Identification of Secondary Traffic Crashes and Recommended Countermeasures

Secondary crashes (SCs) usually occur due to congestion or other prior incidents. SCs are increasingly spotted as a significant issue in traffic operations, leading to reduced capacity, extra traffic delays, increased fuel consumption, and additional emissions. SCs have substantial impacts on traffic management resource allocation. One of the challenges in the traffic safety area of the transportation industry is to determine an adequate method for identifying SCs. The specific objectives of this study are: identification of SCs using spatiotemporal criteria and exploring the contributing risk factors to the identified SCs. Two different approaches were explored to identify SCs. The first approach is based on a “static” method that employs a predefined 2 miles-2 hours fixed spatiotemporal threshold. Four-year (2011 to 2014) crash and traffic data from the Crash Analysis Reporting (CAR) system database were used. The linear referencing tool of Geographic Information Systems (GIS) was applied to identify crashes that fell within the threshold. About 1.49% of all crashes were identified as SCs. A Structural Equation Model (SEM) was developed to investigate the contributing risk factors to the occurrence and severity level of SCs. Model results revealed that a series of driver attributes contributed to the occurrence of SCs, including the influence of alcohol or drug, inattentive driving, fatigue or speeding. Other variables that might lead to higher probabilities of SCs include vehicle attributes (brake defects, motorcycles), roadway conditions (roadway surface, vision obstruction) and environmental factors (raining condition Given that about 40% of SCs were rear-end crashes, this study also examined contributing factors to severity levels of rear-end SCs. Results revealed that the presence of horizontal curves, presence of guardrail, and posted speed limit showed a significant influence on the severity level of SCs. Crash modification factors were also developed by considering the roadway and traffic characteristics. In contrast to the static method, the dynamic approach identifies a dynamic spatiotemporal impact area for each primary incident using the Speed Contour Plot method. This analysis was explored using the Regional Integrated Transportation Information System (RITIS) and the SunGuide™ database for the year of 2014-2017. This study further analyzed contributing risk factors to SCs on I-95 and found that SCs were more likely to occur if primary incident clearance times were longer. It also revealed that SCs were more severe at night and on weekends. It implies that timely emergency responses would have a significant effect on mitigating SCs. These findings point to necessary strategies to mitigate SCs, including improved traffic management policies and implementation of advanced intelligent transportation warning systems. One of the challenges in addressing SCs lies in the lack of quality databases (such as speed data and incident information) to appropriately identify and investigate SCs. Therefore, future efforts may focus on institute a framework that combines all levels of databases from multiple sources, which can help timely identification and investigation of SCs. This would lead to the development and implementation of efficient and effective countermeasures to mitigate SC and enhance safety

Short-term crash risk prediction considering proactive, reactive, and driver behavior factors

Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. Highway crashes are among the most significant challenges to achieving this goal. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to predictive models of crash occurrence and severity. Most of these models are reactive: they use the data about crashes that have occurred in the past to identify the significant crash factors, crash hot-spots and crash-prone roadway locations, analyze and select the most effective countermeasures for reducing the number and severity of crashes. More recently, the advancements have been made in developing proactive crash risk models to assess short-term crash risks in near-real time. Such models could be applied as part of traffic management strategies to prevent and mitigate the crashes. The driver behavior is found to be the leading cause of highway crashes. Nevertheless, due to data unavailability, limited studies have explored and quantified the role of driver behavior in crashes. The Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) offers an unprecedented opportunity to perform an in-depth analysis of the impacts of driver behavior on crashes events. The research presented in this dissertation is divided into three parts, corresponding to the research objectives. The first part investigates the application of advanced data modeling methods for proactive crash risk analysis. Several proactive models for segment level crash risk and severity assessment are developed and tested, considering the proactive data available to most transportation agencies in real time at a regional network scale. The data include roadway geometry characteristics, traffic flow characteristics, and weather condition data. The analysis methods include Random-effect Bayesian Logistics Regression, Random Forest, Gradient Boosting Machine, K-Nearest Neighbor, Gaussian Naive Bayes (GNB), and Multi-layer Feedforward Deep Neural Network (MLFDNN). The random oversampling technique is applied to deal with the problem of data imbalance associated with the injury severity analysis. The model training and testing are completed using a dataset containing records of 10,155 crashes that occurred on two interstate highways in New Jersey over a period of two years. The second part of the study analyzes the potential improvement in the prediction abilities of the proposed models by adding reactive data (such as vehicle characteristics and driver characteristics) to the analysis. Commonly, the reactive data is only available (known) after the crash occurs. In the proposed research, the crash analysis is performed by classifying crashes in multiple groupings (instead of a single group), constructed based on the age of drivers and vehicles to account for the impact of reactive data on driver injury severity outcomes. The results of the second part of the study show that while the simultaneous use of reactive and proactive data can improve the prediction performance of the models, the absolute crash probability values must be further improved for operational crash risk prediction. To this end, in the third part of the study, the Naturalistic Driving Study data is used to calibrate the crash risk models, including the driver behavior risk factors. The findings show significant improvement in crash prediction accuracy with the inclusion of driver behavior risk factors, which confirms the driver behavior to be the most critical risk factor affecting the crash likelihood and the associated injury severity

