Table 2 - uploaded by Naim Bitar
Content may be subject to copyright.
Source publication
Urban traffic congestion is common and the cause for loss of productivity (due to trip delays) and higher risk to passenger safety (due to increased time in the automobile), not to mention an increase in fuel consumption, pollution, and vehicle wear. The fiduciary effect is a tremendous burden for citizens and states alike. One way to alleviate the...
Contexts in source publication
Context 1
... separate NPMRDS data file reports average travel times for roadways geo- referenced to each of the TMC location codes. Table 2 details a description of associated fields. Given the continuous, large scale, and probe-based nature of traffic data, the number of observations reported in variable traffic conditions can fluctuate significantly. ...
Context 4
... addition to performing linear classification, SVMs can efficiently perform non-linear classification using a non-linear kernel. A linear and radial SVM kernel were applied, and results are presented in Figure 108, Table 24, Table 25, and In the end a simple decision tree classifier was chosen based on Occam's razor. ...
Similar publications
Early detection of incidents is one of the key step to reduce incident-related congestion. With the increasing usage of GPS based navigation, promising data-scalable crowdsourced probe data is now available which can provide near-real time traffic speed information. This study utilizes such extensive historical datasets (approximately 500 GB) to ga...
Several efforts have been made during recent years on the control of traffic flow using ramp metering, variable speed limit and lane change control. Most of these techniques reported significant traffic mobility improvements in macroscopic simulations, which cannot be confirmed in some microscopic simulation cases. We demonstrate that the lack of t...
Citations
Este artigo apresenta os resultados de uma pesquisa-ação que teve como finalidade o desenvolvimento de um processo de consolidação de dados para pesquisas de tráfego no Departamento Nacional de Infraestrutura de Transportes (DNIT). A importância das pesquisas de tráfego para o planejamento do sistema viário nacional obrigou o DNIT a desenvolver um projeto que pudesse resgatar as informações de diversas pesquisas antigas de tráfego espalhadas por planilhas eletrônicas e outros tipos de documentos. O artigo apresenta a aplicação do processo de consolidação que resultou na criação de um banco de dados relacional possibilitando o resgate de dados de pesquisas antigas, distribuídas em planilhas eletrônicas que poderiam se perder ou se tornar inacessíveis com o passar dos anos.
The National Performance Management Research Data Set (NPMRDS), made available by Federal Highway Administration in 2013, provides fine-resolution travel-time data, which have been used in numerous network performance management and operations applications. This article discusses corridor-level performance measures computed using the NPMRDS. Three measures are analyzed on a 20.2-mile long corridor in San Antonio, Texas, including corridor travel time, corridor travel-time reliability, and day-to-day variation in travel time. The primary contributions of this article are the analysis of the impact of using two different approaches for travel-time aggregation across segments—instantaneous and time-dependent approaches—and defining a mean absolute error-based method to identify days when travel times significantly deviate from typical traffic conditions. The findings suggest that the temporal patterns of corridor travel times obtained using instantaneous and time-dependent aggregation approaches are similar; however, instantaneous travel-time estimates lead to inaccuracies that become more apparent during peak hours and for longer segments. In addition, it is found that a k-means clustering analysis performed on daily travel-time profiles provides a useful statistic for corridor performance analysis. Using this methodology, 9.23% of weekdays in the 20-month study period are classified as atypical for the corridor. The numerical results reinforce the value of the NPMRDS in estimating corridor performance measures and highlight potential limitations of traditional techniques for evaluating corridor performance measures when applied in practice to support enhanced traffic planning and operations.
