A data-driven systematic approach for identifying and classifying long-haul truck parking locations

Nevland, Erik A.; Gingerich, Kevin; Park, Peter Y.

doi:10.1016/j.tranpol.2020.04.003

Cited by 23 publications

(9 citation statements)

References 14 publications

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“…Nevland et al studied all the locations that trucks can park in urban areas, finding that rest areas and truck stops represent a small share of all the places that trucks can park legally ( 12 ). This research stresses the importance of studying truck parking comprehensively, and not just focusing on facilities dedicated to truck parking, finding that most truck parking occurs at commercial and industrial establishments.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Truck Parking Demand at Commercial and Industrial Establishments

Guerrero¹,

Pulikanti²,

Wieghart³

et al. 2022

Transportation Research Record: Journal of the Transportation R

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In urban areas, there exists a mismatch between where trucks need to park and the availability of spaces, forcing many truck drivers to park in undesignated locations that are unsafe, disrupt traffic, and cause a nuisance to the community. This mismatch also decreases the productivity of the sector, as drivers spend more time searching for spaces, take longer detours, and end their workday early. Most commercial and industrial establishments currently do not allow trucks to park at their facilities beyond the loading or unloading of cargo, which generates parking demand in the surrounding areas. This paper quantifies the truck parking requirements of different commercial and industrial establishments in Phoenix, AZ, to inform land use and development decision-making. GPS data were combined with land use and employment data to identify where, when, and for how long trucks are parking. Parking rates were calculated at 2-digit and 3-digit NAICS level, for three parking metrics: stops per weekday, cumulative stop duration per weekday, and peak occupancy. Linear and negative binomial models were estimated, along with other types of models, to improve the prediction of parking demand. Results show that parking demand is the highest for establishments that store trucks overnight, such as truck terminals, and establishments with large in-house fleets. Parking demand is also high for establishments moving bulky commodities or having significant transportation needs, like in warehousing and the wholesale trade. Manufacturing establishments have a wide range of parking demand rates, based on the bulkiness of products and use of in-house fleets.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling Truck Parking Demand at Commercial and Industrial Establishments

Guerrero¹,

Pulikanti²,

Wieghart³

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…An HCV platoon can use the same horizontal clearance and turning radius as a single HCV. The increased physical dimensions of an LCV will also require more space at truck parking facilities and LCVs may struggle with existing truck parking availability ( 16 , 17 ).…”

Section: Introductionmentioning

confidence: 99%

Operational Impact of the Through-Traffic Signal Prioritization for Heavy Commercial Vehicle Platooning on Urban Arterials

Chowdhury

Park

Gingerich

2022

Transportation Research Record: Journal of the Transportation R

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This study investigated the operational impact of heavy commercial vehicle (HCV) platooning on urban arterials. HCV platooning is an important application of vehicle-to-vehicle (V2V) technology, with urban arterials facilitating an essential component of HCV movements when picking up and delivering goods. HCV platooning has the potential to reduce fuel consumption and emissions. Moreover, the increasing HCV driver shortage problem can be alleviated if the vehicle following behind a lead vehicle can function without a driver by using autonomous technology enabling Society of Automotive Engineers Level 4 or higher. PTV VISSIM was used to develop a set of micro-simulation models that investigated the impact of traffic signal priority (TSP) and low levels (0%, 5%, and 10%) of HCV platooning. The performance measures include travel time and the number of stops. With the existing traffic control system, HCV platooning increased travel time and increased the number of stops for all vehicles including passenger cars and HCVs. TSP with 5% HCV platooning improved travel time and decreased the number of stops for all vehicles. TSP with 10% HCV platooning, however, only decreased travel time and the number of stops for passenger vehicles. The results suggest that a higher penetration rate of HCV platooning may create significant delays and overwhelm the traffic system even with the assistance of TSP. The findings of this study highlight the potential for TSP to mitigate the impact of HCV platooning on traffic congestion. However, the TSP system may not be a panacea that works for all traffic compositions.

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“…The later a search is started, the less likely a suitable parking spot will be found. This is due to a general lack of truck parking spots along motorways (Garber et al, 2004;Sun et al, 2018;Nevland et al, 2020), which is predicted to become even worse because of increased commercial traffic (Bayraktar et al, 2014). The situation is illustrated by the example of the United States but also applies to other countries.…”

Section: Introductionmentioning

confidence: 99%

“…The situation is illustrated by the example of the United States but also applies to other countries. Easily finding a proper, legal, and safe parking spot at the end of a working day for a night's rest is crucial for truck drivers to be able to comply with HOS regulations, reduce fatigue, and improve road safety (Boris and Brewster, 2018;Nevland et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion

Gutmann

Maget

Spangler

et al. 2021

Front. Future Transp.

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For haul truck drivers it is becoming increasingly difficult to find appropriate parking at the end of a shift. Proper, legal, and safe overnight parking spots are crucial for truck drivers in order for them to be able to comply with Hours of Service regulation, reduce fatigue, and improve road safety. The lack of parking spaces affects the backbone of the economy because 70% of all United States domestic freight shipments (in terms of value) are transported by trucks. Many research projects provide real-time truck parking occupancy information at a given stop. However, truck drivers ultimately need to know whether parking spots will be available at a downstream stop at their expected arrival time. We propose a machine-learning-based model that is capable of accurately predicting occupancy 30, 60, 90, and 120 min ahead. The model is based on the fusion of Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) with the help of a feed-forward neural network. Our results show that prediction of truck parking occupancy can be achieved with small errors. Root mean square error metrics are 2.1, 2.9, 3.5, and 4.1 trucks for the four different horizons, respectively. The unique feature of our proposed model is that it requires only historic occupancy data. Thus, any truck occupancy detection system could also provide forecasts by implementing our model.

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A data-driven systematic approach for identifying and classifying long-haul truck parking locations

Cited by 23 publications

References 14 publications

Modeling Truck Parking Demand at Commercial and Industrial Establishments

Modeling Truck Parking Demand at Commercial and Industrial Establishments

Operational Impact of the Through-Traffic Signal Prioritization for Heavy Commercial Vehicle Platooning on Urban Arterials

Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion

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