Joint Choice Model for Airport Passengers’ Travel Mode and Departure Time Based on Agent Theory

2020 ◽  
Vol 2674 (6) ◽  
pp. 177-187
Author(s):  
Danwen Bao ◽  
Tianxuan Zhang ◽  
Shijia Tian ◽  
Zhiwei Di
Keyword(s):  
Decision Making ◽  
Public Transportation ◽  
Travel Behavior ◽  
Time Management ◽  
Imperfect Information ◽  
Bayesian Learning ◽  
Choice Model ◽  
Travel Mode ◽  
Agent Theory ◽  
Departure Time

Numerous strategies have been proposed to modify and transform passengers’ travel mode and departure time with the purpose of mitigating landside traffic pressure of airports. A core solution to tackle this problem is to build a travel behavior model so that pertinent predictions about the extent to which passengers shift their patterns of travel can hopefully be obtained. This paper aims at studying the passengers’ behaviors with respect to the travel mode and departure time based on agent theory. What distinguishes this model from traditional utility maximization theory is that it specifically places emphasis on the decision-making process with imperfect information and bounded rationality. Passengers continuously renew their knowledge of time management and their surrounding environment in the duration of the Bayesian learning process. It is evident that decisions about whether to substitute their current travel mode and departure time will be given thoughtful consideration before traveling, in relation to their presumptive gain and cost for searching. When performing additional searches, passengers tend to depend on a range of decision-making conditions to determine the necessity of converting to a new travel pattern. The process of both searching and deciding can be indicated by production (if–then) rules. These rules basically stem from the data gathered from Nanjing Lukou International Airport (NKG). Furthermore, this paper studies and discusses to what extent passengers will change their travel behaviors under variable costs of public transportation. Finally, this paper provides some recommendations on how to formulate appropriate subway fares.

A Modeling Analysis of Impact from E-Hailing Service on Non-Work Travel Mode in Shanghai, China

2018 ◽  
Vol 2672 (47) ◽  
pp. 125-134 ◽  
Author(s):  
Jiayu Zhong ◽  
Xin Ye ◽  
Ke Wang ◽  
Dongjin Li
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Rapid Development ◽  
Influential Factors ◽  
Travel Mode ◽  
Explanatory Variables ◽  
Bus Travel Time ◽  
Vehicle Travel Time

With the rapid development of mobility services, e-hailing service have been highly prevalent and e-hailing travel has become a part of daily life in many cities in China. At the same time, travelers’ mode choice behaviors have been influenced to some degree by different factors, and in this paper, a web-based retrospective survey initially conducted in Shanghai, China is used to analyze the extent to which various factors are influencing mode choice behaviors. Then, a multinomial-logit-based mode choice model is developed to incorporate the e-hailing auto mode as a new travel mode for non-work trips. The developed model can help to identify influential factors and quantify their impact on mode choice probabilities. The developed model involves a variety of explanatory variables including e-hailing/taxi fare, bus travel time, rail station access/egress distance, trip distance, car in-vehicle travel time as well as travelers’ socioeconomic and demographic characteristics, etc. The model indicates that the e-hailing fare, travel companions and some travelers’ characteristics (e.g., age, income, etc.) are significant factors influencing the choice of e-hailing mode. The alternative-specific constant in the e-hailing utility equation is adjusted to match the observed market share of the e-hailing mode. Based on the developed model, elasticities of LOS attributes are computed and discussed. The research methods used in this paper have the potential to be applied to investigate travel behavior changes under the influence of emerging travel modes. The research findings can aid in evaluating policies to manage e-hailing services and improve their levels of services.

scholarly journals Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Chuan Ding ◽  
Yu Chen ◽  
Jinxiao Duan ◽  
Yingrong Lu ◽  
Jianxun Cui
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Structural Equation ◽  
Choice Model ◽  
Mode Choice ◽  
Influential Factors ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Impact ◽  
Hybrid Choice Model

Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

scholarly journals Incorporating MNL Model into Random Forest for Travel Mode Detection

2021 ◽  
Vol 40 (3) ◽  
pp. 496-501
Author(s):  
Muhammad Awais Shafique ◽  
Eiji Hato
Keyword(s):  
Decision Making ◽  
Random Forest ◽  
Learning Algorithm ◽  
Choice Model ◽  
Mode Choice ◽  
Decision Making Process ◽  
Accelerometer Data ◽  
Choice Modelling ◽  
Travel Mode ◽  
Mode Choice Model

Mode choice models have been used widely to forecast the relative probabilities of using available travel modes. These depend on mode-related and traveler-related characteristics. On the other hand, smartphones are increasingly being used to collect sensors’ data relating to trips made after selection of a suitable mode. Such sensors’ data may be correlated with decision-making process of travelers regarding travel mode selection. Discrete Choice Modelling is used to simulate this decision-making process by computing utilities of various travel alternatives, and then calculating their respective probabilities of being selected. In this paper, multinomial logit (MNL) mode choice model is utilized to enhance the prediction capacity of supervised learning algorithm i.e. Weighted Random Forest. To make the procedure less energy-intensive, GPS data was used only to locate the origin and destination of any trip, to be incorporated in mode choice model. Afterwards only accelerometer data was utilized in feature selection for the learning algorithm. One tenth of the classified data was used to train the algorithm whereas rest was used to test it. Results suggested that with incorporation of MNL, the overall prediction accuracy of learning algorithm was increased from 93.75% to 99.08%.

