Vehicle Travel Destination Prediction Method Based on Multi-source Data

Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input ne...
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Autor*in:	Hu, Jie [verfasserIn] Cai, Shijie [verfasserIn] Huang, Tengfei [verfasserIn] Qin, Xiongzhen [verfasserIn] Gao, Zhangbin [verfasserIn] Chen, Liming [verfasserIn] Du, Yufeng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Vehicle trajectory Multi-source data Destination prediction Deep learning

Anmerkung:	© China Society of Automotive Engineers (China SAE) 2021

Übergeordnetes Werk:	Enthalten in: Automotive innovation - [Singapore] : Springer Singapore, 2018, 4(2021), 3 vom: 18. März, Seite 315-327
Übergeordnetes Werk:	volume:4 ; year:2021 ; number:3 ; day:18 ; month:03 ; pages:315-327

Links:	Volltext

DOI / URN:	10.1007/s42154-021-00136-2

Katalog-ID:	SPR044819153

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245	1	0	\|a Vehicle Travel Destination Prediction Method Based on Multi-source Data
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520			\|a Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction
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650		4	\|a Multi-source data \|7 (dpeaa)DE-He213
650		4	\|a Destination prediction \|7 (dpeaa)DE-He213
650		4	\|a Deep learning \|7 (dpeaa)DE-He213
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700	1		\|a Huang, Tengfei \|e verfasserin \|4 aut
700	1		\|a Qin, Xiongzhen \|e verfasserin \|4 aut
700	1		\|a Gao, Zhangbin \|e verfasserin \|4 aut
700	1		\|a Chen, Liming \|e verfasserin \|4 aut
700	1		\|a Du, Yufeng \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Automotive innovation \|d [Singapore] : Springer Singapore, 2018 \|g 4(2021), 3 vom: 18. März, Seite 315-327 \|w (DE-627)1019335262 \|w (DE-600)2927503-9 \|x 2522-8765 \|7 nnns
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author_variant	j h jh s c sc t h th x q xq z g zg l c lc y d yd
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allfields	10.1007/s42154-021-00136-2 doi (DE-627)SPR044819153 (SPR)s42154-021-00136-2-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Hu, Jie verfasserin aut Vehicle Travel Destination Prediction Method Based on Multi-source Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © China Society of Automotive Engineers (China SAE) 2021 Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction Vehicle trajectory (dpeaa)DE-He213 Multi-source data (dpeaa)DE-He213 Destination prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Cai, Shijie verfasserin aut Huang, Tengfei verfasserin aut Qin, Xiongzhen verfasserin aut Gao, Zhangbin verfasserin aut Chen, Liming verfasserin aut Du, Yufeng verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 3 vom: 18. März, Seite 315-327 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:3 day:18 month:03 pages:315-327 https://dx.doi.org/10.1007/s42154-021-00136-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 3 18 03 315-327
spelling	10.1007/s42154-021-00136-2 doi (DE-627)SPR044819153 (SPR)s42154-021-00136-2-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Hu, Jie verfasserin aut Vehicle Travel Destination Prediction Method Based on Multi-source Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © China Society of Automotive Engineers (China SAE) 2021 Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction Vehicle trajectory (dpeaa)DE-He213 Multi-source data (dpeaa)DE-He213 Destination prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Cai, Shijie verfasserin aut Huang, Tengfei verfasserin aut Qin, Xiongzhen verfasserin aut Gao, Zhangbin verfasserin aut Chen, Liming verfasserin aut Du, Yufeng verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 3 vom: 18. März, Seite 315-327 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:3 day:18 month:03 pages:315-327 https://dx.doi.org/10.1007/s42154-021-00136-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 3 18 03 315-327
allfields_unstemmed	10.1007/s42154-021-00136-2 doi (DE-627)SPR044819153 (SPR)s42154-021-00136-2-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Hu, Jie verfasserin aut Vehicle Travel Destination Prediction Method Based on Multi-source Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © China Society of Automotive Engineers (China SAE) 2021 Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction Vehicle trajectory (dpeaa)DE-He213 Multi-source data (dpeaa)DE-He213 Destination prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Cai, Shijie verfasserin aut Huang, Tengfei verfasserin aut Qin, Xiongzhen verfasserin aut Gao, Zhangbin verfasserin aut Chen, Liming verfasserin aut Du, Yufeng verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 3 vom: 18. März, Seite 315-327 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:3 day:18 month:03 pages:315-327 https://dx.doi.org/10.1007/s42154-021-00136-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 3 18 03 315-327
allfieldsGer	10.1007/s42154-021-00136-2 doi (DE-627)SPR044819153 (SPR)s42154-021-00136-2-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Hu, Jie verfasserin aut Vehicle Travel Destination Prediction Method Based on Multi-source Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © China Society of Automotive Engineers (China SAE) 2021 Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction Vehicle trajectory (dpeaa)DE-He213 Multi-source data (dpeaa)DE-He213 Destination prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Cai, Shijie verfasserin aut Huang, Tengfei verfasserin aut Qin, Xiongzhen verfasserin aut Gao, Zhangbin verfasserin aut Chen, Liming verfasserin aut Du, Yufeng verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 3 vom: 18. März, Seite 315-327 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:3 day:18 month:03 pages:315-327 https://dx.doi.org/10.1007/s42154-021-00136-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 3 18 03 315-327
