Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads

Pattanaik, Vishwajeet; Singh, Mayank; Gupta, Praveen; Singh, Sanjay Kumar

doi:10.1109/tencon.2016.7848689

Cited by 30 publications

(10 citation statements)

References 5 publications

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“…Similar to Pattanaik et al work [37], Hongsakham et al [38] applied the k-means clustering method to cluster the congestion levels on a particular road section in Bangkok, Thailand. Motivated by the studies of Pattanaik et al [37] and Hongsakham et al [38], in this work, the k-means clustering method was used to cluster the congestion severity based on the traffic speed data collected from smartphones in Jakarta, Indonesia. In the traffic study, the RF method has been widely utilized in finding the contributing factors of a traffic accident.…”

Section: Machine Learning Methods In Traffic Studiesmentioning

confidence: 99%

“…The ITS problem studied by [ 36 ] combines the issues of coordinating the traffic signal lamp, balancing the traffic flow, and reducing the travel time. Pattanaik et al [ 37 ] used the k -means clustering method to cluster the severity of congestion in a New Delhi study. They found that their methodology can segment the roads according to the congestion severity.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Yang

Sutrisno

Chan

et al. 2021

Sensors

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Weather change such as raining is a crucial factor to cause traffic congestion, especially in metropolises with the limited sewer system infrastructures. Identifying the roads which are sensitive to weather changes, defined as weather-sensitive roads (WSR), can facilitate the infrastructure development. In the literature, little research focused on studying weather factors of developing countries that might have deficient infrastructures. In this research, to fill the gap, the real-world data associating with Jakarta, Indonesia, was studied to identify WSR based on smartphone sensor data, real-time weather information, and road characteristics datasets. A spatial-temporal congestion speed matrix (STC) was proposed to illustrate traffic speed changes over time. Under the proposed STC, a sequential clustering and classification framework was applied to identify the WSR in terms of traffic speed. In this work, the causes of WSR were evaluated based on the variables’ importance of the classification method. The experimental results show that the proposed method can cluster the roads according to the pattern changes in the traffic speed caused by weather change. Based on the results, we found that the distances to shopping malls, mosques, schools, and the roads’ altitude, length, width, and the number of lanes are highly correlated to WSR in Jakarta.

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Section: Machine Learning Methods In Traffic Studiesmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Yang

Sutrisno

Chan

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The automated traffic management is essential for the future transportation system and recent techniques such as the traffic light control [192], traffic flow management [193] and crash prediction [194] show a path to automated traffic prediction. Further, the automated traffic management needs traffic state prediction which is possible with crowd‐intelligence techniques such as the traffic signal prediction [197], lane changing detection [198], traffic stat prediction [199, 201] and congestion detection [200].…”

Section: Future Of Crowd Intelligence In Transportation Systemmentioning

confidence: 99%

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

Shit

2020

IET Intelligent Transport Systems

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“…Several parameters are observed and measured: vehicles positions, vehicles speeds, and vehicles number. Specific events are detected through value changes of the above mentioned parameters [5][6][7]. We propose algorithms to perform the congestion detection and the speeding violation detection.…”

Section: Events Detectionmentioning

confidence: 99%

“…A lot of researches treated the congestion detection [5][6][7] and the early congestion detection [8][9][10][11] to overcome the low efficiency of transportation, which is a significant problem on roads in urban cities. The numbers of vehicles in an area and their velocities are the two main parameters that indicate whether there is congestion or not.…”

Section: Introductionmentioning

confidence: 99%

Traffic Monitoring on City Roads Using UAVs

Elloumi¹,

Dhaou²,

Escrig³

et al. 2019

Ad-Hoc, Mobile, and Wireless Networks

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Unmanned Aerial Vehicles (UAVs) based systems are a suitable solution for monitoring, more particularly for traffic monitoring. The mobility, the low cost, and the broad view range of UAVs make them an attractive solution for traffic monitoring of city roads. UAVs are used to collect and send information about vehicles and unusual events to a traffic processing center, for traffic regulation. Existing UAVs based systems use only one UAV with a fixed trajectory. In this paper, we are using multiple cooperative UAVs to monitor the road traffic. This approach is based on adaptive UAVs trajectories, adjusted by moving points in UAVs fields of view. We introduced a learning phase to search for events locations with a frequent occurrence and to place UAVs above those locations. Our approach allows the detection of a lot of events and permits the reduction of UAVs energy consumption.

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Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads

Cited by 30 publications

References 5 publications

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

Traffic Monitoring on City Roads Using UAVs

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