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.

Physically sound vehicle-driver model for realistic microscopic simulation

In microscopic traffic simulators, the interaction between vehicles is considered. The dynamics of the system then becomes an emergent property of the interaction between its components. Such interactions include lane-changing, car-following behaviours and intersection management. Although, in some cases, such simulators produce realistic prediction, they do not allow for an important aspect of the dynamics, that is, the driver-vehicle interaction. This paper introduces a physically sound vehicle-driver model for realistic microscopic simulation. By building a nanoscopic traffic simulation model that uses steering angle and throttle position as parameters, the model aims to overcome unrealistic acceleration and deceleration values, as found in various microscopic simulation tools. A physics engine calculates the driving force of the vehicle, and the preliminary results presented here, show that, through a realistic driver-vehicle-environment simulator, it becomes possible to model realistic driver and vehicle behaviours in a traffic simulation.

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.

Integrating driving and traffic simulators to study railway level crossing safety interventions : a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs.

Ensemble-based DDoS detection and mitigation model

This work-in-progress paper presents an ensemble-based model for detecting and mitigating Distributed Denial-of-Service (DDoS) attacks, and its partial implementation. The model utilises network traffic analysis and MIB (Management Information Base) server load analysis features for detecting a wide range of network and application layer DDoS attacks and distinguishing them from Flash Events. The proposed model will be evaluated against realistic synthetic network traffic generated using a software-based traffic generator that we have developed as part of this research. In this paper, we summarise our previous work, highlight the current work being undertaken along with preliminary results obtained and outline the future directions of our work.

Rule-based transit signal priority control method using a real-time transit travel time prediction model

An advanced rule-based Transit Signal Priority (TSP) control method is presented in this paper. An on-line transit travel time prediction model is the key component of the proposed method, which enables the selection of the most appropriate TSP plans for the prevailing traffic and transit condition. The new method also adopts a priority plan re-development feature that enables modifying or even switching the already implemented priority plan to accommodate changes in the traffic conditions. The proposed method utilizes conventional green extension and red truncation strategies and also two new strategies including green truncation and queue clearance. The new method is evaluated against a typical active TSP strategy and also the base case scenario assuming no TSP control in microsimulation. The evaluation results indicate that the proposed method can produce significant benefits in reducing the bus delay time and improving the service regularity with negligible adverse impacts on the non-transit street traffic.

Hazard based models for freeway traffic incident duration

Assessing and prioritising cost-effective strategies to mitigate the impacts of traffic incidents and accidents on non-recurrent congestion on major roads represents a significant challenge for road network managers. This research examines the influence of numerous factors associated with incidents of various types on their duration. It presents a comprehensive traffic incident data mining and analysis by developing an incident duration model based on twelve months of incident data obtained from the Australian freeway network. Parametric accelerated failure time (AFT) survival models of incident duration were developed, including log-logistic, lognormal, and Weibul-considering both fixed and random parameters, as well as a Weibull model with gamma heterogeneity. The Weibull AFT models with random parameters were appropriate for modelling incident duration arising from crashes and hazards. A Weibull model with gamma heterogeneity was most suitable for modelling incident duration of stationary vehicles. Significant variables affecting incident duration include characteristics of the incidents (severity, type, towing requirements, etc.), and location, time of day, and traffic characteristics of the incident. Moreover, the findings reveal no significant effects of infrastructure and weather on incident duration. A significant and unique contribution of this paper is that the durations of each type of incident are uniquely different and respond to different factors. The results of this study are useful for traffic incident management agencies to implement strategies to reduce incident duration, leading to reduced congestion, secondary incidents, and the associated human and economic losses.

Analysis of traffic injury severity in a mega city of a developing country

Despite a considerable amount of research on traffic injury severities, relatively little is known about the factors influencing traffic injury severity in developing countries, and in particular in Bangladesh. Road traffic crashes are a common headline in daily newspapers of Bangladesh. It has also recorded one of the highest road fatality rates in the world. This research identifies significant factors contributing to traffic injury severity in Dhaka – a mega city and capital of Bangladesh. Road traffic crash data of 5 years from 2007 to 2011 were collected from the Dhaka Metropolitan Police (DMP), which included about 2714 traffic crashes. The severity level of these crashes was documented in a 4-point ordinal scale: no injury (property damage), minor injury, severe injury, and death. An ordered Probit regression model has been estimated to identify factors contributing to injury severities. Results show that night time influence is associated with a higher level injury severity as is for individuals involved in single vehicle crashes. Crashes on highway sections within the city are found to be more injurious than crashes along the arterial and feeder roads. There is a lower likelihood of injury severity, however, if the road sections are monitored and enforced by the traffic police. The likelihood of injuries is lower on two-way traffic arrangements than one-way, and at four-legged intersections and roundabouts compare to road segments. The findings are compared with those from developed countries and the implications of this research are discussed in terms of policy settings for developing countries.

