An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities

Culita, Janetta; Caramihai, Simona Iuliana; Dumitrache, Ioan; Moisescu, Mihnea Alexandru; Sacală, Ioan Ștefan

doi:10.3390/s20247209

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…The model-driven method [ 21 ] (2020) is used to build a model based on the understanding of the traffic model, but its accuracy and applicability are limited due to the complexity of the actual traffic environment. The data-driven method focuses on the mapping relationship between data and phenomena, but the demand for data is large, and the understanding and application depth of traffic mechanisms is insufficient.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Shi

2022

Sensors

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As the aggravation of road congestion leads to frequent traffic crashes, it is necessary to relieve traffic pressure through traffic flow prediction. As well, the traffic flow of the target road section to be predicted is also closely related to the adjacent road sections. Therefore, in this paper, a prediction method based on the combination of multiple linear regression and Long-Short-Term Memory (MLR-LSTM) is proposed, which uses the incomplete traffic flow data in the past period of time of the target prediction section and the continuous and complete traffic flow data in the past period of time of each adjacent section to jointly predict the traffic flow changes of the target section in a short time. The accurate prediction of future traffic flow changes can be solved based on the model supposed when the traffic flow data of the target road section is partially missing in the past period of time. The accuracy of the prediction results is the same as that of the current mainstream prediction results based on continuous and non-missing target link flow data. Meanwhile, there is a small-scale improvement when the data time interval is short enough. In the case of frequent maintenance of cameras in actual traffic sections, the proposed prediction method is more feasible and can be widely used.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Shi

2022

Sensors

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“…However, due to the complicated traffic management system, these approaches are unable to provide an acceptable response. As a result, the development of smart approaches can aid in the resolution and improvement of urban management issues [8], [12]- [14]. In the planning of transportation infrastructure, traffic modeling and simulations are important [11], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Improving model predictive control's optimization for urban traffic

Khelafa

Baghdad

Hachimi

2022

IJEECS

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<p><span>When it comes to decreasing traffic congestion and enhancing mobility, traffic forecasting is critical. However, due to the complicated spatio-temporal dynamics of urban transportation networks, which are difficult to describe, this task is tough. Using a model predictive controller (MPC) provides the control of a traffic network's architecture as well as errors in its operations. Based on a real-time simulation, a novel, accurate prediction controller for urban traffic was presented in this study to estimate the number of cars at junctions and their waiting duration. Different optimization approaches were employed and evaluated to improve the MPC's performance. Simulation results demonstrated that the fmincon was very robust and could effectively reduce the number of vehicles in the link, in comparison with other algorithms This study also includes an in-depth analysis of the characteristics of various prediction horizon sets in an MPC. By increasing the prediction horizon, the amplitude of fluctuation became more important, but when Np=4, the fluctuations reduced.</span></p>

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“…The insufficient management and monitoring of buses have caused social issues, e.g., residents have gradually lost confidence in public transportation, social security systems and government programs. Consequently, a greater scientific basis and more statistical analyses are required to support intelligent transportation systems [8,9].…”

Section: Introductionmentioning

confidence: 99%

Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

Yang

Yan

Guo

et al. 2021

Sensors

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The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform’s visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform’s features are provided for intelligent transport system (ITS) design and applications.

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An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities

Cited by 14 publications

References 32 publications

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Improving model predictive control's optimization for urban traffic

Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

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