商业空间消费者行为模型研究综述
收稿日期: 2010-06-01
修回日期: 2010-07-01
网络出版日期: 2010-12-25
基金资助
国家自然科学基金项目(40871080)；高密度人居环境生态与节能(同济大学)教育部重点实验室支持项目。
A Review on the Models in Research of Consumer Behavior in Commercial Space
Received date: 2010-06-01
Revised date: 2010-07-01
Online published: 2010-12-25
本文综述商业空间中消费者行为者研究中应用的主要模型方法。从模型发展历史将其分为集合模型和个体模型阶段；从空间尺度视角分别在宏观、中观、微观层面总结模型的应用。集合模型首先介绍以空间相互作用理论为基础的重力模型，其中包括基本的模型形式、制约的模型形式，以及竞争目的地模型。集合模型的第二部分介绍描述消费行为动态的马尔科夫链模型，着重于从恒定转移概率到变化转移概率的发展和应用。个体模型首先介绍以随机效用理论为基础的离散选择模型，将被广泛应用的多项分对数模型和嵌套分对数模型作为重点。最后介绍作为模拟手段的多代理人技术。综述包括模型的基本原理、相关文献，以及各自特点。总体上认为，模型的选用需要与研究的特性相符合。集合模型的优点是能够把握整体的趋势，缺点是不能满足对异质性高的行为作深入探索；个体模型的优势在于对行为多样性的灵活把握能力，但要通过自下而上的方式逼真地模拟集合现象仍需要深入研究个体间的互动规律。
朱玮, 王德 . 商业空间消费者行为模型研究综述[J]. 地理科学进展, 2010 , 29(12) : 1470 -1478 . DOI: 10.11820/dlkxjz.2010.12.002
This paper reviews the main models used in the research of consumer behavior in commercial space. It takes a historical perspective, divides the development of the models into the stages of aggregate models and individual models, and classifies the model application into macro, meso, and micro scales. For the aggregate models, the paper firstly introduces gravity models based on spatial interaction theory, including the basic model form, constrained forms, and the competing destination model. The second part for the aggregate models section introduces Markov Chain models for describing dynamic consumer behavior, with the emphasis on the development and applications from static transition probabilities to varying transition probabilities. The individual model section introduces discrete choice models based on random utility theory, with the emphasis on the widely applied multinomial logit and nested logit models. This is followed by an introduction to multi-agent technology as a simulation tool. The review includes the fundamentals of underlying theories of the models, related literatures and model features. It is considered that the fitness between the model and the nature of the research is important for model selection. Aggregate models have the advantage of grasping the overall regularities, but are limited in exploring highly heterogeneous behavior. The advantage of individual models is the flexibility to represent heterogeneous behavior, while the idea of bottom-up simulation to form aggregate behavior requires deeper understandings of inter-individual interactions.
Key words： commercial space; consumer behavior; model; review
[1] Johnston R J, Kissling C C. Establishment use patterns within central places. Australian Geographical Studies, 1971, 9(2): 116-132.
[2] Pacione M. Redevelopment of a medium-sized central shopping area: A case study of Clydebank. Tijdschrift voor Economische en Sociale Geografie, 1980, 71(3): 159-168.
[3] Lorch, B J, Smith M J. Pedestrian movement and the downtown enclosed shopping centre. Journal of the American Planning Association, 1993, 59(1): 75-86.
[4] Wilson A G. A family of spatial interaction models, and associated developments. Environment and Planning A, 1971, 3(1): 1-32.
[5] Gibson M, Pullen M. Retail turnover in the East Midlands: A regional application of a gravity model. Regional Studies, 1972, 6(2): 183-196.
[6] Ghosh A. Parameter nonstationarity in retail choice models. Journal of Business Research, 1984, 12(4): 425-436.
[7] Guy C M. Recent advances in spatial interaction modelling: An application to the forecasting of shopping travel. Environment and Planning A, 1987, 19(2): 173-186.
[8] Scott A J. A theoretical model of pedestrian flow. SocioEconomic Planning Sciences, 1974, 8(6): 317-322.
[9] Hagishima S, Mitsuyoshi K, Kurose S. Estimation of pedestrian shopping trips in a neighborhood by using a spatial interaction model. Environment and Planning A, 1987, 19(9): 1139-1152.
[10] Fotheringham A S. A new set of spatial-interaction models: The theory of competing destinations. Environment and Planning A, 1983, 15(1): 15-36.
[11] Fotheringham A S. Some theoretical aspects of destination choice and their relevance to production-constrained gravity models. Environment and Planning A, 1983, 15 (8): 1121-1132.
[12] Fotheringham A S. Modelling hierarchical destination choice. Environment and Planning A, 1986, 18 (3): 401-418.
[13] Fortheringham A S, Trew R. Chain Image and Store-choice Modeling: The Effects of Income and Race. Environment and Planning A, 1993, 25(2): 179-196.
[14] O’Kelly M E. A model of the demand for retail facilities incorporation multistop, multipurpose trips. Geographical Analysis, 1981, 13(2): 134-148.
