Dynamic accessibility mapping using floating car data: A network-constrained density estimation approach

Journal of Transport Geography

Volumn 19, Issue 3, 2011, Pages 379-393

  Li, Qingquan ^a,b   Zhang, Tong ^a,b   Wang, Handong ^a,b   Zeng, Zhe ^a,b,c  

^a WUHAN UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c CHINA UNIVERSITY OF PETROLEUM   (China)

Author keywords

Accessibility mapping; Density estimation; Dynamic; Floating car data; Points of Interest

Indexed keywords

ACCESSIBILITY; EMPIRICAL ANALYSIS; ESTIMATION METHOD; GIS; MAPPING METHOD;

CHINA; HUBEI; WUHAN;

EID: 79953307436     PISSN: 09666923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jtrangeo.2010.07.003     Document Type: Article

References (38)

1. Berkow, M., Monsere, C.M., Koonce, P., Bertini, R.L., Wolfe, M., 2009. Prototype for Data Fusion using Stationary and Mobile Data Sources for Improved Arterial Performance Measurement. The 88th Annual Meeting of the Transportation Research Board. Washington DC.
2. Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C., 2005. On map-matching vehicle tracking data. In: Proceedings of the 31st VLDB Conference, 2005, pp. 853-864.
3. Bricka, S., Bhat. C.R., 2006. A Comparative Analysis of GPS-based and Travel Survey-based Data. The 85th Annual Meeting of the Transportation Research Board, Washington D.C.
4. Borruso G. Network density estimation: a GIS approach for analysing point patterns in a network space. Transactions in GIS 2008, 12(3):377-402.
5. Chan, K., Tam, M., Lam, W. 2009. Using Spatial Travel Time Covariance Relationships for Real-time Estimation of Arterial Travel Times. The 88th Annual Meeting of the Transportation Research Board, Washington, DC.
6. Cheu R.L., Xie C., Lee D.-H. Probe vehicle population and sample size for arterial speed estimation. Computer-Aided Civil and Infrastructure Engineering 2002, 17:53-60.
7. Doherty, S.T., Papinski, D., Lee-Gosselin, M., 2006. An Internet-based Prompted Recall Diary with Automated GPS Activity-trip Detection: System Design. The 85th Annual Meeting of the Transportation Research Board, Washington DC.
8. Dong X., Ben-Akiva M.E., Bowman J.L., Walker J.L. Moving from trip-based to activity-based measures of accessibility. Transportation Research Part A 2006, 40:163-180.
9. Duha R.O., Hart P.E. Pattern Classification and Scene Analysis 1973, Wiley.
10. Esawey, M., Sayed, T., 2009, Travel Time Estimation in an Urban Network Using Sparse Probe Vehicle Data and Historical Travel Time Relationship. The 88th Annual Meeting of the Transportation Research Record. Washington, DC.
11. Geertman S., van Eck J. GIS and models of accessibility potential: an application in planning. International Journal of Geographical Information Systems 1995, 9:67-80.
12. Goodwin, C., Lau, J., 1993. Vehicle navigation and map quality. In: IEEE-IEE vehicle navigation & information systems conference, Ottawa.
13. Handy S.L., Niemeier D.A. Measuring accessibility: an exploration of issues and alternatives. Environment and Planning A 1997, 29:1175-1194.
14. Harris C. The market as a factor in the localization of industry in the US. Annals of the Association of the American Geographers 1954, 44:315-348.
15. Juliano, R.P., 1999. Measuring accessibility using GIS. In: The Proceedings of the 4th International Conference on GeoComputation.
16. Kim H.-M., Kwan M.-P. Space-time accessibility measures: a geocomputational algorithm with a focus on the feasible opportunity set and possible activity duration. Journal of Geographical Systems 2003, 5:71-91.
17. Kong Q.-J., Li Z., Chen Y., Liu Y. An approach to urban traffic state estimation by fusing multisource information. IEEE Transactions on Intelligent Transportation Systems 2009, 10(3):499-511.
18. Liu S., Zhu X. Accessibility analyst: an integrated GIS tool for accessibility analysis in urban transportation planning. Environment and Planning B: Planning and Design 2004, 31:105-124.
19. Liu, H., Ma, W., 2007. Time-dependent Travel Time Estimation Model for Signalized arterial Network. The 86th Annual Meeting of the Transportation Research Record. Washington, DC.
20. Miller H.J. Modeling accessibility using space-time prism concepts within geographical information systems. International Journal of Geographical Information Science 1991, 5(3):287-301.
21. Miller H.J. Measuring space-time accessibility benefits within transportation networks: basic theory and computation methods. Geographical Analysis 1999, 31:187-212.
22. Miller H.J. Place-based versus people-based accessibility. Access to Destinations 2005, 63-89. Elsevier. D. Levinson, K.J. Krizek (Eds.).
23. Miller H.J. A measurement theory for time geography. Geographical Analysis 2005, 37:17-45.
24. O'Kelly M.E., Horner M.W. Aggregate accessibility to population at the county level: US 1940-2000. Journal of Geographical Systems 2003, 5:5-23.
25. Openshaw S. Ecological fallacies and the analysis of areal census data. Environment and Planning A 1984, 16(1):17-31.
26. O'Sullivan D., Morrison A., Shearer J. Using desktop GIS for the investigation of accessibility by public transport: an isochrone approach. International Journal of Geographical Information Science 2000, 14:85-104.
27. Petty, K.F., 1997. Incidents on the Freeway: Detection and Management, Ph.D. Dissertation. University of California.
28. Quddus M.A., Ochieng W.Y., Noland R.B. Current map-matching algorithms for transport applications: state-of-the art and future research directions. Transportation Research Part C 2007, 15:312-328.
29. Quiroga, C.A., 1997. An Integrated GPS-GIS Methodology for Performing Travel Time Studies. Ph.D. Dissertation, Louisiana USA, Louisiana State University.
30. Thomas N.E. Multi-state and multi-sensor incident detection systems for arterial streets. Transportation Research Part C 1998, 6:337-357.
31. Van Lint, J., 2004. Reliable Travel Time Prediction for Freeways. Ph.D. Dissertation. Delft University of Technology, Delft, The Netherlands.
32. van Lint J. Reliable real-time framework for short-Term freeway travel time prediction. Journal of Transportation Engineering 2006, 132(12):921-932.
33. Wei, L., Peng, W., Lin, C., Jung, C., 2009. Exploring spatio-temporal features for traffic estimation on road networks. In: Mamoulis, N., et al. (Eds.), 11th International Symposium n Spatial and Temporal Databases, Lecture Notes in Computer Science 5644, pp. 399-404.
34. Westerman, M., Litjens, R., Linnartz, J.-P., 1996. Integration of Probe Vehicle and Inductive Loop Data: Estimation of Travel Times and Automatic Incident Detection. Research Reports, Institute of Transportation Studies, University of California, Berkeley.
35. Wu Y.-H., Miller H.J. Computational tools for measuring space-time accessibility within transportation network with dynamic flow. Journal of Transportation and Statistics 2002, 4(2/3):1-14.
36. Xie Z., Yan J. Kernel density estimation of traffic accidents in a network space. Computers, Environments, and Urban Systems 2008, 32:396-406.
37. Yoon, J., Noble, B., Liu, M., 2007. Surface street traffic estimation. In: The 5th International Conference on Mobile Systems, Applications, and Services. San Juan, Puerto Rico.
38. Zhang T., Tsou M.-S. Developing a grid-enabled spatial web portal for Internet GIServices and geospatial cyberinfrastructure. International Journal of Geographical Information Science 2009, 23(5):605-630.