Effective Urban Traffic Monitoring by Vehicular Sensor Networks

Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a tr...
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Gespeichert in:

Autor*in:	Xinping Guan [verfasserIn] Ning Lu Bo Yang Rong Du Cailian Chen Xuemin Shen

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	buses Sensors interpolation method entropy urban traffic monitoring matrix completion taxis vehicular sensor networks patrol cars Roads matrix algebra floating cars urban transportation systems real-data-set analysis wireless sensor networks mobile sensors road traffic control traffic matrix spatial entropies Estimation error Vehicles patrol control algorithms interpolation MC methods traffic estimation error traffic-monitoring center sampling rule probe vehicles temporal entropies TMC Monitoring sampling methods Wireless sensor networks Analysis Interpolation

Übergeordnetes Werk:	Enthalten in: IEEE transactions on vehicular technology - New York, NY : IEEE, 1967, 64(2015), 1, Seite 273-286
Übergeordnetes Werk:	volume:64 ; year:2015 ; number:1 ; pages:273-286

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1109/TVT.2014.2321010

Katalog-ID:	OLC1969344024

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520			\|a Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.
650		4	\|a buses
650		4	\|a Sensors
650		4	\|a interpolation method
650		4	\|a entropy
650		4	\|a urban traffic monitoring
650		4	\|a matrix completion
650		4	\|a taxis
650		4	\|a vehicular sensor networks
650		4	\|a patrol cars
650		4	\|a Roads
650		4	\|a matrix algebra
650		4	\|a floating cars
650		4	\|a urban transportation systems
650		4	\|a real-data-set analysis
650		4	\|a wireless sensor networks
650		4	\|a mobile sensors
650		4	\|a road traffic control
650		4	\|a traffic matrix
650		4	\|a spatial entropies
650		4	\|a Estimation error
650		4	\|a Vehicles
650		4	\|a patrol control algorithms
650		4	\|a interpolation
650		4	\|a MC methods
650		4	\|a traffic estimation error
650		4	\|a traffic-monitoring center
650		4	\|a sampling rule
650		4	\|a probe vehicles
650		4	\|a temporal entropies
650		4	\|a TMC
650		4	\|a Monitoring
650		4	\|a sampling methods
650		4	\|a Wireless sensor networks
650		4	\|a Analysis
650		4	\|a Interpolation
700	0		\|a Ning Lu \|4 oth
700	0		\|a Bo Yang \|4 oth
700	0		\|a Rong Du \|4 oth
700	0		\|a Cailian Chen \|4 oth
700	0		\|a Xuemin Shen \|4 oth
773	0	8	\|i Enthalten in \|t IEEE transactions on vehicular technology \|d New York, NY : IEEE, 1967 \|g 64(2015), 1, Seite 273-286 \|w (DE-627)129358584 \|w (DE-600)160444-2 \|w (DE-576)014730871 \|x 0018-9545 \|7 nnns
773	1	8	\|g volume:64 \|g year:2015 \|g number:1 \|g pages:273-286
856	4	1	\|u http://dx.doi.org/10.1109/TVT.2014.2321010 \|3 Volltext
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952			\|d 64 \|j 2015 \|e 1 \|h 273-286

