Freeway Corridor Performance Measurement Based on Vehicle Reidentification

Jeng, Shin-Ting; Tok, Andre; Ritchie, Stephen G.

doi:10.1109/tits.2010.2049105

Cited by 24 publications

(12 citation statements)

References 8 publications

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“…The signature depends on the composition of the vehicle and by matching signatures from two detectors travel times can be estimated. Research has shown that, depending on the driving environment and type of detector, matching rates between 60 % and 100 % can be achieved, see Blokpoel (2009), Ndoye et al (2011) andJeng et al (2010). Since acceleration and retardation affects the signature the matching process is more difficult for urban areas with a lot of stop and start movement.…”

Section: Traffic Signal Detectorsmentioning

confidence: 99%

Highway Traffic State Estimation and Short-term Prediction

Allström¹

2016

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Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors.The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with a n E ns e m bl e K a l ma n F i l t e r ( E nK F ) i t c a n be u s e d t o f u s e d i f f e r e n t t y pe s of poi n t s p e e d measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show that once the model is calibrated, the estimated travel times corresponds well with the ground truth travel times collected from Bluetooth sensors.In order to produce accurate short-term predictions for various networks and conditions it is vital to combine different methods. We have implemented and evaluated a hybrid prediction approach that assimilates parametric and non-parametric short-term traffic state prediction. To predict mainline sensor data we use a neural network, while the CTM-v is ran forward in time in order to predict future traffic states. The results show that both the hybrid approach and the CTM-v prediction without the additional predicted mainline sensor data is superior to a naïve prediction method for longer prediction horizons.

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Section: Traffic Signal Detectorsmentioning

confidence: 99%

Highway Traffic State Estimation and Short-term Prediction

Allström¹

2016

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“…For example, when inductive vehicle signatures are matched across adjacent ILD stations, measures such as section-based speeds, travel times, and densities can be obtained [9], [13], [19]- [22]. These measures can be further analyzed by vehicle type or by lane to yield even more detailed travel information if required.…”

Section: A Inductive Loop Detector (Ild) Signature Technologymentioning

confidence: 99%

“…For end users, they can simply manage the Traffic Monitor HD system and analyze data through a user friendly web interface. C. Core Algorithms of Traffic Monitor HD 1) Vehicle Reidentification: RTREID-2M [5] The Traffic Monitor HD employs a real-time inductive loop signature based vehicle reidentification algorithm, RTREID-2M, that improved the previously developed RTREID-2 [19] algorithm from University of California, Irvine, which pioneered researches on anonymous vehicle tracking using inductive vehicle signatures. RTREID-2 was designed to reidentify vehicles in real-time, and it provides a practical solution because of the use of less computational resources for signature data preprocessing and less communication bandwidth for data transmission.…”

Section: B Traffic Monitor Hd Architecturementioning

confidence: 99%

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A high-definition traffic performance monitoring system with the Inductive Loop Detector signature technology

Jeng¹,

Chu²

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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With continuing emphasis on transportation sustainability and fiscal stewardship, utilizing existing loop detector infrastructure to obtain more accurate, reliable, and comprehensive traffic system performance measures is desired by many transportation agencies. We found that the capability of the Inductive Loop Detector (ILD) signature technology to reidentify and classify vehicles along a section of roadway have the potential to provide better performance measures. Therefore, we proposed a high-definition traffic performance monitoring system (Traffic Monitor HD) based on the ILD signature technology and existing loop infrastructure for both freeway and arterial applications. Compared to the traditional performance measurement system, the advantages of the ILD signature technology allow Traffic Monitor HD to provide more comprehensive and accurate performance measurements, including point-based measures (i.e., vehicle counts, classification, and alerts on problematic detectors), sectionbased measures (i.e., travel time, speed, and estimates on emission), and O-D based measures (i.e., O-D matrix and trip travel time).

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“…In this study, the estimation of average speed by vehicle class is built upon the RTREID-2 model developed by Jeng [15]. A recent study has proved that travel time and speed estimation from RTREID-2 are very close to GPS groudtruth data [25]. Using RTREID-2, the travel time for each re-identified vehicle on a link can be obtained directly.…”

Section: B Vehicle Activity Estimationmentioning

confidence: 99%

Development of a real-time on-road emissions estimation and monitoring system

Liu

Tok

Ritchie

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Transportation has been a significant contributor to total greenhouse gas and criteria air pollutant emissions. Emission mitigation strategies are essential in reducing transportation's impacts on the environment. In order to effectively develop and evaluate on-road emissions reduction strategies, it is important to have an information support system which can estimate and monitor on-road emissions under real world traffic operations. Emission data provided by such a system can be used to identify emission hot spots and their causes, and to develop and evaluate reduction strategies. In this paper, a system is developed to estimate and monitor operational on-road emissions with high accuracy and resolution in real time. The two sets of critical information for emission estimation, vehicle mix and vehicle activity, are directly generated from traffic detection using inductive vehicle signature technology. An initial implementation on a section of the I-405 freeway at Irvine, California is demonstrated. With more widespread deployment, the system can be used to perform before-and-after evaluation of certain mitigation strategies, to develop time sensitive optimal traffic control strategies with the purpose to control emissions, and to provide high fidelity greenhouse gas and air quality information to policymakers, researchers, and the general public.

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Freeway Corridor Performance Measurement Based on Vehicle Reidentification

Cited by 24 publications

References 8 publications

Highway Traffic State Estimation and Short-term Prediction

Highway Traffic State Estimation and Short-term Prediction

A high-definition traffic performance monitoring system with the Inductive Loop Detector signature technology

Development of a real-time on-road emissions estimation and monitoring system

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