Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model: A Case Study in Nanjing, China

WANG Jiechen; WU Jiayi; NI Jianhua; CHEN Jie; XI Changbai

doi:10.1007/s11769-018-0982-2

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Article Navigation > Chinese Geographical Science > 2018 > 28(6): 1048-1060

WANG Jiechen, WU Jiayi, NI Jianhua, CHEN Jie, XI Changbai. Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model: A Case Study in Nanjing, China[J]. Chinese Geographical Science, 2018, 28(6): 1048-1060. doi: 10.1007/s11769-018-0982-2

Citation:

WANG Jiechen, WU Jiayi, NI Jianhua, CHEN Jie, XI Changbai. Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model: A Case Study in Nanjing, China[J]. Chinese Geographical Science, 2018, 28(6): 1048-1060. doi: 10.1007/s11769-018-0982-2

Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model: A Case Study in Nanjing, China

doi: 10.1007/s11769-018-0982-2

1.
Department of Geographic Information Science, Nanjing University, Nanjing 210023, China;
2.
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China;
3.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Funds: Under the auspices of National Natural Science Foundation of China (No. 41571377)

More Information

Corresponding author: WANG Jiechen.E-mail:wangjiechen@nju.edu.cn
Received Date: 2017-10-25
Rev Recd Date: 2018-02-26
Publish Date: 2018-12-27

Abstract

With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.
- time series clustering,
- temporal characteristics of road speed,
- taxi trajectory data,
- urban computation,
- machine-learning

