Floating car based travel times for city logistics

Transportation Research Part C: Emerging Technologies

Volumn 21, Issue 1, 2012, Pages 338-352

  Ehmke, Jan Fabian ^a   Meisel, Stephan ^a   Mattfeld, Dirk Christian ^a  

^a TECHNICAL UNIVERSITY OF BRAUNSCHWEIG   (Germany)

Author keywords

City logistics; Data Mining; Floating Car Data; Routing; Time dependent travel times

Indexed keywords

QUALITY CONTROL; TRAVEL TIME;

CITY LOGISTICS; FLOATING CAR; FLOATING CAR DATA; NETWORK LINKS; ROUTING; SUBSTANTIAL REDUCTION; TIME DEPENDENT; URBAN AREAS;

DATA MINING;

DATA MINING; EFFICIENCY MEASUREMENT; LOGISTICS; ROUTING; TIME DEPENDENT BEHAVIOR; TRAVEL TIME; URBAN AREA; URBAN TRANSPORT;

BADEN-WURTTEMBERG; GERMANY; STUTTGART [BADEN-WURTTEMBERG];

EID: 83255173803     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2011.11.004     Document Type: Article

References (27)

1. Bishop R. Intelligent Vehicle Technology and Trends. Intelligent Transportation Systems Library 2005, Artech House.
2. Brockfeld, E., Lorkowski, S., Mieth, P., Wagner, P., 2007a. Benefits and limits of recent floating car data technology - an evaluation study. In: Proceedings of 11th WCTR Conference, June 24-28th, 2007. Berkeley, USA. http://elib.dlr.de/49618/.
3. Brockfeld, E., Passfeld, B., Wagner, P., 2007b. Validating travel times calculated on the basis of taxi floating car data with test drives. In: Proceedings of 14th ITS Conference, October 9-13th, 2007. Beijing, China. http://elib.dlr.de/50208/.
4. Chrobok R., Kaumann O., Wahle J., Schreckenberg M. Different methods of traffic forecast based on real data. European Journal of Operational Research 2004, (155):558-568.
5. Daganzo C.F. Fundamentals of Transportation and Traffic Operations 1997, Elsevier Science.
6. Dijkstra E.W. A note on two problems in connexion with graphs. Numerische Mathematik 1959, (1):269-271.
7. Eglese R., Maden W., Slater A. A road timetable to aid vehicle routing and scheduling. Computers and Operations Research 2006, (33):3508-3519.
8. Ehmke J.F., Meisel S., Engelmann S., Mattfeld D.C. Data chain management for planning in city logistics. International Journal of Data Mining, Modelling and Management 2009, (1):335-356.
9. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., 1996. From data mining to knowledge discovery: an overview. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining, pp. 1-34.
10. Figliozzi, M.A., 2009. A route improvement algorithm for the vehicle routing problem with time-dependent travel times. In: Proceedings of the 88th Transportation Research Board Annual Meeting. Washington, DC, January 2009. http://web.cecs.pdx.edu/~maf/publications.html.
11. Fleischmann B., Gietz M., Gnutzmann S. Time-varying travel times in vehicle routing. Transportation Science 2004, (38):160-173.
12. Gössel, F., 2005. Informationsentropische, spektrale und statistische Untersuchungen fahrzeuggenerierter Verkehrsdaten unter besonderer Berücksichtigung der Auswertung und Dimensionierung von FCD-Systemen. Dissertation. University of Dresden. http://nbn-resolving.de/urn:nbn:de:swb:14-1116572388287-50504.
13. Greenshields B.D. A study of traffic capacity. Highway Research Board Proceedings 1935, (14):448-477.
14. Gühnemann, A., Schäfer, R.-P., Thiessenhusen, K.-U., Wagner, P., 2004. Monitoring Traffic and Emissions by Floating Car Data. Working Paper ITS-WP-04-07. Institute of Transport Studies, The University of Sydney, Australia. http://elib.dlr.de/6675/.
15. Han, J., Kamber, M., 2000. Data Mining: Concepts and Techniques: Morgan Kaufmann (Morgan Kaufmann Series in Data Management Systems).
16. Ichoua S., Gendreau M., Potvin J.-Y. Vehicle dispatching with time-dependent travel times. European Journal of Operational Research 2003, (144):379-396.
17. Lorkowski, S., Mieth, P., Thiessenhusen, K.-U., Chauham, D., Passfeld, B., Schäfer, R.-P., 2004. Towards area-wide traffic monitoring-applications derived from probe vehicle data. In: Kumares, C. Sinha, Fwa, T.f., Cheu, Ruey L., Lee, Der-Horng (Eds.). In: Proceedings of the 8th International Conference on Applications of Advanced Technologies in Transportation Engineering, ASCE, pp. 389-394. http://elib.dlr.de/6723/.
18. Maden W., Eglese R., Black D. Vehicle routing and scheduling with time-varying data: a case study. Journal of the Operational Research Society 2010, (61):515-522.
19. MacQueen, J., 1967. Some methods for classification and analysis of multivariate observations. In: Le Cam, L.M., Neyman, J. (Eds.), Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281-287.
20. Messelodi S., Modena C.M., Zanin M., De Natale F.G.B., Granelli F., Betterle E., Guarise A. Intelligent extended floating car data collection. Expert Systems with Applications 2009, (36):4213-4227.
21. Pinkofsky, L., 2006. Typisierung von Ganglinien der Verkehrsstärke und ihre Eignung zur Modellierung der Verkehrsnachfrage. Dissertation. University of Braunschweig. http://opus.tu-bs.de/opus/volltexte/2006/847.
22. Potvin J.-Y., Xu Y., Benyahia I. Vehicle routing and scheduling with dynamic travel times. Computers & Operations Research 2006, (33):1129-1137.
23. Simroth A., Zähle H. Travel time prediction using floating car data applied to logistics planning. IEEE Transactions on Intelligent Transportation Systems 2011, 1(12):243-253.
24. Tele Atlas, 2009. Speed Profiles - Intelligent data for optimal routing. White Paper. http://www.teleatlas.com/OurProducts/MapEnhancementProducts/SpeedProfiles/index.htm.
25. Van Woensel T., Kerbache L., Peremans H., Vandaele N. Vehicle routing with dynamic travel times: a queuing approach. European Journal of Operations Research 2008, (286):990-1007.
26. Verband Region Stuttgart (Ed.), (2002): Verkehrsplanung in der Region Stuttgart. German Brochure on Traffic Planning in the Region of Stuttgart.
27. Windt, K., Hülsmann, M., 2007. Changing paradigms in logistics - understanding the shift from conventional control to autonomous cooperation and control. In: Hülsmann, M., Windt, K. (Eds.), Understanding Autonomous Cooperation and Control in Logistics, pp. 1-16.