Visual Cause Analytics for Traffic Congestion

Pi, Mingyu; Yeon, Hanbyul; Son, Hyesook; Jang, Yun

doi:10.1109/tvcg.2019.2940580

Cited by 46 publications

(29 citation statements)

References 75 publications

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“…The trend of improvement ratio for CO emissions and fuel consumption in different schemes shows the same characteristics: (1) The improvement ratio increases with the increase of V/C in the northbound and southbound directions. (2) In most cases, the improvement degree of CO emissions and fuel consumption is positive; only in a small number of cases is the improvement ratio of CO emissions and fuel consumption less than 0, with the maximum negative optimization of 5% under the worst case, indicating that two-leg CFI schemes can reduce emissions and fuel consumption while increasing the number of vehicles, further reflecting the superiority of CFI scheme in improving traffic efficiency.…”

Section: Sensitivity Analysis Of Operational Performancementioning

confidence: 92%

“…Traffic volume in the morning peak hour is highest, so it is selected as the representative traffic volume, the detailed data of which is listed in Table 2. In this table, 0 km/h means the stops of vehicle, which would occur in the following two situations: (1) The traffic lights in the direction for the vehicle are red, so the vehicle needs to stop and wait; (2) The congestion is too severe to move for vehicles, so even the traffic lights in the direction for the vehicle are green.…”

Section: Data Collectionmentioning

confidence: 99%

“…The rapid growth of the number of motor vehicles has brought about serious traffic congestion. The time costs due to delays, the additional economic costs, and the environmental pollution costs caused by urban traffic congestion seriously affect public life and restrict economic development [1,2]. Under this circumstance, sustainable roadways and sustainable transportation, which aim at solving traffic congestion, reducing environmental pollution and optimizing resource utilization, should be vigorously developed, which is of great significance to the urban development and transportation.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and Analysis of CFI Schemes with Different Length of Displaced Left-Turn Lanes with Entropy Method

et al. 2021

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As an unconventional design to alleviate the conflict between left-turn and through vehicles, Continuous Flow Intersection (CFI) has obvious advantages in improving the sustainability of roadway. So far, the design manuals and guidelines for CFI are not enough sufficient, especially for the displaced left-turn lane length of CFI. And the results of existing research studies are not operational, making it difficult to put CFI into application. To address this issue, this paper presents a methodological procedure for determination and evaluation of displaced left-turn lane length based on the entropy method considering multiple performance measures for sustainable transportation, including traffic efficiency index, environment effect index and fuel consumption. VISSIM and the surrogate safety assessment model (SSAM) were used to simulate the operational and safety performance of CFI. The multi-attribute decision-making method (MADM) based on an entropy method was adopted to determine the suitability of the CFI schemes under different traffic demand patterns. Finally, the procedure was applied to a typical congested intersection of the arterial road with heavy traffic volume and high left-turn ratio in Xi’an, China, the results showed the methodological procedure is reasonable and practical. According to the results, for the studied intersection, when the Volume-to-Capacity ratio (V/C) in the westbound and eastbound lanes is less than 0.5, the length of the displaced left-turn lanes can be selected in the range of 80 to 170 m. Otherwise, other solutions should be considered to improve the traffic efficiency. The simulation results of the case showed CFI can significantly improve the traffic efficiency. In the best case, compared with the conventional intersection, the number of vehicles increases by 13%, delay, travel time, number of stops, CO emission, and fuel consumption decrease by 41%, 29%, 25%, 17%, and 17%, respectively.

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Section: Sensitivity Analysis Of Operational Performancementioning

confidence: 92%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation and Analysis of CFI Schemes with Different Length of Displaced Left-Turn Lanes with Entropy Method

et al. 2021

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“…Knowing the cause of traffic jams is very important for domain experts because it provides an efficient way to monitor traffic, infrastructure, and events that affect traffic conditions and city life. In recent work, Pi et al [1] propose a method to find the cause of traffic jams based on deep learning. More clearly, they use traffic flow data from taxis to classify patterns in four classes: accidents, traffic lights, large traffic jams, and free flow.…”

Section: Traffic Jams Detection Analysismentioning

confidence: 99%

“…This problem is significant in major urban centers where traffic jams cause billions of dollars in losses every year. For this reason, it is essential for traffic planning and urban mobility experts to monitor and understand the leading causes of traffic congestions to plan policies and, therefore, identify solutions [1][2][3][4]. Due to the importance of this problem, many cities 1,2 acquire and publish traffic data from different sources such as road sensors.…”

Section: Introductionmentioning

confidence: 99%

JamVis: exploration and visualization of traffic jams

Rodriguez

Ferreira

Poco

2022

Eur. Phys. J. Spec. Top.

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Traffic jams are a significant problem in urban cities that cause pollution and waste fuel, money, and time. Therefore, there is an urgent need to build tools that enable authorities to monitor and understand traffic dynamics and their causes. However, exploring these large complex data presents a challenge to domain experts. This paper proposes JamVis, a web-based visual analytics framework that leverages Waze’s multi-modal spatio-temporal data to this end. JamVis comprises two main components designed based on requirements elicited from domain experts. The first one supports the exploration of Waze’s traffic jam information through multiple linked views. The second component allows identifying events through alerts reported by Waze users about different problems (e.g., potholes, floods, or heavy traffic). A new algorithm called TST-clustering is introduced to perform event detection, which is an adaptation of the DB-Scan algorithm that allows clustering alerts by space, time, and type. Furthermore, to provide an overview of this algorithm’s spatio-temporal results, we introduce a novel visualization called ST-Heatmap. JamVis is validated through three usage scenarios analyzing different events in Rio de Janeiro.

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