Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk. Since the early 21st century, several studies have focused on developing such models. Although the idea has considerably matured over time, the endeavours have been quite discrete and fragmented at best because the fundamental aspects of the overall modelling approach substantially vary. Therefore, a number of transitional challenges have to be identified and subsequently addressed before a ubiquitous proactive safety management system can be formulated, designed and implemented in real-world scenarios. This manuscript conducts a comprehensive review of existing real-time crash prediction models with the aim of illustrating the state-of-the-art and systematically synthesizing the thoughts presented in existing studies in order to facilitate its translation from an idea into a ready to use technology. Towards that journey, it conducts a systematic review by applying various text mining methods and topic modelling. Based on the findings, this paper ascertains the development pathways followed in various studies, formulates the ubiquitous design requirements of such models from existing studies and knowledge of similar systems. Finally, this study evaluates the universality and design compatibility of existing models. This paper is, therefore, expected to serve as a one stop knowledge source for facilitating a faster transition from the idea of real-time crash prediction models to a real-world operational proactive traffic safety management system

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Speed metrics and crash risks: statistical assessment and implications for highway safety policy

This dissertation explores the relationships between measures of crash occurrence, the crash rate and the crash density, and various parameters of speed distributions as measured utilizing automatic traffic recorders (ATR) on highways in Iowa, with special attention to the implications of the findings with regard to highway safety policies such as speed limits and their enforcement. The goal of the research was to determine if crash risk is more related to absolute speed or to some measure of variation of the speed distribution. Data on crashes were obtained from the Iowa DOT crash data base. Roadway segments were selected utilizing criteria to avoid problems of over-long sections as were encountered by Solomon in his 1964 report. Aggregated speed metrics were calculated from raw ATR data provided by the Iowa DOT. Visual Basic programs were developed to calculate the basic speed metrics. Standard statistical tests were used to compare the speed distributions as well as their mean and variance. Logistic regression models were developed to explore the relationship between the dependent variable crash probability and the explanatory variables variance, road type, number of lanes, time of day, and day of week. The evaluation included considering two cases, one with all crashes in the segment and the other with weather-related crashes removed. The hypothesis, that one or more of the speed metrics could be used to determine crash risk, was not supported by the results of the analyses. Recommendations for further research include utilization of new technology, such as (in-vehicle) event data recorders and passive speed measuring devices, to collect non-aggregated speed data

Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.

Improving Traffic Safety at School Zones by Engineering and Operational Countermeasures

Safety issues at school zone areas have been one of the most important topics in the traffic safety field. Although many studies have evaluated the effectiveness of various traffic control devices (e.g., sign, flashing beacon, speed monitoring display), there is a lack of studies exploring different roadway countermeasures and the relationship between school-related factors and crashes. In this study, the most crash-prone school zone was identified in Orange and Seminole Counties, Florida, based on crash rate. Afterward, a microsimulation network was built in VISSIM environment to test different roadway countermeasures in the school zones. Three different countermeasures: two-step speed reduction (TSR), decreasing the number of driveways (DD), and replacing the two-way left-turn lane (TWLTL) to the raised median (RM) were implemented in the microsimulation. Three surrogate safety measures-: (1) time exposed time to collision (TET), (2) time integrated time to collision (TIT) and (3) time exposed rear-end crash risk index (TERCRI) were utilized in this study as indicators for safety evaluation. The higher value of surrogate safety measures indicates higher crash risk. The results showed that both TSR and DD reduced TET, TIT and TERCRI values significantly compare to the base condition. Moreover, the combination of TSR and DD countermeasures outperformed their individual effectiveness. The One-way ANOVA analysis showed that all the sub-scenarios were significantly different from each other. Sensitivity analysis result has proved that all the sub-scenarios in TSR and DD reduced TET, TIT and TERCRI values significantly for different value of TTC threshold. On the other hand, for converting the TWLTL to RM, the crash risk was higher than the base condition because of the turning movements of vehicle. The results of this study could help transportation planners and decision makers to understand the effect of these countermeasures to improve safety at school zones

Water treatment via filtration process

Water is important element for livelihood. About 71% of water covered the Earth surface with 96.5% is salt water, while the remaining 3.5% is fresh water for consumption [1]. The increased of population and urbanization had increase the demand for safe drinking water worldwide. The main challenges in producing potable water is the escalation of polluted water sources

Climate Change Impact Assessment for Surface Transportation in the Pacific Northwest and Alaska

WA-RD 772.

Innovative Revenue Management Practices with Probabilistic Elements

Sale of products with a probabilistic nature, where customers do not know which product they will receive at the time of service, has become popular over the recent years. In the revenue management literature, there has been a growing interest in understanding these modern approaches using analytical techniques. On the other hand, customer-centric revenue management has been replacing the long-standing inventory-centric approach because of the availability of rich data sets by focusing on understanding and predicting customer behavior and then optimizing price and/or quantity related decisions. In this dissertation, we take a customer-centric approach and do not only provide analytical results, but also empirically investigate how customers make their decisions, which is crucial in order to implement appropriate strategies. We first focus on an innovative hotel revenue management practice called standby upgrades, i.e., a practice where the guest is only charged for the discounted upgrade if it is available at the time of arrival. In particular, Chapter 2 discusses how to optimally price standby upgrades and evaluates their benefits through an analytical model. Chapter 3 uses a major hotel chain’s booking and standby upgrades data to investigate the extent of strategic guest behavior through empirical analysis. Then, we focus on another innovative revenue management practice, but in the mega event industry, called team-specific ticket options. Chapter 4 studies fans’ decision-making process for the 2015 College Football season using a unique data set

Design of an Automotive IoT Device to Improve Driver Fault Detection Through Road Class Estimation

Unsafe driver habits pose a serious threat to all vehicles on the road. This thesis outlines the development of an automotive IoT device capable of monitoring and reporting adverse driver habits to mitigate the occurrence of unsafe practices. The driver habits targeted are harsh braking, harsh acceleration, harsh cornering, speeding and over revving the vehicle. With the intention of evaluating and expanding upon the industry method of fault detection, a working prototype is designed to handle initialization, data collection, vehicle state tracking, fault detection and communication. A method of decoding the broadcasted messages on the vehicle bus is presented and unsafe driver habits are detected using static limits. An analysis of the initial design’s performance revealed that the industry method of detecting faults fails to account for the vehicle’s speed and is unable to detect faults on all roadways. A framework for analyzing fault profiles at varying speeds is presented and yields the relationship between fault magnitude and speed. A method of detecting the type of road driven was developed to dynamically assign fault limits while the vehicle traveled on a highway, city street or in traffic. The improved design correctly detected faults along all types of roads and proved to greatly expand upon the current method of fault detection used by the automotive IoT industry today

US 730 corridor refinement plan

168 pp. Tables, figures, appendices. Published October, 2007. Captured September 29, 2009.[T]he US 730 Corridor Refinement Plan was developed to identify circulation and access-management strategies that would address the corridor’s near- and long-term safety needs. [From the Plan