The safety implications of adopting roundabouts in place of conventional signalized intersections have not been adequately assessed. A microscopic simulation model was used to compare the pattern of traffic conflicts at roundabouts with conflicts for signalized intersections. Three indicators of safety performance were defined: (a) time to collision (TTC), (b) deceleration rate to avoid the crash (DRAC), and (c) crash potential index (CPI). For each indicator, traffic conflict profiles were obtained in terms of number of vehicles in conflict and number of conflicts per vehicle for selected directional maneuvers. The exposure time to conflict for each maneuver and vehicle was also determined. Twelve combinations of geometric and traffic attributes (traffic scenarios) were simulated over a 15-min period. The results suggested that roundabouts yield reduced exposure times to rear-end conflicts compared with signalized intersections. On average, signalized intersections also reflected increased number of vehicles in conflict and percentage of vehicles in conflict compared with roundabouts. This relationship was found to be independent of input volumes and pavement surface condition and applied consistently to all safety indicator measures (TTC, DRAC, and CPI).

Safety performance is evaluated by using measures obtained experimentally from a videotaping of traffic operations at an urban roundabout situated in Cosenza, Italy. Five different expressions of safety performance from the perspective of “rear-end” vehicle interactions include maximum deceleration rate to avoid a crash (DRAC), time-to-collision (TTC), proportion of stopping distance (PSD), time integrated time-to-collision (TIT), and crash potential index (CPI). Differences in safety performance are discussed with respect to the type of measure, traffic conditions, and variations in roundabout geometry. The results of this analysis suggest that safety performance is highly sensitive to the way it is measured; different measures can highlight different locations or geometric features of the roundabout as posing potential safety problems. This study underscores the usefulness of safety performance measures for providing meaningful experimental indicators of potential safety problems at roundabouts and how ...

The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

Safety performance measures represent an useful tool for evaluating road safety conditions on the basis of objective parameters deducible from the vehicle kinematics. In this context, safety performances are expressed in terms of indicators representing interactions between different pairs of vehicles belonging to the traffic stream. Safety performance is expressed from the perspective of rear-end vehicle interactions. Differences in safety performance are discussed with respect to type of indicator and traffic conditions. When these indicators reach a certain critical value (threshold), a possible accident scenario is identified. Most common approaches used to acquire vehicle tracking data are based on video image processing algorithms and satellite navigation systems. However, many studies are increasingly interested in the emerging smartphone technologies for tracking people, and hence vehicles. Due to the fact that smartphones are becoming a valid alternative to Tablets, PDAs and laptops, offering phone features coupled with multiple mobile internet applications, smartphone sales will more than triple to 491.9 million units by 2012 from 139.3 million in 2008 (Gartner Inc. forecasts). The main goal of this study is to present a procedure for extracting vehicle tracking data from smartphone sensors and to use them in the estimation of safety performance indicators. The accuracy of tracking data from smartphone sensors is evaluated with respect to GPS tracking measurements. The results of this analysis identify interactions potentially dangerous and highlight high risk zones that reflect locations characterized by high vehicular interactions. This study underscores the usefulness of the smartphones for providing meaningful experimental data to assess potential safety problems

This paper presents a simple concept which has not been, up to now, thoroughly explored in scientific research: the use of information coming from the network of Internet connected mobile devices (on vehicles) to regulate traffic light systems. Three large-scale changes are going to shape the future of transportation and could lead to the regulation of traffic signal system based on floating car data (FCD): (i) the implementation of Internet connected cars with global navigation satellite (GNSS) system receivers and the autonomous car revolution; (ii) the spreading of mobile cooperative Web 2.0 and the extension to connected vehicles; (iii) an increasing need for sustainability of transportation in terms of energy efficiency, traffic safety, and environmental issues. Up to now, the concept of floating car data (FCD) has only been extensively used to obtain traffic information and estimate traffic parameters. Traffic lights regulation based on FCD technology has not been fully researched since the implementation requires new ideas and algorithms. This paper intends to provide a seminal insight into the important issue of adaptive traffic light based on FCD by presenting ideas that can be useful to researchers and engineers in the long-term task of developing new algorithms and systems that may revolutionize the way traffic lights are regulated.