Use of Probe Vehicles to Increase Traffic Estimation Accuracy in Brisbane

Traffic congestion is an increasing problem with high costs in financial, social and personal terms. These costs include psychological and physiological stress, aggressivity and fatigue caused by lengthy delays, and increased likelihood of road crashes. Reliable and accurate traffic information is essential for the development of traffic control and management strategies. Traffic information is mostly gathered from in-road vehicle detectors such as induction loops. Traffic Message Chanel (TMC) service is popular service which wirelessly send traffic information to drivers. Traffic probes have been used in many cities to increase traffic information accuracy. A simulation to estimate the number of probe vehicles required to increase the accuracy of traffic information in Brisbane is proposed. A meso level traffic simulator has been developed to facilitate the identification of the optimal number of probe vehicles required to achieve an acceptable level of traffic reporting accuracy. Our approach to determine the optimal number of probe vehicles required to meet quality of service requirements, is to simulate runs with varying numbers of traffic probes. The simulated traffic represents Brisbane’s typical morning traffic. The road maps used in simulation are Brisbane’s TMC maps complete with speed limits and traffic lights. Experimental results show that that the optimal number of probe vehicles required for providing a useful supplement to TMC (induction loop) data lies between 0.5% and 2.5% of vehicles on the road. With less probes than 0.25%, little additional information is provided, while for more probes than 5%, there is only a negligible affect on accuracy for increasingly many probes on the road. Our findings are consistent with on-going research work on traffic probes, and show the effectiveness of using probe vehicles to supplement induction loops for accurate and timely traffic information.

Freeway traffic estimation in Beijing based on particle filter

Short-term traffic flow data is characterized by rapid and dramatic fluctuations. It reflects the nature of the frequent congestion in the lane, which shows a strong nonlinear feature. Traffic state estimation based on the data gained by electronic sensors is critical for much intelligent traffic management and the traffic control. In this paper, a solution to freeway traffic estimation in Beijing is proposed using a particle filter, based on macroscopic traffic flow model, which estimates both traffic density and speed.Particle filter is a nonlinear prediction method, which has obvious advantages for traffic flows prediction. However, with the increase of sampling period, the volatility of the traffic state curve will be much dramatic. Therefore, the prediction accuracy will be affected and difficulty of forecasting is raised. In this paper, particle filter model is applied to estimate the short-term traffic flow. Numerical study is conducted based on the Beijing freeway data with the sampling period of 2 min. The relatively high accuracy of the results indicates the superiority of the proposed model.

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.

Guide for forecasting traffic on the interstate system for use in preparing cost estimates /

"Circular memorandum to: Division Engineers".

Update of TSC highway traffic noise prediction code (1974) /

Mode of access: Internet.

Evaluación de la distribución de riesgos en los distintos modelos de asociación público-privada para la provisión de infraestructura carretera en México

