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264E-Commercewell as extend the 995ARN using the convenience given by those factors. The mobilecommerce solution of 995ARN will certainly open up a new value-added services for theproject.But, the authors believe that we can still make some expansion in the following aspects:Using the GPRS-model as the Means of system’s communication to replace the GSM-Model, but this model’s application case is relatively small, technology is also not verymature now. Using the technology of system recommendation such as Content-basedRecommendation, Collaborative Filtering Recommendation, Association Rule-basedRecommendation, Utility-based Recommendation, Knowledge-based Recommendation,Hybrid Recommendation and so on, to improve the match effect of rescue services and thevalue of news. In SNS platform, we can design interpersonal retrieval system according to thetheory of “Six Degrees of Separation”. For example, if A is friend of B, and C also is friend ofB, system can design a retrieval path from A to C. It is A-B-C.6. AcknowledgmentThis paper was supported in part by the MOE Project of Key Research Institute of Humanities andSocial Science in Chinese Universities (NO: 07JJD870220) and New Century Educational TalentsPlan of Chinese Education Ministry(NCET-08-0427).The authors would like to express oursincere gratitude to the contributing authors and to the referees for reviewing papers for thisbook.7. ReferencesFerman M.A, Blumenfeld D.E, and Xiaoxue Dai. A simple analytical model of a probe-basedtraffic information system Proce: Intelligent Transportation Systems, 2003 IEEE, p:263~268 vol.1Greenfeld J S(2002). Matching GPS Observations to Locations on A Digital Map. In: Proc. the81th Annual Meeting of the Transportation Research Board, Washington D.CGreenfeld J S.(2002). Matching Gps Observations to Locations On A Digital Map. In: Proc.The 81th Annual Meeting of the Transporation Research Board, Washington D.CGSM Association, www.gsmworld.comHandset Location Technology at http://nings.cn/2008/01/03/cmda-gpsone.htmlhttp://www.mapinfo.comKevin J, Kruzman, homas E.Biedka, and Theodore S. Rappaport.(1997). Wireless PositionLocation Fundamentals, Implementation Strategies, and Sources of Error. IEEEVehicular Technology ConferenceYadong Lang, Juan Wu.(2004). GSM Handset Location Technology. Designing Teachniquesof Posts and TelecomunicationsLing deng. market share of automobile rescue services will nearly reach 200 million,http://www.autopx.cc/article_detail.asp?articleID=1554Liyi Zhang and Qihua Liu(2009). 995ARN: Automobile Rescue Services Mobile CommerceSolution, Journal of Computational Information Systems
A Mobile Commerce Model for Automobile Rescue Services 265Liyi Zhang, Qihua Liu (2008). An E-commerce Model for Automobile Rescue Services, ISM2008Liyi zhang, Shitong zhang(2007). Analysis of China Special Transportation NetworkWebsites.WICOM2007, 3490~3493Liyi zhang, Shitong zhang(2007). Analysis of CSTN's Model and Special TransportationSolution. DCABES2007, 356~360Marko Silventoinen, Timo Rantalainen. Kyamakya(1995). Mobile Station Location in GSM.Wireless communication system symposium, IEEE 27-28 nov, pp53-59Qi Zhu.(2005). Web-Oriented JMS Application Systems. Computer Engineering and Design.Shitong Zhang.(2008). Research and Implementation of Mobile Business Service PlatformWhich Based on Location Based Services:[ Master thesis].Wuhan: WuhanUniversityThe present situation, problem and development trend of automobile rescue service atwww.chevip.com.cnThe SMS Interface Platform at http://zeroliu.blogdriver.com/zeroliu/1215155.html.2007/04/10Vittorio Astarita, Michael Florian(2001). The Use of Mobile Phones in Traffic Managementand Control. 2001 IEEE Intelligent Transportation Systems Conference Proceedings.Okland (CA), USA. Auguest 25-29Vittorio Astarita, Michael Florian.(2001). The Use of Mobile Phones in Traffic Managementand Control. 2001 IEEE Intelligent Transportation Systems Conference Proceedings.Okland (CA), USA. Auguest 25-29Yanying Li, Mike McDonald.(2002). Link Travel Time Estimation Using Single GPSEquipped Probe Vehicle. In: Proc. 2002 IEEE Intelligent Transportation SystemConference Proceedings, 932-938
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VIIWe wish to thank the reviewers o
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IXContentsPrefaceV1. A Conceptual F
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A Conceptual Framework and an Exten
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12E-CommerceThe Application Layer c
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14E-CommerceService description. Th
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16E-Commerceunderstanding about the
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18E-CommerceThis chapter contribute
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20E-CommercePorter M (2001). Strate
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22E-CommerceTo evaluate the e-comme
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24E-CommerceAa 2a a4m1 m 2 m 3m (
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26E-CommerceV v ij , i 1,2,...,
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28E-CommerceIndexFuzzy numberAltern
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30E-CommerceS. Mabuchi (1988). An a
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32E-Commercetechnology implementati
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34E-Commerceapplied to study an ind
