GPS-Derived Measures of Freight Trucks for Rest Areas: A Case-Study Based Analysis

Karam, Ahmed; Illemann, Thorbjørn Martens; Reinau, Kristian Hegner

doi:10.1109/ieem45057.2020.9309748

Cited by 2 publications

(2 citation statements)

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“…In order to identify the vehicle freight activities from GPS data (e.g., stops), algorithms have to be developed [33,34]. Vehicle stop identification is a key aspect faced when analyzing the data, that can be very hard when intermediate short stops (e.g., waiting at traffic lights, congestion) are detected [33,[35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Delivery Pattern through Floating Car Data: Empirical Evidence

Comi

Polimeni

2021

Future Transportation

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This paper investigates the opportunities offered by floating car data (FCD) to infer delivering activities. A discrete trip-chain order model (within the random utility theory) for light goods vehicles (laden weight less than 3.5 tons) is hence proposed, which characterizes delivery tours in terms of the number of stops/deliveries performed. Thus, the main goal of the study is to calibrate a discrete choice model to estimate the number of stops/deliveries per tour by using FCD, which can be incorporated in a planning procedure for obtaining a preliminary assessment of parking demand. The data used refer to light goods vehicles operating in the Veneto region. The database contains more than 8000 tours undertaken in 60 working days. Satisfactory results have been obtained in terms of tour estimation and model transferability.

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Section: Introductionmentioning

confidence: 99%

Forecasting Delivery Pattern through Floating Car Data: Empirical Evidence

Comi

Polimeni

2021

Future Transportation

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“…Analysing the GPS data of commercial vehicles in their study setting, the latter collection of parking places even surpassed the area of the authorised property parcels, proving that traditional data collection methods could not capture a massive portion of parking spots in use. As Karam et al [19] stated, European studies are missing in the field, and they also acknowledge the difficulty of acquiring fleet tracking data. According to them, this is likely due to the reluctance of logistics companies to share sensitive data regarding their operations, as their performance metrics, the extent of their capabilities, or even location of their business partners could be assumed.…”

Section: Introductionmentioning

confidence: 99%

Where Truck Drivers Stop – Application of Vehicle Tracking Data for the Identification of Rest Locations and Driving Patterns

Csendes

Albert

Norina

et al. 2021

Promet

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Road transport plays an essential role in freight transport throughout Europe, therefore, conditions that may hinder seamless operations in this sector require thorough consideration for evidence-based action. Critical amongst these key conditions is how, when, and where truck drivers stop, as a common set of rules strictly regulates their driving times and rest periods, which causes mandatory interruptions in the supply chains. However, approximating reliable estimations of freight traffic flows and road infrastructure usage constitutes a considerable challenge for researchers. This paper presents a robust data processing approach to designate rest area stops and to calculate the pertaining driving and rest times. Drawing on the abundance of navigation information provided by private fleet toll registration services, a comprehensive spatial-temporal truck stop database on all major rest areas along the toll road network in Hungary has been compiled. Based on the assessment and comparison of driving and rest times, driving and parking times have been analysed, including micro-scale analysis of particular rest areas. Both the methods applied and the results achieved can be of strategic interest to better understand truck driving patterns, as well as to develop targeted and cost-effective measures to streamline freight transport operations in other contexts.

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GPS-Derived Measures of Freight Trucks for Rest Areas: A Case-Study Based Analysis

Cited by 2 publications

References 10 publications

Forecasting Delivery Pattern through Floating Car Data: Empirical Evidence

Forecasting Delivery Pattern through Floating Car Data: Empirical Evidence

Where Truck Drivers Stop – Application of Vehicle Tracking Data for the Identification of Rest Locations and Driving Patterns

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