México es de los pocos países en el mundo que ha realizado dos grandes programas para la construcción de autopistas en colaboración con el sector privado. El primero, fue realizado entre 1989 y 1994, con resultados adversos por el mal diseño del esquema de concesiones; y, el segundo con mejores resultados, en operación desde 2003 mediante nuevos modelos de asociación público-privada (APP). El objetivo de la presente investigación es estudiar los modelos de asociación público-privada empleados en México para la provisión de infraestructura carretera, realizando el análisis y la evaluación de la distribución de riesgos entre el sector público y privado en cada uno de los modelos con el propósito de establecer una propuesta de reasignación de riesgos para disminuir el costo global y la incertidumbre de los proyectos. En la primera parte se describe el estado actual del conocimiento de las asociaciones público-privadas para desarrollar proyectos de infraestructura, incluyendo los antecedentes, la definición y las tipologías de los esquemas APP, así como la práctica internacional de programas como el modelo británico Private Finance Initiative (PFI), resultados de proyectos en la Unión Europea y programas APP en otros países. También, se destaca la participación del sector privado en el financiamiento de la infraestructura del transporte de México en la década de 1990. En los capítulos centrales se aborda el estudio de los modelos APP que se han utilizado en el país en la construcción de la red de carreteras de alta capacidad. Se presentan las características y los resultados del programa de autopistas 1989-94, así como el rescate financiero y las medidas de reestructuración de los proyectos concesionados, aspectos que obligaron a las autoridades mexicanas a cambiar la normatividad para la aprobación de los proyectos según su rentabilidad, modificar la legislación de caminos y diseñar nuevos esquemas de colaboración entre el gobierno y el sector privado. Los nuevos modelos APP vigentes desde 2003 son: nuevo modelo de concesiones para desarrollar autopistas de peaje, modelo de proyectos de prestación de servicios (peaje sombra) para modernizar carreteras existentes y modelo de aprovechamiento de activos para concesionar autopistas de peaje en operación a cambio de un pago. De estos modelos se realizaron estudios de caso en los que se determinan medidas de desempeño operativo (niveles de tráfico, costos y plazos de construcción) y rentabilidad financiera (tasa interna de retorno y valor presente neto). En la última parte se efectúa la identificación, análisis y evaluación de los riesgos que afectaron los costos, el tiempo de ejecución y la rentabilidad de los proyectos de ambos programas. Entre los factores de riesgo analizados se encontró que los más importantes fueron: las condiciones macroeconómicas del país (inflación, producto interno bruto, tipo de cambio y tasa de interés), deficiencias en la planificación de los proyectos (diseño, derecho de vía, tarifas, permisos y estimación del tránsito) y aportaciones públicas en forma de obra. Mexico is one of the few countries in the world that has developed two major programs for highway construction in collaboration with the private sector. The first one was carried out between 1989 and 1994 with adverse outcomes due to the wrong design of concession schemes; and, the second one, in operation since 2003, through new public-private partnership models (PPPs). The objective of this research is to study public-private partnership models used in Mexico for road infrastructure provision, performing the analysis and evaluation of risk’s distribution between the public and the private sector in each model in order to draw up a proposal for risk’s allocation to reduce the total cost and the uncertainty of projects. The first part describes the current state of knowledge in public-private partnership to develop infrastructure projects, including the history, definition and types of PPP models, as well as international practice of programs such as the British Private Finance Initiative (PFI) model, results in the European Union and PPP programs in other countries. Also, it stands out the private sector participation in financing of Mexico’s transport infrastructure in 1990s. The next chapters present the study of public-private partnerships models that have been used in the country in the construction of the high capacity road network. Characteristics and outcomes of the highway program 1989-94 are presented, as well as the financial bailout and restructuring measures of the concession projects, aspects that forced the Mexican authorities to change projects regulations, improve road’s legislation and design new schemes of cooperation between the Government and the private sector. The new PPP models since 2003 are: concession model to develop toll highways, private service contracts model (shadow toll) to modernize existing roads and highway assets model for the concession of toll roads in operation in exchange for a payment. These models were analyzed using case studies in which measures of operational performance (levels of traffic, costs and construction schedules) and financial profitability (internal rate of return and net present value) are determined. In the last part, the analysis and assessment of risks that affect costs, execution time and profitability of the projects are carried out, for both programs. Among the risk factors analyzed, the following ones were found to be the most important: country macroeconomic conditions (inflation, gross domestic product, exchange rate and interest rate), deficiencies in projects planning (design, right of way, tolls, permits and traffic estimation) and public contributions in the form of construction works.

Urban transportation energy conservation /

Vol. 1 has title: Analytical procedures for urban transportation energy conservation.

Impact of demographic and migration trends on future travel in metropolitan areas.

Mode of access: Internet.

Consequences of small sample 0-D data collection in the transportation planning process /

Mode of access: Internet.