Investigation into the noise map based on traffic flow prediction in the citywide road network

As one of the measures for decreasing road traffic noise in a city, the control of the traffic flow and the physical distribution is considered. To conduct the measure effectively, the model for predicting the traffic flow in the citywide road network is necessary. In this study, the existing model named AVENUE was used as a traffic flow prediction model. The traffic flow model was integrated with the road vehicles' sound power model and the sound propagation model, and the new road traffic noise prediction model was established. As a case study, the prediction model was applied to the road network of Tsukuba city in Japan and the noise map of the city was made. To examine the calculation accuracy of the noise map, the calculated values of the noise at the main roads were compared with the measured values. As a result, it was found that there was a possibility that the high accuracy noise map of the city could be made by using the noise prediction model developed in this study.

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.

Optimized strategy for integrated traffic and transit signal control

This paper proposes a unique and innovative approach to integrate transit signal priority control into a traffic adaptive signal control strategy. The proposed strategy was named OSTRAC (Optimized Strategy for integrated TRAffic and TRAnsit signal Control). The cornerstones of OSTRAC include an online microscopic traffic f low prediction model and a Genetic Algorithm (GA) based traffic signal timing module. A sensitivity analysis was conducted to determine the critical GA parameters. The developed traffic f low model demonstrated reliable prediction results through a test. OSTRAC was evaluated by comparing its performance to three other signal control strategies. The evaluation results revealed that OSTRAC efficiently and effectively reduced delay time of general traffic and also transit vehicles.

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.

Speech interference and transmission on residential balconies with road traffic noise

Balcony acoustic treatments can mitigate the effects of community road traffic noise. To further investigate, a theoretical study into the effects of balcony acoustic treatment combinations on speech interference and transmission is conducted for various street geometries. Nine different balcony types are investigated using a combined specular and diffuse reflection computer model. Diffusion in the model is calculated using the radiosity technique. The balcony types include a standard balcony with or without a ceiling and with various combinations of parapet, ceiling absorption and ceiling shield. A total of 70 balcony and street geometrical configurations are analyzed with each balcony type, resulting in 630 scenarios. In each scenario the reverberation time, speech interference level (SIL) and speech transmission index (STI) are calculated. These indicators are compared to determine trends based on the effects of propagation path, inclusion of opposite buildings and difference with a reference position outside the balcony. The results demonstrate trends in SIL and STI with different balcony types. It is found that an acoustically treated balcony reduces speech interference. A parapet provides the largest improvement, followed by absorption on the ceiling. The largest reductions in speech interference arise when a combination of balcony acoustic treatments are applied.

The development of a naturalistic car following model for assessing managed motorway systems' safety effects

This thesis highlights the limitations of the existing car following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car following models emulating driver behaviour precise parameters such as headways and Time to Collisions. The comparison evaluates the robustness of each car following model for safety metric reproductions. A new car following model, based on the personal space concept and fish school model is proposed to simulate more precise traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit form VSL systems. This research facilitates assessing Intelligent Transportation Systems on motorways, using microscopic simulation.

Determinants of drink-driving and association between drink-driving and road traffic fatalities in Ghana

Aim The objective is to establish determinants of drink-driving and its association with traffic crashes in Ghana. Methods A multivariable logistic regression was used to establish significant determinants of drink-driving and a bivariate logistic regression to establish the association between drink–driving and road traffic crashes in Ghana. Results In total, 2,736 motorists were randomly stopped for breath testing of whom 8.7% tested positive for alcohol. Among the total participants, 5.5% exceeded the legal BAC limit of 0.08%. Formal education is associated with a reduced likelihood of drink-driving compared with drivers without formal education. The propensity to drink-drive is 1.8 times higher among illiterate drivers compared with drivers with basic education. Young adult drivers also recorded elevated likelihoods for driving under alcohol impairment compared with adult drivers. The odds of drink-driving among truck drivers is OR=1.81, (95% CI=1.16 to 2.82) and two wheeler riders is OR=1.41, (95% CI=0.47 to 4.28) compared with car drivers. Contrary to general perception, commercial car drivers have a significant reduced likelihood of 41%, OR=0.59, (95% CI=0.38 to 0.92) compared with the private car driver. Bivariate analysis conducted showed a significant association between the proportion of drivers exceeding the legal BAC limit and road traffic fatalities, p<0.001. The model predicts a 1% increase in the proportion of drivers exceeding the legal BAC to be associated with a 4% increase in road traffic fatalities, 95% CI= 3% to 5% and vice versa. Conclusion A positive and significant association between roadside alcohol prevalence and road traffic fatality has been established. Scaling up roadside breath test, determining standard drink and disseminating to the populace and formulating policies targeting the youth such as increasing minimum legal drinking age and reduced legal BAC limit for the youth and novice drivers might improve drink-driving related crashes in Ghana.