A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System

Afrin, Tanzina; Yodo, Nita

doi:10.3390/su12114660

Cited by 275 publications

(167 citation statements)

References 67 publications

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“…Our findings seem to fit the current literature, as one can see, in Figure 4 the use of a treemap visualization can be highly scalable while providing an efficient use of space in the dashboard itself [18], defining the problem, scope and target audience is essential prior to the development of any visualization [7,12] and understanding Smart Cities as multilayered entities is indispensable in order to provide any kind of meaningful insights [1,3], especially when dealing with mobility [11,16].…”

Section: Resultsmentioning

confidence: 95%

“…It is known that traffic jams are mainly caused by two intrinsically distinct factors [16]: (a) Daily commuting, a problem caused by a recurrent overload of the road system (mostly during week days) in both the home to work commuting (typically during the morning) and again in the analogous work to home commuting (typically during the afternoon), a problem most cities still struggle to solve; (b) A random event, road accidents or city wide events such as a sports match, can cause sporadic traffic jams. While the second point can bring some great insights on how to manage mobility given an external factor or event, it is the first point the allows us to consistently take a snapshot of the daily dynamics of mobility in a city, in order to characterize such a diverse structure as a city, recurrent events are always desirable.…”

Section: Prerequisitesmentioning

confidence: 99%

See 1 more Smart Citation

Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support in Smart Cities

Alpalhão¹,

Neto²,

Motta³

2022

Data Science, Data Visualization, and Digital Twins

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Being mobility one of the biggest challenge’s cities face today, the COVID-19 pandemic reinforced this challenge and caused a deep structural change in the mobility of the multilayered dynamic framework of Smart Cities. The need to supply decision support systems to city authorities is higher than ever. Planning and managing mobility in Smart Cities has become more challenging, as the amount of information available and the pressure to enforce sustainable and secure policies increases, stakeholders require faster and more targeted actions. Dashboards are powerful tools that can be used in this context to provide, in an understandable manner, multidimensional information otherwise unavailable in classically static visualizations, as these tools offer a reliable foundation for decision support systems. This chapter goes through the required visualization techniques used to produce meaningful dashboards, to both showcase spatial and temporal trends in the context of mobility in Smart Cities following the COVID-19 pandemic. A general framework for analyzing mobility patterns is suggested by gathering methods and techniques recently developed in the literature.

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Section: Resultsmentioning

confidence: 95%

Section: Prerequisitesmentioning

confidence: 99%

Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support in Smart Cities

Alpalhão¹,

Neto²,

Motta³

2022

Data Science, Data Visualization, and Digital Twins

View full text Add to dashboard Cite

show abstract

“…Many traffic state estimation approaches are developed to overcome the challenge of quantifying the congestion on the road networks [20]. Measures are related to the available traffic parameters, such as speed, travel time, congestion indices, delay, volume, and level of service.…”

Section: Traffic State Estimation Approachesmentioning

confidence: 99%

Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

et al. 2020

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The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

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“…This huge number of vehicles results in a tremendous increase in traffic, especially during peak hours, which is an extensive global phenomenon. In the United States, people travelled 6.9 billion extra hours due to traffic congestion in 2014 [ 2 ]. During such rush hours, vehicles stuck in congestion can access remote servers to fulfill the requirements of task execution.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

Waheed

Shah

Khan

et al. 2021

Sensors

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Computation offloading is a process that provides computing services to vehicles with computation sensitive jobs. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) is envisioned as a promising solution to perform task executions in vehicular networks using an emerging concept known as vehicle-as-a-resource (VaaR). In VCBV systems, offloading is the primary technique used for the execution of delay-sensitive applications which rely on surplus resource utilization. To leverage the surplus resources arising in periods of traffic congestion, we propose a hybrid VCBV task coordination model which performs the resource utilization for task execution in a multi-hop fashion. We propose an algorithm for the determination of boundary relay vehicles to minimize the requirement of placement for multiple road-side units (RSUs). We propose algorithms for primary and secondary task coordination using hybrid VCBV. Extensive simulations show that the hybrid technique for task coordination can increase the system utility, while the latency constraints are addressed.

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A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System

Cited by 275 publications

References 67 publications

Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support in Smart Cities

Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support in Smart Cities

Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

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