scholarly journals Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fei Ye ◽  
Changshuai Wang ◽  
Wen Cheng ◽  
Haoxue Liu
Keyword(s):  
Traffic Safety ◽  
Injury Severity ◽  
Red Light ◽  
Random Parameter ◽  
Video Data ◽  
Parameter Estimates ◽  
Individual Parameter ◽  
Electric Bicycle ◽  
Random Parameter Logit ◽  
The Stability

Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.

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.

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 Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Kairan Zhang ◽  
Mohamed Hassan
Keyword(s):  
Traffic Safety ◽  
Injury Severity ◽  
Random Parameter ◽  
Probit Model ◽  
Weather Conditions ◽  
Work Zone ◽  
Work Zones ◽  
Road Project ◽  
Key Indicators ◽  
The Impact

Egypt’s National Road Project is a large infrastructure project aiming to upgrade the existing network of 2500 kilometers as well as constructing new roads of 4000 kilometers to meet today’s need. Increasing highway work zones eventually direct the challenges for traffic safety and mobility. Realizing the need for mitigating the impact of such a challenging scenario, this paper aims to investigate and identify the factors of work zone rear-end crash severity. In this regard, a random parameter ordered probit model was applied to analyze data on the Egyptian long-term highway work zone projects during the period of 2010 to 2017. The factors of speeding and foggy weather conditions are found to be the key indicators for modeling the random parameters. Besides, during the weekend and at nighttime, there is a higher risk of rear-end crash in work zones, while heavy and passenger vehicles are at greater risk in this regard. It is anticipated that the findings of this study would facilitate transport agencies in developing effective measures to ensure safe mobility across work zones.

scholarly journals Analysis of Traffic Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China

2021 ◽  
Vol 18 (2) ◽  
pp. 553
Author(s):  
Guangnan Zhang ◽  
Ying Tan ◽  
Qiaoting Zhong ◽  
Ruwei Hu
Keyword(s):  
Injury Severity ◽  
Negative Impact ◽  
Red Light ◽  
Guangdong Province ◽  
Random Parameter ◽  
Early Morning ◽  
Crash Data ◽  
Primary Means ◽  
Dark Conditions ◽  
Motorcycle Rider

Motorcycles are among the primary means of transport in China, and the phenomenon of motorcyclists running red lights is becoming increasingly prevalent. Based on the traffic crash data for 2006–2010 in Guangdong Province, China, fixed- and random-parameter logit models are used to study the characteristics of motorcyclists, vehicles, roads, and environments involved in red light violations and injury severity resulting from motorcyclists’ running red lights in China. Certain factors that affect the probability of motorcyclists running red lights are identified. For instance, while the likelihood of violating red light signals during dark conditions is lower than during light conditions for both car drivers and pedestrians, motorcyclists have significantly increased probability of a red light violation during dark conditions. For the resulting severe casualties in red-light-running crashes, poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe injury. Regarding the relationship between red light violations and the severity of injuries in crashes caused by motorcyclists running red lights, this study indicated that driving direction and time period have inconsistent effects on the probability of red light violations and the severity of injuries. On the one hand, the likelihood of red light violations when a motorcycle rider is turning left/right is higher than when going straight, but this turning factor has a nonsignificant impact on the severity of injuries; on the other hand, reversing, making a U-turn and changing lanes have nonsignificant effects on the probability of motorcyclists’ red light violations in contrast to going straight, but have a very significant impact on the severity of injuries. Moreover, the likelihood of red light violations during the early morning is higher than off-peak hours, but this time factor has a negative impact on the severity of injuries. Measures including road safety educational programs for targeted groups and focused enforcement of traffic policy and regulations are suggested to reduce the number of crashes and the severity of injuries resulting from motorcyclists running red lights.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

2021 ◽  
pp. 036119812110098
Author(s):  
Miao Yu ◽  
Jinxing Shen ◽  
Changxi Ma
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Research Effort ◽  
Random Parameter ◽  
Policy Implications ◽  
Contributing Factors ◽  
Severe Injuries ◽  
Data Set ◽  
Severity Levels

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

scholarly journals Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

2021 ◽  
Vol 13 (11) ◽  
pp. 6214
Author(s):  
Bumjoon Bae ◽  
Changju Lee ◽  
Tae-Young Pak ◽  
Sunghoon Lee
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Random Effect ◽  
Temporal Correlation ◽  
Negative Binomial Regression ◽  
Spatiotemporal Analysis ◽  
Parameter Estimates ◽  
Crash Frequency ◽  
Crash Frequency Modeling ◽  
Using Data

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

scholarly journals Taste Beats Reputation in New Food Products Choice: The Case of Ready-to-Eat Pomegranate among Young Consumers in Veneto Region (Italy)

Horticulturae ◽  
2021 ◽  
Vol 7 (7) ◽  
pp. 179
Author(s):  
Alice Stiletto ◽  
Erika Rozzanigo ◽  
Elisa Giampietri ◽  
Samuele Trestini
Keyword(s):  
Discrete Choice ◽  
Choice Experiment ◽  
Consumer Preferences ◽  
Random Parameter ◽  
Food Products ◽  
Sensory Features ◽  
Young Consumers ◽  
Random Parameter Logit ◽  
Italian Origin ◽  
Random Parameter Logit Model

This study investigates the preferences for ready-to-eat pomegranate arils in Italy through a discrete choice experiment (DCE) on 264 young consumers in Italy. The aim is to estimate consumers’ willingness to pay (WTP) for the reputational attributes of the product (e.g., the product origin and sales channel) and to discriminate the elicited preferences between tasting and non-tasting situations. To this purpose, a random parameter logit model was employed to assess the heterogeneity in consumer preferences. The results suggest that non-tasters attach a relevant value to the reputational attributes (e.g., +75% WTP for Italian origin). Moreover, considering the sensory features of the products, we found that consumers in this group discriminate against the proposed samples only through their visual characteristics: they prefer the sample with the largest size and red colored arils. In addition, we found that the tasting experience reduced the value attached to the reputational attributes (e.g., −50% WTP for local origin) for consumers, compared to non-tasting situation, thus shifting their preference to the samples that they appreciated the most (high liking). Specifically, we found that consumers in the tasting group preferred the product sample with the highest level of sweetness and the lowest level of sourness and astringency, showing a higher preference for sweetness. The findings contribute to the literature on consumers’ behavior on new food products (NFPs), showing that reputational attributes lose value after the tasting experience. In contrast, the sensory features of the NFPs can help tasters to reduce the information asymmetry, which traditionally represents a hurdle in purchases for new consumers. However, this depends on the individuals’ subjective preferences, as demonstrated by the significant effect of liking levels in discriminating consumers’ choices. To conclude, although these results cannot be extended to the general population, they may give some interesting insights about future trends of NFP demand.

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