Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

Hongsakham, W.; Pattara-Atikom, Wasan; Peachavanish, Ratchata

doi:10.1109/ecticon.2008.4600361

Cited by 24 publications

(8 citation statements)

References 5 publications

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“…Hsiao and Chang [3] introduced a segment based method using cellular network data to generate traffic speed information instead of general distance based technique. Moreover, both works in [4] and [5] proposed method that use Cell Dwell Time (CDT), which is the duration that particular cellular phone remain in particular base station, combining with simple threshold or fuzzy logic technique and combining with neural network or K-means clustering technique respectively.…”

Section: Introductionmentioning

confidence: 99%

Context Aware Traffic Congestion Estimation to Compensate Intermittently Available Mobile Sensors

Raphiphan

Zaslavsky

Prathombutr

et al. 2009

2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware

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Estimating the degree of traffic congestion at run time is crucially important for Intelligent Transportation Systems (ITS) especially when selecting travel routes. One of the challenges when using a mobile sensor (e.g. mobile phone, GPS) as source of traffic data stems from its mobility. This paper proposes a novel approach to fusing mobile data and an algorithm for traffic congestion estimation to compensate intermittent available mobile sensors. Furthermore, our proposed model is also tolerant to incomplete data and it is open to any type of sensors. This approach differs from other traffic congestion estimation techniques in that it utilizes discoverable context even on sensorless road segments in real time instead of entirely relying on a sensor-based data of observed road segment. Implementation and validation through extensive experimentation with real data confirms feasibility of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Context Aware Traffic Congestion Estimation to Compensate Intermittently Available Mobile Sensors

Raphiphan

Zaslavsky

Prathombutr

et al. 2009

2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware

View full text Add to dashboard Cite

show abstract

“…The behavioral aftereffects of this emotional demand of driving are impaired road performance and social or communication deficit when arrive at home or at work [17]. The long term exposure to traffic congestion is often occurred in the form of elevated heart rate as physiological residue and anger as psychological excess in behavior [18].…”

Section: Perceptions Of Traffic Congestionmentioning

confidence: 99%

Traffic Congestion Issues, Perceptions, Experience and Satisfaction of Car Drivers/Owners on Urban Roads

Memon

Khiani

2020

Mehran Univ. res. j. eng. technol.

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Traffic congestion is a major issue of Asian megacities which causes the irritation, anger and frustrations among drivers and owners of vehicles during driving in these cities. This paper aims at understanding traffic congestion issues from the perception and experiences of car drivers and owners and their satisfaction level concerning accessibility of circulation within urban roads of Karachi. The issues addressed in this respect are road construction, traffic flow, road crossing, shopping malls along roads, traffic blockage due to VIP movement, congestion during school timing, animal presence on roads, traffic police behavior, marriage halls along the roads, peak hour traffic jams, traffic jams during sociopolitical and religious rallies, road disputes, traffic congestion due to young inexperienced drivers, alternate road availability and pedestrian bridges. The method of investigating these issues is through 42 questionnaire surveys with car drivers and car owners from June to October 2018 on urban roads of Karachi. The analysis of feedback from respondents showed that, more than 90% respondents were satisfied with construction of pedestrian bridges and annoyed with sociopolitical and religious rallies on main urban roads. More than 80% respondents were satisfied with marriage halls along the roads, felt congestion during peak hours and preferred alternate routes to go home. More than 70% agreed that, young and inexperienced drivers cause traffic congestion and felt unsatisfied and displeased with disputes on the streets among residents, traffic police and drivers. More than 60% respondents were satisfied with the road construction but annoyed with VIP protocol and felt congestion during school timings and quite uncomfortable while crossing the road. More than 50% respondents were unsatisfied with shopping malls on both sides of the road, presence of animals on main roads and behavior of traffic police during traffic jams. Finally, concerning traffic flow the respondents were equally divided in their perception. Thus this research presents a detailed perspective of people regarding traffic congestion issues in Karachi for the appropriate response by decision makers of urban transport planning and urban traffic management institutions in city.

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“…Cellular phone position-based traffic information generation uses the GPS from among the various sensors of a cellular phone to predict speed, congestion, and other conditions on the road [1,2,3,4,5,6,7]. There is also a method of predicting the volume of traffic based on the hours of cellular phone use [8,9,10,11]. However, these methods have the weakness of increasing battery consumption due to the frequent sampling of the GPS in the driver’s cellular phone.…”

Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data

Ji¹,

Hong

2019

Sensors

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In this paper, we propose a method for deep-learning-based real-time road traffic predictions using long-term evolution (LTE) access data. The proposed system generates a road traffic speed learning model based on road speed data and historical LTE data collected from a plurality of base stations located within a predetermined radius from the road. Real-time LTE data were the input for the generated learning model in order to predict the real-time speed of traffic. Since the system was developed using a time-series-based road traffic speed learning model based on LTE data from the past, it is possible for it to be used for a road where the environment has changed. Moreover, even on roads where the collection of traffic data is invalid, such as a radio shadow area, it is possible to directly enter real-time wireless communications data into the traffic speed learning model to predict the traffic speed on the road in real time, and in turn, raise the accuracy of real-time road traffic predictions.

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Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

Cited by 24 publications

References 5 publications

Context Aware Traffic Congestion Estimation to Compensate Intermittently Available Mobile Sensors

Context Aware Traffic Congestion Estimation to Compensate Intermittently Available Mobile Sensors

Traffic Congestion Issues, Perceptions, Experience and Satisfaction of Car Drivers/Owners on Urban Roads

Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data

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