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36E-Commerce PEOU of the public sec
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38E-CommerceNITC (National Informat
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40E-Commercewill describe the mecha
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42E-Commerce(2) The manner of commu
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44E-CommerceIf ADS is employed in a
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46E-Commerce4.2 Case studyThe detai
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48E-CommerceAs a high-speed develop
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50E-Commerce4.3.2 The architecture
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52E-Commerceone conclusion is that
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54E-Commerce
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56E-Commerceorganizations expose de
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58E-Commercerules. Such adapters ne
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60E-CommerceThe third level of inve
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62E-Commercecommon “language” h
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64E-Commercewell described in (Mül
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66E-Commerce3.3 Organizational Comp
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68E-Commercethe interaction of defi
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70E-CommerceAs a first step towards
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72E-Commerce5. Conclusion and Outlo
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74E-CommerceSchmid, B. F., Schroth,
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76E-CommerceThe following section p
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78E-Commerce(a) G cooc(b) G overlap
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80E-CommerceBetweenness centrality
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82E-Commerce4.1.1 Generating Networ
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84E-CommerceThe resultant value som
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86E-CommerceG shareholdershareholdi
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88E-Commerce17: Murata Noda Screen
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90E-Commerceand testing data in the
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92E-Commerce(c) Centrality-based ra
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Ranking Companies Based on Multiple
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Ranking Companies Based on Multiple
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Considering Culture in Designing We
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Consumer Responses to Colors of E-C
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120E-Commerce4. Research methodOur
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122E-Commerceavailable information
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124E-CommerceNewsvine Green. Starti
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126E-CommerceProcedureRespondents w
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128E-Commerce6. Data analysis and r
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130E-CommerceHue 1Newsvine Green (d
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132E-CommerceEffects of graphic cha
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134E-Commercethe consumer retains w
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136E-Commercemaintain, load more ra
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138E-CommerceBellizzi J. A. & Hite
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140E-CommerceHill A. & Scharff L. V
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142E-CommerceSpool J. M., Scanlon T
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144E-CommerceConsequently, we concl
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146E-CommerceDimensionsSystems qual
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148E-Commercefunction deployment (Q
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150E-CommerceUseful help serviceEle
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152E-Commerceplanning, enacting and
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154E-Commercecomplete and measurabl
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156E-Commerce There is "About us" s
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158E-Commerce Validation testing -
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160E-Commercefor the Websites (Lohs
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162E-CommerceSinnappan S., Carlson
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164E-Commercerecommendations of a t
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166E-Commerce3.1 Neighborhood Based
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168E-Commerce3.3 Significance Weigh
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170E-Commerce , u1 0,fR5fuR1 u2else
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172E-CommerceFigure 6 shows the ste
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174E-CommerceMAE0.750.740.730.720.7
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Improving performance in recommende
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Electronic Commerce Readinessin Dev
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