Situational Awareness for Transportation Management: Automated Video Incident Detection and Other Machine Learning Technologies for the Traffic Management Center

IA 65A0541This report provides a synthesis of Automated Video Incident Detection (AVID) systems as well as a range of other technologies available for Automated Incident Detection (AID) and more general traffic system monitoring. In this synthesis, the authors consider the impacts of big data and machine learning techniques being introduced due to the accelerating pace of ubiquitous computing in general and Connected and Automated Vehicle (CAV) development in particular. They begin with a general background on the history of traffic management. This is followed by a more detailed review of the incident management process to introduce the importance of incident detection and general situational awareness in the Traffic Management Center (TMC). The authors then turn their attention to AID in general and AVID in particular before discussing the implications of more recent data sources for AID that have seen limited deployment in production systems but offer significant potential. Finally, they consider the changing role of the TMC and how new data can be integrated into traffic management processes most effectively

Stohastički model za utvrđivanje optimalne putarine

Private participation in the delivery of toll road projects has been used worldwide. It is a model which incorporates private sector knowledge and experience in the management of highway projects and mobilizes private capital through Public-Private Partnerships (PPP). One of the most prevailing characteristics of PPP projects is risk sharing between the public and private partners. Assessment of a project’s financial soundness, a crucial factor for private sector involvement, is the basic underlying process throughout the project’s development until the project reaches financial closure. The traditional cash flow analysis of the financial feasibility of a project has shown weaknesses in many cases. From the pool of delivered projects which have experienced difficulties in their operations, it can be learned that advanced probabilistic models need to be introduced due to their feature of representing uncertainties more realistically. It is important to capture a project’s uncertainties even in early phases of financial analysis since this information helps in the identification of potential financial risks and assists all sides to structure the deal properly. Parameters commonly used for the evaluation of a project’s financial feasibility are the annual debt service cover ratio (ADSCR), the internal rate of return (IRR), and the return on equity (ROE). Although some existing models for analysis of a project may seem difficult for decision makers and stakeholders to interpret and understand, there are prospective ways of describing and representing the problem in more understandable and meaningful ways. This research presents a methodological framework for an early assessment of acceptable toll rates for PPP toll road projects taking into account multiple uncertainties. A toll rate is considered acceptable if it is acceptable for all stakeholders. This approach takes into account predefined financial constraints ADSCR, IRR and ROE on one side, and the project’s uncertainties, such as volatility of traffic volumes, construction costs variation, and operation and maintenance costs variation on the other side. Selected financial parameters represent the preferences or requirements of potential investors that must be fulfilled in order for them to invest in a PPP project. These preferences and financial requirements are based on investors' assessments of a project's risk profile and also depend on activities on capital markets...Учешће приватног сектора у реализацији путних пројеката са наплатом путарине заступљено је широм света. Овај модел користи знање и искуство приватног сектора у управљању путним пројектима и мобилише приватни капитал кроз јавно-приватнo партнерство (ЈПП). Једна од најзначајнијих карактеристика ЈПП пројеката је подела ризика између јавног и приватног партнера. Процена финансијске основаности пројекта, кључног фактора за учешће приватног сектора, основни је процес целокупног развоја пројекта све до закључења финансијског аранжмана за пројекат. Традиционална анализа новчаних токова финансијске оправданости пројекта је показала своје недостатке у доста случајева. Из узорка реализованих пројекта који су имали потешкоће у оперативној фази, може се закључити да је неопходно увести напредне моделе вероватноће због њихове могућности да реалније представе неизвесност. Важно је да се увиде ризици пројекта и у раним фазама финансијске анализе обзиром да ова информација помаже у сагледавању потенцијалних финансијских ризика и омогућава свим заинтересованим странама да правилно склопе споразум. Параметри који се често користе за процену финансијске оправданости пројекта су годишњи рацио покрића дуга, интерна стопа рентабилитета пројекта и интерна стопа повраћаја уложеног капитала. Иако неки од постојећих модела за анализу пројекта могу доносиоцима одлука и кључним интересним групама да делују компликовано за разумевање и интерпретацију, постоје други разумљивији и смисленији начини описивања и презентовања проблема. Ово истраживање представља методолошки оквир за рано утврђивање оптималне висине путарине за ЈПП путне пројекте са наплатом путарине узимајући у обзир различите неизвесности. Путарина се сматра оптималном ако је прихватљива за све кључне учеснике у пројекту. Овај приступ користи унапред дефинисана финансијска ограничења годишњи рацио покрића дуга, интерне стопе рентабилитета пројекта и интерне стопе повраћаја уложеног капитала са једне стране, и ризике пројекта као што су нестабилност обима саобраћаја, варијације у трошковима изградње, и варијације у трошковима управљања и одржавања, са друге стране. Изабрани финансијски параметри представљају приоритете или захтеве потенцијалних инвеститора који морају бити испуњени како би се инвестирало у ЈПП пројекат. Ови приоритети и финансијски захтеви се базирају на процени профила ризика пројекта од стране инвеститора, а такође зависе и од активности на тржишту капитала..