An agent-based choice model for travel mode and departure time and its case study in Beijing

2016 ◽  
Vol 64 ◽  
pp. 133-147 ◽  
Author(s):  
Mingqiao Zou ◽  
Meng Li ◽  
Xi Lin ◽  
Chenfeng Xiong ◽  
Chao Mao ◽  
...  
Keyword(s):  
Choice Model ◽  
Travel Mode ◽  
Agent Based ◽  
Departure Time

scholarly journals Quantifying Travel Physical Energy Expenditure for Urban Travellers – A Case Study of Beijing

2020 ◽  
Vol 32 (2) ◽  
pp. 219-228
Author(s):  
Xin Hong ◽  
Lingyun Meng ◽  
Jian An
Keyword(s):  
Energy Expenditure ◽  
Public Transportation ◽  
Choice Model ◽  
Mode Choice ◽  
Economic Cost ◽  
Multinomial Logit Model ◽  
Travel Mode ◽  
Physical Energy ◽  
Travel Mode Choice

Travel physical energy expenditure for travellers has impact on travel mode choice behaviour. However, quantitative study on travel physical energy expenditure is rare. In this paper, the concept of travel physical energy expenditure coefficient has been presented. A case study has been carried out of young travellers in Beijing to get the value of physical energy expenditure per unit time under three transport modes, walking, car and public transportation. A series of experiments have been designed and conducted, which consider influence factors including age, gender, travel mode, riding posture, luggage level and crowded level. By analysing the travel data of money, travel time and physical energy expenditure, we determined that the value of travel physical energy expenditure coefficient δ is 0.058 RMB/KJ, which means that travellers can pay 0.058 RMB to reduce 1 KJ physical energy expenditure. Next, a travel mode choice model has been proposed using a multinomial logit model (MNL), considering economic cost, time cost and physical energy cost. Finally, the case study based on OD from Xizhimen to Tiantongyuan in Beijing was conducted. It is verified that it will be in better agreement with the actual travel behaviour when we take the physical energy expenditure for different types of travellers into account.

Joint Discrete-Continuous Model of Travel Mode and Departure Time Choices

2017 ◽  
Vol 2669 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Ramin Shabanpour ◽  
Nima Golshani ◽  
Sybil Derrible ◽  
Abolfazl (Kouros) Mohammadian ◽  
Mohammad Miralinaghi
Keyword(s):  
Decision Making ◽  
Goodness Of Fit ◽  
Clustering Algorithm ◽  
Joint Modeling ◽  
Continuous Model ◽  
Joint Model ◽  
Travel Mode ◽  
Joint Decision ◽  
Departure Time ◽  
Decision Making Behavior

This paper presents a cluster-based joint modeling approach to investigating heterogeneous travelers’ behavior toward trip mode and departure time choices by considering those choices as a joint decision. First, a two-step clustering algorithm was applied to classify travelers into six distinct clusters to account for the heterogeneity in their decision-making behavior. Then, a joint discrete-continuous model was proposed for each cluster, in which the travel mode and departure time were estimated by a multinomial logit and a log-linear regression model, respectively. These two models were jointly estimated with a copula approach. For an investigation of the performance of the proposed approach, its results were compared with an aggregate joint model on all nonclustered observations to assess the potential benefits of population clustering. The goodness-of-fit measures and prediction accuracy results demonstrated that the proposed cluster-based joint model significantly outperformed the aggregate joint model. Further, the variations in the estimated parameters of different clusters indicated significant behavioral differences across clusters. Hence, the proposed cluster-based joint model, while offering higher accuracy, possesses a significant potential for transportation policy making because it has the capability to target different types of travelers on the basis of their decision-making behavior.

strategi dalam pengambilan keputusan

2019 ◽  
Author(s):  
Suci Handayani Handayani ◽  
Hade Afriansyah
Keyword(s):  
Decision Making ◽  
Imperfect Information ◽  
Economic Value ◽  
Decision Makers ◽  
Decision Making Process ◽  
Administrative Process ◽  
Left Behind ◽  
Information Time ◽  
Complex Matter ◽  
General Individuals

Decision making is one element of economic value, especially in the era of globalization, and if it is not acceptable in the decision making process, we will be left behind. According to Robins, (2003: 173), Salusu, (2000: 47), and Razik and Swanson, (1995: 476) say that decision making can be interpreted as a process of choosing a number of alternatives, how to act in accordance with concepts, or rules in solving problems to achieve individual or group goals that have been formulated using a number of specific techniques, approaches and methods and achieve optimal levels of acceptance.Decision making in organizations whether a decision is made for a person or group, the nature of the decision is often determined by rules, policies, prescribed, instructions that have been derived or practices that apply. To understand decision making within the organization it is useful to view decision making as part of the overall administrative process. In general, individuals tend to use simple strategies, even if in any complex matter, to get the desired solution, because the solution is limited by imperfect information, time and costs, limited thinking and psychological stress experienced by decision makers.

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