Estimation of Truck Traffic Volume from Single Loop Detectors with Lane-to-Lane Speed Correlation

An algorithm for real-time estimation of truck traffic in multilane freeways was proposed. The algorithm used data from single loop detectors—the most widely installed surveillance technology for urban freeways in the United States. The algorithm worked for those freeway locations that have a truck-free lane and exhibit high lane-to-lane speed correlation. These conditions are met by most urban freeway locations. The algorithm produced real-time estimates of the truck traffic volumes at the location. It also can be used to produce alternative estimates of the mean effective vehicle length, which can improve speed estimates from single loop detector data. The algorithm was tested with real freeway data and produced estimates of truck traffic volumes with only 5.7% error. It also captured the daily patterns of truck traffic and mean effective vehicle length. Applied to loop data on Interstate 710 near Long Beach, California, during the dockworkers’ lockout October 1 to 9, 2002, the algorithm found a 32% reduction in five-axle truck volume.

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Real-Time Traffic Measurement from Single Loop Inductive Signatures

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1804-14 ◽

2002 ◽

Vol 1804 (1) ◽

pp. 98-106 ◽

Cited By ~ 33

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.

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The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors

ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585) ◽

10.1109/itsc.2001.948715 ◽

2002 ◽

Cited By ~ 6

Author(s):

Zhanfeng Jia ◽

Chao Chen ◽

B. Coifman ◽

P. Varaiya

Keyword(s):

Real Time ◽

Single Loop ◽

Loop Detectors ◽

Time Estimates ◽

G Factors

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Monitoring a freeway network in real time using single-loop detectors

International Journal of Vehicle Information and Communication Systems ◽

10.1504/ijvics.2005.007591 ◽

2005 ◽

Vol 1 (1/2) ◽

pp. 119 ◽

Cited By ~ 1

Author(s):

Xiaoping Zhang ◽

Yinhai Wang ◽

Nancy L. Nihan ◽

Hu Dong

Keyword(s):

Real Time ◽

Single Loop ◽

Loop Detectors

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Highway travel time information system based on cumulative count curves and new tracking technologies

Libro de Actas CIT2016. XII Congreso de Ingeniería del Transporte ◽

10.4995/cit2016.2016.3209 ◽

2016 ◽

Author(s):

Margarita Martínez-Díaz ◽

Francesc Soriguera Martí ◽

Ignacio Pérez Pérez

Keyword(s):

Travel Time ◽

Real Time ◽

New Technologies ◽

Time Estimation ◽

Travel Times ◽

Important Indicator ◽

Loop Detectors ◽

Real Time Estimation ◽

Cumulative Count ◽

Time Information

Travel time is probably the most important indicator of the level of service of a highway, and it is also the most appreciated information for its users. Administrations and private companies make increasing efforts to improve its real time estimation. The appearance of new technologies makes the precise measurement of travel times easier than never before. However, direct measurements of travel time are, by nature, outdated in real time, and lack of the desired forecasting capabilities. This paper introduces a new methodology to improve the real time estimation of travel times by using the equipment usually present in most highways, i.e., loop detectors, in combination with Automatic Vehicle Identification or Tracking Technologies. One of the most important features of the method is the usage of cumulative counts at detectors as an input, avoiding the drawbacks of common spot-speed methodologies. Cumulative count curves have great potential for freeway travel time information systems, as they provide spatial measurements and thus allow the calculation of instantaneous travel times. In addition, they exhibit predictive capabilities. Nevertheless, they have not been used extensively mainly because of the error introduced by the accumulation of the detector drift. The proposed methodology solves this problem by correcting the deviations using direct travel time measurements. The method results highly beneficial for its accuracy as well as for its low implementation cost.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3209

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Bayesian nowcasting with adjustment for delayed and incomplete reporting to estimate COVID-19 infections in the United States

10.1101/2020.06.17.20133983 ◽

2020 ◽

Cited By ~ 1

Author(s):

Melanie H. Chitwood ◽

Marcus Russi ◽

Kenneth Gunasekera ◽

Joshua Havumaki ◽

Virginia E. Pitzer ◽

...

Keyword(s):

Real Time ◽

Reproduction Number ◽

District Of Columbia ◽

The United States ◽

Considerable Variability ◽

Us States ◽

Secular Changes ◽

Policy Responses ◽

Time Estimates ◽

Reporting Delays

AbstractReal-time estimates of the true size and trajectory of local COVID-19 epidemics are key metrics to guide policy responses. We developed a Bayesian nowcasting approach that explicitly accounts for reporting delays and secular changes in case ascertainment to generate real-time estimates of COVID-19 epidemiology on the basis of reported cases and deaths. Using this approach, we estimate time trends in infections, symptomatic cases, and deaths for all 50 US states and the District of Columbia from early-March through June 11, 2020. At the beginning of June, our best estimates of the effective reproduction number (Rt) are close to 1 in most states, indicating a stabilization of incidence, but there is considerable variability in the level of incidence and the estimated proportion of the population that has already been infected.One Sentence SummaryA new method to track epidemiologic measures of COVID-19, available in the covidestim package for R.

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Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data

Journal of Advanced Transportation ◽

10.1155/2020/8857502 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Syed Muzammil Abbas Rizvi ◽

Afzal Ahmed ◽

Yongjun Shen

Keyword(s):

Real Time ◽

Time Estimation ◽

Location Estimation ◽

Transmission Model ◽

Incident Detection ◽

Cell Transmission Model ◽

The Real ◽

Loop Detectors ◽

Road Capacity ◽

Study Results

Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.

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Research on Installation Site of Loop Detectors for Link Travel Time Estimation

Challenges and Advances in Sustainable Transportation Systems ◽

10.1061/9780784413364.018 ◽

2014 ◽

Cited By ~ 1

Author(s):

Mengying Cui ◽

Kai Liu ◽

Shengchuan Zhao

Keyword(s):

Travel Time ◽

Time Estimation ◽

Travel Time Estimation ◽

Loop Detectors ◽

Link Travel Time

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A machine for real-time estimation of the tribological characteristics of materials

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-4-61-65 ◽

2020 ◽

Vol 86 (4) ◽

pp. 61-65

Author(s):

M. V. Abramchuk ◽

R. V. Pechenko ◽

K. A. Nuzhdin ◽

V. M. Musalimov

Keyword(s):

Real Time ◽

Time Estimation ◽

Tribological Characteristics ◽

Continuous Processing ◽

Friction Machine ◽

Dynamic Friction Coefficient ◽

Displacement Sensors ◽

Frictional Interaction ◽

Real Time Estimation ◽

Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

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