Novel methods and the maximum likelihood estimation technique for estimating traffic critical gap

Most unsignalised intersection capacity calculation procedures are based on gap acceptance models. Accuracy of critical gap estimation affects accuracy of capacity and delay estimation. Several methods have been published to estimate drivers’ sample mean critical gap, the Maximum Likelihood Estimation (MLE) technique regarded as the most accurate. This study assesses three novel methods; Average Central Gap (ACG) method, Strength Weighted Central Gap method (SWCG), and Mode Central Gap method (MCG), against MLE for their fidelity in rendering true sample mean critical gaps. A Monte Carlo event based simulation model was used to draw the maximum rejected gap and accepted gap for each of a sample of 300 drivers across 32 simulation runs. Simulation mean critical gap is varied between 3s and 8s, while offered gap rate is varied between 0.05veh/s and 0.55veh/s. This study affirms that MLE provides a close to perfect fit to simulation mean critical gaps across a broad range of conditions. The MCG method also provides an almost perfect fit and has superior computational simplicity and efficiency to the MLE. The SWCG method performs robustly under high flows; however, poorly under low to moderate flows. Further research is recommended using field traffic data, under a variety of minor stream and major stream flow conditions for a variety of minor stream movement types, to compare critical gap estimates using MLE against MCG. Should the MCG method prove as robust as MLE, serious consideration should be given to its adoption to estimate critical gap parameters in guidelines.

A new approach for trip generation estimation for use in traffic impact assessments

Findings from an online survey conducted by Queensland University of Technology (QUT) shows that Australia is suffering from a lack of data reflecting trip generation for use in Traffic Impact Assessments (TIAs). Current independent variables for trip generation estimation are not able to create robust outcomes as well. It is also challenging to account for the impact of the new development on public and active transport as well as the effect of trip chaining behaviour in Australian TIA studies. With this background in mind, research is being implemented by QUT to find a new approach developing a combined model of trip generation and mode choice with consideration of trip chaining effects. It is expected that the model will provide transferable outcomes as it is developed based on socio-demographic parameters. Child Care Centres within the Brisbane area have been nominated for model development. At the time, the project is in the data collection phase. Findings from the pilot survey associated with capturing trip chaining and mode choice information reveal that applying questionnaire is able to capture required information in an acceptable level. The result also reveals that several centres within an area should be surveyed in order to provide sufficient data for trip chaining and modal split analysis.

Traffic queue estimation for metered motorway on-ramps through use of loop detector time occupancies

The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is a vital input for dynamic queue management on metered on-ramps. Accurate and reliable queue information enables the management of on-ramp queue in an adaptive manner to the actual traffic queue size and thus minimises the adverse impacts of queue flush while increasing the benefit of ramp metering. The proposed algorithm is developed based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. This projection results are updated with the measurement equation using the time occupancies from mid-link and link-entrance loop detectors. This study also proposes a novel single point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performances and consistently outperformed the benchmarked Single Occupancy Kalman filter (SOKF) method. The improvements over SOKF are 62% and 63% in average in terms of the estimation accuracy (MAE) and reliability (RMSE), respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in congested ramp traffic conditions where long queues may significantly compromise the benchmark algorithm’s performance.

Assessing the Impact of Sensor Accuracy and Sensor Choice on Air Traffic Conflict Risk Estimation using Risk-Ratio Concepts

One of the primary desired capabilities of any future air traffic separation management system is the ability to provide early conflict detection and resolution effectively and efficiently. In this paper, we consider the risk of conflict as a primary measurement to be used for early conflict detection. This paper focuses on developing a novel approach to assess the impact of different measurement uncertainty models on the estimated risk of conflict. The measurement uncertainty model can be used to represent different sensor accuracy and sensor choices. Our study demonstrates the value of modelling measurement uncertainty in the conflict risk estimation problem and presents techniques providing a means of assessing sensor requirements to achieve desired conflict detection performance.

Traffic density estimation of signalised arterials with stop line detector and probe data

This research aims to develop a reliable density estimation method for signalised arterials based on cumulative counts from upstream and downstream detectors. In order to overcome counting errors associated with urban arterials with mid-link sinks and sources, CUmulative plots and Probe Integration for Travel timE estimation (CUPRITE) is employed for density estimation. The method, by utilizing probe vehicles’ samples, reduces or cancels the counting inconsistencies when vehicles’ conservation is not satisfied within a section. The method is tested in a controlled environment, and the authors demonstrate the effectiveness of CUPRITE for density estimation in a signalised section, and discuss issues associated with the method.