On selecting an optimal wavelet for detecting singularities in traffic and vehicular data

Serving as a powerful tool for extracting localized variations in non-stationary signals, applications of wavelet transforms (WTs) in traffic engineering have been introduced; however, lacking in some important theoretical fundamentals. In particular, there is little guidance provided on selecting an appropriate WT across potential transport applications. This research described in this paper contributes uniquely to the literature by first describing a numerical experiment to demonstrate the shortcomings of commonly-used data processing techniques in traffic engineering (i.e., averaging, moving averaging, second-order difference, oblique cumulative curve, and short-time Fourier transform). It then mathematically describes WT’s ability to detect singularities in traffic data. Next, selecting a suitable WT for a particular research topic in traffic engineering is discussed in detail by objectively and quantitatively comparing candidate wavelets’ performances using a numerical experiment. Finally, based on several case studies using both loop detector data and vehicle trajectories, it is shown that selecting a suitable wavelet largely depends on the specific research topic, and that the Mexican hat wavelet generally gives a satisfactory performance in detecting singularities in traffic and vehicular data.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.

Navigational Safety in Port Waters : Proactive Management of Collision Risk Using Traffic Conflicts

Traditionally navigational safety analyses rely on historical collision data which is often hampered because of low collision counts, insufficiency in explaining collision causation, and reactive approach to safety. A promising alternative approach that overcomes these problems is using navigational traffic conflicts or near-misses as an alternative to the collision data. This book discusses how traffic conflicts can effectively be used in modeling of port water collision risks. Techniques for measuring and predicting collision risks in fairways, intersections, and anchorages are discussed by utilizing advanced statistical models. Risk measurement models, which quantitatively measure collision risks in waterways, are discussed. To predict risks, a hierarchical statistical modeling technique is discussed which identifies the factors influencing the risks. The modeling techniques are illustrated for Singapore port data. Results showed that traffic conflicts are an ethically appealing alternative to collision data for fast, reliable and effective safety assessment, thus possessing great potential for managing collision risks in port waters.

Pre-crash traffic flow trend analysis on motorways

Crashes on motorway contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence reduce crashes will help address congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a Short time window around the time of crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques, that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists, and that this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with traffic flow data of one hour prior to the crash using an incident detection algorithm. Traffic flow trends (traffic speed/occupancy time series) revealed that crashes could be clustered with regards of the dominant traffic flow pattern prior to the crash. Using the k-means clustering method allowed the crashes to be clustered based on their flow trends rather than their distance. Four major trends have been found in the clustering results. Based on these findings, crash likelihood estimation algorithms can be fine-tuned based on the monitored traffic flow conditions with a sliding window of 60 minutes to increase accuracy of the results and minimize false alarms.

Pre-crash and non-crash traffic flow trends analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence, reducing the frequency of crashes assists in addressing congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a short time window around the time of a crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists. We will compare them with normal traffic trends and show this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding to traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash. Using the K-Means clustering method with Euclidean distance function allowed the crashes to be clustered. Then, normal situation data was extracted based on the time distribution of crashes and were clustered to compare with the “high risk” clusters. Five major trends have been found in the clustering results for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Based on these findings, crash likelihood estimation models can be fine-tuned based on the monitored traffic conditions with a sliding window of 30 minutes to increase accuracy of the results and minimize false alarms.

Promoting a more positive traffic safety culture in Australia : lessons learnt and future directions

Adopting a traffic safety culture approach, this paper identifies and discusses the ongoing challenge of promoting the road safety message in Australia. It is widely acknowledged that mass media and public education initiatives have played a critical role in the significant positive changes witnessed in community attitudes to road safety in the last three to four decades. It could be argued that mass media and education have had a direct influence on behaviours and attitudes, as well as an indirect influence through signposting and awareness raising functions in conjunction with enforcement. Great achievements have been made in reducing fatalities on Australia’s roads; a concept which is well understood among the international road safety fraternity. How well these achievements are appreciated by the general Australian community however, is not clear. This paper explores the lessons that can be learnt from successes in attitudinal and behaviour change in regard to seatbelt use and drink driving in Australia. It also identifies and discusses key challenges associated with achieving further positive changes in community attitudes and behaviours, particularly in relation to behaviours that may not be perceived by the community as dangerous, such as speeding and mobile phone use while driving. Potential strategies for future mass media and public education campaigns to target these challenges are suggested, including ways of harnessing the power of contemporary traffic law enforcement techniques, such as point-to-point speed enforcement and in-vehicle technologies, to help spread the road safety message.

