Effect of Rain on Probability Distributions Fitted to Vehicle Time Headways

Alhassan, Hashim Mohammed; Ben-Edigbe, Johnnie

doi:10.18517/ijaseit.2.2.173

Cited by 14 publications

(5 citation statements)

References 5 publications

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“…4(b), were the height, slope, base width and top width. In order to present the normal behavior of a driver, the traffic data, mainly the vehicle speed [23] and type of vehicle, were observed when they traversed the observation point during off-peak hours on weekdays from Monday to Wednesday,…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Effectiveness of Road Humps in Reducing Vehicle Speed: Case Study of a University Campus

Mashros

Nor

Kurnia

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Road humps are designed to control the speed of vehicles through the discomfort that drivers experience when crossing over them. Although the use of various types of road humps in low-speed environments is common, its effectiveness has yet to be seen. This paper evaluated the effectiveness of different types of road humps in reducing the speed of vehicles at a university campus. Field observations were carried out to determine geometric designs of five round-top and five flat-top road humps. All recorded design characteristics of road humps were compared with the standard specifications by various authorities. Only road humps that met any one or more of the standard specifications were considered for speed data collection. Speed data were observed at two selected points during off-peak weekdays and in daylight conditions. A comparison of the observed design characteristics of the road humps with the standard specifications by the authorities showed that most of the humps did not meet any design standard except one round-top road hump and two flat-top road humps. However, only one round-top and one flat-top road humps were considered for further analysis. The average speed of vehicles approaching the road humps was found to be higher than the average speed observed on road humps, with a reduction of 46% and 52% for round-top and flat-top road humps, respectively. The results of statistical analysis also showed that both types of road humps were effective in reducing the speed of vehicles. Thus, it can be concluded that road humps, irrespective of their type, have an impact on vehicle speed.

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Section: Methodsmentioning

confidence: 99%

Evaluating the Effectiveness of Road Humps in Reducing Vehicle Speed: Case Study of a University Campus

Mashros

Nor

Kurnia

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The second characteristic is using the weight θ k to approximate the Q value Qt(s, a, θ k ) at the k th iteration. In their study, they used the non-exponential Burr XII type distribution [46], to represent the vehicles' inter-arrival time, which generalizes the Poisson process in which the vehicles' inter-arrival times are distributed exponentially [47].…”

Section: Heuristics Based Approachesmentioning

confidence: 99%

Smart Cities Trafic Light Management Systems Review: Models and Approaches

ZERROUG,

ALIOUAT,

ALIOUAT

et al. 2023

Preprint

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In large cities, the number of vehicles in daily circulation is increasing significantly. In parallel with the evolution of urban structures, the road infrastructure is struggling to keep up with this flow of vehicles. Such a situation could become more and more cumbersome until it leads to unmanageable conditions that could reach complete congestion of the crossroads. Thus, due to its negative impact on the daily lives of vehicle users, many studies have addressed this problem, but the issue is still relevant today and is attracting more attention from researchers, especially with the emerging paradigm of smart cities. To this end, different approaches have been developed to overcome the waiting time hindrance at road junctions and avoid the costly and stressful situation of trafic congestion. In this context, the significant studies conducted so far are analyzed in this paper in order to synthesize the different approaches used. The objective is to highlight the important elements of a successful solution for trafic signal planning at intersections. Such a solution must be consistent with the requirements and environment of the smart city concept. Thus, different solutions to the problem posed, in the form of Smart Trafic Light Management Systems (STLMS), have been based on models applied to one or more intersections and using mathematical optimization techniques, wireless sensor networks, or both. Nevertheless, none of the proposed methods has addressed the problem as a whole, i.e., the coordination and cooperation of STLMS at all intersections of a smart city and the integration of the overall system into the IoT environment.

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“…Our contribution is to investigate the use of MADQN to traffic light controllers at intersections with a high volume of traffic and traffic disruptions (i.e., rainfall). This work is based on simulation using the Burr distribution, which has been shown to model traffic disruptions in traffic networks accurately in [27]. The performance measure is the cumulative delay of vehicles, which includes the average waiting and travelling times caused by congestion.…”

Section: Contributions Of the Papermentioning

confidence: 99%

“…Tab. 3 presents the parameters of simulation for the Burr type XII distribution model, which has various intensities of rainfall, including no rain (NR), light rain (LR), moderate rain (MR), and heavy rain (HR) scenarios [27]. The lower and higher scale parameter β value shrinks and stretches the distribution, respectively.…”

Section: Parameters Of Simulation and Performance Measurementioning

confidence: 99%

See 1 more Smart Citation

Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

Rasheed¹,

Yau²,

Noor³

et al. 2022

Computers, Materials &Amp; Continua

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This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia. Investigation is also performed using a grid traffic network (GTN) to understand that the proposed scheme is effective in a traditional traffic network. Our proposed scheme is evaluated using two simulation tools, namely Matlab and Simulation of Urban Mobility (SUMO). Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30% in the simulations.

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Effect of Rain on Probability Distributions Fitted to Vehicle Time Headways

Cited by 14 publications

References 5 publications

Evaluating the Effectiveness of Road Humps in Reducing Vehicle Speed: Case Study of a University Campus

Evaluating the Effectiveness of Road Humps in Reducing Vehicle Speed: Case Study of a University Campus

Smart Cities Trafic Light Management Systems Review: Models and Approaches

Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

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