PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Exploring on-demand service use in large urban areas: the case of Rome

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Traditional and innovative on-demand transport services, such as taxi, car sharing or dial-a-ride respectively, can provide a level of flexibility to the public transport with the aim to guarantee a better service and to reduce the exploitation costs. In this context, in order to point out the key-factors of on-demand services, this study focuses on traditional on-demand service (such as taxi one), and presents the results of a demand analysis and modelling, obtained processing taxi floating car data (FCD) available for the city of Rome. The GPS position of each taxi is logged every few seconds and it was possible to build a monthly database of historical GPS traces through around 27 thousands of GPS positions recorded per day (more than 750 thousands for the entire month). Further, the patterns of within-day and day-to-day service demand are investigated, considering the origin, the destination and other characteristics of the trips (e.g. travel time). The time-based requests for taxi service were obtained and used to analyse the trip distribution in space and on time. These analyses allow us to forecast trips generated/attracted by each zone within the cities according to land use characteristics and time slices. Therefore, a regression tree analysis was developed and different regressive model specifications with different set of attributes (e.g. number of subway stations, number of zonal employees, population) were tested in order to assess their contribution on describing such a service use.
Rocznik
Strony
77--90
Opis fizyczny
Bibliogr. 41 poz., rys., tab., wykr.
Twórcy
  • University of Rome Tor Vergata, Department of Enterprise Engineering, Rome, Italy
autor
  • University of Rome Tor Vergata, Department of Enterprise Engineering, Rome, Italy
  • University of Rome Tor Vergata, Department of Enterprise Engineering, Rome, Italy
Bibliografia
  • [1] AL-AYYASH, Z., ABOU-ZEID, M., & KAYSI, I., 2016. Modeling the demand for a shared-ride taxi service: An application to an organization-based context. Transport Policy, 48, 169-182.
  • [2] ALONSO, B., BARREDA, R., DELL’OLIO, L., & IBEAS, A., 2018. Modelling user perception of taxi service quality. Transport Policy, 63, 157-164.
  • [3] ALONSO, B., IBEAS, A., MUSOLINO, G., RINDONE, C., VITETTA, A., 2019. Effects of traffic control regulation on Network Macroscopic Fundamental Diagram: A statistical analysis of real data, Transportation Research Part A: Policy and Practice, 126, 136-151.
  • [4] BARANN, B., BEVERUNGEN, D., & MÜLLER, O., 2017. An open-data approach for quantifying the potential of taxi ridesharing. In: Decision Support Systems 99, 86-95.
  • [5] BISCHOFF, J., MACIEJEWSKI, M., & SOHR, A., 2015. Analysis of Berlin’s taxi services by exploring GPS traces”. In: Proc. Int’l Conf. Models and Technologies for Intelligent Transportation System.
  • [6] BRACCIALE, L., BONOLA, M., LORETI, P., BIANCHI G., AMICI, R., & RABUFFI, A., 2014. CRAWDAD dataset roma/taxi, down-loaded from https://crawdad.org/roma/taxi/20140717.
  • [7] BREIMAN, L., FRIEDMAN, J.H., & OLSHEN, R.A., & STONE, C.J., 1984. Classification Regression Trees, Wadsworth International Group, Belmont, California.
  • [8] CAI H., ZHAN X., ZHU J., JIA, X., CHIU, A. S. F., & XUG, M., 2016. Understanding taxi travel patterns. Physica A: Statistical Mechanics and its Applications, 457, 90-597.
  • [9] CASCETTA, E., 2009. Transportation Systems Analysis: Models and Applications. Springer.
  • [10] COMI, A., PERSIA, L., POLIMENI, A., CAMPAGNA, A. & MEZZAVILLA, L., 2019. A methodology to design and assess scenarios within SULPS: the case of Bologna. City Logistics Conference XI, Dubrovnik, Croatia.
  • [11] CROCE, A. I., MUSOLINO, G., RINDONE, C. & VITETTA, A., 2019. Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS. International Journal of Geo-Information, 8(4).
  • [12] CZECH, P., TUROŃ, K., & BARCIK, J., 2018. Autonomous vehicles: basic issues. Scientific Journal of Silesian University of Technology, 100, 15-22.
  • [13] FONT, A., GUISEPPIN, L., BLANGIARDO, M., GHERSI, V. & FULLER, G. W., 2019. A tale of two cities: is air pollution improving in Paris and London?, Environmental Pollution, 249, 1-12.
  • [14] GRAU, J. M. S., & ESTRADA M., 2019. Social optimal shiftsnuzz and fares for the Barcelona taxi sector. Transport Policy, 76, 111-122.
  • [15] HADAVI, S., VERLINDE, S., VERBEKE, W., MACHARIS, C. & GUNS, T. 2018. Monitoring Urban-Freight Transport Based on GPS Trajectories of Heavy-Goods Vehicles, IEEE Transactions on Intelligent Transportation Systems.
  • [16] HE, F., & SHEN Z. J. M., 2015. Modeling taxi services with smartphone-based e-hailing applications. Transportation Research Part C: Emerging Technologies, 58, 93-106.
  • [17] HOTHORN, T., HORNIK, K., & ZEILEIS, A., 2012. Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15(3), 651-674.
  • [18] KHUN, M., & JOHNSON, K., 2013. Applied Predictive Modeling. Springer New York Heidelberg Dordrecht London.
