Congestion Pattern Prediction for a Busy Traffic Zone Based on the Hidden Markov Model

Sun, Tingting; Huang, Zhengfeng; Zhu, Hebing; Huang, Yan; Zheng, Pengjun

doi:10.1109/access.2020.3047394

Cited by 15 publications

(5 citation statements)

References 31 publications

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“…Occasionally, in a busy traffic zone, congestion patterns on multiple connected roads can make a complete jam of the network zone. To address this issue, Sun et al [23] recommended a prediction model of congestion pattern in a heavily congested traffic area using the hidden markov model (HMM). The model initiates a connection between the external (observation state) and internal (hidden state) road traffic state of a busy traffic zone.…”

Section: Related Workmentioning

confidence: 99%

Emergency vehicles classification for traffic signal system using optimized transfer DenseNet201 model

Manguri,

Mohammed

2023

IJEECS

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<span>As a result of the rapid growth of the world population, traffic signaling systems for monitoring and controlling the roads have turned to be an important issue facing humanity. To effectively overcome this problem, an accurate method for congestion reduction on the roads should be used which has a direct relation between the population and the cars’ usage. Various approaches derived from deep and transfer learning have been investigated in this context. This research implemented an optimized transfer learning approach for densely connected convolutional neural network (DenseNet201) models for multiple classifications (non-emergency cars, ambulance, police, and firefighter). Due to the non-availability of public datasets, a customized dataset has been created. This paper aims to improve the performance accuracy of vehicle classification using certain preprocessing algorithms on the input images and testing various optimization methods. The performance accuracy of the proposed model is evaluated using k-folds cross-validation 20:80 for the test and training, respectively. The metrics which are used for comparison with other deep learning techniques are based on exactness, recall, accuracy and F1-score. Test outcomes specify that the proposed transfer model-based optimization outperforms alternative deep learning algorithms regarding vehicle accuracy in classifying and reaches 98.6%.</span>

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Section: Related Workmentioning

confidence: 99%

Emergency vehicles classification for traffic signal system using optimized transfer DenseNet201 model

Manguri,

Mohammed

2023

IJEECS

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“…The proposed model outperformed ARIMA in terms of overall performance. The authors suggested a MapReduce-based distributed long short-term memory (LSTM) with a temporal window and normal distribution that outperformed previous LSTM models (Sun et al , 2021). The authors examined three traffic speed prediction algorithms: convolutional neural network (CNN), LSTM-NN and XGBoost, using a machine learning-based framework.…”

Section: Literature Surveymentioning

confidence: 99%

Real-time traffic congestion prediction using big data and machine learning techniques

Chawla

Hasurkar

Bogadi

et al. 2022

WJE

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Purpose The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality. Design/methodology/approach This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques. Findings The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms. Research limitations/implications The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts. Practical implications To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects. Social implications To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly. Originality/value The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

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“…The execution time of the algorithm is linearly related to the number of geospatial objects, resulting in high time complexity and low semantic enrichment performance in the spatial connection process. For points of interest (POI), Sun et al used an implicit Markov model [19] to label the POI categories for staying segments of spatiotemporal trajectories, but in the regions with intensive POI, staying segments may be related to multiple interest points. Coupled with the low GPS sampling rate, it is difficult to identify effective POIs.…”

Section: Related Workmentioning

confidence: 99%

Efficient Semantic Enrichment Process for Spatiotemporal Trajectories

Zhao

Han

et al. 2021

Wireless Communications and Mobile Computing

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The increasing availability of location-acquisition technologies has enabled collecting large-scale spatiotemporal trajectories, from which we can derive semantic information in urban environments, including location, time, direction, speed, and point of interest. Such semantic information can give us a semantic interpretation of movement behaviors of moving objects. However, existing semantic enrichment process approaches, which can produce semantic trajectories, are generally time-consuming. In this paper, we propose an efficient semantic enrichment process framework to annotate spatiotemporal trajectories by using geographic and application domain knowledge. The framework mainly includes preannotated semantic trajectory storage phase, spatiotemporal similarity measurement phase, and semantic information matching phase. Having observed the common trajectories in the same geospatial object scenes, we propose a semantic information matching algorithm to match semantic information in preannotated semantic trajectories to new spatiotemporal trajectories. In order to improve the efficiency of this approach, we build a spatial index to enhance the preannotated semantic trajectories. Finally, the experimental results based on a real dataset demonstrate the effectiveness and efficiency of our proposed approaches.

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Congestion Pattern Prediction for a Busy Traffic Zone Based on the Hidden Markov Model

Cited by 15 publications

References 31 publications

Emergency vehicles classification for traffic signal system using optimized transfer DenseNet201 model

Emergency vehicles classification for traffic signal system using optimized transfer DenseNet201 model

Real-time traffic congestion prediction using big data and machine learning techniques

Efficient Semantic Enrichment Process for Spatiotemporal Trajectories

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