No abstract

Pedestrians are more likely to be seriously injured in vehicle collisions. In fact, multiple collisions between vehicles and pedestrians occur on residential roads that lack street-to-sidewalk dividers and have numerous blind spots. Traditional traffic safety features and equipment, such as speed bumps and traffic signs, are not always sufficient to prevent pedestrian accidents on such residential roads. Therefore, we suggest a collision risk warning service for residential roads as a solution to this issue. We use CCTVs with computer vision techniques and radar to accurately detect objects in real-time and to trace their trajectories. In addition, we employ a time-to-collision-based method to identify dangerous situations. The service warns drivers and pedestrians about hazardous situations using a light-emitting diode sign board. We applied our service to three different roads on a university campus in Seoul, Korea, and then conducted a user survey to evaluate the service. In summary, more than 90% of respondents stated that the service was necessary for these specific locations, and 76.9% noted that the service significantly contributed to traffic safety on the campus. This implies that the proposed service improved traffic safety and can be applied to various locations on residential roads.

Local roads have numerous blind spots caused by complex geometry, obstacles, and narrow width. Thus, conventional proactive countermeasures, such as passive traffic signs and convex mirrors, have not always been effective in preventing local road collisions. In this paper, we present a novel proactive two-step approach for traffic safety on local roads, comprised of detection of pedestrian-to-vehicle and vehicle-to-vehicle collision risks and warning systems. First, using video surveillance and radars to eliminate blind spots, the system detects road objects, predicts their trajectories and reachable areas, and identifies a potential risk situation. Second, it provides road users such as vehicles and pedestrians with warnings through LED variable message signs, which allows them to react effectively in risky situations. We have applied the system to two local road sites in South Korea, including a university campus in Seoul City and an apartment complex in Daejeon City. The detecting system has been validated using a confusion matrix. We have assessed the warning effect through a before-and-after study and found that the proposed system contributed to the improvement of traffic safety at the case study site in that traffic conflicts decreased by 55–62%.

No abstract

This paper proposes a framework to evaluate the network vulnerability of cities to wildfires. Three cities are selected from the California Public Utilities Commission (CPUC), U.S., fire-threat regions: Orinda, Paradise, and Atascadero. For each city, four different network connectivity measures are calculated, and agent-based evacuation simulations are performed by the Monte Carlo method. In the simulations, the number of isolated vehicles and evacuation time estimates are measured for the following scenarios: (i) no wildfire case with original network; and (ii) wildfire cases with randomly damaged networks that are reduced by 1%, 3%, 5%, 7%, and 10% from the original network. A city-to-city comparison is conducted in relation to network connectivity measures and evacuation simulation results. It is shown that Paradise has the worst network connectivity, and the simulation results reveal that Paradise also has the most sensitive network in relation to random roadway closures caused by wildfire propagation. Thus, among the three cities, Paradise has the most vulnerable network to wildfires as determined through the two analysis results concerning the worst network measures and the simulation results. It is expected that the proposed analysis framework can be generally applied to any city located in a fire-threat region.