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allfields	10.1109/TVT.2014.2321010 doi PQ20160211 (DE-627)OLC1969344024 (DE-599)GBVOLC1969344024 (PRQ)g1504-8683b157dbc6c430226546c6dbed723254152e451e2de4e67de4d69e7d2aa4350 (KEY)0030991520150000064000100273effectiveurbantrafficmonitoringbyvehicularsensorne DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xinping Guan verfasserin aut Effective Urban Traffic Monitoring by Vehicular Sensor Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated. buses Sensors interpolation method entropy urban traffic monitoring matrix completion taxis vehicular sensor networks patrol cars Roads matrix algebra floating cars urban transportation systems real-data-set analysis wireless sensor networks mobile sensors road traffic control traffic matrix spatial entropies Estimation error Vehicles patrol control algorithms interpolation MC methods traffic estimation error traffic-monitoring center sampling rule probe vehicles temporal entropies TMC Monitoring sampling methods Wireless sensor networks Analysis Interpolation Ning Lu oth Bo Yang oth Rong Du oth Cailian Chen oth Xuemin Shen oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 1, Seite 273-286 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:1 pages:273-286 http://dx.doi.org/10.1109/TVT.2014.2321010 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6807778 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165843 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 1 273-286
spelling	10.1109/TVT.2014.2321010 doi PQ20160211 (DE-627)OLC1969344024 (DE-599)GBVOLC1969344024 (PRQ)g1504-8683b157dbc6c430226546c6dbed723254152e451e2de4e67de4d69e7d2aa4350 (KEY)0030991520150000064000100273effectiveurbantrafficmonitoringbyvehicularsensorne DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xinping Guan verfasserin aut Effective Urban Traffic Monitoring by Vehicular Sensor Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated. buses Sensors interpolation method entropy urban traffic monitoring matrix completion taxis vehicular sensor networks patrol cars Roads matrix algebra floating cars urban transportation systems real-data-set analysis wireless sensor networks mobile sensors road traffic control traffic matrix spatial entropies Estimation error Vehicles patrol control algorithms interpolation MC methods traffic estimation error traffic-monitoring center sampling rule probe vehicles temporal entropies TMC Monitoring sampling methods Wireless sensor networks Analysis Interpolation Ning Lu oth Bo Yang oth Rong Du oth Cailian Chen oth Xuemin Shen oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 1, Seite 273-286 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:1 pages:273-286 http://dx.doi.org/10.1109/TVT.2014.2321010 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6807778 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165843 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 1 273-286
allfields_unstemmed	10.1109/TVT.2014.2321010 doi PQ20160211 (DE-627)OLC1969344024 (DE-599)GBVOLC1969344024 (PRQ)g1504-8683b157dbc6c430226546c6dbed723254152e451e2de4e67de4d69e7d2aa4350 (KEY)0030991520150000064000100273effectiveurbantrafficmonitoringbyvehicularsensorne DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xinping Guan verfasserin aut Effective Urban Traffic Monitoring by Vehicular Sensor Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated. buses Sensors interpolation method entropy urban traffic monitoring matrix completion taxis vehicular sensor networks patrol cars Roads matrix algebra floating cars urban transportation systems real-data-set analysis wireless sensor networks mobile sensors road traffic control traffic matrix spatial entropies Estimation error Vehicles patrol control algorithms interpolation MC methods traffic estimation error traffic-monitoring center sampling rule probe vehicles temporal entropies TMC Monitoring sampling methods Wireless sensor networks Analysis Interpolation Ning Lu oth Bo Yang oth Rong Du oth Cailian Chen oth Xuemin Shen oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 1, Seite 273-286 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:1 pages:273-286 http://dx.doi.org/10.1109/TVT.2014.2321010 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6807778 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165843 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 1 273-286
allfieldsGer	10.1109/TVT.2014.2321010 doi PQ20160211 (DE-627)OLC1969344024 (DE-599)GBVOLC1969344024 (PRQ)g1504-8683b157dbc6c430226546c6dbed723254152e451e2de4e67de4d69e7d2aa4350 (KEY)0030991520150000064000100273effectiveurbantrafficmonitoringbyvehicularsensorne DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xinping Guan verfasserin aut Effective Urban Traffic Monitoring by Vehicular Sensor Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated. buses Sensors interpolation method entropy urban traffic monitoring matrix completion taxis vehicular sensor networks patrol cars Roads matrix algebra floating cars urban transportation systems real-data-set analysis wireless sensor networks mobile sensors road traffic control traffic matrix spatial entropies Estimation error Vehicles patrol control algorithms interpolation MC methods traffic estimation error traffic-monitoring center sampling rule probe vehicles temporal entropies TMC Monitoring sampling methods Wireless sensor networks Analysis Interpolation Ning Lu oth Bo Yang oth Rong Du oth Cailian Chen oth Xuemin Shen oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 1, Seite 273-286 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:1 pages:273-286 http://dx.doi.org/10.1109/TVT.2014.2321010 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6807778 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165843 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 1 273-286
allfieldsSound	10.1109/TVT.2014.2321010 doi PQ20160211 (DE-627)OLC1969344024 (DE-599)GBVOLC1969344024 (PRQ)g1504-8683b157dbc6c430226546c6dbed723254152e451e2de4e67de4d69e7d2aa4350 (KEY)0030991520150000064000100273effectiveurbantrafficmonitoringbyvehicularsensorne DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Xinping Guan verfasserin aut Effective Urban Traffic Monitoring by Vehicular Sensor Networks 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated. buses Sensors interpolation method entropy urban traffic monitoring matrix completion taxis vehicular sensor networks patrol cars Roads matrix algebra floating cars urban transportation systems real-data-set analysis wireless sensor networks mobile sensors road traffic control traffic matrix spatial entropies Estimation error Vehicles patrol control algorithms interpolation MC methods traffic estimation error traffic-monitoring center sampling rule probe vehicles temporal entropies TMC Monitoring sampling methods Wireless sensor networks Analysis Interpolation Ning Lu oth Bo Yang oth Rong Du oth Cailian Chen oth Xuemin Shen oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 64(2015), 1, Seite 273-286 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:64 year:2015 number:1 pages:273-286 http://dx.doi.org/10.1109/TVT.2014.2321010 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6807778 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165843 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 64 2015 1 273-286
