The analysis of urban taxi carpooling impact from taxi GPS data

• Autorzy: Xiao Q. , He R. , Ma C.

Identyfikatory

DOI

10.5604/01.3001.0012.6514

• Języki publikacji: EN

Abstrakty

EN

Taxi is an important part of urban passenger transportation system. The research and analysis of taxi trip behavior is the key to meet the demand of urban passenger transport and solve the traffic congestion problem. Based on the GPS data of taxis in Nanjing, the statistical method is used to analyze the taxi characteristics of the average number of passengers, the average passenger time, the no-load distance and the passenger distance. By using the double logarithmic coordinate, the trip distance and trip time of taxi passengers are analyzed, it is found that the average trip distance of taxi passengers is mainly concentrated in 3-20 km, and the average trip time of taxi passengers is mainly concentrated in 10-30 minutes. Using the information entropy theory to construct the equilibrium model of taxi passenger-carrying point, and analyze the spatial distribution of taxi, it is found that the distribution of urban taxi is unbalanced. The peak clustering algorithm is used to determine the location of passenger gathering points, and the hot spot of taxi trip is analyzed, it is found that the hot spots of taxi trip are mainly concentrated in the central city of Nanjing. Combined with the results of urban taxi trip analysis, from the perspective of taxi and passenger, we found that the number of urban taxis, the passenger carrying rate of taxis, the duration period of passenger trip, the duration and distance of passenger trip and the location of passenger trip points will have an impact on the urban taxi carpooling in Nanjing. By using the probability model of urban taxi carpooling, this paper discusses and analyzes the influence of these factors on urban taxi carpooling. The research in this paper can provide a reference for the effective implementation of urban taxi carpooling policy.

Słowa kluczowe

EN

taxi carpooling; taxi passenger-carrying points; equilibrium degree; trip characteristic

PL

taksówka; punkty przewożenia pasażerów; równowaga; charakterystyka podróży

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2018
• Tom: Vol. 47, iss. 3
• Strony: 109--120
• Opis fizyczny: Bibliogr. 23 poz., rys., tab., wykr.

Twórcy

autor

Xiao Q.

• School of Traffic and Transportation, Lanzhou Jiao tong University, Lanzhou, China

autor

He R.

• School of Traffic and Transportation, Lanzhou Jiao tong University, Lanzhou, China

autor

Ma C.

• School of Traffic and Transportation, Lanzhou Jiao tong University, Lanzhou, China

Bibliografia

• [1] ALEXANDROV, G. A. & YAMAGATA, Y., 2007. A peaked function for modelling temperature dependence of plant productivity. Eco-logical Modelling. 200(1-2), 189-192.
• [2] CASTRO, P. S., & ZHANG, D. & CHEN, C. & LI, S., 2013. From taxi GPS traces to social and community dynamics: a survey. ACM Comput Surv. 46 (2), (article 17).
• [3] CHEN X. W., 2018. A Spatial and Temporal Analysis of the Socioeconomic Factors Associated with Breast Cancer in Illinois Using Geographically Weighted Generalized Linear Regression. Journal of Geovisualization and Spatial Analysis. 2 (1), 1-16.
• [4] CHEN, Y. M. & WU, K. S. & LI, X. J., 2013. A kind of outlier mining algorithm based on information entropy. Control and Decision. 28 (6), 867-872.
• [5] HE, W. & LI, H. W., 2014. Intelligent carpool routing for urban ridesharing by mining GPS trajectories, IEEE Transactions on Intelligent Transportation Systems. 15(5), 2286-2296.
• [6] HUANG, S. C. & JIAU, M. K. & LIN C. H., 2015. A genetic-algorithm-based approach to solve carpool service problems in cloud computing. IEEE Transactions on Intelligent Transportation Systems. 16 (1), 352-364.
• [7] KIM, J. W. & MAHMASSANI, H. S., 2015. Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories. Transportation Research Part C: Emerging Technologies. 59, 375-390.
• [8] LIANG, X. & ZHENG X. & LU, W., 2012. The scaling of human mobility by taxis is exponential. Phys. A. 391, 2135-2144.
• [9] LIU, Y. & KANG, C. & GAO, S., 2012. Understanding characteristics of intraurban trips using taxi trajectory data. J. Geogr. Syst. 14 (4), 463-483.
• [10] LIU, Y. & WANG, F. & XIAO, Y. & GAO, S., 2012. Urban land uses and traffic source-sink areas: evidence from GPS-enabled taxi data in Shanghai. Landsc Urban Plan. 106 (1), 73-87.
• [11] LI, L. & CHEN, X. & ZHANG, L., 2014. Multimodel ensemble for freeway traffic state estimations, IEEE Trans. Intell. Transp. Syst. 15 (3), 1323-1336.
• [12] LIU, X. & GONG, L. & GONG, Y. X. & LIU, Y., 2015. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography. 43, 78-90.
• [13] LIU, X. J. & LI, Q. L. & LI, Y. J., 2015. Learning with information entropy method for transportation image retrieval. International Journal of Multimedia and Ubiquitous Engineering. 10 (7), 317-328.
• [14] MA, Z. L. & SHAO, C. F. & HU, D. W., 2012. Temporal-spatial analysis model of traffic accident frequency on expressway. Journal of Traffic and Transportation Engineering. 12, 93-99.
• [15] MILEV, M. & INVERARDI, P. N. & TAGLIANI, A., 2012. Moment information and entropy evaluation for probability densities. Applied Mathematics and Computation. 218(9), 5782-5795.
• [16] PU, H. Z. & ZHEN, Z. Y. & WANG, D. B., 2007. Improved jumping gene genetic algorithm for multi-peak function optimization. Nanjing Hangkong Hangtian Daxue Xuebao/Journal of Nanjing University of Aeronautics and Astronautics. 39 (6), 829-832.
• [17] QUDDUS, M. & WASHINGTON S., 2015. Shortest path and vehicle trajectory aided map-matching for low frequency GPS data. Transportation Research Part C: Emerging Technologies. 55, 328-339.
• [18] STÉPHANE, G. A. & LUK, K. B. & YASAR, A. U. H., 2014. Multi-agent simulation of individual mobility behaviour in carpooling. Transportation Research Part C. 45, 83-98.
• [19] TOTH, C., 2015. Carpooling in Hungary: Can it reduce the GHG emissions of personal transport?. European Transport -Trasporti Europe. 58 (4), 1-24.
• [20] XIAO, Q. & HE, R. C., 2017. Carpooling scheme selection for taxi carpooling passengers: a multi-objective model and optimisation algorithm. Archives of Transport. 42(2), 85-92.
• [21] XIAO Q., HE, R. C., YU J. N., 2018. Method of taxi carpooling probability and wait time based on Poisson distribution. China Journal of Highway and Transport. 31 (5), 151-159.
• [22] YOON, J. & NOBLE, B. & LIU, M., 2007. Surface street traffic estimation. Proceedings of the 5th International ACM Conference on Mobile Systems: Applications and Services. New York, USA, 220-232.
• [23] ZHANG, J. D. & XU, J. & LIAO, S. S., 2013. Aggregating and sampling methods for processing GPS data streams for traffic state estimation, IEEE Trans. Intell. Transp. Syst. 14 (4), 1629-1641.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).