Nested logit model formation to analyze airport parking behavior based on stated preference survey studies

Qin, Huanmei; Jian-qiang, Gao; Zhang, Guohui; Chen, Yanyan; Wu, Shengbiao

doi:10.1016/j.jairtraman.2016.10.011

Cited by 41 publications

(19 citation statements)

References 18 publications

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“…In recent work, Qin et al (2017) used an NL model to analyze the parking choice behavior of air travelers at airports. At the upper level of the nested model, the choice between off-site and on-site parking was modeled, whereas at the lower level the choice between different connection models from the parking facility to the airport terminals was modeled.…”

Section: Parking Choice Modelingmentioning

confidence: 99%

A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas

Chiara

Cheah

Azevedo

et al. 2020

Transportation Science

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Understanding factors that drive the parking choice of commercial vehicles at delivery stops in cities can enhance logistics operations and the management of freight parking infrastructure, mitigate illegal parking, and ultimately reduce traffic congestion. In this paper, we focus on this decision-making process at large urban freight traffic generators, such as retail malls and transit terminals, that attract a large share of urban commercial vehicle traffic. Existing literature on parking behavior modeling has focused on passenger vehicles. This paper presents a discrete choice model for commercial vehicle parking choice in urban areas. The model parameters were estimated by using detailed, real-world data on commercial vehicle parking choices collected in two commercial urban areas in Singapore. The model analyzes the effect of several variables on the parking behavior of commercial vehicle drivers, including the presence of congestion and queueing, attitudes toward illegal parking, and pricing (parking fees). The model was validated against real data and applied within a discrete-event simulation to test the economic and environmental impacts of several parking measures, including pricing strategies and parking enforcement.

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Section: Parking Choice Modelingmentioning

confidence: 99%

A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas

Chiara

Cheah

Azevedo

et al. 2020

Transportation Science

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“…The larger the value is, the higher the accuracy of the model is. In practice, it indicates an accurate model when ρ 2 is within 0.2-0.4 [49]. The ρ 2 of the models in Table 4 are 0.527, 0.853 and 0.854, respectively, which shows the accuracy of the model is high and the model can be used to describe the travel mode choice behaviors of passengers waiting at bus station.…”

Section: Estimation Results Of Sem-logit Integration Modelmentioning

confidence: 90%

Research on Passenger’s Travel Mode Choice Behavior Waiting at Bus Station Based on SEM-Logit Integration Model

Han

Wei

et al. 2018

Sustainability

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Abstract:To improve the mode share of public transport and reduce the transition to private transport of passengers waiting at bus station, the mechanism of passengers' decision-making procedure and influence factors of the travel mode choice were analyzed. Some latent variables such as safety, comfort, convenience, flexibility and economy were selected to reflect the satisfaction degree of passengers on the service level of public transport. Taking Jinan City as an example, the questionnaire of passengers' travel choice behavior at bus station was designed and carried out. Based on the structure equation model (SEM), the relationship between the satisfaction degree and some latent variables such as safety and comfort was discussed. The SEM method analysis shows that, of the influence level of the latent variables to the service level of public transport, flexibility is the most significant variable affecting passenger's satisfaction degree followed by safety, convenience, comfort and economy. Travel mode choice model of passengers waiting at bus station was established with an integration approach of SEM and nested logit (NL) model. The SEM-NL integration model results reveal that gender, monthly income, purpose of the trip, travel distance, safety and convenience service level have a significant effect on the choice of the upper model (public transport or private transport). Passenger's age, vehicle ownership and bus ride frequency have great influence on the choice of the lower mode (ORB: original route bus; ARB: alternative route bus; Taxi; and Shared bike). Sensitivity analysis reveals that the transition probabilities from private transport to public transport can reach the highest point (respectively, 69.85%, 68.84% and 35.51%) when safety service reaches level 4, convenience service reaches level 3, or comfort service reaches level 2, indicating that the safety level equal to 4, convenience level equal to 3 and comfort level equal to 2 are the key threshold to increase the public transport mode share. Some proposals such as ensuring good accessibility of public transport, shortening the transfer distance of different routes, creating a comfortable travel environment and integrating bus ticket system have been put forward for the sustainable development of public transport system.

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“…The absolute value of t-test value of variables is greater than 1.96, which indicates that the variables have a significant impact on tourists' behavior adjustment at 95% confidence level. The goodness ratio is 0.203, which means that the accuracy of the MNL model is higher [63,64]. There is a strong ability to explain tourists' behavior adjustment under RTFDSI.…”

Section: Estimation Results Of Tousists' Behavior Adjustment Modelmentioning

confidence: 97%

“…The t-test is used to judge the influence degree of influencing factors on decision-making behavior, when the absolute value of the t-test value is greater than 1.96, it shows that the influencing factors have a significant impact on the decision-making behavior at 95% confidence interval. The goodness ratio ρ 2 is used to measure the accuracy of the model, when ρ 2 reaches 0.2-0.4, the accuracy of the model is high [63,64].…”

Section: Estimation Methods Of Multinomial Logit Modelmentioning

confidence: 99%

Spatial-Temporal Response Patterns of Tourist Flow under Real-Time Tourist Flow Diversion Scheme

et al. 2020

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This paper excavates tourist decision-making mechanism under the real-time tourist flow diversion scheme (RTFDS) and evaluates the tourist flow diversion effect of RTFDS. To meet the objectives, the stated preference survey and tourist flow survey of the Summer Palace were implemented. The tourist behavior adjustment model and tourist flow diversion simulation model were established. The results show that: (a) For core tourist spots, 66.5% and 16.5% of tourists will choose “behavior adjustment” and “no longer adjustment” under RTFDS, these behavior adjustments all shorten tourists’ residence time in tourist spots; (b) When the tourist congestion perception degree equals 4 or 5, tourists tend to adopt behavior adjustment or the individuals adopt no longer adjustment instead of cognitive adjustment when they face low tourist congestion perception degree, which equals 1 or 2; (c) When core tourist spots’ residence time is reduced by 10% and 20%, there are 60% and 73% time nodes where core tourist spots’ tourist flow density is less than or equal to the condition of null information, there are 73% and 60% time nodes where periphery tourist spots’ density is more than the condition of null information. The simulation results showed that some tourists could be guided from core tourist spots to periphery tourist spots through releasing RTFDS information. The research can provide theoretical support for the implementation of RTFDS, and alleviate the congestion inside the tourist attraction.

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Nested logit model formation to analyze airport parking behavior based on stated preference survey studies

Cited by 41 publications

References 18 publications

A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas

A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas

Research on Passenger’s Travel Mode Choice Behavior Waiting at Bus Station Based on SEM-Logit Integration Model

Spatial-Temporal Response Patterns of Tourist Flow under Real-Time Tourist Flow Diversion Scheme

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