Traffic congestion propagation identification method in smart cities

Abstract

Managing the frequent traffic congestion (traffic jams) of the road networks of large cities is a major challenge for municipal traffic management organizations. In order to manage these situations, it is crucial to understand the processes that lead to congestion and propagation, because the occurrence of a traffic jam does not merely paralyze one street or road, but could spill over onto the whole vicinity (even an entire neighborhood). Solutions can be found in professional literature, but they either oversimplify the problem, or fail to provide a scalable solution. In this article, we describe a new method that not only provides an accurate road network model, but is also a scalable solution for identifying the direction of traffic congestion propagation. Our method was subjected to a detailed performance analysis, which was based on real road network data. According to testing, our method outperforms the ones that have been used to date.

Identification of the Possible Security Issues of the 464XLAT IPv6 Transition Technology

“…Other areas of applications include any kinds of IoT [19] and/or LoRaWAN systems [20], where IPv6 may be needed in the access network due to the high number of devices, but some legacy devices still need IPv4 support. Further important application segment is the intelligent transportation systems in smart cities [21] The double translation mechanism of 464XLAT proved its effectiveness in terms of IPv4 literals communications over IPv6 infrastructure. However, it has some security issues and vulnerabilities such as DoS attack possibility.…”

“…Other areas of applications include any kinds of IoT [19] and/or LoRaWAN systems [20], where IPv6 may be needed in the access network due to the high number of devices, but some legacy devices still need IPv4 support. Further important application segment is the intelligent transportation systems in smart cities [21] for the same reason.…”

Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion

“…When a road meets the above two conditions, it is a congested road. The failed edges are e (8,13), e (8,4), e (9, 14), e (9, 10), e (9, 8), e (14,15), e (14,18), e (14,13), e (15,19), e (15,16), e (15,10), and e (18,19). The values of these failed roads are represented in red in Figure 4.…”

“…Task sequence e (9,14) , e (9,10) , e (8,9) , e (8,4) , e (8,13) , e (13,14) 1 e (9,14) , e (9,10) , e (8,4) , e (13,14) , e (8,13) 2 e (8,9) , e (9,10) 3 e (9,14) , e (9,10) , e (8,4) , e (13,14) , e (8,13) , e (8,9) 150 times…”

“…Much research work has been conducted on different aspects to avoid road congestion, such as traffic flow propagation [3][4][5], traffic congestion prediction [6][7][8][9], and the correlation of traffic congestion [10,11]. But it is inevitable that road congestion can propagate to adjacent nodes in an urban road network [12].…”