Academic literature on the topic 'Traffic estimation Australia'
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Journal articles on the topic "Traffic estimation Australia"
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Dey, Subhrasankha, Stephan Winter, and Martin Tomko. "Origin–Destination Flow Estimation from Link Count Data Only." Sensors 20, no. 18 (September 13, 2020): 5226. http://dx.doi.org/10.3390/s20185226.
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All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin–destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin–destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin–destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin–destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.
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Emami, Azadeh, Majid Sarvi, and Saeed Asadi Bagloee. "A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles." Journal of Modern Transportation 27, no. 4 (November 24, 2019): 341–54. http://dx.doi.org/10.1007/s40534-019-00200-y.
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AbstractThis paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.
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Aboura, Khalid, and Bijan Samali. "The Information System for Bridge Networks Condition Monitoring and Prediction." International Journal of Information Technologies and Systems Approach 5, no. 1 (January 2012): 1–18. http://dx.doi.org/10.4018/jitsa.2012010101.
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This paper introduces an information system for estimating lifetime characteristics of elements of bridges and predicting the future conditions of networks of bridges. The Information System for Bridge Networks Condition Monitoring and Prediction was developed for the Roads and Traffic Authority of the state of New South Wales, Australia. The conceptual departure from the standard bridge management systems is the use of a novel stochastic process built out of the gamma process. The statistical model was designed for the estimation of infrastructure lifetime, based on the analysis of more than 15 years of bridge inspection data. The predictive curve provides a coherent mathematical model for conducting target level constrained and funding based maintenance optimization.
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Iwanowicz, Damian. "Assessment of selected methods of estimating the maximum back-of-queue size on a signal-controlled intersection approach." Journal of Civil Engineering and Transport 4, no. 2 (December 31, 2022): 49–63. http://dx.doi.org/10.24136/tren.2022.008.
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The article presents the characteristics and evaluation of the accuracy of estimating the maximum length of the queue of vehicles at signalized intersections by commonly used methods in the world. The analyzes were based on the latest editions of the guidelines in the United States, Canada, Australia, Germany and Poland. In order to carry out accuracy analyzes, traffic tests were carried out at 5 intersection inlets in three different cities in Poland (Bydgoszcz, Torun, Warsaw), covering all phases of vehicle queue formation during individual periods of the signaling cycle (effective red and green signal). In total, the analysis had the results of tests from 81 hours of observation ~23,000 behaviors of vehicle drivers. Based on the analyzes it was found in particular: (1) slight differences in the construction of mathematical models of the considered calculation methods, with the exception of the US HCM model from 2016; (2) small errors in estimating the maximum queue length in unsaturated vehicle flow states (~3-5 vehicles/cycle and ~2-6 vehicles/15 minutes interval); (3) quite large errors in estimating the maximum queue length in saturated and oversaturated vehicle flow states (~11-16 vehicles/cycle and ~15-18 vehicles/15 minutes interval); (4) the main impact on estimation errors in oversaturation traffic states is not taking into account or incorrect determination of the 'so-called' initial queue length of the period preceding the analyzed period.
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Ma, Zhen-Liang, Luis Ferreira, Mahmoud Mesbah, and Ahmad Tavassoli Hojati. "Modeling Bus Travel Time Reliability with Supply and Demand Data from Automatic Vehicle Location and Smart Card Systems." Transportation Research Record: Journal of the Transportation Research Board 2533, no. 1 (January 2015): 17–27. http://dx.doi.org/10.3141/2533-03.