[15] Borgers A W J, Timmermans H J P. A model of pedestrian route choice and demand for retail facilities within innercity shopping areas. Geographical Analysis, 1986, 18 (2): 115-128.
[16] Kurose S, Hagishima S. A method for identifying accessibility properties of pedestrian shopping networks. Journal of Retailing and Consumer Services, 1995, 2(2): 111-118.
[17] Borgers A W J, Timmermans H J P. City centre entry points, store location patterns and pedestrian route choice behaviour: A microlevel simulation model. Socio-Economic Planning Sciences, 1986, 20(1): 25-31.
[18] McFadden D. Conditional logit analysis of qualitative choice behavior//Zarembka P. Frontiers in Econometrics. New York: Academic Press, 1974: 105-142.
[19] Ben-Akiva M, Lerman S. Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, US: The MIT Press, 1985.
[20] Train K E. Discrete Choice Methods with Simulation. Cambridge, UK: Cambridge University Press, 2003.
[21] McFadden D. Modeling the choice of residential location// Karlqvist A, Lundqvist L, Snickars F, et al. Weibull Spatial Interaction Theory and Planning Models. Amsterdam: North-Holland, 1978: 75-96.
[22] Recker W W, Kostyniuk L P. Factors influencing destination choice for the urban grocery shopping trip. Transportation, 1978, 7(1): 19-33.
[23] Timmermans H, Borgers A. Choice set constrains and spatial decision-making processes. Sistemi Urbani, 1985, 3: 211-220.
[24] Borgers AW J, Timmermans H J P. Choice model specification, substitution, and spatial structure effects: A simulation experiment. Regional Science and Urban Economics, 1987, 17(1): 29-47.
[25] Borgers AW J, Timmermans H J P. Spatial choice, substitutability and spatial structure effects: An extended multinomial logit model//Wieber J C. Analyse de Systemes et Modeles Mathematiques. Paris: Les Belles Lettres, 1987: 35-40.
[26] Borgers A W J, Timmermans H J P. A context-sensitive model of spatial choice behavior//Golledge R G, Timmermans H J P. Behavioural Modelling in Geography and Planning, London: Croom Helm, 1988: 159-178.
[27] Fotheringham A S. Consumer store choice and choice set definition. Marketing Science, 1988, 7(3): 299-310.
[28] Fotheringham A S, Trew R. Chain image and store-choice modeling: the effects of income and race. Environment and Planning A, 1993, 25(2): 179-196.
[29] Oppewal H, Timmermans H J P. Modelling the effects of shopping centre size and store variety on consumer choice behavior. Environment and Planning A, 1997, 29 (6): 1073-1090.
[30] Van der Waerden P, Borgers A, Timmermans H. The impact of the parking situation in shopping centers on store choice behavior. GeoJournal, 1998, 45(4): 309-315.
[31] 张文佳, 柴彦威. 居住空间对家庭购物出行决策的影 响. 地理科学进展, 2009, 28(3): 362-369.
[32] Kitamura R. Incorporating trip chaining into analysis of destination choice. Transportation Research B, 1984, 18 (1): 67-81.
[33] Arentze T, Borgers A, Timmermans H. A model of multi-purpose shopping trip behaviour. Papers in Regional Science, 1993, 72(3): 239-256.
[34] Dellaert B G C, Arentze T A, Bierlaire M, et al. Investigating consumers’tendency to combine multiple shopping purposes and destinations. Journal of Marketing Research, 1998, 35(2): 177-188.
[35] Arentze T A, Timmermans H J P. A multipurpose shopping trip model to assess retail agglomeration effects. Journal of Marketing Research, 2005, 42(1): 109-115.
[36] 隽志才, 李志瑶, 宗芳. 基于活动链的出行需求预测方 法综述. 公路交通科技, 2005, 22(6): 108-113.
[37] 张文佳, 柴彦威. 时空制约下的城市居民活动: 移动系 统: 活动分析法的理论和模型进展. 国际城市规划, 2009, 24(4): 60-68.
[38] Saito S, Ishibashi K. A Markov Chain model with covariates to forecast consumer’s shopping trip chain within a central commercial district. FourthWorld Congress of Regional Science Association International, Mallorca, Spain.
[39] 朱玮, 王德, 齐藤参郎. 南京东路消费者的入口消费行 为研究. 城市规划, 2005, 29(5): 14-21.
[40] 朱玮, 王德, 齐藤参郎. 南京东路消费者的回游消费行 为研究. 城市规划, 2006, 20(2): 9-17.
[41] 朱玮, 王德. 南京东路消费者的空间选择行为与回游轨 迹. 城市规划, 2008, 32(3): 33-40.
[42] 王德, 朱玮, 黄万枢, 等. 基于人流分析的上海世博会规 划方案评价与调整. 城市规划, 2009, 33(8): 26-32.
[43] Zhu W, Timmermans H, Wang D. Temporal variation in consumer spatial behavior in shopping streets. Journal of Urban Planning and Development, 2006, 132(3): 166-171.
