Benchmark Comparison of Methods for Approximate Neighborhood Estimation of Road Segments in Large-Scale Traffic Networks

Author(s):  
Athanasios I. Salamanis ◽  
George A. Gravvanis ◽  
Christos K. Filelis-Papadopoulos ◽  
Dimitrios Michail
Computing ◽  
2020 ◽  
Vol 102 (11) ◽  
pp. 2333-2360
Author(s):  
Tarique Anwar ◽  
Chengfei Liu ◽  
Hai L. Vu ◽  
Md. Saiful Islam ◽  
Dongjin Yu ◽  
...  

Author(s):  
Mingjian Wu ◽  
Karim El-Basyouny ◽  
Tae J. Kwon

Speeding is a leading factor that contributes to approximately one-third of all fatal collisions. Over the past decades, various passive/active countermeasures have been adopted to improve drivers’ compliance to posted speed limits to improve traffic safety. The driver feedback sign (DFS) is considered a low-cost innovative intervention that is being widely used, in growing numbers, in urban cities to provide positive guidance for motorists. Despite their documented effectiveness in reducing speeds, limited literature exists on their impact on reducing collisions. This study addresses this gap by designing a before-and-after study using the empirical Bayes method for a large sample of urban road segments. Safety performance functions and yearly calibration factors are developed to quantify the sole effectiveness of DFS using large-scale spatial data and a set of reference road segments within the city of Edmonton, Alberta, Canada. Likewise, the study followed a detailed economic analysis based on three collision-costing criteria to investigate if DFS was indeed a cost-effective intervention. The results showed significant collision reductions that ranged from 32.5% to 44.9%, with the highest reductions observed for severe speed-related collisions. The results further attested that the benefit–cost ratios, combining severe and property-damage-only collisions, ranged from 8.2 to 20.2 indicating that DFS can be an extremely economical countermeasure. The findings from this study can provide transportation agencies in need of implementing cost-efficient countermeasures with a tool they need to design a long-term strategic deployment plan to ensure the safety of traveling public.


2019 ◽  
Vol 1 (2-3) ◽  
pp. 161-173 ◽  
Author(s):  
Vilhelm Verendel ◽  
Sonia Yeh

Abstract Online real-time traffic data services could effectively deliver traffic information to people all over the world and provide large benefits to the society and research about cities. Yet, city-wide road network traffic data are often hard to come by on a large scale over a longer period of time. We collect, describe, and analyze traffic data for 45 cities from HERE, a major online real-time traffic information provider. We sampled the online platform for city traffic data every 5 min during 1 year, in total more than 5 million samples covering more than 300 thousand road segments. Our aim is to describe some of the practical issues surrounding the data that we experienced in working with this type of data source, as well as to explore the data patterns and see how this data source provides information to study traffic in cities. We focus on data availability to characterize how traffic information is available for different cities; it measures the share of road segments with real-time traffic information at a given time for a given city. We describe the patterns of real-time data availability, and evaluate methods to handle filling in missing speed data for road segments when real-time information was not available. We conduct a validation case study based on Swedish traffic sensor data and point out challenges for future validation. Our findings include (i) a case study of validating the HERE data against ground truth available for roads and lanes in a Swedish city, showing that real-time traffic data tends to follow dips in travel speed but miss instantaneous higher speed measured in some sensors, typically at times when there are fewer vehicles on the road; (ii) using time series clustering, we identify four clusters of cities with different types of measurement patterns; and (iii) a k-nearest neighbor-based method consistently outperforms other methods to fill in missing real-time traffic speeds. We illustrate how to work with this kind of traffic data source that is increasingly available to researchers, travellers, and city planners. Future work is needed to broaden the scope of validation, and to apply these methods to use online data for improving our knowledge of traffic in cities.


MethodsX ◽  
2019 ◽  
Vol 6 ◽  
pp. 1147-1163 ◽  
Author(s):  
Amila Jayasinghe ◽  
Kazushi Sano ◽  
C. Chethika Abenayake ◽  
P.K.S. Mahanama

1998 ◽  
Vol 14 (1) ◽  
pp. 109-122 ◽  
Author(s):  
Paul M. Wortman ◽  
Joshua M. Smyth ◽  
John C. Langenbrunner ◽  
William H. Yeaton

AbstractA comparison of two assessment methods, consensus among experts and research synthesis of the scientific literature, was performed using a surgical procedure, carotid endarterectomy (CE), as an example. These two methods have been widely advocated as being scientifically valid. While the comparison revealed a number of areas of general agreement, important differences between the two methods emerged. For example, 30-day mortality for asymptomatic patients was considered an effective outcome (ranked first) by the synthesis, but only “quivocal” (ranked third) of six major indicators reported by the consensus method. The synthesis results are also consistent with other literature reviews as well as with recent large-scale randomized trial results. A number of factors that could account for differences between the two methods were examined. Overall, use of consensus panels may be appropriate early in the development of an intervention where the evidence is sparse, while quantitative research synthesis is preferable when a number of high-quality studies have been performed.


2004 ◽  
Vol 14 (04) ◽  
pp. 579-601 ◽  
Author(s):  
MICHAEL HERTY ◽  
AXEL KLAR

Simplified dynamic models for traffic flow on networks are derived from network models based on partial differential equations. We obtain simplified models of different complexity like models based on ordinary differential equations or algebraic models. Optimization problems for all models are investigated. Analytical and numerical properties are studied and comparisons are given for simple traffic situations. Finally, the simplified models are used to optimize large scale networks.


2020 ◽  
Vol 34 (13) ◽  
pp. 2987-2999
Author(s):  
Zaiyong Zhang ◽  
Wenke Wang ◽  
Chengcheng Gong ◽  
Ming Zhang

2019 ◽  
Vol 33 (02) ◽  
pp. 1950001
Author(s):  
Dayong Wang ◽  
Guozhu Jia ◽  
Hengshan Zong ◽  
Wei He

Robustness of infrastructure networks is essential for our modern society. Cascading failures are the cause of most large-scale network outages. We study the cascading failure of networks due to overload, using the betweenness centrality of an edge as the measure of its initial load. Taking into account the congestion effect of a slightly overloading edge, we define two capacities (the basic capacity and the removal capacity) of every edge and give three possible states (the free state, the congestion state, and the removal state) of every edge according to its current load. We propose a new method to dynamically adjust two capacities of the slightly overloading edge and study the dynamical features of cascading propagation induced by removing the edge with the highest load in two artificial networks, two traffic networks, and two power grids. We mainly focus on the relationship between the capacity parameters and two robust metrics. By simulations, we find two interesting and counterintuitive results, i.e. enhancing the basic capacity of every edge may weaken the network robustness, and fixing the basic capacity of every edge, simply improving the removal capacity of every edge sometimes makes the whole network more invulnerable. These findings show that investing more maintenance resources to alleviate flow congestion is not always better to avoid the cascading propagation, which is similar to Braess’s paradox in traffic networks.


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