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Travel time reliability is an important aspect of bus service quality. Despite a significant body of research on private vehicle reliability, little attention has been paid to bus travel time reliability at the stop-to-stop link level on different types of roads. This study aims to identify and quantify the underlying determinants of bus travel time reliability on links of different road types with the use of supply and demand data from automatic vehicle location and smart card systems collected in Brisbane, Australia. Three general bus-related models were developed with respect to the main concerns of travelers and planners: average travel time, buffer time, and coefficient of variation of travel time. Five groups of alternative models were developed to account for variations caused by different road types, including arterial road, motorway, busway, and central business district. Seemingly unrelated regression equations estimation were applied to account for cross-equation correlations across regression models in each group. Three main categories of unreliability contributory factors were identified and tested in this study, namely, planning, operational, and environmental. Model results provided insights into these factors that affect bus travel time and its variability. The most important predictors were found to be the recurrent congestion index, traffic signals, and passenger demand at stops. Results could be used to target specific strategies aimed at reducing unreliability on different types of roads.
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Al-Masaeid, Hashem R. "Capacity and performance of roundabouts." Canadian Journal of Civil Engineering 26, no. 5 (October 1, 1999): 597–605. http://dx.doi.org/10.1139/l99-018.
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Empirical and theoretical approaches for assessing the capacity and performance of roundabout entries have provided inconsistent results. It was believed that an efficient modeling of gap-acceptance behavior of drivers entering into a roundabout would provide more insight for assessing roundabout operation. Accordingly, this study used logit modeling to predict the probability that a randomly selected driver will accept a given gap in the circulating traffic stream based upon roundabout and gap characteristics. Also, the study has developed move-up time models using both the roundabout geometry and circulating traffic characteristics. The developed gap-acceptance and move-up time models have implications in roundabout capacity estimation analysis. Finally, the results of the developed models are incorporated into the Australian and German gap theoretical models to judge the feasibility of using these theoretical models for Jordan conditions. Compared with an empirical data set, the Australian theoretical model with a minimum headway of 0.5 s provides reasonable results.Key words: capacity analysis, probabilistic approach, roundabouts.
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Vander Hoorn, Stephen, and Sabine Knapp. "Predicting Traffic and Risk Exposure in the Maritime Industry." Safety 5, no. 3 (July 1, 2019): 42. http://dx.doi.org/10.3390/safety5030042.
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Maritime regulators, port authorities, and industry require the ability to predict risk exposure of shipping activities at a micro and macro level to optimize asset allocation and to mitigate and prevent incidents. This article introduces the concept of a strategic planning tool by making use of the multi-layered risk estimation framework (MLREF), which accounts for ship specific risk, vessel traffic densities, and meets ocean conditions at the macro level. This article’s main contribution is to provide a traffic and risk exposure prediction routine that allows the traffic forecast to be distributed across the shipping route network to allow for predicting scenarios at the macro level (e.g., covering larger geographic areas) and micro level (e.g., passage way, particular route of interest). In addition, the micro level is introduced by providing a theoretical idea to integrate location specific spatial rate ratios along with the effect of the risk control option to perform sensitivity analysis of risk exposure prediction scenarios. Aspects of the risk exposure estimation routine were tested via a pilot study for the Australian region using a comprehensive and unique combination of datasets. Sources of uncertainties for risk assessments are described in general and discussed along with the potential for future developments and improvements.
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Dehghani-Zadeh, Maryam, and Mehdi Fallah Tafti. "Estimating saturation flow under weak discipline traffic conditions, case study: Iran." Archives of Transport 46, no. 2 (June 30, 2018): 47–60. http://dx.doi.org/10.5604/01.3001.0012.2102.
