2023
Tian, Zhihui; Feng, Tao; Yao, Baozhen; Hu, Yan; Zhang, Jing
Where to park an autonomous vehicle? Results of a stated choice experiment Journal Article
In: Transportation Research Part A: Policy and Practice, vol. 175, pp. 103763, 2023, ISSN: 0965-8564.
Abstract | Links | BibTeX | Tags: Autonomous vehicles, Context effects, Parking location choice
@article{TIAN2023103763,
title = {Where to park an autonomous vehicle? Results of a stated choice experiment},
author = {Zhihui Tian and Tao Feng and Baozhen Yao and Yan Hu and Jing Zhang},
url = {https://www.sciencedirect.com/science/article/pii/S0965856423001830},
doi = {https://doi.org/10.1016/j.tra.2023.103763},
issn = {0965-8564},
year = {2023},
date = {2023-01-01},
journal = {Transportation Research Part A: Policy and Practice},
volume = {175},
pages = {103763},
abstract = {The future innovation and growing popularity of autonomous vehicles have the potential to significantly impact the spatiotemporal distribution of parking demand. However, little knowledge is gained on how people will choose to park their autonomous cars. In principle, an autonomous vehicle is not necessarily parked close by like traditional vehicles leveraging the automated driving and parking capability, still, the decision made by people is important for policymakers in urban and transportation planning. This study attempts to gain useful insights to understand people’s parking location choices for autonomous vehicles. A stated choice experiment was designed, allowing people to choose a parking location for autonomous vehicles in varied contexts, including time windows, picking-up times, and the requirement for on-time arrival at the next activity. We found that similar to conventional cars people generally prefer cheaper and/or closer parking lots for autonomous vehicles. However, the distance between a parking lot and the activity location is relatively longer in the case of autonomous vehicles. The amount of time an autonomous vehicle spends in congestion while picking up the users influences the choice of parking locations. Moreover, substantial preference heterogeneity between individual people was found in the parking choice behavior. The maximum value of access time for autonomous cars is 34 $/h which is higher than the empirical value of walking time for conventional cars. Results of elasticity indicate that the influence of parking fees is larger than that of access time and congestion time.},
keywords = {Autonomous vehicles, Context effects, Parking location choice},
pubstate = {published},
tppubtype = {article}
}
The future innovation and growing popularity of autonomous vehicles have the potential to significantly impact the spatiotemporal distribution of parking demand. However, little knowledge is gained on how people will choose to park their autonomous cars. In principle, an autonomous vehicle is not necessarily parked close by like traditional vehicles leveraging the automated driving and parking capability, still, the decision made by people is important for policymakers in urban and transportation planning. This study attempts to gain useful insights to understand people’s parking location choices for autonomous vehicles. A stated choice experiment was designed, allowing people to choose a parking location for autonomous vehicles in varied contexts, including time windows, picking-up times, and the requirement for on-time arrival at the next activity. We found that similar to conventional cars people generally prefer cheaper and/or closer parking lots for autonomous vehicles. However, the distance between a parking lot and the activity location is relatively longer in the case of autonomous vehicles. The amount of time an autonomous vehicle spends in congestion while picking up the users influences the choice of parking locations. Moreover, substantial preference heterogeneity between individual people was found in the parking choice behavior. The maximum value of access time for autonomous cars is 34 $/h which is higher than the empirical value of walking time for conventional cars. Results of elasticity indicate that the influence of parking fees is larger than that of access time and congestion time.
2013
Feng, T.; Arentze, T.; Timmermans, H.
Capturing preference heterogeneity of truck drivers’ route choice behavior with context effects using a latent class model Journal Article
In: European Journal of Transport and Infrastructure Research, vol. 13, iss. 4, 2013, ISSN: 15677141.
Abstract | Links | BibTeX | Tags: Context effects, Freight transport, Heterogeneity, Latent class model, Route choice behavior
@article{Feng2013,
title = {Capturing preference heterogeneity of truck drivers' route choice behavior with context effects using a latent class model},
author = {T. Feng and T. Arentze and H. Timmermans},
doi = {10.18757/ejtir.2013.13.4.3004},
issn = {15677141},
year = {2013},
date = {2013-01-01},
journal = {European Journal of Transport and Infrastructure Research},
volume = {13},
issue = {4},
abstract = {This paper investigates heterogeneity in truck drivers' route choice preferences. A latent class model is estimated to identify heterogeneous segments of drivers. A stated choice experiment designed for identifying route choice behavior of truck drivers provides the data for model estimation. The effects of road pricing and environmental bonus are examined considering context dependency. Results reveal that size of truck is a significant segmentation variable of preferences for route attributes. Drivers of light trucks care more about congestion than drivers of heavy trucks, and are highly sensitive to road pricing and slightly sensitive to a road bonus. Drivers of heavy trucks are more sensitive to road category and urban area than drivers of light trucks, and are insensitive to bonus and slightly sensitive to pricing.},
keywords = {Context effects, Freight transport, Heterogeneity, Latent class model, Route choice behavior},
pubstate = {published},
tppubtype = {article}
}
This paper investigates heterogeneity in truck drivers’ route choice preferences. A latent class model is estimated to identify heterogeneous segments of drivers. A stated choice experiment designed for identifying route choice behavior of truck drivers provides the data for model estimation. The effects of road pricing and environmental bonus are examined considering context dependency. Results reveal that size of truck is a significant segmentation variable of preferences for route attributes. Drivers of light trucks care more about congestion than drivers of heavy trucks, and are highly sensitive to road pricing and slightly sensitive to a road bonus. Drivers of heavy trucks are more sensitive to road category and urban area than drivers of light trucks, and are insensitive to bonus and slightly sensitive to pricing.