Volume 42 Issue 2
Apr.  2024
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CHENG Guozhu, LI Weijun, FENG Tianjun. A Method for Allocating Bus Passenger Flow by Considering Comprehensive Cost[J]. Journal of Transport Information and Safety, 2024, 42(2): 166-174. doi: 10.3963/j.jssn.1674-4861.2024.02.017
Citation: CHENG Guozhu, LI Weijun, FENG Tianjun. A Method for Allocating Bus Passenger Flow by Considering Comprehensive Cost[J]. Journal of Transport Information and Safety, 2024, 42(2): 166-174. doi: 10.3963/j.jssn.1674-4861.2024.02.017
shu

A Method for Allocating Bus Passenger Flow by Considering Comprehensive Cost

doi: 10.3963/j.jssn.1674-4861.2024.02.017
  • 1.

    School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China

  • 2.

    School of Transportation Science & Engineering, Jilin Jianzhu University, Changchun 130118, China

  • Received Date: 2023-03-10
    Available Online: 2024-09-14
    Abstract
  • The inefficiencies and inaccuracies of traditional survey methods for bus passenger flow data need to be addressed. Moreover, bus passenger flow allocation methods are inadequate due to the incomplete consideration of travel cost and significant disparities in travel cost among individuals. Therefore, a study on a bus passenger flow allocation method that considers comprehensive cost is conducted. A mobile signaling data platform based on Data-as-a-Service is developed as a source of data for bus passenger flow allocation. Spatial relationships between users and traffic zones are determined by matching latitude and longitude coordinates. Using a data warehousing tool, data dictionary indexes are filtered to define parameters such as time, speed, and origin-destination types. Transportation modes are identified through time matching and path matching. User proportions are extrapolated to the national population to obtain the bus commuting origin-destination (OD) matrix during the morning peak period for permanent residents. Travel time cost, congestion cost, and fare cost for bus passengers are analyzed. A bus passenger flow allocation model is established based on the principle of maximizing individual benefits while considering comprehensive cost. The problem of bus passenger flow allocation between traffic zones is transformed into a directed weighted graph path selection problem. A hybrid algorithm combining depth-first search and successive averages method is employed to solve this problem, facilitating bus travel plan selection and passenger flow allocation. Taking typical traffic zones in Harbin as a case study, bus passenger flow allocation is conducted and compared with results from the traditional Logit path selection probability model and manual surveys. The results show that the average absolute percentage error between the proposed model and manual surveys is 4%, compared to 17.5% for the Logit model. After allocating passenger flows using the proposed model, the extreme difference, variance, and total sum of individual travel cost are 0.03, 0.000 1, and 1 108.35, respectively, compared to 3.28, 1.58, and 1 127.02 for the Logit model. These results validate the accuracy of the proposed model in allocating passenger flows and highlight the necessity of considering comprehensive cost. After passenger flow allocation, the difference in individual travel cost is smaller, aligning better with the principle of maximizing benefits.

     

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    • References(30)
  • [1]
    徐猛, 刘涛, 钟绍鹏, 等. 城市智慧公交研究综述与展望[J]. 交通运输系统工程与信息, 2022, 22(2): 91-108.

    XU M, LIU T, ZHONG S P, et al. Urban smart public transport studies: a review and prospect[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 91-108. (in Chinese)
    [2]
    柳伍生, 周向栋, 谭倩. 多元数据下的公交站点客流不确定性分析[J]. 交通运输系统工程与信息, 2018, 18(2): 149-156.

    LIU W S, ZHOU X D, TAN Q. Uncertainty analysis to passenger flow of bus stations based on multivariate data fusion[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(2): 149-156. (in Chinese)
    [3]
    赵建东, 申瑾, 刘麟玮. 多源数据驱动CNN-GRU模型的公交客流量分类预测[J]. 交通运输工程学报, 2021, 21(5): 265-273.

    ZHAO J D, SHEN J, LIU L W. Bus passenger flow classification prediction driven by CNN-GRU model and multi-source data[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 265-273. (in Chinese)
    [4]
    许智宏, 王怡峥, 王利琴, 等. 基于Hive的海量公交客流起讫点挖掘方法[J]. 科学技术与工程, 2020, 20(20): 8300-8309.