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Intersections, as the critical elements and the major bottleneck points of urban street networks, may have inconsistent performances in different countries. This is largely due to the fact that the factors affecting their performance e.g. driving behavior, vehicle characteristics, control methods, and environmental conditions may vary from one country to another. It is, therefore required to take into account these factors when developing or applying available models and methodologies for their capacity analysis or signal control setting. This is particularly important for the countries with heterogeneous and weak discipline traffic streams such as Iran. Meanwhile, estimating the saturation flow rate, which is a key parameter in capacity and delay analysis and in optimal timing of traffic signals, is of great importance. In this study, the possibility of identifying and or developing appropriate models for estimating the saturation flow rate at the signalized intersections in these situations has been explored. For this purpose, a case study performed at the signalized intersections located in the city of Yazd, a medium sized city located in the middle of Iran. Using the data obtained from several intersections together with the application of analytical procedures proposed by American, Australian, Canadian, Indonesian, Iranian and Malaysian highway capacity guides, the saturation flow rate was estimated from both field observations and analytical methods. A comparison of these results indicated that in the protected left-turn situations, the Australian guide produced the best comparable results with the field data. On the other hand, in the permitted left-turn situations, the method proposed in the American Highway Capacity Manual guide produced the best comparable results with the field data. Furthermore, three new models were developed for estimating the saturation flow rate in three different situations namely, unopposed mixed straight and turning traffic movements, opposed mixed straight and turning traffic movements and merely straight through movement. The effective width, traffic composition, and opposite oncoming through traffic flow were considered as the effective parameters in the proposed models. Moreover, using the multivariate regression analysis, the Passenger Car Equivalent coefficients for motorcycles and heavy vehicles were calculated as 0.51 and 2.09, respectively.
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Peiris, Sujanie, Janneke Berecki-Gisolf, Stuart Newstead, Bernard Chen, and Brian Fildes. "Development of a Methodology for Estimating the Availability of ADAS-Dependent Road Infrastructure." Sustainability 13, no. 17 (August 24, 2021): 9512. http://dx.doi.org/10.3390/su13179512.
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Advanced driver assist systems are being promoted with the expectation that enhanced driver support will mitigate road trauma. While these technologies are optimised for certain road and traffic conditions, not all roads across Australasia are equipped with ADAS-supportive infrastructure. This study developed a desk-top methodology for using road classes (disaggregated by remoteness levels) to estimate the presence of quality roads, road delineation and speed signage in Victoria, Australia. Aerial imagery and mapping data were used to assess a number of random locations based on a developed protocol. The methodology demonstrated that in Victoria, major and arterial roads across all remoteness levels had high-quality sealed surfaces but 42% of all remote roads were unsealed. Delineation (crucial for lane support systems) were absent across 73% of sub-arterial roads independent of remoteness, and absent across 96% of sub-arterial roads in regional and remote areas. Speed sign availability across remote and regional areas was sparse, with only 65% of all roads assessed having signage. Results are reflective of Victoria’s road funding model and consistent with on-road audits conducted by other researchers. This methodology enables the proportion ADAS-ready roads to be estimated so the benefits of ADAS technologies can be quantified and investments into ADAS-supportive infrastructure be readily allocated.
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Black, J. A., S. E. Samuels, U. Vandebona, E. Masters, J. C. Trinder, B. Morrison, and R. Tudge. "Road Traffic Noise Prediction Using Object-Oriented and Geographic Information System Technologies." Transportation Research Record: Journal of the Transportation Research Board 1601, no. 1 (January 1997): 77–83. http://dx.doi.org/10.3141/1601-12.
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It is now some 4 years since the New South Wales Roads and Traffic Authority (RTA) interim noise policy was released. The document sets out RTA policies on traffic noise along with guidelines relating to assessment and control. It is perhaps the most extensive document on the topic ever produced by an Australian road authority. The underlying philosophy of the policy is the importance of noise as an issue that must be addressed in all aspects of RTA road planning, design, construction, maintenance, and operation programs. Some recent advances in the traffic noise arena are considered. A spatial decision support system is then outlined for road planning that has been developed as part of an Australian Research Council industry collaborative research grant involving RTA and the University of New South Wales. This is built on object-oriented programming and geographic information system technologies and involves a library containing models in eight domains amounting to 33,000 lines of code. The noise estimation procedure included in this system is described together with how it has been adapted and implemented in an object-oriented way. Speculation is made on how the prototype may be integrated into the emerging corporate responsibilities of RTA in the area of communication and consultation.
Dissertations / Theses on the topic "Traffic estimation Australia"
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Muhunthan, Bala. "Traffic loads for estimating the endurances and residual strength of highway bridges." Thesis, 1996. http://hdl.handle.net/2440/122492.
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