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCube

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCub

Traffic modeling, forecasting and assignment in large-scale networks

Today, the development and evaluation of traffic management strategies heavily relies on microscopic traffic simulation models. In case detailed input (i.e. od matrix, signal timings, etc.) is extracted and incorporated in these simulators, they can provide valuable traffic state predictions. However, as this type of information is almost never available at the large-scale and traffic represents chaotic behavior in saturated networks, microscopic simulation models remain intractable and unstable. An alternative is a recently discovered network traffic model; macroscopic fundamental diagram (MFD). Nevertheless, large-scale traffic management strategies remain a big challenge partly due to unpredictability of choices of travelers (e.g. route, departure time and mode choice). Part I of the thesis is an attempt to fill this gap. Chapter 2, 3 and 4 elaborate new aspects of large-scale traffic modeling, and integrate route choice behavior into the modeling. Chapter 2 proposes a dynamic traffic assignment (DTA) model to establish equilibrium conditions in multi-region urban networks where the modeling is done through MFD dynamics. The method handles the stochastic components of the aggregated model through a sampling approach. In addition, the assignment model enables us to consider the response of drivers to changing traffic conditions in an aggregated modeling framework. Chapter 3 extends the DTA model presented in Chapter 2 to a route guidance system, where drivers are given a sequence of subregions to follow. Two aggregated models, region- and subregion-based models, are introduced to develop the guidance scheme and to test its effect, respectively. Notably, the challenge here is to translate certain variables across the traffic models without a loss of significance and assure certain degree of consistency. Chapter 4 extracts and reconstructs aggregated route choice patterns through an extensive GPS data set from taxis in a mega city. Observed GPS trajectories are first grouped together to provide a physical evidence for consistent route patterns. Second, in order to investigate the consistency of equilibrium assumptions considered in Chapter 2, observed trajectories are replaced with shortest path trajectories, and aggregated route choice patterns are reconstructed. Part II introduces novel travel time prediction and variability models. Travel time is a crucial performance measure in assessing the efficiency of transportation systems, and it provides a common index for both practitioners and travelers. Chapter 5 develops a travel time prediction model that jointly exploits traffic flow fundamentals and advanced data mining techniques. The prediction method detects the congestion patterns through the identification of active bottlenecks, and clusters the days with similar traffic patterns. This approach basically allows the model to train its predictions with relevant historical data sets. The method is applicable in oversaturated conditions and consistent with physics of traffic flow. Nevertheless, travelers not only consider travel time on average, but also value its variation. Day-to-day travel time variability, addressing the travel time variations of vehicles crossing the same route at the same period of time on different days, reveals interesting patterns. Departure time periods with similar mean travel times in the onset and offset of congestion exhibit quite different variance values. This phenomenon causes counter-clockwise hysteresis loops on the mean-variance curves. Chapter 6 investigates the empirical implications of hysteresis shape within the context of day-to-day travel time variability