    XU Z H, WANG Y Z, WANG L Q, et al. A methodology of massive bus passenger origin-destination mining based on Hive[J]. Science Technology and Engineering, 2020, 20(20): 8300-8309. (in Chinese)
    [5]
    李淑庆, 刘耀鸿, 邱豪基. 基于IC卡与GPS数据的公交通勤出行特征分析[J]. 重庆交通大学学报(自然科学版), 2021, 40(10): 171-177, 184. doi: 10.3969/j.issn.1674-0696.2021.10.20

    LI S Q, LIU Y H, QIU H J. Commuting characteristics of public transport based on IC card and GPS data[J]. Journal of Chongqing Jiaotong University (Natural Science), 2021, 40(10): 171-177, 184. (in Chinese) doi: 10.3969/j.issn.1674-0696.2021.10.20
    [6]
    张文胜, 卢梦, 朱冀军, 等. 基于公交IC卡和AVL数据的公交客流OD推算[J]. 计算机应用与软件, 2021, 38(7): 100-105.

    ZHANG W S, LU M, ZHU J J, et al. OD calculation of bus passenger flow based on IC card and AVL data[J]. Computer Applications and Software, 2021, 38(7): 100-105. (in Chinese)
    [7]
    柳伍生, 周向栋, 匡凯. 基于IC卡数据的公交下车站点区间不确定性客流推导方法[J]. 铁道科学与工程学报, 2018, 15(11): 2988-2994.

    LIU W S, ZHOU X D, KUANG K. The method of deriving passenger flow of bus alighting stops based on smart card data and interval uncertainty[J]. Journal of Railway Science and Engineering, 2018, 15(11): 2988-2994. (in Chinese)
    [8]
    费晔. 基于IC卡数据的公交出行OD推算方法研究[J]. 计算机应用与软件, 2018, 35(8): 190-194. doi: 10.3969/j.issn.1000-386x.2018.08.035

    FE Y. OD matrix estimation method for public transportation trip based on IC card data[J]. Computer Applications and Software, 2018, 35(8): 190-194. (in Chinese) doi: 10.3969/j.issn.1000-386x.2018.08.035
    [9]
    ALEJANDRO T, DAVID H, JOHN R. Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand[J]. Transportation Research Part A: Policy and Practice, 2013, 53(6): 36-52.
    [10]
    ESTEVE C, FRANCISCA R. A heuristic method for a congested capacitated transit assignment model with strategies[J]. Transportation Research Part B: Methodological, 2017, 106(7): 293-320.
    [11]
    TAN Q, ZHOU X D, LIU W S. Transit assignment modeling approaches based on interval uncertainty of urban public transit net impedance[J]. Tehnicki Vjesnik-Technical Gazette, 2021, 28(5): 1582-1589.
    [12]
    TAN Q, LI Y D, LI W. Bus passenger flow allocation model considering interval uncertain impedance under big data[J]. Journal of Rail Way Science and Engineering, 2021, 18(8): 2191-2199.
    [13]
    ZHANG S J, JIA S P, MAO B H, et al. Passenger flow assignment model considering the queuing process of commuters at feeder bus stations[J]. Journal of Harbin Institute of Technology, 2019, 51(3): 121-126.
    [14]
    何流, 朱治邦, 戚湧, 等. 基于多源数据的公交OD估计及客流分配模型[J]. 武汉理工大学学报(交通科学与工程版), 2023, 1(1): 1-13. doi: 10.3963/j.issn.2095-3844.2023.01.001

    HE L, ZHU Z B, QI Y, et al. Transit OD estimation and assignment model based on multi-source data[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2023, 1(1): 1-13. (in Chinese) doi: 10.3963/j.issn.2095-3844.2023.01.001
    [15]
    张晓亮, 赵淑芝, 刘华胜, 等. 高峰时段公交客流分配的Logit模型改进[J]. 吉林大学学报(工学版), 2014, 44(6): 1616-1621.

    ZHANG X L, ZHAO S Z, LIU H S, et al. Improvement of public transportation assignment Logit model during peakperiod[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(6): 1616-1621. (in Chinese)
    [16]
    裴玉龙, 杨世军, 潘恒彦. 考虑车内拥挤状态的公交弹性发车间隔优化[J]. 东北大学学报(自然科学版), 2021, 42(11): 1663-1672. doi: 10.12068/j.issn.1005-3026.2021.11.020

    PEI Y L, YANG S J, PAN H Y. Optimization of buses'flexible departure intervals considering crowdedness state[J]. Journal of Northeastern University (Natural Science), 2021, 42(11): 1663-1672. (in Chinese) doi: 10.12068/j.issn.1005-3026.2021.11.020
    [17]
    王伟, 丁黎黎, 张文思等. 考虑车厢内拥挤效应的随机公交配流悖论[J]. 交通科学与工程, 2019, 35(1): 79-85, 100.

    WANG W, DING L L, ZHANG W S, et al. Stochastic transit assignment paradox considering congestion effect in the carriages[J]. Journal of Transport Science and Engineering, 2019, 35(1): 79-85, 100. (in Chinese)
    [18]
    杨熙宇, 暨育雄. 考虑公交车内拥挤的区间公交优化设计[J]. 同济大学学报(自然科学版), 2017, 45(2): 209-214.

