Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network

Tavassoli, Ahmad; Mesbah, Mahmoud; Hickman, Mark

doi:10.1007/s11116-019-10004-y

Cited by 9 publications

(6 citation statements)

References 42 publications

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“…a given number of count sections). With the advances in the field of ICT, in addition to traffic counts, also other kinds of data sources have been implemented to carry out reliable estimation of O-D matrices, such as GPS data [233], video recordings [234], mobile phone data [235], e-ticketing and automatic fare collection systems [236][237][238][239][240][241][242][243][244].…”

Section: Estimation Techniques For Travel Demand Flowsmentioning

confidence: 99%

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Botte

D’Acierno

2018

Urban Rail Transit

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The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown.

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Section: Estimation Techniques For Travel Demand Flowsmentioning

confidence: 99%

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Botte

D’Acierno

2018

Urban Rail Transit

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“…In transportation, these efforts have been mainly focusing on the aspects of data acquisitionmostly in terms of data collection, information extraction and cleaning and modelling analysis. The analyses most commonly performed are basedto name but a fewon Floating Car Data (Li et al, 2021;Chen et al, 2021b;Astarita et al, 2019Astarita et al, , 2020, mobile phone data (Franco et al, 2020;Zhao et al, 2020;Huang et al, 2018;Wang et al, 2018;Zhou et al, 2018), payment and transit card data (Arbex and Cunha, 2020;Tavassoli et al, 2020;Sulis et al, 2018;Yap et al, 2018;Utsunomiya et al, 2006), GPS enabled mobile phone data (Bachir et al, 2019;Bwambale et al, 2017) and social media (Liao et al, 2021;Yao and Qian, 2021;Lock and Pettit, 2020;Hu et al, 2020;Chaniotakis and Antoniou, 2015;Zheng et al, 2016). Of particular interest in regards to the increased data availability is the evolution of pervasive systems (e.g., GPS handsets, cellular networks) and especially the connectivity that has been available to a growing number of individuals, that allow the sharing of different information types such as spatial, temporal, and textual information.…”

Section: Introductionmentioning

confidence: 99%

Investigating social media spatiotemporal transferability for transport

Chaniotakis

Abouelela

Antoniou

et al. 2022

Communications in Transportation Research

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“…For instance, travel diary-based approaches have been transformed into electronic travel diaries with global positioning systems (GPS), thereby speeding up data transfer from users to research groups and providing details for understanding more in-depth travel patterns [26][27][28][29][30]. Innovation has also driven transport system modelling: reverse assignment procedures have been developed to update network and demand model parameters [11,18]; In particular, in transit modelling, smart-card data were used to estimate the origin-destination matrix as well as calibrate and validate assignment models ( [17,[31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

et al. 2021

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Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented.

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Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network

Cited by 9 publications

References 42 publications

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Investigating social media spatiotemporal transferability for transport

Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

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