Mobile phone data in transportation research: methods for benchmarking against other data sources

Landmark, Andreas D.; Arnesen, Petter; Södersten, Carl-Johan; Hjelkrem, Odd André

doi:10.1007/s11116-020-10151-7

Cited by 13 publications

(4 citation statements)

References 47 publications

(107 reference statements)

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“…By using alternative methods of obtaining the OD demand matrix for a large network, such as network modularity partitioning [23], or including alternative demand data sources (e.g. mobile phone [44]), the analysis could be applied to smaller windows of time than nine months, allowing the analysis of seasonal trends in traffic routing.…”

Section: A Future Workmentioning

confidence: 99%

Data-Driven Traffic Assignment Through Density-Based Road-Specific Congestion Function Estimation

Roocroft,

Ramli,

Punzo

2024

IEEE Access

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The ability to build accurate traffic assignment models on large-scale major road networks is essential for effective infrastructure planning. Static traffic assignment models often utilize standard formulations of congestion functions which suffer from various inaccuracies. Conversely, newer approaches in the literature rely on inverse optimisation to provide enhanced accuracy but incur significantly heavy computational costs. The work in this article develops density-based congestion function fitting in order to compute traffic assignment patterns. Computational efficiency makes the method amenable to be used on real-world networks at national scale. The methodology is applied on the motorway network connecting the primary metropolitan areas in England using Motorway Incident Detection and Automatic Signalling system data. The results demonstrate that the use of density-based congestion functions provides significant improvement in terms of computational runtime in the order of 11,000 times (22 secs vs 68 hours). Correspondingly, prediction error from this method (3.9 to 6.9% for time prediction and 10.4 to 10.7% for flow prediction) slightly outperforms the state-of-the-art Inv-Opt method (5.3 to 8.8% for time prediction and 10.5 to 11% for flow prediction). The increased accuracy provides greater confidence in modelling results for applications such as cost-benefit analysis and price of anarchy calculations.

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Section: A Future Workmentioning

confidence: 99%

Data-Driven Traffic Assignment Through Density-Based Road-Specific Congestion Function Estimation

Roocroft,

Ramli,

Punzo

2024

IEEE Access

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“…Both types of cellular data have been widely applied to research topics such as travel behavior, human mobility, and social networks since the year 2000 ( 23 – 31 ). Despite the large volume of data, cellular data are limited by their spatial and temporal resolution, which is determined by the density of cell towers and users’ cellphone usage levels ( 32 ). On a positive note, however, the collection of cellular data requires less advanced mobile devices and can raise fewer user privacy concerns.…”

Section: Literature Reviewmentioning

confidence: 99%

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

Yang

Luo

Ashoori

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance planning, and more. Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations. These efforts require significant labor and cost for expansions. Researchers and private sector companies have also explored alternative solutions, such as probe vehicle data, although this still suffers from a low penetration rate. In recent years, along with the technological advancement in mobile sensors and mobile networks, the quantity of mobile device location data (MDLD) has been growing dramatically in spatiotemporal coverage of the population and its mobility. This paper presents a big-data driven framework that can ingest terabytes of MDLD and estimate vehicle volume over a larger geographical area with a larger sample size. The proposed framework first employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, that is, annual vehicle-miles traveled and annual average daily traffic. The proposed framework is implemented on the all-street network in the State of Maryland using MDLD for the entire year of 2019. The results demonstrate that our proposed framework produces reliable vehicle volume and also its transferability and generalization ability.

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“…This trend has been driven largely by the widespread and almost ubiquitous use of cell phones in many societies, and increasingly available data of population’s location with high spatiotemporal resolution. Such data is useful in many different research fields [ 23 ]. For example, during the Covid-19 pandemic, movement data from the mobile network was used to indicate spatial patterns of infection and spreading (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Towards more realistic measures of accessibility to emergency departments in Sweden

Hassler,

Andersson Granberg,

Steins

et al. 2024

Int J Health Geogr

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Background Assuring that emergency health care (EHC) is accessible is a key objective for health care planners. Conventional accessibility analysis commonly relies on resident population data. However, the allocation of resources based on stationary population data may lead to erroneous assumptions of population accessibility to EHC. Method Therefore, in this paper, we calculate population accessibility to emergency departments in Sweden with a geographical information system based network analysis. Utilizing static population data and dynamic population data, we investigate spatiotemporal patterns of how static population data over- or underestimates population sizes derived from temporally dynamic population data. Results Our findings show that conventional measures of population accessibility tend to underestimate population sizes particularly in rural areas and in smaller ED’s catchment areas compared to urban, larger ED’s—especially during vacation time in the summer. Conclusions Planning based on static population data may thus lead to inequitable distributions of resources. This study is motivated in light of the ongoing centralization of ED’s in Sweden, which largely depends on population sizes in ED’s catchment areas.

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Mobile phone data in transportation research: methods for benchmarking against other data sources

Cited by 13 publications

References 47 publications

Data-Driven Traffic Assignment Through Density-Based Road-Specific Congestion Function Estimation

Data-Driven Traffic Assignment Through Density-Based Road-Specific Congestion Function Estimation

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

Towards more realistic measures of accessibility to emergency departments in Sweden

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