基于出租车运营数据和POI数据的出行目的识别

摘要/Abstract

摘要：

为了有效获取出租车乘客出行目的，提出了一种基于出租车运营数据和POI(Point of Interest)数据的出行目的识别方法.构建了基于乘客出行特征和下车所属POI点类别的乘客出行目的识别模型，该方法从出行特征及乘客下车点最终可能到达的目的地所属POI点类型两个方面确定乘客的出行目的.为了验证所提方法的有效性及实用性，对成都地区展开了出租车出行调查，并利用调查数据对模型进行了精度验证.结果发现，相比于现有的利用出行特征推断出行目的的方法，本文提出的决策树+POI(II)能够提高最终识别准确度15.76%.最后，将所提方法应用于成都 1周的实际运营数据中，成功地识别出219 942名乘客的出行目的，说明该方法能够应用于实际数据量较大的出行目的识别.本文提出的方法，可以作为出行调查的辅助手段.

关键词: 城市交通, 出租车运营数据, POI点数据, 出行目的识别

Abstract:

An effective trip-purpose-identification method is proposed based on taxi operating data and POI (point of interest) data to obtain the trip purpose of taxi passengers. A trip-purpose-identification model on the account of trip characteristic and type of drop-off points, which determines the destinations of taxi passengers from two aspects including trip characteristic and POI type of possible destinations. To verify the validity and practicability of the proposed method, an investigation into servicing taxi in Chengdu is launched, and the accuracy of the proposed model is verified by exploiting collected data. It turns out that the proposed method, decision-making tree plus POI(II), can improve the ultimate identification accuracy up to 15.76% compared with the existing method that deduces trip purpose upon trip characteristic. Finally, the proposed method is successfully applied to identify the trip purposes of actual one-week operation data, containing 219 942 taxi passengers, which implies this method can be applied into trip purpose identification with large-scale operation data. The proposed method can be used as an assistant method to trip survey.

Key words: urban traffic, taxi operating data, POI data, trip-purpose-identification

中图分类号:

U491

罗孝羚，蒋阳升. 基于出租车运营数据和POI数据的出行目的识别[J]. 交通运输系统工程与信息, 2018, 18(5): 60-66.

LUO Xiao-ling, JIANG Yang-sheng. Trip-purpose-identification Based on Taxi Operating Data and POI Data[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(5): 60-66.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19696.shtml

http://www.tseit.org.cn/CN/Y2018/V18/I5/60

参考文献

[1] RAANAAS R K, ANDERSON D. A questionnaire survey of Norwegian taxi drivers’musculoskeletal health, and work-related risk factors[J]. International Journal of Industrial Ergonomics, 2008, 38(3): 280-290.

[2] DENG Z, JI M. Deriving rules for trip purpose identification from GPS travel survey data and land use data: A machine learning approach[J]. Traffic and Transportation Studies, 2010(2010): 768-777.

[3] COOLS M, MOONS E, CREEMERS L, et al. Changes in travel behavior in response to weather conditions: Do type of weather and trip purpose matter?[J]. Transportation Research Record: Journal of the Transportation Research Board, 2010 (2157): 22-28.

[4] FURTH P, STRATHMAN J, HEMILY B. Part 4, marketing and fare policy: Making automatic passenger counts mainstream, accuracy, balancing algorithms, and data structures[J]. Transportation Research Record: Journal of the Transportation Research Board, 2005: 205-216.

[5] MATAS A. Demand and revenue implications of an integrated public transport policy: The case of madrid[J]. Transport Reviews, 2004(24): 195-217.

[6] EL- GENEIDY A M, HORNING J, KRIZEK K J. Analyzing transit service reliability using detailed data from automatic vehicular locator systems[J]. Journal of Advanced Transportation, 2011(45): 66-79.

[7] TRÉPANIER M, CHAPLEAU R, TRANCHANT N. Individual trip destination estimation in transit smart card automated fare collection system[J]. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 2007(11): 1-15.

[8] MA X, WU Y J, WANG Y, et al. Mining smart card data for transit riders’ travel patterns[J]. Transportation Research Part C: Emerging Technologies, 2013(36): 1-12.

[9] BARRY J, NEWHOUSER R, RAHBEE A, et al. Origin and destination estimation in New York City with automated fare system data[J]. Transportation Research Record: Journal of the Transportation Research Board, 2002: 183-187.

[10] MUNIZAGA M A, PALMA C. Estimation of a disaggregate multimodal public transport origindestination matrix from passive smartcard data from Santiago, Chile[J]. Transportation Research Part C: Emerging Technologies, 2012(24): 9-18.

[11] ALFRED CHU K, CHAPLEAU R. Enriching archived smart card transaction data for transit demand modeling[J]. Transportation Research Record: Journal of the Transportation Research Board, 2008: 63-72.

[12] MORENCY C, TRÉPANIER M, AGARD B. Measuring transit use variability with smart-card data[J]. Transport Policy, 2007(14): 193-203.

[13] ZHAN X, HASAN S, UKKUSURI S V, et al. Urban link travel time estimation using large-scale taxi data with partial information[J]. Transportation Research Part C: Emerging Technologies, 2013(33): 37-49.

[14] 丁涛杰, 史殿习, 李永谋. 基于出租车GPS数据的道路平均速度估计方法[J]. 计算机技术与发展, 2015, 25 (7): 15-19. [DING T J, SHI D X, LI Y M. Estimation method for average traffic speed based on GPS data of taxi[J]. Computer Technology and Development, 2015, 25(7): 15-19.]

[15] 姜桂艳, 常安德, 李琦, 等. 基于出租车GPS数据的路段平均速度估计模型[J]. 西南交通大学学报, 2011, 46 (4): 638-644. [JIANG G Y, CHANG A D, LI Q, et al. Estimation models for average speed of traffic flow based on GPS data of taxi[J]. Journal of Southwest Jiaotong University, 2011, 46(4): 638-644.]

[16] 白竹, 王健, 胡晓伟. 城市出租车系统运营效率评价研究[J]. 交通运输系统工程与信息, 2014, 14(3): 227-233. [BAI Z, WANG J, HU X W. Operational efficiency evaluation of urban taxi system[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(3): 227-233.]

[17] FLIER H, GELASHVILI R, GRAFFAGNINO T, et al. Mining railway delay dependencies in large-scale realworld delay data[J]. Robust and Online Large-scale Optimization, 2009(5868): 354-368.

[18] LIAO S H, CHU P H, HSIAO P Y. Data mining techniques and applications- A decade review from 2000 to 2011[J]. Expert Systems with Applications, 2012, 39(12): 11303-11311.

[19] KUSAKABE T, ASAKURA Y. Behavioural data mining of transit smart card data: A data fusion approach[J]. Transportation Research Part C: Emerging Technologies, 2014(46): 179-191.

[20] TIERNEY K, DECKER S, PROUSSALOGLOU K, et al. Travel survey manual[R]. 1996.

[21] SHEN L, STOPHER P R. A process for trip purpose imputation from global positioning system data[J]. Transportation Research Part C: Emerging Technologies, 2013(36): 261-267.