References

[1]	Balasubramaniyan R, Hüllermeier E, Weskamp N et al., 2005. Clustering of gene expression data using a local shape-based similarity measure. Bioinformatics, 21(7):1069-1077. doi: 10.1093/bioinformatics/bti095
[2]	Chen J, Shaw S L, Yu H B et al., 2011. Exploratory data analysis of activity diary data:a space-time GIS approach. Journal of Transport Geography, 19(3):394-404. doi:10.1016/j.jtrangeo. 2010.11.002
[3]	Cui J X, Liu F, Janssens D et al., 2016. Detecting urban road network accessibility problems using taxi GPS data. Journal of Transport Geography, 51:147-157. doi:10.1016/j.jtrangeo. 2015.12.007
[4]	Ding C, He X F, 2004. Cluster structure of K-means clustering via principal component analysis. In:Dai H H, Srikant R, Zhang C Q (eds). Advances in Knowledge Discovery and Data Mining. Berlin, Heidelberg:Springer, 414-418. doi: 10.1007/978-3-540-24775-3_50
[5]	Filho R H, Maia J E B, 2010. Network traffic prediction using PCA and K-means. In:Proceedings of 2010 Network Opera-tions and Management Symposium. Osaka, Japan:IEEE, 938-941. doi: 10.1109/NOMS.2010.5488338
[6]	Ghosh-Dastidar S, Adeli H, 2006. Neural network-wavelet mi-crosimulation model for delay and queue length estimation at freeway work zones. Journal of Transportation Engineering, 132(4):331-341. doi:10.1061/(ASCE)0733-947X(2006)132:4 (331)
[7]	Kim J, Krishnapuram R, Davé R, 1996. Application of the least trimmed squares technique to prototype-based clustering. Pat-tern Recognition Letters, 17(6):633-641. doi: 10.1016/0167-8655(96)00028-1
[8]	Košmelj K, Batagelj V, 1990. Cross-sectional approach for clus-tering time varying data. Journal of Classification, 7(1):99-109. doi: 10.1007/BF01889706
[9]	Lee J, Hao Q, Jia Y H et al., 2009. Continuous traffic flow de-pendency analysis of urban periphery freeway consecutive section base on principle component analysis. In:Proceedings of 2nd International Conference on Intelligent Computation Technology and Automation. Changsha, Hunan, China:IEEE, 533-536. doi: 10.1109/ICICTA.2009.594
[10]	Liao T W, 2005. Clustering of time series data-a survey. Pattern Recognition, 38(11):1857-1874. doi:10.1016/j.patcog.2005. 01.025
[11]	Lippi M, Bertini M, Frasconi P, 2013. Short-term traffic flow forecasting:an experimental comparison of time-series analysis and supervised learning. IEEE Transactions on Intelligent Transportation Systems, 14(2):871-882. doi:10.1109/TITS. 2013.2247040
[12]	Liu K, Yamamoto T, Morikawa T, 2009. Feasibility of using taxi dispatch system as probes for collecting traffic information. Journal of Intelligent Transportation Systems, 13(1):16-27. doi: 10.1080/15472450802644447
[13]	Liu X, Gong L, Gong Y X et al., 2015. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43:78-90. doi: 10.1016/j.jtrangeo.2015.01.016
[14]	Liu Y, Wang F H, Xiao Y et al., 2012. Urban land uses and traffic ‘source-sink areas’:evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106(1):73-87. doi: 10.1016/j.landurbplan.2012.02.012
[15]	Qi G D, Li X L, Li S J et al., 2011. Measuring social functions of city regions from large-scale taxi behaviors. In:Proceedings of 2011 IEEE International Conference on Pervasive Computing and Communications Workshops. Seattle, WA, USA:IEEE, 384-388. doi: 10.1109/PERCOMW.2011.5766912
[16]	Quddus M A, Ochieng W Y, Noland R B, 2007. Current map-matching algorithms for transport applications:state-of-the art and future research directions. Transportation Research Part C:Emerging Technologies, 15(5):312-328. doi: 10.1016/j.trc.2007.05.002
[17]	Serrano-Cinca C, Fuertes-Callén Y, Mar-Molinero C, 2005. Measuring DEA efficiency in internet companies. Decision Support Systems, 38(4):557-573. doi:10.1016/j.dss.2003. 08.004
[18]	Stathopoulos A, Karlaftis M G, 2001. Spectral and cross-spectral analysis of urban traffic flows. In:Proceedings of 2001 IEEE Intelligent Transportation Systems. Oakland, CA, USA:IEEE, 820-825. doi: 10.1109/ITSC.2001.948766
[19]	Toplak W, Koller H, Dragaschnig M et al., 2010. Novel road clas-sifications for large scale traffic networks. In:Proceedings of the 13th International IEEE Conference on Intelligent Trans-portation Systems. Funchal, Portugal:IEEE, 1264-1270. doi: 10.1109/ITSC.2010.5625182
[20]	Townshend J R G, Justice C O, Kalb V, 1987. Characterization and classification of South American land cover types using satellite data. International Journal of Remote Sensing, 8(8):1189-1207. doi: 10.1080/01431168708954764
[21]	Vlahogianni E I, Geroliminis N, Skabardonis A, 2008. Empirical and analytical investigation of traffic flow regimes and transi-tions in signalized arterials. Journal of Transportation Engi-neering, 134(12):512-522. doi:10.1061/(ASCE)0733-947X (2008)134:12(512)
[22]	Vlahogianni E I, Karlaftis M G, 2012. Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures. Nonlinear Dynamics, 69(4):1949-1963. doi: 10.1007/s11071-012-0399-x
[23]	Zhan X Y, Hasan S, Ukkusuri S V et al., 2013. Urban link travel time estimation using large-scale taxi data with partial infor-mation. Transportation Research Part C:Emerging Technolo-gies, 33:37-49. doi: 10.1016/j.trc.2013.04.001
[24]	Zhang W, Xu J M, Wang H F, 2007a. Urban traffic situation cal-culation methods based on probe vehicle data. Journal of Transportation Systems Engineering and Information Tech-nology, 7(1):43-48. doi: 10.1016/S1570-6672(07)60007-5
[25]	Zhang Wei, Xu Jianmin, Lin Mianfeng, 2007b. Map matching algorithm of large scale probe vehicle data. Journal of Trans-portation Systems Engineering and Information Technology, 7(2):39-45. (in Chinese)
[26]	Zhang Xirui, Fang Zhixiang, Li Qingquan et al., 2015. A spa-tio-temporal analysis on the heterogeneous distribution of urban road network capacity based on floating car data. Journal of Geo-Information Science, 17(3):336-343. (in Chinese)
[27]	Zhuang L J, Gong J F, He Z C et al., 2012. Framework of experi-enced route planning based on taxis' GPS data. In:Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems. Anchorage, AK, USA:IEEE, 1026-1031. doi: 10.1109/ITSC.2012.6338867