    YANG X Y, JI Y X. Design of short-turning services for an urban bus corridor considering passengers'congestion[J]. Journal of Tongji University (Natural Science), 2017, 45(2): 209-214. (in Chinese)
    [19]
    LANDMARK A D, ARNESEN P, SODERSTEN C J, et al. Mobile phone data in transportation research: methods for benchmarking against other data sources[J]. Transportation, 2021, 48(5): 2883-2905. doi: 10.1007/s11116-020-10151-7
    [20]
    周涛, 赵必成, 俞博. 基于CRISP-DM的交通大数据分析方法及实践--以重庆市手机信令数据和RFID数据为例[J]. 城市交通, 2017, 15(5): 42-51.

    ZHOU T, ZHAO B C, YU B. Transportation big data analysis methodology based on CRISP-DM: an example of cellular signaling and RFID data in Chongqing[J]. Urban Transport of China, 2017, 15(5): 42-51. (in Chinese)
    [21]
    NI L L, WANG X K, CHEN X Q. A spatial econometric model for travel flow analysis and real-world applications with massive mobile phone data[J]. Transportation Research Part C: Emerging Technologies, 2018, 86(12): 510-526.
    [22]
    MEI D W, XIU C L, FENG X H, et al. Study of the school-residence spatial relationship and the characteristics of travel-to-school distance in Shenyang[J]. Sustainability, 2019, 11(16): 4432-4447. doi: 10.3390/su11164432
    [23]
    钟舒琦, 邓如丰, 邓红平, 等. 基于兴趣点与导航数据的手机信令数据出行方式识别[J]. 中山大学学报(自然科学版), 2020, 59(3): 87-96.

    ZHONG S Q, DENG R F, DENG H P, et al. Recognition of traffic mode of mobile phone data based on the combination of point of interest data and navigation data[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2020, 59(3): 87-96. (in Chinese)
    [24]
    CHEN X X, XU X D, CHAO Y. Trip mode inference from mobile phone signaling data using logarithm gaussian mixture model[J]. Journal of Transport and Land Use, 2020, 13(1): 429-445. doi: 10.5198/jtlu.2020.1554
    [25]
    MISHALANI R G, MCCORD M R, REINHOLD T. Use of mobile device wireless signals to determine transit route-level passenger origin-destination flows: Methodology and empirical evaluation[J]. Transportation Research Record, 2016, 2544(1): 123-130. doi: 10.3141/2544-14
    [26]
    罗霞, 李树超, 刘硕智, 等. 基于AVL和IC卡数据的公交通勤特性研究[J]. 计算机仿真, 2020, 37(6): 111-116, 256. doi: 10.3969/j.issn.1006-9348.2020.06.024

    LUO X, LI S C, LIU S Z, et al. Research on commuting characteristics of bus based on AVL and IC card data[J]. Computer Simulation, 2020, 37(6): 111-116, 256. (in Chinese) doi: 10.3969/j.issn.1006-9348.2020.06.024
    [27]
    HE S X, DONG J N, LIANG S D, et al. An approach to improve the operational stability of a bus line by adjusting bus speeds on the dedicated bus lanes[J]. Transportation Research Part C: Emerging Technologies, 2019, 107(8): 54-69.
    [28]
    裴玉龙, 申晨, 翟双柱. 常规干线公交网络空间方向分布及结构特性分析方法[J]. 交通信息与安全, 2023, 41(1): 140-150. doi: 10.3963/j.jssn.1674-4861.2023.01.015

    PEI Y L, SHEN C, ZHAI S Z. A method for analyzing the distribution of spatial orientation and structural characteristics of trunk bus network[J]. Journal of Transport Information and Safety, 2023, 41(1): 140-150. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.01.015
    [29]
    邵敏华, 李田野, 孙立军. 常规公交乘客对车内拥挤感知阻抗调查与建模[J]. 同济大学学报(自然科学版), 2012, 40(7): 1031-1034. doi: 10.3969/j.issn.0253-374x.2012.07.012

    SHAO M H, LI T Y, SUN L J. Survey method and model of passengers'cost perception of crowding level in bus[J]. Journal of Tongji University (Natural Science), 2012, 40(7): 1031-1034. (in Chinese) doi: 10.3969/j.issn.0253-374x.2012.07.012
    [30]
    李田野, 邵敏华. 考虑舒适性的公交乘客出行时间价值对比[J]. 交通科学与工程, 2011, 27(3): 82-86.

    LI T Y, SHAO M H. Comparison of value of public transport time for passengers considering congestion[J]. Journal of Transport Science and Engineering, 2011, 27(3): 82-86. (in Chinese)
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