Repeatability &amp; reproducibility: Implications of using GPS data for freight activity chains

Joubert, Johan W.; Meintjes, Sumarie

doi:10.1016/j.trb.2015.03.007

Cited by 35 publications

(22 citation statements)

References 21 publications

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“…Use of data from private freight companies allows for identification of congestion spots [18,42,[51][52][53] Greenhouse Gas (GHG) emission analyses Using freight company data to evaluate and determine high-emission zones through emission modelling [42,[54][55][56] Parking pattern analyses Determining truck stopping locations, and analyses of staying times, parking area utilization and parking demand [34,42,57] Crash cause analyses Data can be used for crash analysis in relation to location, speed, weather conditions, etc. [58][59][60] Travel time analyses Freight transport analysis over time allows for better understanding of travel time patterns and driving patterns, as well as peak periods of freight transport [36,42] Travel speed analyses Analysing speed of trucks and providing analyses on travel speed [11,16,39,61] Route choice analyses Better understanding of route choice can assist traffic management and resource allocation [52,62,63] OD-Matrix analyses Freight GPS-data enables automatic creation of OD-matrices [11,15,18,[63][64][65][66][67] Production-Consumption (PC) matrix analyses Combination of GPS-data and other data types allows for the creation of PC-matrices [68] The three categories were further discussed with the participants through a semi-structured interview in order to identify the most important traffic measures and data usages from the perspective of the system users, i.e., the participants. The results from the discussions and interviews with stakeholders revealed three important key traffic measures as follows: OD-matrices, driving patterns, and parking pattern analyses.…”

Section: Congestion Analysesmentioning

confidence: 99%

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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Section: Congestion Analysesmentioning

confidence: 99%

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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“…A obtenção de dados de GPS para o estudo e planejamento do transporte de carga urbano apresenta algumas vantagens sobre os metodos de obtenção de dados tradicionais, como: a maior precisaõ em relação aos dados obtidos de pesquisas, menor ou nenhuma dependencia de interação com o motorista; o baixo custo de aquisiçaõ destes dados bem como a facilidade de obte-los para um longo perıódo de tempo (Joubert e Meintjes, 2015).…”

Section: Análise De Dados Gpsunclassified

“…Entretanto, pode-se citar tambeḿ fatores externos que podem prejudicar a coleta de dados GPS, como a existencia de muitos edifıćios no meio urbano, que interferem na quantidade de sateĺites visıveis, podendo afetar sua precisão ao fornecer o posicionamento (latitude, longitude, altitude) que depende do número de sateĺites disponıveis, sendo necessário um mıńimo de quatro sateĺites para determinar a localização (Joubert e Meintjes, 2015).…”

Section: Análise De Dados Gpsunclassified

Análise e visualização de dados de rastreamento para caracterização da logística urbana

et al. 2017

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RESUMOEste estudo tem como obje$vo aplicar técnicas de análise e visualização de dados GPS (Global Posi oning System) para entender o comportamento do transporte urbano de carga em algumas capitais do país, através de indicadores logís$cos como distribuição dos pontos de entrega, distribuição do volume de caminhões ao longo do tempo (dia da semana e hora do dia), etc. Para tanto, u$lizaram-se duas bases de dados de rastreamento de veículos, dis$ntas, sendo a primeira fornecida por duas empresas varejistas que já possuíam tais dados por questões de segurança e monitoramento do comportamento dos motoristas, e a segunda disponibilizada por uma empresa que presta serviços de fornecimento de mapas e possui dados de diversas empresas, em sua maioria prestadores de serviço de monitoramento de veículos. Para o processamento dos dados foram usadas ferramentas de análise de grandes volumes de dados geoespaciais (geospa al big data). Para ambas as bases foi possível analisar, para o período de 06 a 10 de outubro de 2014 (segunda à sexta), o padrão de distribuição dos caminhões por dia da semana, cujas análises apontam para uma quan$dade menor de veículos circulando na segunda-feira, em relação aos outros dias da semana (terça à sexta). RESUMOThis study aims to apply GPS (Global Posi$oning System) data analysis and visualiza$on techniques in order to understand the urban logis$cs' behavior in some ci$es of Brazil, through logis$cal indicators such as distribu$on of delivery points, volume of distribu-$on of trucks over $me (day and $me of day), etc. For that, two dis$nct databases of vehicle tracking were used: the first provided by two large retailers that already had such data for risk management and monitoring of drivers' behavior; and the second one belongs to a map services provider whose data is derived from several companies, mostly vehicle tracking companies. To process this amount of data, it was necessary to use geospa$al big data analysis tools. In both databases, it was possible to analyze, for the period 06-10 of October 2014 (Monday to Friday), the volume distribu$on paHern per day, and the results point to a smaller volume of vehicles circula$ng on Monday in comparison to other working days (Tuesday to Friday). Palavras-chaves:Logís$ca urbana, Big data geoespacial, Dados GPS, Visualização de dados. Keywords:Urban logis$cs, Geospa$al big data, GPS data, Data visualiza$on. DOI:10.14295/transportes.v25i3.1353 INTRODUÇÃOOs congestionamentos de trânsito são um dos grandes problemas que afetam as grandes cidades. Tais aglomerações urbanas ganham consideravel atenção devido ao seu tamanho populacional e suas inluencias econômica, sociocultural, ambiental e polıtica.Em tais centros urbanos vem-se observando crescente consumo e movimentação de produtos, tornando imperativo buscar soluções para uma utilização mais e iciente da escassa infraestrutura viária urbana. E# importante destacar ainda, a tendencia de que o tráfego urbano de veıćulos de carga venha a

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“…As operational systems in logistics become more developed and the management of processes becomes automated, everything that is measured creates data, and all data that is stored can be used for modelling (Witlox, 2015). The use of new sources for modelling freight transport activities is already clearly visible in the availability of GPS based analysis of trips for urban transport (Joubert and Meintjes, 2015) and maritime traffic (Shelmerdine, 2015). The data used here concerns observations of vehicle or vessel locations, including a time stamp and other information related to the shipper or carrier.…”

Section: New Directionsmentioning

confidence: 99%

The issues in modelling freight transport at the national level

Jong¹,

Tavasszy

Bates³

et al. 2016

Case Studies on Transport Policy

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Repeatability & reproducibility: Implications of using GPS data for freight activity chains

Cited by 35 publications

References 21 publications

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

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

Análise e visualização de dados de rastreamento para caracterização da logística urbana

The issues in modelling freight transport at the national level

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