Random Parameters Ordered Probit Model Research Articles

Assessment of transit bus electricity consumption using a random parameters approach

The shift towards sustainable transportation emphasizes the crucial role of electric transit buses in promoting cleaner and more efficient public transit systems. This study conducts an explanatory analysis of electric bus electricity consumption, with a focus on the impacts of detailed driver behavior and vehicle operation. A random parameters ordered probit model is applied to an electric bus dataset collected from Guangzhou, China. Findings indicate that some factors exhibit consistent impacts on electricity consumption across bus fleets, such as the acceleration rate at bus stops and the number of stops induced by traffic conditions. However, variability exists within the bus fleet regarding the effects of other factors, such as the average speed and handbrake usage. These results underscore the need for targeted strategies, such as maintenance, route optimization, driver training, and policy adjustments in bus operations, to decrease electricity consumption and improve the efficiency of public transit systems.

Safety Assessment of Suburban-Type Arterial Roadways: New Findings Using Heterogeneity Models

This study investigates the safety performance of suburban-type roads (STRs), a category of medium-speed arterial roads characterized by high vehicular traffic and limited pedestrian and cyclist mobility. Such roadways present unique challenges in road safety, often overlooked in traditional road design focused predominantly on driver needs. By employing a correlated random parameter ordered probit model to analyze police-reported crash severity data from STRs, this research uncovers critical insights into the factors influencing crash injury severities on these roads. Our findings highlight the nuanced impact of various dimensions, such as road design, traffic volume, and environmental conditions, on STR safety; the results offer a detailed understanding of crash contexts on STRs and point toward targeted interventions for enhancing road safety. These interventions include the potential redesign of roadways to accommodate diverse user needs and the implementation of speed regulation measures tailored to specific road characteristics. This research contributes significantly to the existing body of knowledge on suburban road safety, providing evidence-based recommendations for improving the overall safety of these increasingly prevalent road types. The implications of these findings are far-reaching, offering a foundation for future studies to explore diverse methodologies in road safety analysis and to extend this research to other geographic contexts.

An empirical analysis of the factors influencing Scottish residents’ compliance with COVID-19 travel restrictions

This study uses survey data to investigate the factors affecting compliance with the Scottish Government’s COVID-19 travel restrictions. The survey contained self-reported data (n = 819) regarding various aspects of Scottish residents’ COVID-19 travel behaviour. For a question gauging compliance with COVID-19 travel restrictions, ∼57% of respondents complied fully, ∼35% complied mostly and ∼7% were slightly compliant or non-compliant. A random parameters ordered probit model, with allowances for heterogeneity in the means of random parameters, was estimated to unveil the factors affecting levels of compliance. The model estimation results showed that various sociodemographic characteristics of respondents, including age, gender and household income significantly affected compliance. Other significant independent variables were dominated by perceptual and behavioural characteristics of respondents, such as public trust in the Scottish Government, COVID-19 risk perceptions, COVID-19 information channels and travel mode choices at different stages of the pandemic. Two variables gauging car ownership (those with no car) and COVID-19 information channels (received through online sources) produced statistically significant random parameters, which suggests heterogeneous levels of compliance among these groups. In addition, one instance of heterogeneity in the mean of the online sources random parameter was discovered, such that levels of compliance among this group were dependent on a variable gauging highest education level. Policy implications are discussed in terms of tailoring the Scottish Government’s public health messaging to certain demographics, while the most effective channels for delivering public health information, in the case of COVID-19 and future pandemics, are also explored.

Estimating injury severity for motorized and non-motorized vehicle-involved crashes: Insights from random-parameter ordered probit model with heterogeneity in means and variances

The use of advanced models to investigate the determinants of injury severity outcomes for motorized and non-motorized-involved crashes are sparse. Therefore, random-parameter ordered probit models with heterogeneity in means and variances were developed to estimate factors affecting injury severity for motorized and non-motorized-involved crashes. Data covering a five-year period comprising 5976 and 634 cases for motorized and non-motorized-involved crashes respectively, was retrieved from the database of the National Road Safety Authority, State Insurance Company and Driver and Vehicle Licensing Authority in Ghana and used for model estimation. The results show that factors have varying significant effects on injury severity outcomes for motorized and non-motorized models. Marginal effects indicate that old age occupants, head-on-collision, exceeding a posted speed limit of 100 km/h and crash during weekends contributed greatly to the likelihood of severe injury outcomes in motorized model. Additionally, male non-motorists, non-use of helmet, rear-end collision, right-angle collision and crash on urban roads and during weekends, contributed significantly to the severe injury outcomes of non-motorized models. The direction of effect of the factors on severe injury was observed to have varying degrees of estimated coefficients. The difference in estimated coefficients shows that crashes involving non-motorized vehicles were more likely to result in severe injury compared to motorized vehicles. The motorized model had heterogeneity in means of five (5) random parameters observed, while the non-motorized model had heterogeneity in means of four (4) random parameters observed with two variables affecting the variance of three random parameters. Based on the results, various countermeasures were proposed to enhance road traffic safety.

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

As intercity buses are a mode that moves large-scale occupancy between regions, it accounts for the mode share-means for mid- to long-distance movement in South Korea. However, the study of intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and Random Parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of this study, the influencing factors that reflect heterogeneity with random parameters were driver’s condition: drowsiness, vehicle size: medium, crash type: vehicle–pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation.