Traffic safety risks trends and patterns analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestion. Hence, reducing the frequency of crashes assist in addressing congestion issues (Meyer, 2008). Analysing traffic conditions and discovering risky traffic trends and patterns are essential basics in crash likelihood estimations studies and still require more attention and investigation. In this paper we will show, through data mining techniques, that there is a relationship between pre-crash traffic flow patterns and crash occurrence on motorways, compare them with normal traffic trends, and that this knowledge has the potentiality to improve the accuracy of existing crash likelihood estimation models, and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash occurrence. K-Means clustering algorithm applied to determine dominant pre-crash traffic patterns. In the first phase of this research, traffic regimes identified by analysing crashes and normal traffic situations using half an hour speed in upstream locations of crashes. Then, the second phase investigated the different combination of speed risk indicators to distinguish crashes from normal traffic situations more precisely. Five major trends have been found in the first phase of this paper for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Moreover, the second phase explains that spatiotemporal difference of speed is a better risk indicator among different combinations of speed related risk indicators. Based on these findings, crash likelihood estimation models can be fine-tuned to increase accuracy of estimations and minimize false alarms.

Frontier Techniques for Measuring and Estimating Airport Efficiency : An Empirical Review with Practical Guidelines for Analysis and Future Research

Airport efficiency is important because it has a direct impact on customer safety and satisfaction and therefore the financial performance and sustainability of airports, airlines, and affiliated service providers. This is especially so in a world characterized by an increasing volume of both domestic and international air travel, price and other forms of competition between rival airports, airport hubs and airlines, and rapid and sometimes unexpected changes in airline routes and carriers. It also reflects expansion in the number of airports handling regional, national, and international traffic and the growth of complementary airport facilities including industrial, commercial, and retail premises. This has fostered a steadily increasing volume of research aimed at modeling and providing best-practice measures and estimates of airport efficiency using mathematical and econometric frontiers. The purpose of this chapter is to review these various methods as they apply to airports throughout the world. Apart from discussing the strengths and weaknesses of the different approaches and their key findings, the paper also examines the steps faced by researchers as they move through the modeling process in defining airport inputs and outputs and the purported efficiency drivers. Accordingly, the chapter provides guidance to those conducting empirical research on airport efficiency and serves as an aid for aviation regulators and airport operators among others interpreting airport efficiency research outcomes.

A methodology for estimating exposure-controlled crash risk using traffic Police crash data

Exposure control or case-control methodologies are common techniques for estimating crash risks, however they require either observational data on control cases or exogenous exposure data, such as vehicle-kilometres travelled. This study proposes an alternative methodology for estimating crash risk of road user groups, whilst controlling for exposure under a variety of roadway, traffic and environmental factors by using readily available police-reported crash data. In particular, the proposed method employs a combination of a log-linear model and quasi-induced exposure technique to identify significant interactions among a range of roadway, environmental and traffic conditions to estimate associated crash risks. The proposed methodology is illustrated using a set of police-reported crash data from January 2004 to June 2009 on roadways in Queensland, Australia. Exposure-controlled crash risks of motorcyclists—involved in multi-vehicle crashes at intersections—were estimated under various combinations of variables like posted speed limit, intersection control type, intersection configuration, and lighting condition. Results show that the crash risk of motorcycles at three-legged intersections is high if the posted speed limits along the approaches are greater than 60 km/h. The crash risk at three-legged intersections is also high when they are unsignalized. Dark lighting conditions appear to increase the crash risk of motorcycles at signalized intersections, but the problem of night time conspicuity of motorcyclists at intersections is lessened on approaches with lower speed limits. This study demonstrates that this combined methodology is a promising tool for gaining new insights into the crash risks of road user groups, and is transferrable to other road users.

Residential balconies with road traffic noise : a combined source image and radiosity model

Several significant studies have been made in recent decades toward understanding road traffic noise and its effects on residential balconies. These previous studies have used a variety of techniques such as theoretical models, scale models and measurements on real balconies. The studies have considered either road traffic noise levels within the balcony space or inside an adjacent habitable room or both. Previous theoretical models have used, for example, simplified specular reflection calculations, boundary element methods (BEM), adaptations of CoRTN or the use of Sabine Theory. This paper presents an alternative theoretical model to predict the effects of road traffic noise spatially within the balcony space. The model includes a specular reflection component by calculating up to 10 orders of source images. To account for diffusion effects, a two compartment radiosity component is utilised. The first radiosity compartment is the urban street, represented as a street with building facades on either side. The second radiosity compartment is the balcony space. The model is designed to calculate the predicted road traffic noise levels within the balcony space and is capable of establishing the effect of changing street and balcony geometries. Screening attenuation algorithms are included to determine the effects of solid balcony parapets and balcony ceiling shields.

