Exploiting Cellular Networks for Road Traffic Estimation: A Survey and a Research Roadmap

Valerio, Danilo; D’Alconzo, Alessandro; Ricciato, Fabio; Wiedermann, Werner

doi:10.1109/vetecs.2009.5073548

Cited by 50 publications

(32 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…vehicular speed, directions of travel, travel times, traffic info) in one or more points of the city [11][12][13]15,16].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cell phone big data to compute mobility scenarios for future smart cities

Tosi

2017

Int J Data Sci Anal

View full text Add to dashboard Cite

Efficient mobility is a key aspect for the future smart cities. The real added value for smart cities is the real-time optimization of vehicular and public transportation flows to reduce traffic congestions, costs, and emissions. Observing constantly the behaviour of people moving around the city can help policy makers to act promptly and to fix congested flows dynamically. In this paper, we describe from a technical point-of-view an original use of big data (coming from the cellular network of the Vodafone Italy Telco operator) to compute mobility patterns for smart cities. The paper also discusses five innovative mobility patterns that describe different mobility scenarios of the city, starting from how people move around point-of-interests of the city in real time. The mobility patterns have been experimentally validated in a real industrial setting and for the Milan metropolitan city. The study conducted confirmed the quality of the patterns and their importance in smart cities, by showing how cell phone big data can complete other sources of people information. These mobility patterns can be exploited by policy makers to improve the mobility in a city, or by Navigation Systems and Journey Planners to provide final users with accurate travel plans.

show abstract

“…vehicular speed, directions of travel, travel times, traffic info) in one or more points of the city [11][12][13]15,16].…”

Section: Related Workmentioning

confidence: 99%

“…Mobility patterns describe the behaviour of individual citizens in living the city and in affecting the behaviour of other people [1][2][3][4]10,15,17]. In this paper, we discuss new algorithms to collect and elaborate in real time the big data coming from the VI cellular network to compute mobility patterns.…”

Section: Introductionmentioning

confidence: 99%

Cell phone big data to compute mobility scenarios for future smart cities

Tosi

2017

Int J Data Sci Anal

View full text Add to dashboard Cite

show abstract

“…Further investigation focusing on the use of cellular network data for inferring real-time mobility information has been conducted through surveys and data classication identifying the advantages and limitations of this method. Particularly, a discussion on the ways to increase the accuracy of known cellular positioning techniques through a traffic congestion estimation service application and studying the trajectory of several thousand of anonymous mobile phone users are found in [20,21] A new promising technology has been proposed in the traffic and urban planning community based on the interception of Bluetooth Media Access Control (MAC) addresses for the monitoring of traffic conditions that will open new measurement possibilities [22,23,18,2,5,16]. The uniqueness of the detected MAC addresses makes the Bluetooth technology particularly appropriate to monitor the mobility conditions.…”

Section: Related Workmentioning

confidence: 99%

Sensing Bluetooth Mobility Data: Potentials and Applications

Filgueiras

Rossetti

Kokkinogenis

et al. 2014

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Abstract. Information related to mobility dynamics constitutes an important factor to be considered in traffic management to improve the efficiency of existing systems. We present a proof-of-concept deployment of sensors using the Bluetooth technology to detect traffic flow conditions. Besides traditional method consisting of a network of stationary sensors, we present a novel approach that uses sensors deployed in moving vehicles. Both approaches complement the most common methods of traffic sensing while being more cost-effective and easily available. Early experimental results show the variety of information available through both approaches from Origin/Destination matrices to travel times and insights into mobility neighborhoods. These matrices are important to improve traffic management increasing the efficiency of urban mobility networks.

show abstract

“…GPS-equipped probe car reporting has a low accuracy rate when the penetration rate of GPS-equipped probe cars is too low. The CFVD can be obtained from mobile phones, which have high penetration in many countries [9], and some studies pointed that CFVD could be used to estimate traffic status with high accuracy [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Collecting traffic information using CFVD is economic and low cost.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads

Lai

Kuo

2016

IJGI

View full text Add to dashboard Cite

Abstract:In recent years, cellular floating vehicle data (CFVD) has been a popular traffic information estimation technique to analyze cellular network data and to provide real-time traffic information with higher coverage and lower cost. Therefore, this study proposes vehicle positioning and speed estimation methods to capture CFVD and to track mobile stations (MS) for intelligent transportation systems (ITS). Three features of CFVD, which include the IDs, sequence, and cell dwell time of connected cells from the signals of MS communication, are extracted and analyzed. The feature of sequence can be used to judge urban road direction, and the feature of cell dwell time can be applied to discriminate proximal urban roads. The experiment results show the accuracy of the proposed vehicle positioning method, which is 100% better than other popular machine learning methods (e.g., naive Bayes classification, decision tree, support vector machine, and back-propagation neural network). Furthermore, the accuracy of the proposed method with all features (i.e., the IDs, sequence, and cell dwell time of connected cells) is 83.81% for speed estimation. Therefore, the proposed methods based on CFVD are suitable for detecting the status of urban road traffic.

show abstract

Exploiting Cellular Networks for Road Traffic Estimation: A Survey and a Research Roadmap

Cited by 50 publications

References 6 publications

Cell phone big data to compute mobility scenarios for future smart cities

Cell phone big data to compute mobility scenarios for future smart cities

Sensing Bluetooth Mobility Data: Potentials and Applications

Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads

Contact Info

Product

Resources

About