Macroscopic flow characterization at T-junctions

Khan, Daud; Khan, Zahoor; Imran, Waheed; Khattak, Khurram S.; Gulliver, T. Aaron

doi:10.1016/j.trip.2022.100591

Cited by 5 publications

(7 citation statements)

References 28 publications

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“…Firstly, the sheer volume of vehicles, particularly during peak hours or in densely populated areas, overwhelms roads, causing congestion. Limited road capacity exacerbates this problem, leading to bottlenecks and traffic snarls, especially where infrastructure is lacking [11]. Additionally, incidents like accidents and breakdowns disrupt traffic flow, compounded by inadequate alternative routes.…”

Section: Issue Due To Congestionmentioning

confidence: 99%

Unscrambling traffic congestion and increasing sustainability in special urban intersection

Ajimon,

Bose

et al. 2024

E3S Web of Conf.

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Traffic congestion in India's densely populated urban centres is a major challenge exacerbated by rapid urbanization and a surge in vehicles surpassing infrastructure growth. Gridlocks and slow-moving vehicles are common due to inadequate road networks and poorly planned intersections, leading to longer commutes, productivity loss, and heightened pollution and safety concerns. Mitigation efforts include infrastructure upgrades and sustainable transportation promotion, implementing Indian Road Congress (IRC) signal design guidelines for intersection optimization and traffic flow management. Pedestrian safety remains critical, requiring comprehensive strategies integrating public transportation enhancement, road infrastructure optimization, and alternative travel modes promotion. Utilizing advanced technology for traffic monitoring, alongside urban planning initiatives prioritizing efficient land use and mixed-use developments, is crucial. Furthermore, policies promoting telecommuting and flexible work schedules can help alleviate the burden of peak-hour traffic. Flyovers offer relief by allowing vehicles to bypass congested areas, improving traffic flow. Congestion indirectly contributes to pollution, emphasizing the importance of catalytic converters in reducing harmful emissions from vehicle exhaust. This comprehensive approach aims to create smoother and more sustainable transportation systems while addressing congestion and pollution issues effectively.

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Section: Issue Due To Congestionmentioning

confidence: 99%

Unscrambling traffic congestion and increasing sustainability in special urban intersection

Ajimon,

Bose

et al. 2024

E3S Web of Conf.

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“…These parameters provide insight into local traffic flow behaviour (such as speed vs flow, density vs speed, and density vs flow, to name a few). Furthermore, these parameters are imperative for calibrating and validating mathematical traffic flow models and simulation software for better road network design and management [7,8,9,10,11]. In the existing literature, varying solutions have been proposed for vehicular flow characterization and are categorized as either intrusive or non-intrusive sensors [12,13].…”

Section: A Research Contextmentioning

confidence: 99%

Vehicular Flow Characterization: An Internet of Video Things-Based Solution

Zakhil

Khan

Khattak

et al. 2023

PakJET

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Intelligent transportation systems (ITS) have emerged as the optimal solution to address urban mobility challenges. However, to effectively implement ITS, detailed traffic flow statistics are imperative. Various solutions have been proposed, including intrusive/non-intrusive sensors and compute vision-based solutions. However, these solutions have limitations in the number of measured traffic flow parameters, cost or performance under different traffic conditions. To overcome these limitations, we propose an Internet-of-Video-Things (IoVT) based solution. The sensor node (fabricated using Raspberry Pi Zero W, Pi camera, power bank, and Wi-Fi device) can live-streaming roadside traffic video to a remote Dell server located at our lab with Camlytics (commercially available traffic analysis software) installed. The proposed solution was field tested with a 45-minute live-streamed video of 720p at 25 frames per second. Results show that the proposed solution can measure more traffic flow parameters than intrusive and non-intrusive sensors, with an accuracy of 84.3% for vehicle count and speed estimation. Other parameters were also calculated, such as time/distance headway, spatial/temporal densities, heat maps, and trajectories. Additionally, the proposed solution can count pedestrians with an accuracy of 76.3%.

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“…The LWR model is a widely used first-order macroscopic traffic model that can characterize small changes in flow [62]. Thus, this model cannot accurately characterize traffic at abrupt changes such as traffic capacity drops and stop-and-go traffic [45], [62]. The LWR model for one-dimensional flow is given by…”

Section: Pedestrian Dynamics Modelingmentioning

confidence: 99%

“…This model ignores pedestrian behavior [42]. Further, it assumes only small changes in velocity [43]- [45], and does not consider delay [46] and stopand-go behavior [46]- [48]. A fluid dynamic pedestrian model was proposed in [49] which is based on the Boltzmann gas kinetic model.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Traffic Characterization Based on Pedestrian Response

et al. 2022

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Pedestrian behavior is studied during normal and emergency evacuation of two groups of students. One group consists of grade nine high school students while the other group includes diploma (college) students. The evacuation time, number of steps, step frequency, and velocity are observed. It is found that the number of steps, step frequency, and velocity are larger during an emergency evacuation. These results are used to develop a macroscopic pedestrian response model. This model is compared with the Lighthill, Whitham, and Richards (LWR) model for pedestrian traffic using the First Order Centered (FORCE) scheme. Pedestrian parameters from the experiments are used for model evaluation. The direction of pedestrian movement is changed multiple times to observe pedestrian alignment behavior. It has been found that the proposed model performs more realistically than the LWR model.

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Macroscopic flow characterization at T-junctions

Cited by 5 publications

References 28 publications

Unscrambling traffic congestion and increasing sustainability in special urban intersection

Unscrambling traffic congestion and increasing sustainability in special urban intersection

Vehicular Flow Characterization: An Internet of Video Things-Based Solution

Pedestrian Traffic Characterization Based on Pedestrian Response

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