Identifying intercity freight trip ends of heavy trucks from GPS data

Yang, Yitao; Jia, Bin; Yan, Xiao-Yong; Li, Jiangtao; Yang, Zhenzhen; Gao, Ziyou

doi:10.1016/j.tre.2021.102590

Cited by 29 publications

(13 citation statements)

References 61 publications

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“…Speed thresholds used in the literature above vary from 3 to 8.5 mph. Thresholds may also be used to filter out stops, through using dwell time thresholds (Aziz et al, 2016;Hwang et al, 2017;Akter et al, 2018;Camargo et al, 2017;Holguin-Veras et al, 2020;Chankaew et al, 2018;Thakur et al, 2015;Yang et al, 2022b;Yang et al, 2022a;Kuppam et al, 2014;You and Ritchie, 2018), or through trip distance thresholds (Chankaew et al, 2018;Thakur et al, 2015). And finally, thresholds may also be used to merge stops together.…”

Section: Stop Inferencementioning

confidence: 99%

“…Sharman and Roorda (2011) propose combining clusters whose medians lie in the same parcel boundary. Yang et al (2022b) use Voronoi diagrams constructed using geocoded freight POIs, and use that to filter out stops (as many as 73.2%).…”

Section: Stop Inferencementioning

confidence: 99%

“…As compared to work on finding trip ends or stops, work on finding trips is not so prevalent. Hwang et al (2017) Inferring tours, which are collections of trips, of a truck as part of its activity is uncommon and mostly done as a step to prepare input for truck tour models (You and Ritchie, 2018;Kuppam et al, 2014). You and Ritchie (2018) introduce the idea of closed tours as a collection of trips departing from a depot and returning to the same location.…”

Section: Trip and Tour Inferencementioning

confidence: 99%

“…These are based on the idea that freight related stops must take some time to be completed. While used widely (You and Ritchie, 2018; Akter et al, 2018;Camargo et al, 2017;Holguin-Veras et al, 2020;Chankaew et al, 2018;Thakur et al, 2015;Yang et al, 2022bYang et al, , 2022aKuppam et al, 2014), we study the effectiveness and interaction of this filter criteria on other aspects of a comprehensive pipeline such as clustering and hub finding. Apart from a visual inspection, we suggest looking at the distributions of the number of stops per trip and the marginal decrease in number of stops on increasing the stop duration threshold, to determine the stop duration threshold, .…”

Section: δTmentioning

confidence: 99%

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Inferring truck activities using privacy-preserving truck trajectories data

Choudhry

Qian

2023

J. Int. Con. Veh.

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Global Navigation Satellite System (GNSS) data is an inexpensive and ubiquitous source of activity data. Global Positioning System (GPS) is an example of such data. Although there have been several studies about inferring device activity using GPS data from a consumer device, freight GPS data presents unique challenges for example having low and variable frequency, having long transmission gaps, and frequent and unpredictable device ID resetting for preserving privacy. This study aims to provide an end-to-end, generic data analytical framework to infer multiple aspects of truck activity such as stops, trips and tours. We use popular existing methods to construct the data processing pipeline and provide insights into their practical usage. We also propose improved data filters to different aspects of the data processing pipeline to address challenges found in privacy-preserving freight GPS data. We use freight data across four weeks from the greater Philadelphia region with variable transmission frequency ranging from one second to several hours to perform experiments and validate our methods. Our findings indicate that auxiliary information such as land use can be helpful in fine tuning stop inference,but spatio-temporal information contained in timestamped GPS pings is still the most powerful source of false stop identification. We also find that a combination of simple clustering techniques can provide a way to perform fast and reasonable clustering of the same stop.

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Section: Stop Inferencementioning

confidence: 99%

Section: Stop Inferencementioning

confidence: 99%

Section: Trip and Tour Inferencementioning

confidence: 99%

Section: δTmentioning

confidence: 99%

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Inferring truck activities using privacy-preserving truck trajectories data

Choudhry

Qian

2023

J. Int. Con. Veh.

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“…The truck trajectory data contain information such as location, time, and speed, reflecting the movement status of trucks and the transportation process of goods, which provides a new data source for the study of cargo circulation. Based on the truck trajectory data, previous studies have developed trajectory data mining methods to identify freight information (Zhu et al, 2021;Yang et al, 2022b), analyze freight activity (Gan et al, 2019;Siripirote et al, 2020), and evaluate freight emissions (Xu et al, 2021;Cheng et al, 2022). In this study, we first extracted the trip chain of APC through the national truck trajectory data in 2018 to construct the flow network of Beijing APC in four seasons.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal interaction pattern of the Beijing agricultural product circulation

Zhao

Cheng

2023

J. Geogr. Sci.

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Identifying the spatiotemporal interaction pattern of agricultural product circulation (APC) is crucial for agricultural resource adjustment and food security. Current studies are mostly based on static statistical data over an entire year or a specific period, which cannot describe the spatial pattern of APC and its seasonal variation on a fine spatiotemporal scale. Thus, this study extracts an APC trip chain based on national truck trajectory data and constructs the flow network of the Beijing APC with the city as the spatial unit and the season as the temporal unit. The spatial interaction pattern and seasonal variation in APC are then analyzed from the network spatial form, city node function role, and transportation corridors. The results are as follows: (1) Compared with methods based on static statistical data, the proposed method provides a more complete and refined depiction of the spatiotemporal interaction pattern of APC. (2) The flow network of the Beijing APC involves 316 cities in China, of which 143 cities play a major role with typical seasonal characteristics. These cities can be divided into perennial core cities, perennial major cities, core cities in winter-spring, major cities in winter-spring, core cities in summer-autumn, and major cities in summer-autumn, contributing 2.6%–40.3% to the Beijing APC. (3) There are 6 transportation corridors for the Beijing APC. The Beijing-Tianjin-Hebei corridor and coastal corridor contribute 53.5% and 12.8% of the annual supply, respectively, with a balanced supply in all seasons. The Beijing-Kunming corridor and Beijing-Guangzhou corridor contribute 14.3% and 9.0%, respectively, with much higher supplies in winter and spring. The northeast and northwest corridors contribute 7.3% and 3.3%, respectively, mainly in the summer and autumn. These results help deepen the understanding of agricultural product supply patterns and provide a reference for the design and optimization of agricultural product transportation routes.

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