language	English
source	Enthalten in IEEE transactions on vehicular technology 64(2015), 1, Seite 273-286 volume:64 year:2015 number:1 pages:273-286
sourceStr	Enthalten in IEEE transactions on vehicular technology 64(2015), 1, Seite 273-286 volume:64 year:2015 number:1 pages:273-286
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topic_facet	buses Sensors interpolation method entropy urban traffic monitoring matrix completion taxis vehicular sensor networks patrol cars Roads matrix algebra floating cars urban transportation systems real-data-set analysis wireless sensor networks mobile sensors road traffic control traffic matrix spatial entropies Estimation error Vehicles patrol control algorithms interpolation MC methods traffic estimation error traffic-monitoring center sampling rule probe vehicles temporal entropies TMC Monitoring sampling methods Wireless sensor networks Analysis Interpolation
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author	Xinping Guan
spellingShingle	Xinping Guan ddc 620 bkl 53.70 bkl 53.74 misc buses misc Sensors misc interpolation method misc entropy misc urban traffic monitoring misc matrix completion misc taxis misc vehicular sensor networks misc patrol cars misc Roads misc matrix algebra misc floating cars misc urban transportation systems misc real-data-set analysis misc wireless sensor networks misc mobile sensors misc road traffic control misc traffic matrix misc spatial entropies misc Estimation error misc Vehicles misc patrol control algorithms misc interpolation misc MC methods misc traffic estimation error misc traffic-monitoring center misc sampling rule misc probe vehicles misc temporal entropies misc TMC misc Monitoring misc sampling methods misc Wireless sensor networks misc Analysis misc Interpolation Effective Urban Traffic Monitoring by Vehicular Sensor Networks
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topic_title	620 DNB 53.70 bkl 53.74 bkl Effective Urban Traffic Monitoring by Vehicular Sensor Networks buses Sensors interpolation method entropy urban traffic monitoring matrix completion taxis vehicular sensor networks patrol cars Roads matrix algebra floating cars urban transportation systems real-data-set analysis wireless sensor networks mobile sensors road traffic control traffic matrix spatial entropies Estimation error Vehicles patrol control algorithms interpolation MC methods traffic estimation error traffic-monitoring center sampling rule probe vehicles temporal entropies TMC Monitoring sampling methods Wireless sensor networks Analysis Interpolation
topic	ddc 620 bkl 53.70 bkl 53.74 misc buses misc Sensors misc interpolation method misc entropy misc urban traffic monitoring misc matrix completion misc taxis misc vehicular sensor networks misc patrol cars misc Roads misc matrix algebra misc floating cars misc urban transportation systems misc real-data-set analysis misc wireless sensor networks misc mobile sensors misc road traffic control misc traffic matrix misc spatial entropies misc Estimation error misc Vehicles misc patrol control algorithms misc interpolation misc MC methods misc traffic estimation error misc traffic-monitoring center misc sampling rule misc probe vehicles misc temporal entropies misc TMC misc Monitoring misc sampling methods misc Wireless sensor networks misc Analysis misc Interpolation
topic_unstemmed	ddc 620 bkl 53.70 bkl 53.74 misc buses misc Sensors misc interpolation method misc entropy misc urban traffic monitoring misc matrix completion misc taxis misc vehicular sensor networks misc patrol cars misc Roads misc matrix algebra misc floating cars misc urban transportation systems misc real-data-set analysis misc wireless sensor networks misc mobile sensors misc road traffic control misc traffic matrix misc spatial entropies misc Estimation error misc Vehicles misc patrol control algorithms misc interpolation misc MC methods misc traffic estimation error misc traffic-monitoring center misc sampling rule misc probe vehicles misc temporal entropies misc TMC misc Monitoring misc sampling methods misc Wireless sensor networks misc Analysis misc Interpolation
topic_browse	ddc 620 bkl 53.70 bkl 53.74 misc buses misc Sensors misc interpolation method misc entropy misc urban traffic monitoring misc matrix completion misc taxis misc vehicular sensor networks misc patrol cars misc Roads misc matrix algebra misc floating cars misc urban transportation systems misc real-data-set analysis misc wireless sensor networks misc mobile sensors misc road traffic control misc traffic matrix misc spatial entropies misc Estimation error misc Vehicles misc patrol control algorithms misc interpolation misc MC methods misc traffic estimation error misc traffic-monitoring center misc sampling rule misc probe vehicles misc temporal entropies misc TMC misc Monitoring misc sampling methods misc Wireless sensor networks misc Analysis misc Interpolation
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title	Effective Urban Traffic Monitoring by Vehicular Sensor Networks
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title_full	Effective Urban Traffic Monitoring by Vehicular Sensor Networks
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title_sort	effective urban traffic monitoring by vehicular sensor networks
title_auth	Effective Urban Traffic Monitoring by Vehicular Sensor Networks
abstract	Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.
abstractGer	Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.
abstract_unstemmed	Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.
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title_short	Effective Urban Traffic Monitoring by Vehicular Sensor Networks
url	http://dx.doi.org/10.1109/TVT.2014.2321010 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6807778 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165843
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