Estimating an Injury Crash Rate Prediction Model based on severity levels evaluation: The case study of single-vehicle run-off-road crashes on rural context

Abstract In general in case of crash situations the quality of collected data is very limited and several information are usually unreliable. Thus it is recognised that a significant effort is required in order to improve the quality of the crash prediction models moreover a crucial role is played by the identification of the factors influencing the crashes occurrence and the levels of severity estimation. In this paper two injury crash rate prediction models related to single-vehicle run-off-road crashes type are calibrated and in particular significant attributes estimated are identified not only with roadway geometric characteristics and surface conditions, but also with gender/number-of-drivers. To this aim a survey of injury crashes on two-lane rural roads collected in the Southern Italy was considered and analysed. Finally before the calibration step, a preliminary analysis of the data was provided through the estimation of the levels of severity by multinomial logit; in fact by this model only segments with highest values of severity are identified and involved in the calibration procedure

Use of Harsh-Braking Data from Connected Vehicles as a Surrogate Safety Measure

Traffic safety may be analyzed with the use of surrogate safety measures, measures of safety that do not incorporate collision data but rather rely on the concept of traffic conflicts. Use of these measures provides several benefits over use of more traditional analysis methods with historical crash data. Surrogate measures eliminate the need to wait for crashes to occur to conduct a safety analysis. The amount of time required for enough crash data to accumulate can be significant, delaying safety analyses. Similarly, these measures allow for safety analysis to be conducted prior to crashes occurring, potentially calling attention to hazardous areas which may be altered to prevent crashes. In addition to these benefits, traffic conflicts occur much more frequently than collisions, generating many more data points which in turn make statistical methods of analysis more effective. Evaluating surrogate safety measures for a particular transportation network is most effectively done with the use of traffic microsimulation or with connected vehicle data. Traffic microsimulation (such as the use of PTV VISSIM) will generate kinematic data that may then be used for computation of surrogate safety measures. A significant amount of research has been done on this topic, resulting in the establishment of algorithms for calculation of several different surrogate measures and validation of these measures. Kinematic data from connected vehicles has also been used for the calculation of surrogate safety measures. One data point collected by connected vehicles is harsh braking events which could serve as a surrogate safety measure. Because drivers usually brake more gently if given the opportunity to do so, harsh braking events indicate that a traffic conflict has occurred or is about to occur. Such events take away the driver’s opportunity to brake gently. This research establishes statistical models which relate harsh braking events to crashes on intersections and segments in Salt Lake City, Utah. The findings indicate that harsh braking events have the effect of reducing expected crashes because they represent traffic conflicts which were remedied through the use of harsh braking as an evasive action. The presence of schools and the presence of left turn lanes were also found to be statistically significant crash predictors. In addition to this research work a paper outlining the existing state of safety analysis with surrogate safety measures and evaluating the usefulness and practicality of various existing measures is presented

Feasibility Analysis of a Connected Vehicle Wrong-Way Driver Countermeasure System

Despite existing countermeasures for addressing wrong-way driving, crashes relating to wrong-way driving continue to occur on Texas roads. These crashes tend to be more severe than typical crashes since they tend to be head-on collisions at high speeds. This study considers a countermeasure designed to use connected vehicle communications, on high-speed, controlled access, freeway-type facilities. This study quantifies the impacts of a connected vehicle wrong-way driving countermeasure (CV-WWD) system, translates them into a benefit-cost ratio that represents the economic value of the system, and performs the analysis on a generic case and case study to draw conclusions on potential deployment needs for the system. To determine the probability that a vehicle received a warning about the wrong-way driver (WWD) early enough to be able to make an informed decision earlier than if they were not equipped, calculations were done to determine vehicle presence, connected vehicle capability probability, and successful warning message transmission. The increased time for response was translated into reduced crash probability for various market penetration rates (MPRs) of connected vehicles. Each analysis used the baseline scenario as the case where the MPR of zero, representing no connected vehicles, was used as a baseline for the economic analysis. Reduced crash probability for a single event was used to estimate the benefit over the life of the system. The benefit-cost ratio was this benefit divided by the cost of the system. The findings of the study indicate that the WWD crash rate is the driving factor for economic feasibility. Each traffic density considered had similar MPRs for feasibility across each crash rate, with a rate of one WWD crash ever five years needing about 37 percent MPR and a rate of once a year only needing 17 percent MPR to break even. The case study on US-75 in downtown Dallas, TX, which has a crash rate of 1.8 WWD crashes per year, showed that a system installed there could be feasible with an MPR as low as seven percent. These results show that the system has potential to be economically feasible at low MPRs with a sufficiently high crash rate

Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management

The decision to select the best Intelligent Transportation System (ITS) technologies from available options has always been a challenging task. The availability of connected vehicle/automated vehicle (CV/AV) technologies in the near future is expected to add to the complexity of the ITS investment decision-making process. The goal of this research is to develop a multi-criteria decision-making analysis (MCDA) framework to support traffic agencies’ decision-making process with consideration of CV/AV technologies. The decision to select between technology alternatives is based on identified performance measures and criteria, and constraints associated with each technology. Methods inspired by the literature were developed for incident/bottleneck detection and back-of-queue (BOQ) estimation and warning based on connected vehicle (CV) technologies. The mobility benefits of incident/bottleneck detection with different technologies were assessed using microscopic simulation. The performance of technology alternatives was assessed using simulated CV and traffic detector data in a microscopic simulation environment to be used in the proposed MCDA method for the purpose of alternative selection. In addition to assessing performance measures, there are a number of constraints and risks that need to be assessed in the alternative selection process. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. This research utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied to an ITS investment case study to support freeway bottleneck management. The results of this dissertation indicate that utilizing CV data for freeway segments is significantly more cost-effective than using point detectors in detecting incidents and providing travel time estimates one year after CV technology becomes mandatory for all new vehicles and for corridors with moderate to heavy traffic. However, for corridors with light, there is a probability of CV deployment not being effective in the first few years due to low measurement reliability of travel times and high latency of incident detection, associated with smaller sample sizes of the collected data

Geospatial and statistical methods to model intracity truck crashes

In recent years, there has been a renewed interest in statistical ranking criteria to identify hot spots on road networks. These criteria potentially represent high crash risk zones for further engineering evaluation and safety improvement. Many studies also focused on the development of crash estimation models to quantify the safety effects of geometric, traffic, and environmental factors on expected number of total, fatal, injury, and/or property damage crashes at specific locations. However, freight safety, specifically truck safety, was meagerly addressed. Trucks and long-combination vehicles (LCVs) that carry approximately 70% freight have significant potential in triggering crash occurrences on roads, mostly severe crashes. Truck transportation is therefore attracting more and more attention due to its effect on safety and operational performance as well as rapid industrial growth. Most of the past research on truck safety focused on intercity or Interstate truck trips. Intracity truck safety related studies or research was hardly pursued. The major research objectives of this dissertation are: 1) to develop a geospatial method to identify high truck crash zones, 2) to evaluate the use of different ranking methods for prioritization and allocation of resources, 3) to investigate the relations between intracity truck crash occurrences and various predictor variables (on- and off-network characteristics) to provide greater insights regarding crash occurrence and effective countermeasures, and 4) to develop truck crash prediction models. The prioritization of high truck crash zones was performed by identifying truck crash hot spots and ranking them based on several parameters. Geospatial methods along with statistical methods were deployed to understand the relationships between geometric road conditions, land use characteristics, demographic, and socio-economic characteristics and truck crashes. Truck crash estimation models were then developed using selected on- and off- network characteristics data. To assess the suitability of these models, several goodness-of-fit statistics were computed. The geospatial methods and development of truck crash estimation models are illustrated using data for the city of Charlotte, North Carolina for the year 2008. It was found that on-off network characteristics, socio-economic characteristics and demographic characteristics that are within the 0.5-mile proximity have a vital influence on truck crash occurrence. The findings from the research are expected to provide information and methods on identifying truck crash zones and the likelihood of a truck crash occurrence due to intracity trips and its relationship with on- and off-network characteristics of a region. Furthermore, this research is expected to aid significantly in the process of selecting meaningful countermeasures to improve safety of users on roads

Application of big data in transportation safety analysis using statistical and deep learning methods

