Discharge Characteristics Analysis of Queued-up Vehicles at Signal-Controlled Intersections Under Heterogeneous Traffic Conditions

Mondal, Satyajit; Gupta, Ankit

doi:10.1007/s40999-018-0343-7

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…This is known as Little's law and is generally true [27]- [31]. The main attributes of the queuing model are the population source, the number of servers, the arrival with service patterns, and the queue discipline [33].…”

Section: 1mentioning

confidence: 99%

Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations

Alem

2024

Int. J. Ind. Optim.

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Modern urban environments present a dynamically growing field where, notwithstanding shared goals, several mutually conflicting interests frequently collide. However, it has a big impact on the city's socioeconomic standing, waiting lines and queues are common occurrences. This results in extremely long lines for vehicles and people on incongruous routes, service coagulation, customer murmuring, unhappiness, complaints, and looking for other options, sometimes illegally. The root cause is corruption, which leads to traffic jams, stops and packs vehicles beyond their safe carrying capacity, and violates passengers' human rights and freedoms. This study focused on optimizing the time passengers had to wait in public vehicle stations. This applied research employed both data-gathering sources and mixed approaches. Then, 166 samples of key informants of transport stations were taken using the Slovin sampling formula. The time vehicles, including the drivers and auxiliary drivers ‘Weyala', had to wait was also studied. To maximize the service level at vehicle stations, a queuing model was subsequently devised ‘Menaharya’. Time, cost, and quality encompass performance, scope, and suitability for the intended purposes. The study also focused on determining the minimal response time required for passengers and vehicles queuing to reach their ultimate destinations within the transportation stations in Tepi, Mizan, and Bonga. A new bus station system was modeled and simulated by Arena simulation software in the chosen study area. 84% improvement on cost reduced by 56.25%, time 4 hours to 1.5 hours, quality, safety and designed load performance calculations employed. Stakeholders are asked to implement the model and monitor the results obtained.

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Section: 1mentioning

confidence: 99%

Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations

Alem

2024

Int. J. Ind. Optim.

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“…This study examines the behavior of motorcycles towards saturated flow on through movements because in Denpasar City for large traffic flows, the arrangement of intersections with more signals is done through movement to reduce conflict at signalized intersections. Through movements are free movements that are not affected by conflict [18,19] while according to [2,20] through movements are movements (turn right, go straight, turn left) in each approach that is given the green light simultaneously, and given 1 phase per approach so that vehicles turning right do not have to wait to enter opposite traffic. In this study, what is meant by through movement is the movement of traffic at an intersection approach that has 1 phase or an intersection whose approach has the same phase as other approaches but the movement does not affect or be influenced by the movements of other approaches.…”

Section: Introductionmentioning

confidence: 99%

The Saturated Flow Modeling on Motorcycle Behavior Based on Through Movements at Signalized Intersections

Kariyana¹,

Suthanaya²,

Wedagama³

et al. 2021

cea

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In developing countries such as Indonesia the proportion of motorcycles to other vehicles is reaching 76%, which is very high. The proportion of motorcycles in Bali Province exceeds 85% while in Denpasar City is 82%. Previous studies suggest that a high proportion of motorcycles will certainly affect the saturated flow. Therefore, the effect of motorcycles on saturation flows should not be neglected. With a high proportion of motorcycles that will affect the behavior of motorcyclists at irregular intersections such as in front of the stop line, besides other vehicles and behind other vehicles or in the flow, the behavior of the motorcycle affects the saturated flow. In calculating saturated flow, the Indonesian Highway Capacity Manual (IHCM) does not have specific measures that adopt the behavior of motorcycles. If the motorcycle is not properly regulated, it will interfere with the movement of other vehicles when the green light is on. The behavior to be examined in this study is the behavior of motorcycles in front of the stop line, outside the current (beside flow), or in the current (inside flow). In this study, a model was developed to determine saturation flow based on the behavior of motorcycles for through movements at signalized intersections with or without ESSM.

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“…As society develops, cities continue to expand, accompanied by the increase in transportation and the increase in road traffic pressure [1,2]. The development of traffic sign recognition (TSR) technology makes the intelligent construction of road traffic networks possible [3].…”

Section: Introductionmentioning

confidence: 99%

Image Recognition and Safety Risk Assessment of Traffic Sign Based on Deep Convolution Neural Network

2020

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A neural network model based on deep learning is utilized to explore the traffic sign recognition (TSR) and expand the application of deep intelligent learning technology in the field of virtual reality (VR) image recognition, thereby assessing the road traffic safety risks and promoting the construction of intelligent transportation networks. First, a dual-path deep CNN (TDCNN) TSR model is built based on the convolutional neural network (CNN), and the cost function and recognition accuracy are selected as indicators to analyze the training results of the model. Second, the recurrent neural network (RNN) and long-short-term memory (LSTM) RNN are utilized to assess the road traffic safety risks, and the prediction and evaluation effects of them are compared. Finally, the changes in safety risks of road traffic accidents are analyzed based on the two key influencing factors of the number of road intersections and the speed of vehicles traveling. The results show that the learning rate of the network model and the number of hidden neurons in the fully-connected layer directly affect the training results, and there are differences in the choices between the early and late training periods. Compared with RNN, the LSTM network model has higher evaluation accuracy, and its corresponding root square error (RSE) is 0.36. The rational control of the number of intersections and the speed of roads traveled has a significant impact on improving the safety level and promoting road traffic efficiency. The VR image recognition algorithm and safety risk prediction method based on a neural network model positively affect the construction of an intelligent transport network.

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Discharge Characteristics Analysis of Queued-up Vehicles at Signal-Controlled Intersections Under Heterogeneous Traffic Conditions

Cited by 16 publications

References 14 publications

Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations

Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations

The Saturated Flow Modeling on Motorcycle Behavior Based on Through Movements at Signalized Intersections

Image Recognition and Safety Risk Assessment of Traffic Sign Based on Deep Convolution Neural Network

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