Exploring injury severity of bicycle-motor vehicle crashes: A two-stage approach integrating latent class analysis and random parameter logit model

2021 ◽  
pp. 1-27
Author(s):  
Zhiyuan Sun ◽  
Yuxuan Xing ◽  
Jianyu Wang ◽  
Xin Gu ◽  
Huapu Lu ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Logit Model ◽  
Latent Class ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Random Parameter ◽  
Motor Vehicle Crashes ◽  
Two Stage ◽  
Random Parameter Logit ◽  
Random Parameter Logit Model

scholarly journals A Random Parameter Logit Model of Immediate Red-Light Running Behavior of Pedestrians and Cyclists at Major-Major Intersections

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Wencheng Wang ◽  
Zhenzhou Yuan ◽  
Yanting Liu ◽  
Xiaobao Yang ◽  
Yang Yang
Keyword(s):  
Logit Model ◽  
Motor Vehicle ◽  
Red Light ◽  
Random Parameter ◽  
Light Period ◽  
Education Administration ◽  
Red Light Running ◽  
Speed Change ◽  
Random Parameter Logit ◽  
Random Parameter Logit Model

It is a dangerous behaviour for pedestrians and nonmotorized vehicles to cross intersections without waiting when they arrive at intersections during the red-light period. This paper investigates three typical signalized major-major intersections in the center of Beijing, by collecting and analyzing 1368 samples of pedestrians and nonmotorized vehicles. A random parameter logit model (RPLM) is established, with immediate red-light running (IRLR) behaviour as the dependent variable. The results show that the number of people waiting upon arrival, number of people crossing upon arrival, traffic mode, motor vehicle phase upon arrival, and speed change upon arrival have significant effects on IRLR behaviour. Accordingly, we suggest enforcing education administration on cyclists to reduce cyclists’ IRLR behaviour. Thus, people’s red-light running (RLR) behaviour will further decrease with fewer cyclists’ IRLR behaviour.

scholarly journals The Effects of Life Course Events on Car Ownership and Sustainable Mobility Tools Adoption Decisions: Results of an Error Component Random Parameter Logit Model

2021 ◽  
Vol 13 (12) ◽  
pp. 6816
Author(s):  
Gaofeng Gu ◽  
Tao Feng ◽  
Chixing Zhong ◽  
Xiaoxi Cai ◽  
Jiang Li
Keyword(s):  
Life Course ◽  
Logit Model ◽  
Random Parameter ◽  
Error Component ◽  
Car Ownership ◽  
Sustainable Mobility ◽  
Car Sharing ◽  
Random Parameter Logit ◽  
Electric Bike ◽  
Random Parameter Logit Model

Life course events can change household travel demand dramatically. Recent studies of car ownership have examined the impacts of life course events on the purchasing, replacing, and disposing of cars. However, with the increasing diversification of mobility tools, changing the fleet size is not the only option to adapt to the change caused by life course events. People have various options with the development of sustainable mobility tools including electric car, electric bike, and car sharing. In order to determine the impacts of life course events on car ownership and the decision of mobility tool type, a stated choice experiment was conducted. The experiment also investigated how the attributes of mobility tools related to the acceptance of them. Based on existing literature, we identified the attributes of mobility tools and several life course events which are considered to be influential in car ownership decision and new types of mobility tools choice. The error component random parameter logit model was estimated. The heterogeneity across people on current car and specific mobility tools are considered. The results indicate people incline not to sell their current car when they choose an electric bike or shared car. Regarding the life course events, baby birth increases the probability to purchase an additional car, while it decreases the probability to purchase an electric bike or joining a car sharing scheme. Moreover, the estimation of error components implies that there is unobserved heterogeneity across respondents on the sustainable mobility tools choice and the decision on household’s current car.

scholarly journals U.S. Consumers' Willingness to Pay for Food Labeled ‘Genetically Modified’

