Influence of Proximal Land Use and Network Characteristics on Link Travel Time

Mane, Ajinkya S.; Pulugurtha, Srinivas S.

doi:10.1061/(asce)up.1943-5444.0000599

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…Travel patterns are governed by the area type, socioeconomic characteristics, and land use categories of surrounding land (27)(28)(29)(30)(31). These components of urban areas assist in identifying planning strategies to construct new facilities or implement transportation projects.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, temporal aspects and road characteristics like functional class (10), flow/occupancy (19,23), signal timings (21), and upstream/downstream road characteristics (26,31) were incorporated into the models. Surrounding characteristics like land use, socioeconomic characteristics, and demographic variables were also emphasized in the travel time estimation models (29,31). While there are comprehensive research studies on passenger car/transit travel time estimation, their applicably to trucks remains debatable.…”

Section: Conclusion and Scope Of Future Researchmentioning

confidence: 99%

“…In addition, the linear models (without intercept) have lower QIC and QICC values and lower errors than the other models. Office, industrial, and commercial land uses can have a significant influence on the travel times and delays, in general (31). Specifically, truck travel times in the evening or morning peak hours can delay delivery of shipments, truck loading, or scheduling.…”

Section: Conclusion and Scope Of Future Researchmentioning

confidence: 99%

“…Like the travel demand in an area, the travel time patterns of a road vary based on the facility type, the surrounding conditions, and the area type. Further, the travel time of a road link is influenced by the land use and socioeconomic characteristics of the surrounding area ( 27 , 31 ). The complex interactions between the surrounding area characteristics and the travel patterns require a research design that considers the spatial parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Incorporating a geographic information system (GIS)-based approach makes it possible to capture associated spatial data for analysis and modeling ( 30 , 32 ). Past studies on travel time prediction incorporated spatial modeling frameworks to capture and quantify the influence of surrounding land use and demographic characteristics ( 31 ). An understanding of the association between the road, surrounding land use, and demographic characteristics with truck travel times helps us to understand better their complex relationships ( 32 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Modeling Road Link-Level Truck Travel Times Using On- and Off-Network Characteristics

Duvvuri

Pulugurtha

2022

Transportation Research Record

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Travel times of freight trucks (or trucks) play a major role in trip planning, identification of efficient routes, and allocating resources for implementation of strategies. While research on travel time estimation models for passenger cars or traffic stream is documented in the literature, their applicability for trucks remains debatable. Truck travel is influenced by the road characteristics, surrounding land use, and demographics of an area. The focus of this research, therefore, is on estimating the truck travel times using the on-network (road) and off-network (land use and demographics) characteristics. Truck travel time data for 501 road links in North Carolina from 2019 were processed for four times of the day and two days of the week. Generalized estimating equations (GEE) were used with the average truck travel time per mile (ATTTPM) as the dependent variable. Spatial proximity (buffers of 0.25 mi, 0.50 mi, and 1 mi) and spatial weights (distance decay functions like 1/d, 1/d2, and 1/d3) were explored to check the best possible approach for capturing off-network characteristics. The office (business park and administrative areas), transportation (rest areas and parking facilities), heavy commercial, and light industrial land uses have an increasing influence on the ATTTPM. The linear model developed using data from a 0.25-mi buffer is best suited to estimate the ATTTPM. Off-peak hour delivery incentives or truck priority systems on road links near these land uses can be implemented to improve truck mobility. The methodology illustrated in this research is transferable and could be used for estimating truck travel times across a region.

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