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model: A Case Study in Nanjing, China

1. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China;

2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China;

3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Funds: Under the auspices of National Natural Science Foundation of China (No. 41571377)

Corresponding author: WANG Jiechen.E-mail:wangjiechen@nju.edu.cn

Received Date: 2017-10-25

Rev Recd Date: 2018-02-26

Publish Date: 2018-12-27

Key words:

time series clustering /
temporal characteristics of road speed /
taxi trajectory data /
urban computation /
machine-learning

Abstract: With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.

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Reference (27)

[1]	Balasubramaniyan R, Hüllermeier E, Weskamp N et al., 2005. Clustering of gene expression data using a local shape-based similarity measure. Bioinformatics, 21(7):1069-1077. doi:10.1093/bioinformatics/bti095
[2]	Chen J, Shaw S L, Yu H B et al., 2011. Exploratory data analysis of activity diary data:a space-time GIS approach. Journal of Transport Geography, 19(3):394-404. doi:10.1016/j.jtrangeo. 2010.11.002
[3]	Cui J X, Liu F, Janssens D et al., 2016. Detecting urban road network accessibility problems using taxi GPS data. Journal of Transport Geography, 51:147-157. doi:10.1016/j.jtrangeo. 2015.12.007
[4]	Ding C, He X F, 2004. Cluster structure of K-means clustering via principal component analysis. In:Dai H H, Srikant R, Zhang C Q (eds). Advances in Knowledge Discovery and Data Mining. Berlin, Heidelberg:Springer, 414-418. doi:10.1007/978-3-540-24775-3_50
[5]	Filho R H, Maia J E B, 2010. Network traffic prediction using PCA and K-means. In:Proceedings of 2010 Network Opera-tions and Management Symposium. Osaka, Japan:IEEE, 938-941. doi:10.1109/NOMS.2010.5488338
[6]	Ghosh-Dastidar S, Adeli H, 2006. Neural network-wavelet mi-crosimulation model for delay and queue length estimation at freeway work zones. Journal of Transportation Engineering, 132(4):331-341. doi:10.1061/(ASCE)0733-947X(2006)132:4 (331)
[7]	Kim J, Krishnapuram R, Davé R, 1996. Application of the least trimmed squares technique to prototype-based clustering. Pat-tern Recognition Letters, 17(6):633-641. doi:10.1016/0167-8655(96)00028-1
[8]	Košmelj K, Batagelj V, 1990. Cross-sectional approach for clus-tering time varying data. Journal of Classification, 7(1):99-109. doi:10.1007/BF01889706
[9]	Lee J, Hao Q, Jia Y H et al., 2009. Continuous traffic flow de-pendency analysis of urban periphery freeway consecutive section base on principle component analysis. In:Proceedings of 2nd International Conference on Intelligent Computation Technology and Automation. Changsha, Hunan, China:IEEE, 533-536. doi:10.1109/ICICTA.2009.594
[10]	Liao T W, 2005. Clustering of time series data-a survey. Pattern Recognition, 38(11):1857-1874. doi:10.1016/j.patcog.2005. 01.025
[11]	Lippi M, Bertini M, Frasconi P, 2013. Short-term traffic flow forecasting:an experimental comparison of time-series analysis and supervised learning. IEEE Transactions on Intelligent Transportation Systems, 14(2):871-882. doi:10.1109/TITS. 2013.2247040
[12]	Liu K, Yamamoto T, Morikawa T, 2009. Feasibility of using taxi dispatch system as probes for collecting traffic information. Journal of Intelligent Transportation Systems, 13(1):16-27. doi:10.1080/15472450802644447
[13]	Liu X, Gong L, Gong Y X et al., 2015. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43:78-90. doi:10.1016/j.jtrangeo.2015.01.016
