Estimation of an Urban OD Matrix Using Different Information Sources

Sbai, Asma; Zuylen, Henk J van; Li, Jie; Zheng, Fangfang; Ghadi, Fattehallah

doi:10.1007/978-3-319-62395-5_14

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…FCD are also considered as the basis for estimating O-D matrices [16,39,58,[63][64][65][66][67][68]. Carrese et al [64] proposed the dynamic estimation O-D matrix approach using FCD.…”

Section: O-d Demand Flow Estimationmentioning

confidence: 99%

“…Sbaï et al [66] proved the necessity of data fusion from several tele sources such as FCD (taxis with GPS detectors), loop detectors, and smartphones. Among the tele sources studied, Sbaï et al [66] estimated FCD as being more reliable for travel demand assessment but noted the problem of a small share of taxis in the total traffic. This resulted in difficulties for data extrapolation to the whole population.…”

Section: O-d Demand Flow Estimationmentioning

confidence: 99%

“…This resulted in difficulties for data extrapolation to the whole population. Sbaï et al [66] also revealed the differences in taxi drivers' behaviour from that of regular car drivers, such as higher travel speed and more frequent stops to enable passengers to board or alight.…”

Section: O-d Demand Flow Estimationmentioning

confidence: 99%

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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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“…FCD are also considered as the basis for estimating O-D matrices [16,39,58,[63][64][65][66][67][68]. Carrese et al [64] proposed the dynamic estimation O-D matrix approach using FCD.…”

Section: O-d Demand Flow Estimationmentioning

confidence: 99%

Section: O-d Demand Flow Estimationmentioning

confidence: 99%

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

et al. 2021

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“…These can include beacon-based probe vehicles [17]- [19], active probe vehicles such as floating car data [65]- [67], and passive probe-vehicles [68], [69]. Data from probe vehicles does not provide a complete record of traffic volume because of only a fraction of the traffic has been equipped [65] and because fleet probe vehicles (e.g., transit and taxi vehicles) may not be representative of the global mobility pattern since these vehicles operate for other primary purposes and could therefore inherit highlybiased traffic characteristics [70], [71].…”

Section: Related Workmentioning

confidence: 99%

Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data

et al. 2019

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There has been a recent push towards using opportunistic sensing data collected from sources like automatic vehicle location (AVL) systems, mobile phone networks, and global positioning system (GPS) tracking to construct origin-destination (O-D) matrices, which are an effective alternative to expensive and time-consuming traditional travel surveys. These data have numerous drawbacks: they may have inadequate detail about the journey, may lack spatial and temporal granularity, or may be limited due to privacy regulations. Taxi trajectory data is an opportunistic sensing data type that can be effectively used for O-D matrix construction because it addresses the issues that plague other data sources. This paper presents a new approach for using taxi trajectory data to construct a taxi O-D matrix that is dynamic in both space and time. The model's origin and destination zone sizes and locations are not fixed, allowing the dimensions to vary from one matrix to another. Comparisons between these spatiotemporal-varying O-D matrices cannot be made using a traditional method like matrix subtraction. Therefore, this paper introduces a new measure of similarity. Our proposed approaches are applied to the taxi trajectory data collected from Lisbon, Portugal as a case study. The results reveal the periods in which taxi travel demand is the highest and lowest, as well as the periods in which the highest and lowest regular taxi travel demand patterns take shape. This information about taxi travel demand patterns is essential for informed taxi service operations management. INDEX TERMS Dynamic origin-destination matrix, adaptive zoning scheme, origin-destination matrix similarity measure, taxi trajectory data, taxi travel demand.

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Evaluating the Effect of Multiple Data Types on a Large Scale Static Origin–Destination Matrix Estimation, Case Study of Tehran, Iran

Farzaneh Movahed,

Mesbah

2024

Iran J Sci Technol Trans Civ Eng

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Estimation of an Urban OD Matrix Using Different Information Sources

Cited by 6 publications

References 20 publications

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

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

Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data

Evaluating the Effect of Multiple Data Types on a Large Scale Static Origin–Destination Matrix Estimation, Case Study of Tehran, Iran

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