Estimation of Urban Mobility using Public Mobile Network

被引:0
作者
Vidovic, Kresimir [1 ]
Mandzuka, Sadko [2 ]
Brcic, Davor [2 ]
机构
[1] Ericsson Nikola Tesla, Krapinska 44, Zagreb, Croatia
[2] Univ Zagreb, Fac Transport & Traff Sci, Vukeliceva 4, Zagreb, Croatia
来源
PROCEEDINGS OF 2017 INTERNATIONAL SYMPOSIUM ELMAR | 2017年
关键词
Intelligent Transport Systems; Call Data Records; Urban mobility; Mobility indicator; Urban mobility index; SYSTEM; ANFIS;
D O I
暂无
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Urban mobility of the population is usually estimated using procedures focused on specific domains (transport, logistics, environment, society, etc.), Using the limited sets of domain related data and indicators related to targeted subsets of the total urban population (users of public transport, cyclists, drivers, etc.). The paper proposes a new approach for estimating the urban mobility of population based on data on telecommunication activities (voice calls, text messaging, Internet access) of users in public telecommunications networks. The paper describes the procedure for determination of indicators of urban mobility (number of trips, travel time, distance), and their values is obtained by analyzing a subset of data from mobile phone operators Call Data Records database. The presented method provides an estimate of the movement of users in order to estimate urban mobility by forming an origin destination matrix, travel time matrix and distance matrices between the respective zones. The new model for integrating urban mobility indicators into urban mobility index is proposed, by utilizing an expert system based on fuzzy-logic ANFIS method.
引用
收藏
页码:21 / 24
页数:4
相关论文
共 35 条
[1]   Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning [J].
Al-Hmouz, Ahmed ;
Shen, Jun ;
Al-Hmouz, Rami ;
Yan, Jun .
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, 2012, 5 (03) :226-237
[2]   Measuring urban sustainable [J].
Alberti, M .
ENVIRONMENTAL IMPACT ASSESSMENT REVIEW, 1996, 16 (4-6) :381-424
[3]  
Anastasi E., 2013, P SUST INT ICT SUST
[4]  
Brcic D., 2014, 3 INT C ROAD RAIL IN
[5]  
Brcic D., 2016, UPRAVLJANJE PROMETNO
[6]  
Calabrese F, 2013, TRANSPORT RES C-EMER, V26
[7]   The promises of big data and small data for travel behavior (aka human mobility) analysis [J].
Chen, Cynthia ;
Ma, Jingtao ;
Susilo, Yusak ;
Liu, Yu ;
Wang, Menglin .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 68 :285-299
[8]   From traces to trajectories: How well can we guess activity locations from mobile phone traces? [J].
Chen, Cynthia ;
Bian, Ling ;
Ma, Jingtao .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2014, 46 :326-337
[9]  
Colak S., 2015, TRB 2015 ANN M, P1
[10]  
ETSI and 3GPP, 2016, 32240 ETSI 3GPP TS