Study on Real-Time Traffic Data Modification Method Based on the Improved Etkin Interpolation Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1072-1076
Author(s):  
Qiang Gao ◽  
Yuan Li Gu ◽  
Teng Hua Zhang
Keyword(s):  
Real Time ◽  
Successive Approximation ◽  
Original Data ◽  
Successive Approximation Method ◽  
Engineering Practice ◽  
Interpolation Algorithm ◽  
Traffic Data ◽  
Time Data ◽  
Real Time Traffic ◽  
Point Data

Identification and modification of real-time traffic data has been the basic and critical part in the intelligent transportation system.Through the research to a large number of data,the original data is divided into the correct data,the irregular time-point data,inaccurate detection data,missing data and event data. Etkin interpolation algorithm is to gain the values of specified missing value by a successive approximation method with high order polynomial and implemented by using a successive approximation of multiple linear combinations.The paper selects improved Etkin interpolation algorithm to correct the traffic data and makes use of the DongZhiMen Bridge North 728 meters' 2001 detector data for example.The algorithm not only considers the practicability in the engineering practice,but also improves the accuracy of real-time data.

Crash risk analysis during fog conditions using real-time traffic data

2018 ◽  
Vol 114 ◽  
pp. 4-11 ◽  
Author(s):  
Yina Wu ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee
Keyword(s):  
Risk Analysis ◽  
Real Time ◽  
Crash Risk ◽  
Traffic Data ◽  
Real Time Traffic

Real-Time Traffic Measurement from Single Loop Inductive Signatures

2002 ◽  
Vol 1804 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Seri Oh ◽  
Stephen G. Ritchie ◽  
Cheol Oh
Keyword(s):  
Real Time ◽  
The United States ◽  
Traffic Data ◽  
Traffic Surveillance ◽  
Vehicle Class ◽  
Traffic Measurement ◽  
Single Loop ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Traffic Data Collection

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

Evaluating Factors Influencing the Severity of Three-Plus Multiple-Vehicle Crashes using Real-Time Traffic Data

2018 ◽  
Vol 2672 (38) ◽  
pp. 128-137 ◽  
Author(s):  
Angela E. Kitali ◽  
Emmanuel Kidando ◽  
Paige Martz ◽  
Priyanka Alluri ◽  
Thobias Sando ◽  
...  
Keyword(s):  
Real Time ◽  
Random Effect ◽  
Random Effect Model ◽  
Probit Model ◽  
Severe Injury ◽  
Traffic Data ◽  
Vehicle Crashes ◽  
Lighting Condition ◽  
Crash Data ◽  
Real Time Traffic

Multiple-vehicle crashes involving at least two vehicles constitute over 70% of fatal and injury crashes in the U.S. Moreover, multiple-vehicle crashes involving three or more vehicles (3+) are usually more severe compared with the crashes involving only two vehicles. This study focuses on developing 3+ multiple-vehicle crash severity models for a freeway section using real-time traffic data and crash data for the years 2014–2016. The study corridor is a 111-mile section on I-4 in Orlando, Florida. Crash injury severity was classified as a binary outcome (fatal/severe injury and minor/no injury crashes). For the purpose of identifying the reliable relationship between the 3+ severe multiple-vehicle crashes and the identified explanatory variables, a binary probit model with Dirichlet random effect parameter was used. More specifically, Dirichlet random effect model was introduced to account for unobserved heterogeneity in the crash data. The probit model was implemented using a Bayesian framework and the ratios of the Monte Carlo errors were monitored to achieve parameter estimation convergence. The following variables were found significant at the 95% Bayesian credible interval: logarithm of average vehicle speed, logarithm of average equivalent 10-minute hourly volume, alcohol involvement, lighting condition, and number of vehicles involved (3, or >3) in multiple-vehicle crashes. Further analysis involved analyzing the posterior probability distributions of these significant variables. The study findings can be used to associate certain traffic conditions with severe injury crashes involving 3+ multiple vehicles, and can help develop effective crash injury reduction strategies based on real-time traffic data.

Research on the Traffic Simulation Platform Based on the Real-Time Mobile Phone Data

2012 ◽  
Vol 253-255 ◽  
pp. 1365-1368
Author(s):  
Ge Qi Qi ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Traffic Simulation ◽  
Rapid Development ◽  
Mobile Phone Data ◽  
Simulation Platform ◽  
Traffic Data ◽  
The Real ◽  
Real Time Traffic ◽  
Wide Range

With the rapid development of the society, the transportation system has become more complicated and vulnerable. For simulating the real-time traffic condition of the whole city, a wide range of OD matrix data are needed which are hard to collect in whole based on the present conventional methods. The paper raises a feasible design of the traffic simulation platform based on the real-time mobile phone data. The popularity and development of mobile phones make the vast amounts of real-time traffic data can be collected and usable. With the help of the GIS module, dynamic OD traffic generation module and other related modules, the real-time mobile phone data will be converted to the valuable traffic data and applied to the traffic simulation platform.

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