Smart Traffic Scheduling for Crowded Cities Road Networks

Alkhatib, Ahmad Aa; Maria, Khulood Abu; AlZu’bi, Shadi; Maria, Eman Abu

doi:10.1016/j.eij.2022.10.002

Cited by 16 publications

(4 citation statements)

References 22 publications

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“…Therefore, minimizing the average delay can effectively reduce the time for vehicles to stay at intersections, improve the efficiency of vehicles passing through intersections, and relieve traffic pressure. In terms of average delay

, the calculation is mainly based on the sum of random delay

and consistent delay

[ [23] , [24] , [25] ]. The calculation process of random delay

and consistent delay

is shown in (5) , (6) :

Where,

is the saturation of the

inlet in phase

is the actual traffic volume at the

inlet in phase

, and

is the flow ratio at the

inlet in phase

.…”

Section: Building An Optimization Model For Traffic Signal Control At...mentioning

confidence: 99%

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

Wei,

2024

Heliyon

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, the calculation is mainly based on the sum of random delay

and consistent delay

[ [23] , [24] , [25] ]. The calculation process of random delay

and consistent delay

is shown in (5) , (6) :

Where,

is the saturation of the

inlet in phase

is the actual traffic volume at the

inlet in phase

, and

is the flow ratio at the

inlet in phase

.…”

Section: Building An Optimization Model For Traffic Signal Control At...mentioning

confidence: 99%

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

Wei,

2024

Heliyon

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“…Alkhatib A et al proposed an intelligent urban road traffic control management system for the intelligent EVs scheduling in congested urban road networks, taking into account urban traffic flow. This effectively reduced the actual running time of EVs [18]. In addition, Raja G et al proposed a multi-agent deep rein-forcement path optimization algorithm for emergency rescue vehicles, utilizing 6G net-works and autonomous IoVs.…”

Section: Related Workmentioning

confidence: 99%

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

Wang,

Geng

2024

IEEE Access

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The current emergency vehicle priority control methods in road traffic net-works are difficult to cope with the increasing traffic demand. Therefore, a traffic control method based on multi-vehicle collaborative lane change strategy, fleet convergence and gap adjustment model is proposed and its effectiveness is verified. These experiments confirmed that the speed of vehicle Cj+1 under strategy 1 showed a positive correlation with time at 0-5s, reaching an extreme value of 16.85 m/s around 4s, and a negative correlation after 5s. Under strategy 2, its speed showed a negative correlation with time at 0-4s, and a parallel relation-ship after 4s. The multi-vehicle col-laborative lane changing strategy was validated. Except for fleet C with a density of +0.0333m-1, the actual adjustment time of vehicle C1 was gradually increasing at all other densities. The maximum time for B1 adjustment was 9.358s, and the maximum time for C1 adjustment was 10.798s. The longitudinal relative dis-placement of C1 was larger than that of B1. In addition, compared with Model T, the research method increased the average vehicle speed by 12.64% under four different flow rates. Compared with Model Y, the average flow rate of the research method under the four experimental flows was 1.45%. Overall, the research method is effective and feasible in the priority selection control of road traffic net-works. It improves the operational efficiency of emergency vehicle sections and can be effectively applied in actual traffic net-works.INDEX TERMS Internet of Vehicles, Emergency situations, Multi-vehicle col-laborative lane changing strategy, Gap adjustment model, Flow

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“…This model can achieve better management of congestion. Alkhatib et al [23] proposed to use intelligent traffic management system to optimize traffic management. Intelligent traffic lights are used to realize dynamic control of traffic flow.…”

Section: Relevant Workmentioning

confidence: 99%

Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion

Dui

Zhang

et al. 2023

Mathematics

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Urban road networks have promoted high-quality travel for residents by increasing connectivity and intelligence. But road congestion has not been effectively alleviated, causing a loss of time and energy. At present, the recovery of urban road networks mainly considers removing the failed edges. Considering the recovery cost and time, it is important to take active maintenance behavior to restore these networks. One of the key problems is dispatching traffic workers reasonably to achieve timely maintenance. In this paper, a flow-distribution-based process and execution (FD-PE) model is established for solving congestion. The maintenance centers (MC) study the reasons for and spread of congestion by edge flow. Based on the genetic algorithm (GA), two models of maintenance for urban road networks are developed, which include a single MC-centered dispatching plan and the co-scheduling of MCs. Both models aim at minimizing recovery time and allocating maintenance resources. The road network in Zhengzhou is borrowed as a case to explain the feasibility of the proposed models. The results show that on the premise of dividing network regions, it is reasonable to take a single MC to recover congestion. Compared with a single MC, the co-scheduling of MCs may save more time.

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Smart Traffic Scheduling for Crowded Cities Road Networks

Cited by 16 publications

References 22 publications

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion

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