allfieldsSound	10.1007/s42154-021-00136-2 doi (DE-627)SPR044819153 (SPR)s42154-021-00136-2-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Hu, Jie verfasserin aut Vehicle Travel Destination Prediction Method Based on Multi-source Data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © China Society of Automotive Engineers (China SAE) 2021 Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction Vehicle trajectory (dpeaa)DE-He213 Multi-source data (dpeaa)DE-He213 Destination prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Cai, Shijie verfasserin aut Huang, Tengfei verfasserin aut Qin, Xiongzhen verfasserin aut Gao, Zhangbin verfasserin aut Chen, Liming verfasserin aut Du, Yufeng verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 3 vom: 18. März, Seite 315-327 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:3 day:18 month:03 pages:315-327 https://dx.doi.org/10.1007/s42154-021-00136-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 3 18 03 315-327
language	English
source	Enthalten in Automotive innovation 4(2021), 3 vom: 18. März, Seite 315-327 volume:4 year:2021 number:3 day:18 month:03 pages:315-327
sourceStr	Enthalten in Automotive innovation 4(2021), 3 vom: 18. März, Seite 315-327 volume:4 year:2021 number:3 day:18 month:03 pages:315-327
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Vehicle trajectory Multi-source data Destination prediction Deep learning
dewey-raw	620
isfreeaccess_bool	false
container_title	Automotive innovation
authorswithroles_txt_mv	Hu, Jie @@aut@@ Cai, Shijie @@aut@@ Huang, Tengfei @@aut@@ Qin, Xiongzhen @@aut@@ Gao, Zhangbin @@aut@@ Chen, Liming @@aut@@ Du, Yufeng @@aut@@
publishDateDaySort_date	2021-03-18T00:00:00Z
hierarchy_top_id	1019335262
dewey-sort	3620
id	SPR044819153
language_de	englisch
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author	Hu, Jie
spellingShingle	Hu, Jie ddc 620 misc Vehicle trajectory misc Multi-source data misc Destination prediction misc Deep learning Vehicle Travel Destination Prediction Method Based on Multi-source Data
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topic_title	620 ASE Vehicle Travel Destination Prediction Method Based on Multi-source Data Vehicle trajectory (dpeaa)DE-He213 Multi-source data (dpeaa)DE-He213 Destination prediction (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213
topic	ddc 620 misc Vehicle trajectory misc Multi-source data misc Destination prediction misc Deep learning
topic_unstemmed	ddc 620 misc Vehicle trajectory misc Multi-source data misc Destination prediction misc Deep learning
topic_browse	ddc 620 misc Vehicle trajectory misc Multi-source data misc Destination prediction misc Deep learning
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title	Vehicle Travel Destination Prediction Method Based on Multi-source Data
ctrlnum	(DE-627)SPR044819153 (SPR)s42154-021-00136-2-e
title_full	Vehicle Travel Destination Prediction Method Based on Multi-source Data
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author_browse	Hu, Jie Cai, Shijie Huang, Tengfei Qin, Xiongzhen Gao, Zhangbin Chen, Liming Du, Yufeng
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title_sort	vehicle travel destination prediction method based on multi-source data
title_auth	Vehicle Travel Destination Prediction Method Based on Multi-source Data
abstract	Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction © China Society of Automotive Engineers (China SAE) 2021
abstractGer	Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction © China Society of Automotive Engineers (China SAE) 2021
abstract_unstemmed	Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. Finally, the cyclic encoding is reduced by 48.60% in the data dimension compared to one-hot encoding. Accurate destination prediction will help improve the efficiency of automotive human–computer interaction © China Society of Automotive Engineers (China SAE) 2021
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title_short	Vehicle Travel Destination Prediction Method Based on Multi-source Data
url	https://dx.doi.org/10.1007/s42154-021-00136-2
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author2	Cai, Shijie Huang, Tengfei Qin, Xiongzhen Gao, Zhangbin Chen, Liming Du, Yufeng
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up_date	2024-07-04T02:27:43.512Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR044819153</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220112042258.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210813s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42154-021-00136-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR044819153</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42154-021-00136-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hu, Jie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Vehicle Travel Destination Prediction Method Based on Multi-source Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© China Society of Automotive Engineers (China SAE) 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data, such as weather, time, and points of interest (POI). This study proposes a destination prediction method based on multi-source data, and a multi-input neural network model is established. In terms of the coding of vehicle trajectory data, a GeoHash to vector (Geo2vec) model is proposed to realize the characterization of the trajectory. As for the coding of temporal features, a cyclic coding model is proposed based on trigonometric functions. For the coding of POI, an origin–destination POI matrix (OD-POI) model is proposed based on the state transition probability. Experimental results show that in terms of the average distance and root-mean-square distance deviations, Geo2vec reveals reductions of 4.51% and 5.63% compared to word to vector (Word2vec), and cyclic encoding shows reductions of 6.35% and 6.67% compared to label encoding; further, the method of OD-POI state transition probability is reduced by 5.85% and 6.4%, and the model based on multi-source data is 17.29% and 17.65% lower than the model based on trajectory data only. 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Vehicle Travel Destination Prediction Method Based on Multi-source Data

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