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Traditionally, data have been collected to measure and improve the performance of incident management (IM). While these data are less detailed than crash records, they are timelier and contain useful attributes typically not reported in the crash database. This paper proposes the use of Getis–Ord (Gi*) spatial statistics to identify hot spots on freeways from an IM database while selected impact attributes are incorporated into the analysis. The Gi* spatial statistics jointly evaluate the spatial dependency effect of the frequency and attribute values within the framework of the conceptualized spatial relationship. The application of the method was demonstrated through a case study by using the incident database from the Houston, Texas, Transportation Management Center (TranStar). The method successfully identified the clusters of high-impact accidents from more than 30,000 accident records from 2006 to 2008. The accident duration was used as a proxy measure of its impact. The proposed method could be modified, however, to identify the locations with high-valued impacts by using any other attributes, provided that they were either continuous or categorical in nature and could provide meaningful implications. With improved intelligent transportation system infrastructure and communication technology, hot spot analyses performed with IM data of freeway network and arterials in the vicinity have become a much more promising alternative. Freeway management agencies can use the results of hot spot analysis to provide visualized information to aid the decision-making process in the design, evaluation, and management of IM strategies and resources. The limitations of the method and possible future research are discussed in the closing section of the paper.

Crash-based safety evaluation is often hampered by randomness, lack of timeliness, and rarity of crash occurrences. This situation is particularly true for technology-driven safety improvement projects that are frequently updated or replaced by newer ones before adequate crash data for a reliable and defensible before-and-after evaluation can be gathered. Surrogate safety data are commonly used as alternatives to crash data; however, the current practice is resource intensive and prone to human errors. The advent of connected vehicle technology allows vehicles to communicate with each other and with the infrastructure wirelessly. Through this platform, vehicle movements and signal status at facilities can be automatically and continually monitored in real time. This study explored the viability of long-term monitoring of connected vehicle data for evaluation of safety performance. A safety monitoring application that used connected vehicle data to detect potential safety indicators at signalized intersections was proposed. As limited saturation of onboard equipment (OBE) was expected in near-term evolution, development focused on a roadside equipment application to process data elements from OBE by way of vehicle-to-infrastructure communications through standard message sets. A microscopic simulation was designed to evaluate effectiveness of the proposed safety indicators in detection of degrading safety performance. A signalized intersection test bed was created in VISSIM, while the wireless communications capability and the application were implemented in a car-to-devices application programming interface. Evaluation results indicated that the application could effectively detect changes in safety performance at full market penetration. Future research needs to quantify the combination of penetration rates and monitoring periods that can yield effective detection of changes in safety performance at intersections with varying operating characteristics.

Traffic signal operators are often faced with the challenge of assessing the performance of signal operation and troubleshooting day-to-day operational issues. In many cases, the problems can be specific to a certain time of day or exclusive traffic patterns; this specificity makes it more difficult to pinpoint the problems without spending hours or even days at the sites. With limited resources and budget constraints, public agencies need to find a more efficient and cost-effective approach to operating and maintaining an ever-increasing number of signals within their jurisdictions. To address this challenge, the authors developed a portable tool that consists of a field-hardened computer that interfaces with the traffic signal cabinet through special enhanced bus interface units. The toolbox consists of a monitoring tool and an analysis tool. The monitoring tool monitors and logs relevant events within the cabinet that provide input for analyzing intersection operations. These inputs include signal status, detector call status (including pedestrian calls), preempt status, and coordination status. The analysis tool then analyzes the log files for each day and produces user-friendly reports in hourly average and cycle-based formats. The measures of effectiveness produced by the tool include signal data (e.g., phase time, phase failures, queue clearance time), pedestrian data (e.g., pedestrian calls per hour and average time to service pedestrian), and preempt data (e.g., type of preempt, time of preempt, and duration of preempt). The analyst can install the tool in the signal cabinet and log the data for subsequent evaluation and troubleshooting in the office. The tool was deployed and field-evaluated at two signalized intersections in Texas. The results generated from the tool were found to be in good agreement with observations at the test sites.

Pavement marking test decks are an effective way to evaluate the quality of marking in the field. Transverse test decks provide accelerated wear on markings in the wheelpath area and can provide a side-by-side comparison of different pavement marking materials. The drawback is that the relationship between transverse and long-line pavement marking test decks is relatively unknown. This study was developed to provide better understanding of the relationship between the accelerated wear area on a transverse marking and how it relates to typical wear on a longitudinal marking. The objective of the study was to develop a model for predicting long-line pavement marking retroreflectivity values from transverse pavement marking test deck data. These models and associated parameters can be used to estimate the retroreflectivity of an edge line marking or the amount of time it will take for the edge line marking to reach a given retroreflectivity level. The user needs only the transverse retroreflectivity readings and an initial or assumed initial edge line retroreflectivity value.

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Based upon the results in this study, a simple method to evaluate the risk of right-angle collisions at signalized intersections was provided. Using a count of short PETs at a studied intersection or individual conflict zone within an intersection, the corresponding annual frequency of right-angle collisions can be estimated. Also given is a guideline to help evaluate whether there is excessive risk of right-angle collisions. Contacts AbstractCrash-based safety analysis is set back by several shortcomings such as randomness and rarity of crash occurrences, lack of timeliness, and inconsistency in crash reporting. Non-crash-based safety analysis has been around for more than three decades but its potential was limited due to a difficulty in the data collection and the evaluation. Recent advancement in digital videos and image detection technology renewed our interest in facilitating the data collection and improving the evaluation method. Two image detection systems for the measurement of traffic characteristics were evaluated: (a) a commercial video detection system and (b) proprietary image processing software. The measurement evaluation revealed that both systems were still not sufficiently accurate for the safety evaluation purpose and thus a manual measurement from digitized video clips was preferred for a collection of evaluation data. We proposed a novel application of extreme value theory to safety evaluation based on observable traffic characteristics. The proposed method was evaluated by applying to right-angle collisions at signalized intersections. A traffic characteristic so-called post-encroachment time (PET) was collected at selected intersections as a surrogate safety measure. Based on PET characteristics, risk and frequency of rightangle crashes at the studied intersection or individual conflict zone can be estimated using only the data collected at the location. For comparison, a traditional approach to safety analysis using Poisson and negative binomial regression analyses was also examined. Both evaluation methods -extreme value approach and regression -indicate a significant relationship between PETs and historical crash data. Simulation experiments were carried out to examine the effect of observation period on a variance of estimates obtained the proposed method. Advantages and problems with the proposed method are described in this study. A simple method for an evaluation of the risk of right-angle collisions at signalized intersections is also provided in the appendix.17.