Analysis of Selected Distractive Stimuli on the Driver Attention

Dizertační práce se zaměřuje na problematiku nepozornosti řidičů jako jeden z hlavních faktorů přispívajících ke vzniku dopravních nehod. V úvodu je vymezena důležitost řešené tematiky prostřednictvím rozsáhlé rešerše. V rámci teoretického rozboru je uvedena terminologie v oblasti analýzy nehod související s problematikou této dizertační práce. Dále byly analyzovány stávající metody využívané pro analýzu chování řidičů. Byly představeny rovněž výsledky existujících výzkumných studií s důrazem zejména na problematiku vlivu rušivých vlivů na pozornost řidiče. Na základě provedené rešerše byly identifikovány výzkumné otázky, z nichž část je předmětem řešení této práce. Vymezená problémová situace a rovněž formulované problémy poukazují zejména na nezbytnost nalezení a validaci relevantních moderních metod pro analýzu vlivu vybraných rušivých vlivů na pozornost řidiče, resp. vybrané fáze procesu zpracování informace řidičem. Série provedených měření pak poskytuje rovněž kvantifikované údaje zejména s ohledem na časovou náročnost vnímání vybraných rušivých podnětů řidičem.The dissertation thesis focuses on the problem of driver inattention as one of the main factors contributing to the traffic accidents occurrence. The importance of this topic has been introduce on the basis of the literature review. The theoretical analysis includes terminology in the field of accident analysis related to the topic of this dissertation thesis. Existing methods for driver behavior analysis have been introduced as well as the selected resuls of existing research studies focused on the driver inattention. Research gaps have been identified on the basis of the research review, on some of them has been focused this work. The defined problem situation points to the need to find and verify the relevant modern methods for analysis of driver inattention, respectivaly influence of selected distracting stimuli on the stages of driver information processing. The series of measurements also provides quantified data, especially with regard to the perception duration of selected distracting stimuli.

Путешествие

The textbook «Travelling» is the most complete specialized textbook for training specialists in English in the field of travelling. It is based on the most recent data on the main directions of the travelling industry development. The value and timeliness of this tutorial will help you on the one hand – to improve your English skills, and on the other – to make better your knowledge in your professional context. The structure of this textbook is that – four chapters: the cities of the world (English, American and others); travelling by railway, air and sea. In the book there are a lot of authentic texts in English, developed exercises, dialogues, charts, colour inserts. This tutorial allows you to learn the professional vocabulary quickly and easily and ameliorate your level of English.Даний підручник «Подорожі» є найповнішим спеціалізованим навчальним посібником із професійної підготовки фахівців з англійської мови у сфері подорожей. Він побудований на базі найсучасніших даних із основних напрямків розвитку туристичної галузі. У цьому полягає цінність і своєчасність даного навчального підручника, який допоможе, з одного боку, удосконалити знання англійської мови, а з іншого – поліпшити свої знання у професійній сфері. Структура даного підручника така – чотири розділі: великі і малі міста (прогулянка містом, історія міст, англійські міста, американські міста, міста світу); подорож залізницею, авіаподорож, морська подорож. У підручнику велика кількість автентичних текстів англійською мовою; розроблені вправи, діалоги, схеми, кольорові вкладиші. Даний підручник дозволяє швидко і легко засвоїти професійну лексику і підвищити свій рівень знання англійської мови