scholarly journals MODELO CUASI-DINÁMICO EN AIMSUN. COMPARATIVA CONTRA MODELOS ESTÁTICOS Y DINÁMICOS

2016 ◽  
Author(s):  
Jordi Casas
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Time Estimation ◽  
Real Data ◽  
Travel Time Estimation ◽  
Traffic Demand ◽  
Mesoscopic Models ◽  
Demand Models ◽  
Main Criticism

Traditionally traffic demand models require as input the impedance of a demand with respect to the network supply; mode choice or departure choice for example, take into account the travel time for each option. Bearing this in mind, the main criticism of using static models to evaluate travel times is that the estimated travel time could diverge considerably because these models have no capacity constraints. On the other hand, dynamic models, such as mesoscopic models, have a level of detail that is sometimes unnecessarily high for the final requirements. The Quasi-dynamic model developed in Aimsun could contribute to a more realistic estimate of the travel time while avoiding the need for a full dynamic model. This paper presents the integration of a Quasi-dynamic model inside the integrated framework of Aimsun and evaluates a comparison of all models in terms of travel time estimation. The evaluation is performed using real networks validated with real data sets.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4127

A Methodology to Assess the Quality of Travel Time Estimation and Incident Detection Based on Connected Vehicle Data

2018 ◽  
Vol 2672 (42) ◽  
pp. 203-212 ◽  
Author(s):  
Md Shahadat Iqbal ◽  
Samaneh Khazraeian ◽  
Mohammed Hadi
Keyword(s):  
Data Acquisition ◽  
Travel Time ◽  
Regression Models ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Incident Detection ◽  
Connected Vehicle ◽  
Traffic Demand ◽  
Vehicle Data ◽  
Existing Data

Connected vehicle (CV) technologies are expected to have a significant influence on the investment decisions of transportation system management and operations (TSMO) in the near future. One of the potential applications is the use of CV data to support various TSMO processes. This study investigates the use of CV data as an alternative to existing data acquisition techniques in providing two critical functions to support TSMO: travel time estimation and incident detection. In support of this investigation, the study develops regression models to estimate the accuracy and reliability of travel time measurement and latency of incident detection as functions of the traffic demand level and the proportion of CV in the traffic stream. The developed regression models are used in conjunction with a prediction of CV proportions in future years to determine when the CV technology can provide sufficient data quality to replace existing data acquisition systems. The results can be used by TSMO programs and agencies to plan their investment in data acquisition alternatives in future years.

scholarly journals Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu
Keyword(s):  
Travel Time ◽  
Extreme Learning Machine ◽  
Road Networks ◽  
Time Estimation ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Travel Time Estimation ◽  
Trace Data ◽  
Learning Machine ◽  
Travel Time Forecasting

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

Real-time Travel Time Estimation with Sparse Reliable Surveillance Information

2020 ◽  
Vol 4 (1) ◽  
pp. 1-23
Author(s):  
Wen Zhang ◽  
Yang Wang ◽  
Xike Xie ◽  
Chuancai Ge ◽  
Hengchang Liu
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Time Travel ◽  
Travel Time Estimation
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