The emergence of new sensors and data sources provides large scale high-resolution big data from instantaneous vehicular movements, driver decision and states, surrounding environment, roadway characteristics, weather condition, etc. Such a big data can be served to expand our understanding regarding the current state of the transportation and help us to proactively evaluate and monitor the system performance. The key idea behind this dissertation is to identify the moments and locations where drivers are exhibiting different behavior comparing to the normal behavior. The concept of driving volatility is utilized which quantifies deviation from normal driving in terms of variations in speed, acceleration/deceleration, and vehicular jerk. This idea is utilized to explore the association of volatility in different hierarchies of transportation system, i.e.: 1) Instance level; 2) Event level; 3) Driver level; 4) Intersection level; and 5) Network level. In summary, the main contribution of this dissertation is exploring the association of variations in driving behavior in terms of driving volatility at different levels by harnessing big data generated from emerging data sources under real-world condition, which is applicable to the intelligent transportation systems and smart cities. By analyzing real-world crashes/near-crashes and predicting occurrence of extreme event, proactive warnings and feedback can be generated to warn drivers and adjacent vehicles regarding potential hazard. Furthermore, the results of this study help agencies to proactively monitor and evaluate safety performance of the network and identify locations where crashes are waiting to happen. The main objective of this dissertation is to integrate big data generated from emerging sources into safety analysis by considering different levels in the system. To this end, several data sources including Connected Vehicles data (with more than 2.2 billion seconds of observations), naturalistic driving data (with more than 2 million seconds of observations from vehicular kinematics and driver behavior), conventional data on roadway factors and crash data are integrated

Ανάπτυξη προτύπου προσομοίωσης για την πρόβλεψη και τη διαχείριση έκτακτων συμβάντων σε δίκτυα αυτοκινητόδρομων

177 σ.Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management.Η Οδική Ασφάλεια αποτελεί πεδίο ερευνητικού ενδιαφέροντος για μηχανικούς κατά τις τελευταίες δεκαετίες. Ανεξάρτητα από τις εφαρμοζόμενες μεθόδους προτυποποίησης, σημαντικός παράγοντας ανακρίβειας πρότερων διερευνήσεων είναι η ομαδοποίηση δεδομένων. Ωστόσο, οι περισσότεροι αυτοκινητόδρομοι είναι πλέον εξοπλισμένοι με συστήματα παρακολούθησης, τα οποία καθιστούν διαθέσιμα μη ομαδοποιημένα κυκλοφοριακά δεδομένα. Η διαθεσιμότητα των δεδομένων αυτών δεν έχει επαρκώς αξιοποιηθεί ερευνητικά. Στόχος της διατριβής είναι η αξιοποίηση των κυκλοφοριακών δεδομένων αυτοκινητόδρομων που συλλέγονται σε πραγματικό χρόνο κατά τη στιγμή εκδήλωσης ατυχήματος. Για το σκοπό αυτό, μελετήθηκε η επίδραση διάφορων κυκλοφοριακών παραμέτρων στον τύπο οδικού ατυχήματος, αλλά και στο επίπεδο σοβαρότητας τραυματισμού των επιβαινόντων. Παράλληλα, ελήφθησαν υπόψιν παράγοντες σχετιζόμενοι με το περιβάλλον και τη γεωμετρία. Εφαρμόστηκαν μοντέλα probit σε τετραετή δεδομένα συμβάντων από το κοινό τμήμα των αυτοκινητόδρομων Α4-Α86 στην περιοχή Ile-de-France της Γαλλίας. Τα εμπειρικά αποτελέσματα καταδεικνύουν ότι ο τύπος ατυχήματος μπορεί –σχεδόν αποκλειστικά- να εκτιμηθεί από τις επικρατούσες κυκλοφοριακές συνθήκες. η αύξηση του κυκλοφοριακού φόρτου φαίνεται να ασκεί σταθερή επίδραση στη σοβαρότητα των ατυχημάτων, ενώ η επίδραση της ταχύτητας διαφοροποιείται ανάλογα με το επίπεδο του κυκλοφοριακού φόρτου. Στη συνέχεια, αναπτύσσεται πλαίσιο για την ένταξη κυκλοφοριακών δεδομένων πραγματικού χρόνου στη διαχείριση συμβάντων. Τέλος, τα πορίσματα της διατριβής χρησιμοποιούνται στη διερεύνηση εφαρμογών με απώτερο στόχο τον περιορισμό της προδιάθεσης πρόκλησης συμβάντων και τη βελτιωμένη διαχείρισή τους.Ζωή Δ. Χριστοφόρο

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