[44] Borgers A W J, Timmermans H J P. Modeling pedestrian behavior in downtown shopping areas//Proceedings of the 9th International Conference on Computers in Urban Planning and Urban Management, London, 2005: http://128.40.111.250/cupum/searchpapers/detail.asp?pID=83.
[45] Borgers A W J, Kemperman A D A M, Timmermans H J P. Pedestrian behaviour in down-town shopping areas: Differentiating between hedonic and utilitarian shoppers// Proceedings of the 12th RARSS Conference, Orlando, 2005.
[46] Dijkstra J, Timmermans H, de Vries B. Empirical estimation of agent shopping patterns for simulating pedestrian movement//Proceedings of the 10th International Conference on Computers in Urban Planning and Urban Management, Igguasu Falls, Brazil, 2007.
[47] Antonini G, Bierlaire M, Weber M. Discrete choice models of pedestrian walking behavior. Transportation Research B, 2006, 40(8): 667-687.
[48] Borgers A W J, Smeets I M E, Kemperman A D A M, et al. Simulation of micro pedestrian behaviour in shopping streets//van Leeuwen J P H, Timmermans J P. Progress in Design & Decision Support Systems. Heeze, The Netherlands, 2006: 101-116.
[49] O’Sullivan D, Haklay M. Agent-based models and individualism: Is the world agent-based? Environment and Planning A, 2000, 32(8): 1409-1425.
[50] Dijkstra J, Jessurun J, Timmermans H J P. A multi-agent cellular automata model of pedestrian movement// Schreckenberg M, Sharma S D. Pedestrian and Evacuation Dynamics, Berlin: Springer-Verlag, 2001: 173-181.
[51] Dijkstra J, Timmermans H. Towards a multi-agent model for visualizing simulated user behavior to support the assessment of design performance. Automation in Construction, 2002, 11(2): 135-145.
[52] Kerridge J, Hine J, Wigan M. Agent-based modeling of pedestrian movements: The question that need to be asked and answered. Environment and Planning B, 2001, 28(3): 327-341.
[53] Walmsley D J, Lewis G J. The pace of pedestrian flows in cities. Environment and Behavior, 1989, 21(2): 123-150.
[54] Willis A, Gjersoe N, Havard C, et al. Human movement behaviour in urban spaces: Implications for the design and modeling of effective pedestrian environments. Environment and Planning B, 2004, 31(2): 805-828.
[55] Lee J Y S, Lam W H K. Variation of walking speeds on a unidirectional walkway and on a bidirectional stairway. Transportation Research Record, 2006, 1982: 122-131.
[56] Haklay M, O’Sullivan D, Thurstain-Goodwin M.‘So go downtown’: Simulating pedestrian movement in town centers. Environment and Planning B, 2001, 28(3): 343-359.
[57] Silverman B G, Johns M, Cornwell J, et al. Human behavior models for agents in simulators and games part I: Enabling science with PMFserv. Presence, 2006, 15(2): 139-162.
[58] Silverman B G, Bharathy G, O’Brien K, et al. Human behavior models for agents in simulators and games part II: Gamebot engineering with PMFserv. Presence, 2006, 15 (2): 163-185.
[59] Lee J Y S, Lam W H K, Wong S C. Pedestrian simulation model for Hong Kong underground stations//Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, Oakland, US, 2001: 554-558.
[60] Batty M, Desyllas J, Duxbury E. The discrete dynamics of small-scale spatial events: Agent-based models of mobility in carnivals and street parades. International Journal of Geographical Information Science, 2003, 17(7): 673-698.
[61] Johansson A, Helbing D. Pedestrian flow optimization with a genetic algorithm based on boolean grids//Waldau N, Gattermann P, Knoflacher H, et al. Pedestrian and Evacuation Dynamics 2005. Berlin: Springer, 2007: 267-272.
[62] 陈鹏. 基于多智能主体的人群流动形态动态模拟研 究
[D]. 同济大学, 2006.
[63] 朱玮, 王德, Timmermans H. 多代理人系统在商业街消 费者行为模拟中的应用: 以上海南京东路为例. 地理学 报, 2009, 64(4): 445-455.
[64] Borgers A W J, Kemperman A D A M, Timmermans H J P, et al. Alternative ways of measuring activities and movement patterns of transients in urban areas: International experiences//Proceedings ICTSC Conference, Annecy, France, 2008-5-25
[2010-3-20]. http://isctsc.let.fr/papiers/workshop%20final%20version/11%20B8%20Borgers%20et%20al.doc.
[65] Handerson L F. On the fluid mechanics of human crowd motion. Transportation Research, 1974, 8(6): 509-515.
[66] Helbing D, Molnar P. Social force model for pedestrian dynamics. Physical Review E, 1995, 51(5): 4282-4286.
[67] Schadschneider A. Cellular automaton approach to pedestrian dynamics: Theory//Schreckenberg M, Sharma S. Pedestrian and Evacuation Dynamics. Berlin: Springer, 2001: 75-86.
/
| 〈 |
|
〉 |