Increasing the complex intersections functioning efficiency by restriction of left-turn traffic flows

Lobashov, Oleksii; Boikiv, Mykola

doi:10.23939/tt2020.01.054

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Tables 24 and C3 (Appendix C) also depict the contrast of one or two compared cities associated with p-values for the metropolitan size of urban cities group for one-year and quarter-year periods successively. In general, the level of similarity for the metropolitan group of three selected cities was largely in line with what has been reported in other studies (Casado-Sanz et al, 2020;Hosseinpour et al, 2014;Lobashov and Boikiv, 2020;Rolison et al, 2018;Saleem and Persaud, 2017;Sun et al, 2021a;Vandenbulcke et al, 2014;Wang et al, 2009). For example, two contrasts only were observed with similar average log counts of cluster values (p-value = 0.4683 and 1) for both St. Louis and Springfield for one-year periods as in Table 24.…”

Section: Cluster Average Log Counts Comparisonsupporting

confidence: 90%

Measuring and comparing the spatiotemporal evolution of accident hot spots

Hamami¹

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[EMBARGOED UNTIL 6/1/2023] One reality of transportation systems is that vehicular accidents can happen practically anywhere and at any time. An increasing body of research suggests though that spatial and/or temporal dependencies (i.e., clusters or hot spots) among accidents likely exist. Along with understanding where and when such spatiotemporal dependencies may occur, another important facet to consider is the geographic extent or area associated with the hot spots. For example, an accident hot spot may involve a small, isolated portion of the transportation system or a much more expansive geographic area. Better delineation and quantification of the morphological characteristics of accident hot spots can provide valuable decision support for planning for accident hot spot mitigation and prevention. As the size and shape of accident hot spots may evolve over time, the capability to track such dynamics is vital, especially with respect to the identification of effecting processes of hot spot occurrence as well as assessments of the efficacy of efforts to mitigate factors underlying hot spot development. For example, a hot spot that is increasing in size over time may indicate areas that are started with a small cluster size then become a part of a larger cluster size during the next periods. Likewise, a cluster that is decreasing in size may signify that some areas may participate with a big cluster size then decrease to a smaller cluster size later. Besides understanding the trend of cluster size evolution during multiple time periods, another important aspect to consider is the percentage or amount of similarity/dissimilarity of cluster morphological characteristics between different urban areas. Better understanding and quantification of morphological characteristics of accident hot spots' similarity characteristics over different urban area levels can also provide valuable decision support for accident safety planning and improve accident alleviation and prevention. To this end, a Geographical Information Systems (GIS) based framework is outlined to facilitate the analysis and comparison of the morphological characteristics of hot spots over time. The analysis framework is applied to a case study of vehicular accidents reported over a two-year period for Morphological analysis and a three-year period for statistical comparison computing to demonstrate its practical utility. The application results of the morphological analysis indicate that patterns of change in hot spot morphology can be effectively quantified, and a variety of informative spatial and temporal patterns can be detected. In addition, the results of cluster statistical comparison computing revealed that the level or percentage of cluster characteristics similarity between urban areas decreases with the increment of the urban city size and road levels complexity.

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Section: Cluster Average Log Counts Comparisonsupporting

confidence: 90%

Measuring and comparing the spatiotemporal evolution of accident hot spots

Hamami¹

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Studying the effectiveness of project solutions using the transport modeling

Plesak

2024

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One of the factors that lead to an increase in congestion on the road network is the growth of motorization levels in the city. Under such conditions, the number of private vehicles on city streets is increasing as delays at intersections and queues. As a result, the passage of the intersection is delayed, as drivers are forced to wait longer for their turn. When reconstructing a street or road, it is proposed to change the traffic management, the number of lanes and the configuration of some intersections to solve the problems described above. However, after the project is implemented, traffic flows are mostly redistributed, and the proposed changes may not be effective. It is imperative to conduct transport modeling, which allows checking the effectiveness of decisions and adjusting the project in the early stages, but not after implementation, to prevent the negative impact of such situations. When performing transport modeling, unnecessary investments that may arise after implementing the project can be avoided, or the feasibility of its implementation can be checked. The object of research is a section of the road network located in a settlement. It is worth noting that all approaches to intersections have high traffic volumes, which should be taken into account when creating project documentation. This article presents a design solution that includes reconfiguring the intersection, increasing the number of lanes, and conducting transport microscopic modeling of the existing and projected conditions to determine the effectiveness of changing the traffic management on the studied section of the road network. According to the results of the microscopic modeling, delays and queues on the approaches to the intersection have been reduced, which indicates that the decision was correct. PTV VISSIM software was used to perform transport modeling. After testing the simulation model and analyzing the results, it is possible to assess the impact of design decisions on the road network and, if necessary, make adjustments.

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Increasing the complex intersections functioning efficiency by restriction of left-turn traffic flows

Cited by 2 publications

References 9 publications

Measuring and comparing the spatiotemporal evolution of accident hot spots

Measuring and comparing the spatiotemporal evolution of accident hot spots

Studying the effectiveness of project solutions using the transport modeling

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