Modeling Freight Truck Trips in Birmingham Using Tour-Based Approach

Doustmohammadi, Ehsan; Sisiopiku, Virginia P.; Sullivan, Andrew

doi:10.4236/jtts.2016.65035

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…With the use of multiday GPS data, this study also provides opportunities for investigating the regularity and variability for long-haul freight truck trips. Other potential applications for splitting truck GPS trips using the method proposed in this paper are, for example, truck travel demand forecasting [25,26], freight fleeting performances [27], fuel consumption and emission estimations [28], roadway bottlenecks identification [22], and so forth. For the logistics and trucking industry, it would be interesting to know the punctuality of a truck on a route to ensure timely delivery and performance stability [29], which will be showcased later in this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Origin-Destination Trips from GPS Data – Application in Travel Time Reliability of Dedicated Trucks

Zhao

2022

Promet

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The advancement of data collection technologies has brought an upsurge in GPS applications. For example, travel behaviour research has benefited from the integration of multiple sources of Global Positioning System (GPS) data. However, the effective use of such data is still impeded by the challenge in data processing. For instance, GPS data, despite providing detailed spatial movement information, do not label the starting and finishing points of a trip, especially for commercial trucks. Hence, there is a critical need to develop a trip identification method to effectively use the trajectory data provided by GPS without additional information. This paper focused on identifying trips from the raw GPS data. Specifically, a systematic method is proposed to extract trips on the basis of origin-destination (OD) pairs by using a 5-step procedure. An application was provided on estimating the performance of travel time reliability using three metrics based on the OD trips for each dedicated truck. The application showed that, in general, trucks on long-distance routes have less reliable travel times compared to trucks on short-distance routes. This paper provides an example of using GPS data, without further information, to study travel time for freight performance and similar needs of punctuality in logistics.

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Section: Literature Reviewmentioning

confidence: 99%

Identifying Origin-Destination Trips from GPS Data – Application in Travel Time Reliability of Dedicated Trucks

Zhao

2022

Promet

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“…Additionally, data collection and availability are proving to be a large obstacle in the development and improvement of truck demand models (Bastida & Holguin-Veras, 2009;Samimi et al, 2010;Hunt & Stefan, 2007;Ruan et al, 2011;Greaves & Figliozzi, 2008;Roorda et al, 2010). Fortunately, some research studies have tested the use of GPS in collecting better data for modeling and found it to increase the quality of the models (Kuppam et al, 2014;Wheeler & Figliozzi, 2011;Greaes & Figliozzi, 2008;Doustmohammadi & Sisiopiku, 2016;Doustmohammadi et al, 2016b). Despite the issues being faced, researchers have been working on several variations of truck demand models.…”

Section: Literature Review Of Freight Transportationmentioning

confidence: 99%

“…Activity-based, or tour-based, modeling approaches have been shown to provide the most accurate movement predictions (Samimi et al, 2010;Figliozzi, 2007;Kuppam et al, 2014;Gliebe et al, 2007;Hunt & Stefan, 2007;Ruan et al, 2011;Kim et al, 2011;Bradley et al, 2010;Doustmohammadi & Sisiopiku, 2016;Doustmohammadi et al, 2016b). Additionally, the ability to combine modeling techniques, primarily combing tour-based modeling with other types of modeling can meet specific needs (Alho, 2011;Fischer et al, 2005;Jansuwan et al, 2014;Holguín-Veras & Thorson, 2000;Holguín-Veras & Patil, 2008;Daly, 1982;Doustmohammadi et al, 2016a;Doustmohammadi et al, 2016b;Holguín-Veras et al, 2008; (Cambridge, 1996;Cambridge, 2007). The models in QRFM have been shown to be useful in forecasting truck in a medium sized community (Anderson et al, 2013;Cambridge, 2007).…”

Section: Literature Review Of Freight Transportationmentioning

confidence: 99%

Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities

Doustmohammadi¹,

Anderson²,

Doustmohammadi³

2019

CUS

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This paper uses data from a trucking origin/destination study conducted with global positioning system (GPS) technology to develop a truck trip generation model for medium sized urban communities-in this study taken to be communities between 200,000 and 1,000,000 people. The difficulty with developing truck trip generation equations centers on the limitation of data. For passenger transportation, data are collected from household surveys. For truck transportation, if available, data are typically collected from a small collection of shippers/businesses within the urban area and extrapolated to cover the entire study area. Because of the data limitations, truck transportation is typically indirectly modeled or as an afterthought. Increasing truck volumes, coupled with cost saving strategies such as just-in-time delivery systems, require that transportation policymakers analyze infrastructure needs and make investment decisions that explicitly include truck volumes as a component. This paper contains a case study using a medium sized urban area and a GPS collected set of truck origins and destinations to develop a truck specific trip generation equation using standard employment data. The paper presents the models developed and validates the models to the case study community. The paper concludes that the trip generation equations developed can be incorporated into medium sized community travel models to provide a framework for truck planning that can be used to improve resource allocation decisions.

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“…Truck transportation plays a key role in serving freight transportation needs and supporting the economy, but it also contributes to urban congestion and pollution. However, comprehensive modeling of truck trips is still limited due to the complexity of freight movements and the unavailability of detailed truck trip data [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Road Network Connectivity and Freight Transportation for Supporting the Development of the Northern Zone of Aceh

Herman¹,

Hasyim²,

Saleh³

et al. 2019

Journal of Southwest Jiaotong University

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Road network connectivity significantly affects merchandise, transport, and people’s lives. Freight transportation network models are utilized as frameworks of transport policy decisions to estimate the impacts of infrastructure projects on traffic. The goods supply delivery modes in Aceh, Indonesia, are dominated by road-based transportation, where up to 95% of daily needs, such as food, are carried using trucks, buses, and other vehicles. This condition emerged owing to a lack of infrastructure and facilities. Thus, the main purpose of this study is to analyze the effect of road network connectivity on goods delivery in the northern zone of Aceh. This research adopted a quantitative technique (survey questionnaire) and involved as many as 420 respondents. The data were analyzed using structural equation modeling (SEM) with Analysis of Moment Structures. The result of the study shows the value of regressions weight is 0.375 or 37.5%. It indicates the road network connectivity variable has a significant relationship with the transport of goods. Furthermore, the road network connectivity of the area has the strongest link or significantly influences the activities of regional development. Therefore, the government in the northern zone of Aceh can formulate road network policies oriented towards the development of the area’s new economy and support the implementation of the Arun Lhokseumawe Special Economic Zone, which is the business area and trade laws are different from the rest of the country.

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Modeling Freight Truck Trips in Birmingham Using Tour-Based Approach

Cited by 6 publications

References 8 publications

Identifying Origin-Destination Trips from GPS Data – Application in Travel Time Reliability of Dedicated Trucks

Identifying Origin-Destination Trips from GPS Data – Application in Travel Time Reliability of Dedicated Trucks

Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities

Road Network Connectivity and Freight Transportation for Supporting the Development of the Northern Zone of Aceh

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