Abstract
With the objective of deriving useful insights into measures against traffic congestion at service areas (SAs) and parking areas (PAs) on expressways and ensuring efficient use of SAs/PAs, this study investigated the decisions on where a truck is parked (i.e., choice of an SA or a PA), how long it is parked (i.e., parking time), and their influential factors. To this end, this study used the trajectory data of 1600 trucks recorded in 6-min intervals by in-vehicle digital tachographs on the Sanyo and Chugoku Expressways in Japan from October 2013 to March 2014. First, the aspect of repeated choice of each truck (i.e., habitual behavior) toward a specific SA/PA was clarified. Next, a multilevel discrete–continuous model (Type II Tobit model) was developed to reveal the factors affecting the above decisions. The modeling results confirmed the existence of habitual behavior and showed that trucks were more likely to be parked a longer time at an SA/PA when it is closer to the destination. It appears that truck drivers may adjust their time at the SA/PA close to the destination to comply with the arrival time, which is often predetermined by the owner of the transported goods. Furthermore, the availability of restaurants and shops, and the number of parking spaces available for trucks and trailers are important determinants of parking time, whereas the existence of a convenience store is important to the choice of the SA/PA. Parking experience has an extremely strong positive effect on the parking choice and use. Moreover, increasing the number of parking lots may induce its longer use.
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Notes
The information in Table 1 is further used to specify some SA/PA-specific variables used in the later analysis.
The installation of digital tachographs has become mandatory in Japan for trucks having the vehicle total weight of seven ton or more or trucks having the maximum loading capability of four ton or more since April 2017.
During the period covered by the data, the midnight discount rate of the expressways was 50%, although it is now reduced to 30% (December 2017). At that time, the SA/PA parking congestion was very severe and therefore it is worthwhile to focus on this period.
We need to admit the possibility of the selection bias due to the use of the data from this particular digital tachographs system.
Threshold values of 300 were set via a trial and error process by checking the locations of stopping trucks around the SAs/PAs on GIS.
Because onboard devices sometimes do not stop, a trip was considered to have ended if there were no observations for 3 h.
The bottom and top of the box are the 25th and 75th percentiles; the band near the middle of the box is the 50th percentile (the median). The dots at the end of the boxplot represent outliers (less than Q1 − 1.5 × IQR; greater than Q3 + 1.5 × IQR, where IQR denotes the interquartile range (Q3–Q1)).
There are several price discount policies on the expressways in Japan based on the time period. For more details on discount policies, visit the East Nippon Expressway Company website (https://www.driveplaza.com/traffic/tolls_etc/etc_dis_night/).
The facilities at the SAs/PAs are improved sometimes. Moreover, the number of parking lots would have been determined according to the number of trucks that request for parking. These facts suggest the existence of reverse causalities. However, because the SA/PA facilities are available to all users and not for truck drivers alone, it is natural to assume the causal direction from the facilities or number of parking lots to choice/usage behavior.
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Acknowledgements
This study was funded by the West Nippon Expressway Company Limited, and JSPS KAKENHI Grant Numbers 15H02271 and 17K18905.
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HSeya: Literature Research and Review, Manuscript writing and Editing, Data analysis. J Zhang: Manuscript writing and Editing. M Chikaraishi: Manuscript writing and Editing. Y Jiang: Literature Research and Review, Manuscript Writing and Editing.
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Seya, H., Zhang, J., Chikaraishi, M. et al. Decisions on truck parking place and time on expressways: an analysis using digital tachograph data. Transportation 47, 555–583 (2020). https://doi.org/10.1007/s11116-018-9899-y
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DOI: https://doi.org/10.1007/s11116-018-9899-y