Freight Traffic Analytics from National Truck GPS Data in Thailand

Chankaew, Nattaphon; Sumalee, Agachai; Siripirote, Treerapot; Threepak, Thanunchai; Ho, H.W.; Lam, William H. K.

doi:10.1016/j.trpro.2018.11.023

Cited by 22 publications

(19 citation statements)

References 19 publications

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“…Data are extracted from GPS positions of a sample of freight vehicles (FCD) travelling along a road network inside a study area in the Calabria Region (Italy). Several papers available in the literature deal with the contribution of GPS technology in the characterization of goods movements (see, among others, [5,6] for the urban case and [7] for the national case) and, in general, in the estimation and diagnosis of traffic conditions [8,9].…”

Section: Datamentioning

confidence: 99%

Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data

et al. 2020

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According to the literature, the path choice decision process of a user of a (road) transport network, named path choice problem (PCP), is composed of two levels/models: the definition of perceived alternative paths (choice set) and the choice of one path in the path choice set. The path choice probability can be estimated with two models: a choice model of the path choice set and a choice model of a path (Mansky paradigm). In this research, the paper’s contribution concerns two elements: extension of the PCP paradigm (two-level models) consolidated in the literature to the route choice decision process (vehicle routing problem (VRP)) and identification of common elements in the PCP and VRP concerning the criteria in the two decision levels and the procedure for route and path selection and choice. The experiment concerns the comparison of observed routes with simulated and optimized routes of commercial vehicles to analyse the level of similarity and coverage. The observed routes are extracted from floating car data (FCD) from commercial vehicles travelling inside a study area inside the Calabria Region (Southern Italy). The comparison is executed in terms of similarity of the sequences of nodes visited between observed routes and simulated/optimized routes.

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Section: Datamentioning

confidence: 99%

Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data

et al. 2020

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“…Yet, GPS freight data were mainly used for commodity location tracking; freight activity in-depth analysis was still scarce ( 11 ). For instance, Chankaew et al ( 12 ) demonstrate the capability of using GPS data to better understand truck commodity flows, and the potential characterization of stops according to the freight activity distributions. However, freight activity remained difficult to characterize because of GPS data limitations.…”

Section: Literature Reviewmentioning

confidence: 99%

Mechanistic Identification of Freight Activity Stops from Global Positioning System Data

Holguín‐Veras

Encarnación

Pérez-Guzmán

et al. 2020

Transportation Research Record: Journal of the Transportation R

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The identification of freight pick-ups and deliveries, referred to as “freight activity” in this paper, is crucial to characterizing freight operations and assessing the performance of freight transportation systems. However, identifying freight activity stops from global positioning system (GPS) data is challenging, particularly in urban freight where congested traffic is common. This paper presents a mechanistic—because it is based on the physics of driving patterns—procedure to identify freight activity stops from raw GPS data. The procedure was implemented to identify stops in three distinct case studies that present a wide range of traffic conditions: Barranquilla, Colombia; Dhaka, Bangladesh; and New York City, United States. The results show that the procedure achieves an average accuracy of above 98.6% when identifying freight activity stops. The results of the proposed procedure were compared with results from support vector machines, random forest, and k nearest neighbors. The mechanistic procedure outperformed these methods in correctly classifying freight activity using second-by-second GPS data.

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“…Recently, scholars have developed data processing systems, which collect and process freight data from private logistics companies. The main aims of Such studies are to develop data analytics to produce valuable freight traffic measures, which can support public authorities when making planning decisions in the road sectors [13]. Data analytics can be defined as the application of an algorithmic approach to a set of data with the objective of deriving insights and measures [14].…”

Section: Introductionmentioning

confidence: 99%

“…For example, traffic measures derived from processing GPS data can include Origin-Destination (OD)-matrices, average travelling speed, and travelling time reliability [10,15,16]. Chankaew et al (2018) created a system for processing GPS-data from freight transport in Thailand to extract travelling patterns of freight trucks. Additionally, they included land use data to create an activity-based OD-matrix to form better understanding of the patterns of freight movement [13].…”

Section: Introductionmentioning

confidence: 99%

“…Chankaew et al (2018) created a system for processing GPS-data from freight transport in Thailand to extract travelling patterns of freight trucks. Additionally, they included land use data to create an activity-based OD-matrix to form better understanding of the patterns of freight movement [13]. Hyun et al, developed a system to create OD-matrices to determine the most used freight corridors to decide on the best locations for installing loop-detectors [17].…”

Section: Introductionmentioning

confidence: 99%

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Towards Deriving Freight Traffic Measures from Truck Movement Data for State Road Planning: A Proposed System Framework

Karam

Illemann

Reinau

et al. 2020

IJGI

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To make the right decisions on investments, operations, and policies in the public road sector, decision makers need knowledge about traffic measures of trucks, such as average travel time and the frequency of trips among geographical zones. Private logistics companies daily collect a large amount of freight global positioning system (GPS) and shipment data. Processing such data can provide public decision makers with detailed freight traffic measures, which are necessary for making different planning decisions. The present paper proposes a system framework to be used in the research project “A new system for sharing data between logistics companies and public infrastructure authorities: improving infrastructure while maintaining competitive advantage”. Previous studies ignored the fact that the primary step for delivering valuable and usable data processing systems is to consider the final user’s needs when developing the system framework. Unlike existing studies, this paper develops the system framework through applying a user-centred design approach combining three main steps. The first step is to identify the specific traffic measures that satisfy the public decision makers’ planning needs. The second step aims to identify the different types of freight data required as inputs to the data processing system, while the third step illustrates the procedures needed to process the shared freight data. To do so, the current work employs methods of literature review and users’ need identification in applying a user-centralized approach. In addition, we develop a systematic assessment of the coverage and sufficiency of the currently acquired data. Finally, we illustrate the detailed functionality of the data processing system and provide an application case to illustrate its procedures.

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Freight Traffic Analytics from National Truck GPS Data in Thailand

Cited by 22 publications

References 19 publications

Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data

Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data

Mechanistic Identification of Freight Activity Stops from Global Positioning System Data

Towards Deriving Freight Traffic Measures from Truck Movement Data for State Road Planning: A Proposed System Framework

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