Using big GPS trajectory data analytics for vehicle miles traveled estimation

Fan, Junchuan; Fu, Cheng; Stewart, Kathleen; Zhang, Lei

doi:10.1016/j.trc.2019.04.019

Cited by 41 publications

(31 citation statements)

References 21 publications

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“…Sun et al (2019) use GPS trajectory of floating cars to predict traffic congestion using the Hidden Markov Model to match GPS trajectory data to the road network, estimate the average speed of a road section for map matching and predict the traffic congestion using deep learning techniques. Zhang et al (2013) and Fan et al (2019) have come up with a method of estimating VMT and AADT of local roads and state-wide roads from vehicle GPS data with trajectory information.…”

Section: Related Workmentioning

confidence: 99%

“…We also evaluate our work with other independent studies. For example, Fan et al (2019) have also proposed workflow where vehicle GPS data was used to estimate traffic volume. They used Apache Spark-based geo-computing framework to estimate Maryland AADT accurately and stably.…”

Section: Traffic Volume Estimationmentioning

confidence: 99%

“…They used Apache Spark-based geo-computing framework to estimate Maryland AADT accurately and stably. Table 1 shows a comparison of Fan et al (2019) and this study. The GPS data used by Fan et al (2019) was acquired only from vehicles, while our dataset contains unclassified smartphone users.…”

Section: Traffic Volume Estimationmentioning

confidence: 99%

“…Table 1 shows a comparison of Fan et al (2019) and this study. The GPS data used by Fan et al (2019) was acquired only from vehicles, while our dataset contains unclassified smartphone users. The objective of study for Fan et al (2019) were traffic count sites, but we used highway traffic sections for AADT estimation study.…”

Section: Traffic Volume Estimationmentioning

confidence: 99%

See 3 more Smart Citations

Exploration of Municipal Mobility Using Smartphone GPS Data

Peng¹,

Wang²,

Liu³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Due to the advances in location-acquisition techniques, smartphone GPS location data has emerged with opportunity for research, development, innovation, and business. A variety of research has been developed to study human behaviour through exploring patterns from these data. In this paper, we use smartphone GPS location data to investigate municipal mobility. Kernel density estimation and emerging hot spot analysis are used to the GPS dataset to demonstrate GPS user (point) distribution across space and time. Flow maps are capable of tracking clustering behaviours and direction maps drawn upon the orientation of vectors can precisely identify location of the events. Case study with Indianapolis metropolitan area traffic rush hour verifies the effectiveness of these methods. Furthermore, we identify smartphone GPS data of vehicles and develop a concise and effective method for traffic volume estimation for county highway network. It is shown that the developed smartphone GPS data analytics is powerful for predicting reliable annual average daily traffic estimation. The study showcases the capability of GPS location data in identifying municipal mobility patterns for both citizens and vehicles.

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Section: Related Workmentioning

confidence: 99%

Section: Traffic Volume Estimationmentioning

confidence: 99%

Section: Traffic Volume Estimationmentioning

confidence: 99%

Section: Traffic Volume Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Exploration of Municipal Mobility Using Smartphone GPS Data

Peng¹,

Wang²,

Liu³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Recent efforts in developing TI tools focus on Geographic Information Systems (GIS) and collaborative decision making approaches, which are mainly applied to urban land planning and urban transport (including urban logistics) [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Logistic Profile Generation via Clustering Analy

Regal¹

2020

IJMLC

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The process of characterizing a city to generate logistic profiles involves the analysis of many different aspects. These profiles are based on secondary sources of data, mainly road network infrastructure, socioeconomic data and population density. Following previous research, the final profiles are given by a K-Means algorithm, which uses principal component analysis (PCA) for correlation analysis. A caveat in this method is that prior research has shown that PCA is sensitive to outliers and high dimensionality, which may mislead the following analysis and research. As such, this paper proposes a methodology to evaluate the performance of different clustering techniques to generate logistic profiles, applying it to a case study in the city of Lima, Perú.

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Examining Passenger Vehicle Miles Traveled and Carbon Emissions in the Boston Metropolitan Area

Aslanyan

Jiang

2021

Urban Informatics and Future Cities

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With spatial analytic, econometric, and visualization tools, this book chapter investigates greenhouse gas emissions for the on-road passenger vehicle transport sector in the Boston metropolitan area in 2014. It compares greenhouse gas emission estimations from both the production-based and consumption-based perspectives with two large-scale administrative datasets-the vehicle odometer readings (from individual vehicle annual inspection) and the road inventory data (containing roadsegment level geospatial and traffic information). Based on spatial econometric models that examine socioeconomic and built environment factors contributing to the vehicle miles traveled (VMT) at the census tract level, it offers insights to help cities reduce VMT and carbon footprint for passenger vehicle travel. Finally, it recommends a pathway for cities and towns in the Boston metropolitan area to curb VMT and mitigate carbon emissions to achieve climate goals of carbon neutrality.

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Using big GPS trajectory data analytics for vehicle miles traveled estimation

Cited by 41 publications

References 21 publications

Exploration of Municipal Mobility Using Smartphone GPS Data

Exploration of Municipal Mobility Using Smartphone GPS Data

Logistic Profile Generation via Clustering Analy

Examining Passenger Vehicle Miles Traveled and Carbon Emissions in the Boston Metropolitan Area

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