A Review on Traffic Prediction Methods for Intelligent Transportation System in Smart Cities

Chen, Xiangyang; Chen, Ruqing

doi:10.1109/cisp-bmei48845.2019.8965742

Cited by 20 publications

(7 citation statements)

References 27 publications

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“…Researchers of smart city traffic modeling have explored earlier linear and statistical theories [10] [9] [11] [12] [9]. With the maturity of recent advanced algorithms and artificial intelligence, and the emergence of big data owing to advances in data collection technology, a new background of traffic big data has been created, allowing researchers to propose more accurate approaches for smart city modeling and prediction, as well as more accurate investigations of urban traffic conditions and their interference in traffic flow [8] [13] [14] [1] [15] [16].…”

Section: Related Workmentioning

confidence: 99%

“…Given the number of samples, automatic labelling is an important issue. In [16], a review of existing traffic prediction approaches was presented, and some possible future development trends were identified.…”

Section: Related Workmentioning

confidence: 99%

“…Another important research issue is related to the influencing factors, also called meta-parameters, which interfere highly in the congestion state and externally influence the traffic. Nevertheless, in some contexts of traffic vulnerability, the control system service, designed for controlling the traffic, is no longer efficient, and the traffic flow prediction becomes more complex owing to such internal factors [16]. An accurate definition of a meta-parameter conditions the accuracy of prediction but is not faithfully considered in the literature.…”

Section: Related Workmentioning

confidence: 99%

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Entropy-Based Traffic Flow Labeling for CNN-Based Traffic Congestion Prediction From Meta-Parameters

et al. 2022

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Traffic congestion affects quality of life by inducing frustration and wasting time. The congestion is also critical to vehicles with high emergencies such as ambulances or police cars. This leads to additional CO2 emissions. Traffic management requires the accurate modeling of congestion levels. Two main observable parameters identify the congestion state of a city: vehicle speed and density. Congestion has an intuitive definition rather than a quantitative one, and is associated with the disorder and randomness occurring in traffic parameters. Therefore, statistical analysis offers an efficient and natural framework for modeling such disorders. In this study, a differential-entropy-based approach was proposed for labelling purposes. Subsequently, supervised congestion prediction from traffic meta-parameters based on a convolutional neural network was proposed. Traffic parameters includes node localization, date, day of the week, time of day, special road conditions, and holidays. The proposed model is validated on the CityPulse dataset, which is a set of vehicle traffic records, collected in Aarhus city in Denmark over a period of six months, for 449 observation nodes. Simulation results on the CityPulse dataset illustrate that the proposed approach yields accurate prediction rates for different nodes considered. The proposed system can prevent traffic congestion by reorienting the drivers to follow other itineraries.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Entropy-Based Traffic Flow Labeling for CNN-Based Traffic Congestion Prediction From Meta-Parameters

et al. 2022

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“…Studies pointed out that effective control and management of traffic and accurate prediction of travel time are the two major challenges faced in the application of ITS. However, despite the emphasis on these challenges, only a few researchers have rendered solutions based on the functionality of ITS (Van Arem et al, 1997;Papageorgio et al, 2003;Barros et al, 2015;Chen and Chen, 2019). In addition, only few papers have compared the benefits and shortcomings of the suggested solutions (Chhatpar et al, 2018;De Souza et al, 2017;Avatefipour and Sadry, 2018).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives

Ravish

Swamy²

2021

Transport and Telecommunication Journal

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Recent years have witnessed a colossal increase of vehicles on the roads; unfortunately, the infrastructure of roads and traffic systems has not kept pace with this growth, resulting in inefficient traffic management. Owing to this imbalance, traffic jams on roads, congestions, and pollution have shown a marked increase. The management of growing traffic is a major issue across the world. Intelligent Transportation Systems (ITS) have a great potential in offering solutions to such issues by using novel technologies. In this review, the ITS-based solutions for traffic management and control have been categorized as traffic data collection solutions, traffic management solutions, congestion avoidance solutions, and travel time prediction solutions. The solutions have been presented along with their underlying technologies, advantages, and drawbacks. First, important solutions for collecting traffic-related data and road conditions are discussed. Next, ITS solutions for the effective management of traffic are presented. Third, key strategies based on machine learning and computational intelligence for avoiding congestion are outlined. Fourth, important solutions for accurately predicting travel time are presented. Finally, avenues for future work in these areas are discussed.

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“…In recent years, transportation research focuses on traffic congestion control and minimization of congestion time, mostly to resolve the above problems. Developed countries invest in intelligent transportation research where researchers collect traffic data through loop detectors, Radio Frequency Identification (RFID), and sensor networks to model and predict the traffic pattern [28]. Such models can play a pivotal role in developing congestion prediction applications for commuters, travelers, and traffic management authorities.…”

Section: Introductionmentioning

confidence: 99%

Modeling Traffic Congestion in Developing Countries using Google Maps Data

Pramanik¹,

Rahman²,

Anam³

et al. 2020

Preprint

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Traffic congestion research is on the rise, thanks to urbanization, economic growth, and industrialization. Developed countries invest a lot of research money in collecting traffic data using Radio Frequency Identification (RFID), loop detectors, speed sensors, high-end traffic light, and GPS. However, these processes are expensive, infeasible, and non-scalable for developing countries with numerous non-motorized vehicles, proliferated ride-sharing services, and frequent pedestrians. This paper proposes a novel approach to collect traffic data from Google Map's traffic layer with minimal cost. We have implemented widely used models such as Historical Averages (HA), Support Vector Regression (SVR), Support Vector Regression with Graph (SVR-Graph), Auto-Regressive Integrated Moving Average (ARIMA) to show the efficacy of the collected traffic data in forecasting future congestion. We show that even with these simple models, we could predict the traffic congestion ahead of time. We also demonstrate that the traffic patterns are significantly different between weekdays and weekends.

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A Review on Traffic Prediction Methods for Intelligent Transportation System in Smart Cities

Cited by 20 publications

References 27 publications

Entropy-Based Traffic Flow Labeling for CNN-Based Traffic Congestion Prediction From Meta-Parameters

Entropy-Based Traffic Flow Labeling for CNN-Based Traffic Congestion Prediction From Meta-Parameters

Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives

Modeling Traffic Congestion in Developing Countries using Google Maps Data

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