  • [19] LI, B., CAI, Z., JIANG, L., SU, S. & HUANG, X., 2019. Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression, Cities, 87, 68-86.
  • [20] LIU, X., GONG, L., GONG, Y., LIU, Y., 2015. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43, 78-90.
  • [21] MACIEJEWSKI M., 2014. Benchmarking minimum passenger waiting time in online taxi dispatching with exact offline optimization methods. Archives of Transport, 30 (2), 67-75.
  • [22] MOREIRA-MATIAS, L., GAMA, J., FERREIRA, M., MENDES-MOREIRA, J. & DAMAS, L., 2016. Time-evolving O-D matrix estimation using high-speed GPS data streams, Expert Systems with Applications, 44, 275-288.
  • [23] MOREIRA-MATIAS, L., GAMA, J., FER-REIRA, M., MENDES-MOREIRA, J. & DAMAS, L., 2013a. Predicting Taxi–Passenger Demand Using Streaming Data, IEEE Transactions on Intelligent Transportation Systems, 14(3), 1393-1402.
  • [24] MOREIRA-MATIAS, L., GAMA, J., FER-REIRA, M., MENDES-MOREIRA, J. & DAMAS, L., 2013b. On predicting the taxi-passenger demand: a real-time approach. In: Correia L., Reis L.P., Cascalho J. (eds) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science, vol 8154. Springer, Berlin, Heidelberg.
  • [25] NUZZOLO, A. & COMI, A., 2017. Real-time modelling of normative travel strategies on unreliable dynamic transit networks: a framework analysis, Modelling Intelligent Multi-Modal Transit Systems, Nuzzolo, A. and Lam, W. H. K. (eds), CRC Press, Taylor & Francis Group, Boca Raton (FL, USA), 130 – 151.
  • [26] NUZZOLO A., COMI, A., PAPA, E., & POLIMENI, A., 2019. Understanding taxi travel demand patterns through GPS data. E. G. Nathanail and I. D. Karakikes (Eds.): CSUM 2018, AISC 879, 445-452, Springer Nature Switzerland AG.
  • [27] PIRAS, G., PINI, F. & GARCIA, D. A., 2019. Correlations of PM10 concentrations in urban areas with vehicle fleet development, rain precipitation and diesel fuel sales, Atmospheric Pollution Research, 10(4), 1165-1179.
  • [28] POLIMENI, A. & VITETTA, A., 2014. Vehicle routing in urban areas: an optimal approach with cost function calibration. Transportmetrica B: transport dynamics, 2(1), 1-19.
  • [29] QUINLAN, J.R., 1993. C4.5: Programs for machine learning. San Francisco, CA: Morgan Kaufmann Publishers Inc.
  • [30] RAMEZANI, M., & NOURINEJAD, M., 2018. Dynamic modeling and control of taxi services in large-scale urban networks: A macroscopic approach”, Transportation Research Part C: Emerging Technologies, 98, 203-219.
  • [31] ROMA CAPITALE, DIPARTIMENTO MOBILITÀ E TRASPORTI (2015). Piano generale del traffico urbano di Roma Capitale.
  • [32] RUSSO, F. & COMI, A. (2016). Urban Freight Transport Planning towards Green Goals: Synthetic Environmental Evidence from Tested Results, Sustainability, 8 (4), 381.
  • [33] SUMAN, H.K. & BOLIA, N.B., 2019. A Review of Service Assessment Attributes and Improvement Strategies for Public Transport, Transportation in Developing Economies, 5 (1).
  • [34] SUMP (2013). Guidelines. Developing and Implementing a Sustainable Urban Mobility Plan; European Commission: Brussels, Belgium.
  • [35] TANG, J., LIU, F., WANG, Y., & WANG, H., 2015. Uncovering urban human mobility from large scale taxi GPS data. Physica A: Statistical Mechanics and its Applications, 438, 140-153.
  • [36] WANG, W., PAN, L., YUAN, N., ZHANG, S., & LIU, D., 2015. A comparative analysis of intra-city human mobility by taxi. Physica A: Statistical Mechanics and its Applications, 420, 134-147.
  • [37] WONG, K. I., WONG, S.C., & YANG, H., 2001. Modeling urban taxi services in congested road networks with elastic demand. Transportation Research Part B: Methodological, 35(9), 819-842.
  • [38] WONG, K.I., WONG, S.C., YANG, H., & WU, J.H., 2008. Modeling urban taxi services with multiple user classes and vehicle modes. Transportation Research Part B: Methodological, 42(10), 985-1007.
  • [39] XIAO Q. & HE R., 2017. Carpooling scheme selection for taxi carpooling passengers: a multi-objective model and optimisation algorithm. The Archives of Transport, 42 (2), 67-75.
  • [40] YANG, Z., FRANZ, M.L., ZHU, S., MAHMOUDI, J., NASRI, A. & ZHANG L., 2018. Analysis of Washington, DC taxi demand using GPS and land-use data. Journal of Transport Geography, 66, 35-44.
  • [41] ZHENG, Z., RASOULI, S., & TIMMER-MANS, H., 2018. Modeling taxi driver anticipatory behavior. Computers, Environment and Urban Systems, 69, 133-141
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-764dc020-0ebb-4e4b-8fc9-a85b945172e4
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.