽  
Road Traffic ◽  
Simulation Analysis ◽  
Dynamic Traffic Assignment ◽  
Urban Traffic ◽  
Assignment Method ◽  
Assignment Model ◽  
Path Assignment ◽  
Effective Path

Based on the Transcad all-or-nothing assignment model is a kind of static method; do not consider the travel time will be affected by traffic flow. The insufficient is that it does not conform to reality. In order to solve the all-or-nothing assignment model that to putting all of traffic flow on the shortest path, the text through the defining of the effective path and the traffic flow of the effective path to improve all-or-nothing assignment model. So the other road traffic flow can also assign and the results can reflect the assignment of urban traffic directly. It can be used to study the dynamic traffic assignment and traffic simulation analysis.

Model and algorithm of stochastic dynamic traffic assignment based on dynamic rainfall intensity

2021 ◽  
pp. 1-11
Author(s):  
Xun Ji ◽  
Chunfu Shao
Keyword(s):  
Cost Function ◽  
Traffic Flow ◽  
Rainfall Intensity ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Network ◽  
Travel Cost ◽  
Stochastic Dynamic ◽  
Dynamic Traffic ◽  
Rainy Weather

Frequent occurrence of urban rainy weather, especially rainstorm weather, affects transportation operation and safety, so it is essential that effective intervention measures to recover disordered traffic be adopted and then analyzed for their influence on the dynamic network. Therefore, models and algorithm to show dynamic traffic flow of traffic network in rainy weather are a fundamental need and have drawn great interest from governments and scholars. In this paper, innovative content contains a travel cost function considering rainfall intensity; considering the travel cost function, a dynamic traffic assignment model based on dynamic rainfall intensity is built. Then a corresponding algorithm is designed. Moreover, this study designs three scenarios under rainfall and analyzes the influence of the rainfall on an example network. The results show that rainfall has a significant effect on traffic flow. The finding proved the proposed models and algorithm can express the development trend of path flow rate on a dynamic network under rainfall.

CAPACITY ASSIGNMENT MODEL TO DEFENSE CASCADING FAILURES

2009 ◽  
Vol 20 (07) ◽  
pp. 991-999 ◽  
Author(s):  
J. J. WU ◽  
H. J. SUN ◽  
Z. Y. GAO
Keyword(s):  
Traffic Flow ◽  
Cascading Failures ◽  
Traffic Networks ◽  
Practical Application ◽  
Assignment Rule ◽  
Scale Free ◽  
Capacity Assignment ◽  
Assignment Model ◽  
Equilibrium Assignment ◽  
Theoretical Significance

How to alleviate the damages of cascading failures triggered by the overload of edges/nodes is common in complex networks. To describe the whole cascading failures process from edges overloading to nodes malfunctioning and the dynamic spanning clustering with the evolvement of traffic flow, we propose a capacity assignment model by introducing an equilibrium assignment rule of flow in artificially created scale-free traffic networks. Additionally, the capacity update rule of node is given in this paper. We show that a single failed edge may undergo the cascading failures of nodes, and a small failure has the potential to trigger a global cascade. It is suggested that enhancing the capacity of node is particularly important for the design of any complex network to defense the cascading failures. Meanwhile, it has very important theoretical significance and practical application worthiness in the development of effective methods to alleviate the damage of one or some failed edges/nodes.

Modeling Traffic Flows with Fluid Flow Model

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Paulus Setiawan Suryadjaja ◽  
◽  
Maclaurin Hutagalung ◽  
Herman Yoseph Sutarto ◽  
◽  
...  
Keyword(s):  
Fluid Flow ◽  
Traffic Flow ◽  
Flow Model ◽  
Intelligent Transportation System ◽  
Macroscopic Model ◽  
Chain Process ◽  
Data Traffic ◽  
Flow Data ◽  
Order Markov Chain ◽  
Parameter Values

This Research presents a macroscopic model of traffic flow as the basis for making Intelligent Transportation System (ITS). The data used for modeling is The number of passing vehicles per three minutes. The traffic flow model created in The form of Fluid Flow Model (FFM). The parameters in The model are obtained by mixture Gaussian distribution approach. The distribution consists of two Gaussian distributions, each representing the mode of traffic flow. In The distribution, intermode shifting process is illustrated by the first-order Markov chain process. The parameters values are estimated using The Expectation-maximization (EM) algorithm. After The required parameter values are obtained, traffic flow is estimated using the Observation and transition-basedmost likely estimates Tracking Particle Filter (OTPF). To Examine the accuracy of the model has been made, the model estimation results are compared with the actual traffic flow data. Traffic flow data is collected on Monday 20 September 2017 at 06.00 to 10.00 on DipatiukurRoad, Bandung. The proposed model has accuracy with MAPE value below 10%, or falls into highly accurate categories

Explaining monotonic ranking functions

2020 ◽  
Vol 14 (4) ◽  
pp. 640-652
Author(s):  
Abraham Gale ◽  
Amélie Marian
Keyword(s):  
Decision Making ◽  
Selection Process ◽  
A Priori ◽  
Data Distribution ◽  
Synthetic Data ◽  
Decision Processes ◽  
Decision Makers ◽  
Ranking Functions ◽  
High School Admissions ◽  
Parameter Values

Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards explainability and transparency in decision processes and results, as well as demands to justify the fairness of the processes. In this paper, we focus on providing metrics towards explainability and transparency of ranking functions, with a focus towards making the ranking process understandable, a priori , so that decision-makers can make informed choices when designing their ranking selection process. We propose transparent participation metrics to clarify the ranking process, by assessing the contribution of each parameter used in the ranking function in the creation of the final ranked outcome, using information about the ranking functions themselves, as well as observations of the underlying distributions of the parameter values involved in the ranking. To evaluate the outcome of the ranking process, we propose diversity and disparity metrics to measure how similar the selected objects are to each other, and to the underlying data distribution. We evaluate the behavior of our metrics on synthetic data, as well as on data and ranking functions on two real-world scenarios: high school admissions and decathlon scoring.

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