[14]	Liu Y, Wang F H, Xiao Y et al., 2012. Urban land uses and traffic ‘source-sink areas’:evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106(1):73-87. doi:10.1016/j.landurbplan.2012.02.012
[15]	Qi G D, Li X L, Li S J et al., 2011. Measuring social functions of city regions from large-scale taxi behaviors. In:Proceedings of 2011 IEEE International Conference on Pervasive Computing and Communications Workshops. Seattle, WA, USA:IEEE, 384-388. doi:10.1109/PERCOMW.2011.5766912
[16]	Quddus M A, Ochieng W Y, Noland R B, 2007. Current map-matching algorithms for transport applications:state-of-the art and future research directions. Transportation Research Part C:Emerging Technologies, 15(5):312-328. doi:10.1016/j.trc.2007.05.002
[17]	Serrano-Cinca C, Fuertes-Callén Y, Mar-Molinero C, 2005. Measuring DEA efficiency in internet companies. Decision Support Systems, 38(4):557-573. doi:10.1016/j.dss.2003. 08.004
[18]	Stathopoulos A, Karlaftis M G, 2001. Spectral and cross-spectral analysis of urban traffic flows. In:Proceedings of 2001 IEEE Intelligent Transportation Systems. Oakland, CA, USA:IEEE, 820-825. doi:10.1109/ITSC.2001.948766
[19]	Toplak W, Koller H, Dragaschnig M et al., 2010. Novel road clas-sifications for large scale traffic networks. In:Proceedings of the 13th International IEEE Conference on Intelligent Trans-portation Systems. Funchal, Portugal:IEEE, 1264-1270. doi:10.1109/ITSC.2010.5625182
[20]	Townshend J R G, Justice C O, Kalb V, 1987. Characterization and classification of South American land cover types using satellite data. International Journal of Remote Sensing, 8(8):1189-1207. doi:10.1080/01431168708954764
[21]	Vlahogianni E I, Geroliminis N, Skabardonis A, 2008. Empirical and analytical investigation of traffic flow regimes and transi-tions in signalized arterials. Journal of Transportation Engi-neering, 134(12):512-522. doi:10.1061/(ASCE)0733-947X (2008)134:12(512)
[22]	Vlahogianni E I, Karlaftis M G, 2012. Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures. Nonlinear Dynamics, 69(4):1949-1963. doi:10.1007/s11071-012-0399-x
[23]	Zhan X Y, Hasan S, Ukkusuri S V et al., 2013. Urban link travel time estimation using large-scale taxi data with partial infor-mation. Transportation Research Part C:Emerging Technolo-gies, 33:37-49. doi:10.1016/j.trc.2013.04.001
[24]	Zhang W, Xu J M, Wang H F, 2007a. Urban traffic situation cal-culation methods based on probe vehicle data. Journal of Transportation Systems Engineering and Information Tech-nology, 7(1):43-48. doi:10.1016/S1570-6672(07)60007-5
[25]	Zhang Wei, Xu Jianmin, Lin Mianfeng, 2007b. Map matching algorithm of large scale probe vehicle data. Journal of Trans-portation Systems Engineering and Information Technology, 7(2):39-45. (in Chinese)
[26]	Zhang Xirui, Fang Zhixiang, Li Qingquan et al., 2015. A spa-tio-temporal analysis on the heterogeneous distribution of urban road network capacity based on floating car data. Journal of Geo-Information Science, 17(3):336-343. (in Chinese)
[27]	Zhuang L J, Gong J F, He Z C et al., 2012. Framework of experi-enced route planning based on taxis' GPS data. In:Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems. Anchorage, AK, USA:IEEE, 1026-1031. doi:10.1109/ITSC.2012.6338867

Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model: A Case Study in Nanjing, China

doi: 10.1007/s11769-018-0982-2

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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