Data collection techniques

The objective of this chapter is to provide an overview of traffic data collection that can and should be used for the calibration and validation of traffic simulation models. There are big differences in availability of data from different sources. Some types of data such as loop detector data are widely available and used. Some can be measured with additional effort, for example, travel time data from GPS probe vehicles. Some types such as trajectory data are available only in rare situations such as research projects.

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.

A framework for generating realistic traffic for distributed denial-of-service attacks and flash events

An intrinsic challenge associated with evaluating proposed techniques for detecting Distributed Denial-of-Service (DDoS) attacks and distinguishing them from Flash Events (FEs) is the extreme scarcity of publicly available real-word traffic traces. Those available are either heavily anonymised or too old to accurately reflect the current trends in DDoS attacks and FEs. This paper proposes a traffic generation and testbed framework for synthetically generating different types of realistic DDoS attacks, FEs and other benign traffic traces, and monitoring their effects on the target. Using only modest hardware resources, the proposed framework, consisting of a customised software traffic generator, ‘Botloader’, is capable of generating a configurable mix of two-way traffic, for emulating either large-scale DDoS attacks, FEs or benign traffic traces that are experimentally reproducible. Botloader uses IP-aliasing, a well-known technique available on most computing platforms, to create thousands of interactive UDP/TCP endpoints on a single computer, each bound to a unique IP-address, to emulate large numbers of simultaneous attackers or benign clients.

A comparison of road traffic crashes along mountainous and non-mountainous roads in Sabah, Malaysia

Constrained topography and complex road geometry along rural mountainous roads often represent a demanding driving situation. As a result, traffic crashes along mountainous roads are likely to have different characteristics to crashes on roads in flatter areas; however, there is little research on this topic. The objective of this study is to examine the characteristics of road traffic crashes on rural mountainous roads and to compare these with the characteristics of crashes on non-mountainous roads. This paper explores and compares general crash characteristics including crash type, crash severity, roadway geometric features and environmental factors, and road user/vehicle characteristics. Five years of road traffic crash data (2008-2012) for Sabah were obtained from the Malaysian Institute of Road Safety Research. During this period, a total of 25,439 crashes occurred along federal roads in Sabah, of which 4,875 crashes occurred in mountainous areas. Categorical data analysis techniques were used to examine the differences between mountainous and non-mountainous crashes. Results show that the odds ratio of ‘out-of-control’ crashes and the crash involvement due to speeding are respectively about 4.2 times and 2.8 times higher on mountainous than non-mountainous roads. Other factors and crash characteristics that increase the odds of crashes along mountainous roads compared with non-mountainous roads include horizontal curved sections compared with straight sections, single-vehicle crashes compared with multi-vehicle crashes and weekend crashes compared with weekday crashes. This paper identifies some of the basic characteristics of crashes along rural mountainous roads to aid future research on traffic safety along mountainous roads.

A comparison of road traffic crashes along mountainous and non-mountainous roads in Sabah, Malaysia

Constrained topography and complex road geometry along rural mountainous roads often represent a demanding driving situation. As a result, traffic crashes along mountainous roads are likely to have different characteristics to crashes on roads in flatter areas; however, there is little research on this topic. The objective of this study is to examine the characteristics of road traffic crashes on rural mountainous roads and to compare these with the characteristics of crashes on non-mountainous roads. This paper explores and compares general crash characteristics including crash type, crash severity, roadway geometric features and environmental factors, and road user/vehicle characteristics. Five years of road traffic crash data (2008-2012) for Sabah were obtained from the Malaysian Institute of Road Safety Research. During this period, a total of 25,439 crashes occurred along federal roads in Sabah, of which 4,875 crashes occurred in mountainous areas. Categorical data analysis techniques were used to examine the differences between mountainous and non-mountainous crashes. Results show that the odds ratio of ‘out-of-control’ crashes and the crash involvement due to speeding are respectively about 4.2 times and 2.8 times higher on mountainous than non-mountainous roads. Other factors and crash characteristics that increase the odds of crashes along mountainous roads compared with non-mountainous roads include horizontal curved sections compared with straight sections, single-vehicle crashes compared with multi-vehicle crashes and weekend crashes compared with weekday crashes. This paper identifies some of the basic characteristics of crashes along rural mountainous roads to aid future research on traffic safety along mountainous roads.