2006 ◽  
Vol 35 (2) ◽  
pp. 299-310 ◽  
Author(s):  
Benjamin Onyango ◽  
Rodolfo M. Nayga ◽  
Ramu Govindasamy
Keyword(s):  
Logit Model ◽  
Genetically Modified ◽  
Random Parameter ◽  
Nationwide Survey ◽  
Choice Modeling ◽  
Modeling Framework ◽  
A Value ◽  
Label Information ◽  
Random Parameter Logit ◽  
Random Parameter Logit Model

This study analyzes U. S. consumers' choice of cornflakes under five different labeling statements. Using a nationwide survey and choice modeling framework, results indicate that consumers value labeling statements differently, depending on the information contained on the label. The random parameter logit model results indicated that, compared to cornflakes that have no label information, cornflakes labeled “contains no genetically modified com” have a value of 10 percent more, the label “USDA approved genetically modified com” has a value of 5 percent more, and the label “com genetically modified to reduce pesticide residues in your food” has a value of 5 percent more. The results also suggest that consumers negatively valued the label “contains genetically modified com,” paying 6.5 percent less, and the label “may contain genetically modified com,” paying 1 percent less than the product that has no label information.

scholarly journals The Bundling Strategy: The One-Click Effect on Loss Aversion

2019 ◽  
Vol 44 (4) ◽  
pp. 704-712 ◽  
Author(s):  
Juan Luis Nicolau ◽  
Ricardo Sellers
Keyword(s):  
Loss Aversion ◽  
Logit Model ◽  
Random Parameter ◽  
Expected Loss ◽  
Random Parameter Logit ◽  
The One ◽  
Different Levels ◽  
Price Responsiveness ◽  
Random Parameter Logit Model ◽  
Airline Ticket

This research aims to determine different levels of loss aversion in the context of price responsiveness and service bundling. Considering that nonlinearities in price responses may exist in a bundling strategy, this research tests the existence of different degrees of loss aversion, depending on whether an individual books one service independently of another (e.g., an airline ticket independently of accommodation) or as part of a bundle (e.g., a package that includes an airline ticket plus accommodation). We estimate a random parameter logit model. Empirical application shows that people who book a flight independently of accommodation are more loss averse than those who book a package that includes flight and accommodation. To explain this result, we propose the one-click effect so that people who find a price higher than expected (loss aversion) are more willing to accept it if the product is included in a bundle.

scholarly journals Can I Trust You? Estimation Models for e-Bikers Stop-Go Decision before Amber Light at Urban Intersection

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jing Cai ◽  
Jianyou Zhao ◽  
Yusheng Xiang ◽  
Jing Liu ◽  
Gang Chen ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Logit Model ◽  
Random Parameter ◽  
Autonomous Driving ◽  
Analytical Models ◽  
Contributing Factors ◽  
Acceleration Rate ◽  
Signal Change ◽  
Random Parameter Logit ◽  
Random Parameter Logit Model

Electric bike (e-bike) riders’ inappropriate go-decision, yellow-light running (YLR), could lead to accidents at intersection during the signal change interval. Given the high YLR rate and casualties in accidents, this paper aims to investigate the factors influencing the e-bikers’ go-decision of running against the amber signal. Based on 297 cases who made stop-go decisions in the signal change interval, two analytical models, namely, a base logit model and a random parameter logit model, were established to estimate the effects of contributing factors associated with e-bikers’ YLR behaviours. Besides the well-known factors, we recommend adding approaching speed, critical crossing distance, and the number of acceleration rate changes as predictor factors for e-bikers’ YLR behaviours. The results illustrate that the e-bikers’ operational characteristics (i.e., approaching speed, critical crossing distance, and the number of acceleration rate change) and individuals’ characteristics (i.e., gender and age) are significant predictors for their YLR behaviours. Moreover, taking effects of unobserved heterogeneities associated with e-bikers into consideration, the proposed random parameter logit model outperforms the base logit model to predict e-bikers’ YLR behaviours. Providing remarkable perspectives on understanding e-bikers’ YLR behaviours, the predicting probability of e-bikers’ YLR violation could improve traffic safety under mixed traffic and fully autonomous driving condition in the future.

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