Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

Abstract

Intelligent transportation system (ITS) has become a crucial section of transportation and traffic management systems in the past decades. As a result, transportation agencies keep improving the quality of transportation infrastructure management information for accessibility and security of transportation networks. The goal of this paper is to evaluate the impact of two competing risks: “natural deterioration” of ITS devices and hurricane-induced failure of the same components. The major devices employed in the architecture of this paper include closed circuit television (CCTV) cameras, automatic vehicle identification (AVI) systems, dynamic message signals (DMS), wireless communication systems and DMS towers. From the findings, it was evident that as ITS infrastructure devices age, the contribution of Hurricane Category 3 as a competing failure risk is higher and significant compared to the natural deterioration of devices. Hurricane Category 3 failure vs. natural deterioration indicated an average hazard ratio of 1.5 for CCTV, AVI and wireless communications systems and an average hazard ratio of 2.3 for DMS, DMS towers and portable DMS. The proportional hazard ratios of the Hurricane Category 1 compared to the devices was estimated as <0.001 and that of Hurricane Category 2 < 0.5, demonstrating the lesser impact of the Hurricane Categories 1 and 2. It is expedient to envisage and forecast the impact of hurricanes on the failure of wireless communication networks, vehicle detection systems and other message signals, in order to prevent vehicle to infrastructure connection disruption, especially for autonomous and connected vehicle systems.

An Overview on the Current Status and Future Perspectives of Smart Cars

“…For instance, in the case of a pedestrian preparing to cross the road, a device on the roadside, connected to the mobile network and equipped with a camera capable of detecting the action, sends an alert message to the connected vehicles in the proximity. This situation represents infrastructure-to-vehicle (I2V) communication [9]. In the scenario represented by a connected bicycle and vehicle, moving towards each other, in case of detection of the risk of collision, both receive an alert message on the presence of the other vehicle.…”

“…First of all, the smart car is a self-driving car whose automation level [8,9], as shown in Figure 1, is high (level 4) or complete (level 5). In the first case, it is a substantially full autonomous driving, in which the vehicle can perform all the essential functions of safety and regularly monitor road conditions for the entire trip, but not in extreme weather conditions.…”

Method of Effective Implementation of Intelligent Hardware Complexes in the Management of Passenger Transportation Processes within Urban Agglomerations

“…One of the important directions in the development of the infrastructure of the federal city of St. Petersburg, a city of one million people with a complex system of transportation services, is to ensure an increase in the efficiency and quality of the transportation system [1][2][3].…”

OPC UA Publish-Subscribe and VSOME/IP Notify-Subscribe Based Gateway Application in the Context of Car to Infrastructure Communication

“…It is considered useful where the GPS signal is not available, for example in a tunnel. Because of the increased number of approaches regarding connected and autonomous vehicle applications, studies as [ 12 ] are analyzing risk models for assessing intelligent transportation systems devices. Studies as [ 13 ] are proposing deep reinforcement learning for left-turn in the case of connected and automated vehicle control at signalized intersections in vehicle-to-infrastructure environment, focusing on centralizing data.…”