Browsing by Author "Fu, Chiang"

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A latent class generalised nested logit model and its application to modelling carrier choice with market segmentation
(2013-09-01)
Wen, Chieh-Hua
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Huang, Wan-Wen
;
Fu, Chiang
;
Chou, Pei-Yu
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運輸與物流管理系註：原交通所+運管所
This article develops a new latent class (LC) model with a generalised nested logit (GNL) formulation to enhance the methodology of market segmentation analysis. The standard generalised nested logit (or the cross-nested logit model) is a special case of the latent class generalised nested logit model (LCGNL) that accounts for heterogeneity in individuals' preferences by a number of segments and simultaneously identifies segment sizes and individual profiles. In addition, the LCGNL model allows flexible substitution patterns among alternatives. This extends the standard LC model with the multinomial logit formulation, such that the independence from irrelevant alternatives property does not hold within segments. The proposed model was used to identify potential segments of travellers' preferences towards air and bus carriers. The estimation results of the LCGNL models indicate that degrees of competition vary across carriers and that differential sensitivity in preference parameters exists between segments. The LCGNL model outperforms the other models; therefore, it is a better approach for analysing carrier choice behaviour.
Freeway crash frequency modeling under time-of-day distribution
(2017-01-01)
Chiou, Yu-Chiun
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Sheng, Yu-Chun
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Fu, Chiang
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This study aims to identify key factors affecting crash frequencies under various times of the day, so as to propose effective and time-specific countermeasures. Two approaches are proposed and compared. The clustering approach combines a crash count model to predict total number of crashes and a clustering model to divide segments into clusters according to their time-of-day distribution patterns of crash frequency. The multivariate approach treats the crash frequencies of various times of the day as target variables and accommodates potential correlation among them. Crash datasets of Taiwan Freeway No. 1 are used to estimate and validate the models. Four times of the day, late-night/dawn (24-06), morning/noon (07-13), afternoon/evening (14-19), and night (20-23) are formed according to crash count distribution. In terms of Adj-MAPE and RMSE, the clustering approach performs better than the multivariate approach. According to the clustering results, segments in metropolitan areas have higher crash frequency in the afternoon/evening, while those in rural areas have higher crash frequency in late-night/dawn, suggesting the time-of-day distributions of crash frequency markedly differ among segments. Time-specific countermeasures are then proposed accordingly. (C) 2017 The Authors. Published by Elsevier B.V.
Latent class nested logit model for analyzing high-speed rail access mode choice
(2012-03-01)
Wen, Chieh-Hua
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Wang, Wei-Chung
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Fu, Chiang
;
運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
This paper explores access mode choice behavior, using a survey data collected in Taiwan. The latent class nested logit model is used to capture flexible substitution patterns among alternatives and preference heterogeneity across individuals while simultaneously identifying the number, sizes, and characteristics of market segments. The results indicate that a four-segment latent class nested logit model with individual characteristics in segment membership functions is the most preferred specification. Most high-speed rail travelers were cost-sensitive to access modes, and thus strategies that reduce the access costs can be more effective than reducing the access times. (C) 2011 Elsevier Ltd. All rights reserved.
Modeling crash frequency and severity using multinomial-generalized Poisson model with error components
(2013-01-01)
Chiou, Yu-Chiun
;
Fu, Chiang
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Since the factors contributing to crash frequency and severity usually differ, an integrated model under the multinomial generalized Poisson (MGP) architecture is proposed to analyze simultaneously crash frequency and severity-making estimation results increasingly efficient and useful. Considering the substitution pattern among severity levels and the shared error structure, four models are proposed and compared-the MGP model with or without error components (EMGP and MGP models, respectively) and two nested generalized Poisson models (NGP model). A case study based on accident data for Taiwan's No. 1 Freeway is conducted. The results show that the EMGP model has the best goodness-of-fit and prediction accuracy indices. Additionally, estimation results show that factors contributing to crash frequency and severity differ markedly. Safety improvement strategies are proposed accordingly. (C) 2012 Elsevier Ltd. All rights reserved.
Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach
(2013-03-01)
Chiou, Yu-Chiun
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Hwang, Cherng-Chwan
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Chang, Chih-Chin
;
Fu, Chiang
;
運輸與物流管理系註：原交通所+運管所
This study simultaneously models crash severity of both parties in two-vehicle accidents at signalized intersections in Taipei City, Taiwan, using a novel bivariate generalized ordered probit (BGOP) model. Estimation results show that the BGOP model performs better than the conventional bivariate ordered probit (BOP) model in terms of goodness-of-fit indices and prediction accuracy and provides a better approach to identify the factors contributing to different severity levels. According to estimated parameters in latent propensity functions and elasticity effects, several key risk factors are identified driver type (age >65), vehicle type (motorcycle), violation type (alcohol use), intersection type (three-leg and multiple-leg), collision type (rear ended), and lighting conditions (night and night without illumination). Corresponding countermeasures for these risk factors are proposed. (C) 2012 Elsevier Ltd. All rights reserved.
"Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach"(Reprinted from Accident Analysis and Prevention, vol.51, pg 175, 2013)
(2013-12-01)
Chiou, Yu-Chiun
;
Hwang, Cherng-Chwan
;
Chang, Chih-Chin
;
Fu, Chiang
;
運輸與物流管理系註：原交通所+運管所
This study simultaneously models crash severity of both parties in two-vehicle accidents at signalized intersections in Taipei City, Taiwan, using a novel bivariate generalized ordered probit (BGOP) model. Estimation results show that the BGOP model performs better than the conventional bivariate ordered probit (BOP) model in terms of goodness-of-fit indices and prediction accuracy and provides a better approach to identify the factors contributing to different severity levels. According to estimated parameters in latent propensity functions and elasticity effects, several key risk factors are identified driver type (age >65), vehicle type (motorcycle), violation type (alcohol use), intersection type (three-leg and multiple-leg), collision type (rear ended), and lighting conditions (night and night without illumination). Corresponding countermeasures for these risk factors are proposed. (C) 2012 Elsevier Ltd. All rights reserved.
Responses of drivers and motorcyclists to congestion charge
(2017-01-01)
Chiou, Yu-Chiun
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Fu, Chiang
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
To effectively mitigate traffic congestion, many densely populated and well-developed cities worldwide decided to charge in-town traffic a congestion fee and the effectiveness of the congestion charge policy has been proven. However, a successful congestion charge scheme mainly depends on a clear understanding of urban travelers' responses. To do so, this study aims to model behaviors of travelers, including car drivers and motorcyclists, in response to in-town congestion charge in Taipei City and to propose feasible in-town congestion charge scheme accordingly. Four possible choices of travelers are defined as: 1) Pay for the charge, 2) Shift to off-peak hours/Cancel the trip, 3) Shift to public transportation, and 4) Shift to other private modes. Due to the possible correlation among alternatives and the potential heterogeneity among travelers, Multinomial Logit model (MNL), Nested Logit model (NL) and Mixed nested Logit model (MXNL) are estimated and compared based on a large-scale post-mail questionnaire survey. A total of 5,906 valid questionnaires were returned, including 3,450 car drivers and 2,536 motorcyclists. Among them, a total of 355 drivers and 314 motorcyclists who have morning peak-hour commuting experiences in Taipei City were selected. The estimation results show the existence of correlation among alternatives and heterogeneity for car drivers. Additionally, motorcyclists are much more sensitive to the charge than car drivers. The proportion of drivers and motorcyclists who are discouraged by various congestion charges are also predicted based on the estimated model. Suggestions for implementation of congestion charge are then proposed accordingly. (c) 2017 The Authors. Published by Elsevier B.V.
The adoption behaviours of freeway electronic toll collection: A latent class modelling approach
(2013-01-01)
Chiou, Yu-Chiun
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Jou, Rong-Chang
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Kao, Chu-Yun
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Fu, Chiang
;
運輸與物流管理系註：原交通所+運管所
This study develops an electronic toll collection (ETC) disaggregate choice model based on a large-scale questionnaire survey of car drivers in Taiwan. To acknowledge the difference in preferences among car drivers, the latent class logit model is used to classify respondents into different groups without subjective segmentation. The estimation results show that six groups of car drivers are optimally distinguished. The price of an e-pass and the discount for ETC tolls are identified as the two most important factors affecting ETC adoption. The study also finds that significant differences in adoption behaviours exist among groups. Effective marketing strategies for different groups are then proposed accordingly. (C) 2012 Elsevier Ltd. All rights reserved.
The adoption behaviours of freeway electronic toll collection: A latent class modelling approach
(2013-01-01)
Chiou, Yu-Chiun
;
Jou, Rong-Chang
;
Kao, Chu-Yun
;
Fu, Chiang
;
運輸與物流管理系註：原交通所+運管所
This study develops an electronic toll collection (ETC) disaggregate choice model based on a large-scale questionnaire survey of car drivers in Taiwan. To acknowledge the difference in preferences among car drivers, the latent class logit model is used to classify respondents into different groups without subjective segmentation. The estimation results show that six groups of car drivers are optimally distinguished. The price of an e-pass and the discount for ETC tolls are identified as the two most important factors affecting ETC adoption. The study also finds that significant differences in adoption behaviours exist among groups. Effective marketing strategies for different groups are then proposed accordingly. (C) 2012 Elsevier Ltd. All rights reserved.