Understanding driverless car adoption: Random parameters ordered probit model for Brisbane, Melbourne and Sydney

With advanced policy and technology support, driverless cars are expected to be a reality on Australian roads in next 5–10 years. The pace at which driverless cars emerge as a mainstream travel mode greatly depends on public adoption and acceptance; this is likely to be based on multiple factors. The impact these factors may have on various adoption time horizons, however, is rarely discussed in contemporary research. Therefore, this study focuses on illustrating the impact of factors associated with the adoption of driverless cars in Australia. A survey was conducted on 2608 participants living in metropolitan Brisbane, Melbourne, and Sydney. A total of 1108 participants expressed their preference towards driverless car adoption over three different time intervals, ranging from now to 10 years following deployment. Four random parameters ordered probit models were developed using the survey data—three separate models for Brisbane, Melbourne, and Sydney, and one for the combined dataset. Significance differences among models were evaluated using the likelihood ratio test. The results showed a significant difference among cities in terms of contributing factors towards driverless car adoption timeline. Additionally, different groups, considering the following factors—gender, age, household members, level of education, employment status, income, driving licence duration, travel distance to school, travel distance for social outings, car ownership, familiarity with driving aids and driverless car riding—exhibited a mixed interest regarding the adoption timeline for driverless cars. Other groups, considering the following factors exhibited a unidirectional interest—age, level of education, workplace type, income, employment status, travel distance to work, travel distance to school, travel distance for social outings, number of employed in a household, occupation, driver's licence age, number of smart tech use, driverless car trip purpose, feature preference in a driverless car, and use of driving aids. The study offers insights for future planning and policy direction related to driverless car adoption.

Exploring Factors Contributing to Crash Injury Severity at Al-Diwaniyh City Streets: Random Parameter Ordered Probit Model Technique

Traffic crashes are among the problems that affect societies because the losses caused by these crashes directly impact the citizen due to the psychological suffering it leaves. Therefore, care must be taken, and strenuous efforts must be made in order to reduce the percentage of crash victims and losses. Depending on the characteristics of the local transportation environment, the study outcomes of traffic crash data may differ. Therefore, in order to comprehend the factors that contribute to and are involved in traffic crashes in Iraq, it is important to concentrate on the city's unique characteristics. So, the purpose of this study is to pinpoint the contributing factors that substantially impact how serious traffic crashes are in Al-diwaniya City for the years (2017-2022). RStudio was used to examine complex interactions and assess the fundamental elements that influence the severity of the crash by applying the ordered Probit model, the random parameter ordered Probit model and the marginal effect of the significant factors. The results showed a random parameter ordered Probit model well performed over the ordered Probit model. Moreover, many statistically significant variables were found to impact how severe the injury is, like road features, vehicle characteristics, crash cause, crash reason, time distribution, and crash type. Addressing these contributing factors through effective policies and programs promoting safe driving practices can create safer roads for everyone.

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

Purpose This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs. Design/methodology/approach The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously. Findings The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability. Originality/value The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

Quantification of cut-in risk and analysis of its influencing factors: a study using random parameters ordered probit model

In the cut-in scenario, drivers are forced to experience a smaller headway distance, which may easily lead to rear-end crashes and reduced road traffic efficiency. Quantitatively evaluating cut-in risks and considering the heterogeneity of driving maneuvers to explore the influencing factors of cut-in risks using microscopic driving behavior data are still limited. In this study, a cut-in risk index (CIRI) was proposed to evaluate the cut-in risk based on fault tree analysis (FTA). To consider the heterogeneity of driving maneuvers, a random parameter ordered probit (RPOP) model was employed to recognize the key determinants of risky cut-in maneuvers. The results obtained in this study show that during the cut-in process, the cut-in vehicle has the highest crash risk with the preceding vehicle in the current lane compared to other surrounding vehicles. The proposed surrogate measure can objectively quantify cut-in risk. The present study suggests that the driver not only needs to pay attention to the following vehicle in the target lane, but also pay more attention to the preceding vehicle in the current lane during cut-in. Quantifying cut-in risks and exploring its influencing factors are essential for road traffic control, thereby improving driving safety and traffic efficiency.

Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: A correlated random parameters ordered probit approach with heterogeneity in means

In Thailand in 2016, more than 70% of all deaths due to road accidents were motorcyclist deaths. This study uses a correlated random parameters ordered probit model with heterogeneity in means (CRPOPHM) to obtain insight into differences in the significant factors determining the severity of motorcyclist injury between motorcycle crashes in urban and rural roadways, using data on motorcycle crashes in Thailand from 2016 to 2019. Using a rating system for injury severity level from minor injury to severe injury and to fatal injury, a wide range of potential risk factors are considered, including rider characteristics and actions, roadway characteristics, environmental and temporal characteristics, and crash characteristics. The findings indicate that, although some factors are significant in both urban and rural models, factors such as male rider, illegally overtaking, drowsiness, four-lane or wider highway, flush or depressed median, road on slope, weekend, nighttime with light, crash with van or minibus, and rear-ending or sideswiping crash, are significant only in the rural model, whereas the factors barrier median, occurring between 18:00 and 23:59, and striking a passenger car are statistically significant in only the urban model. These findings further suggests that difference in effect of unobserved characteristics could be seen in different crash locations, and splitting the model estimation between both location types could be done to develop effective guidance for policies to mitigate the severity of motorcyclist injuries. In addition, practical policy-related recommendations drawn from the results of the analysis are provided. With respect to methodology, the proposed CRPOPHM method outperforms lower-ordered models in terms of statistical fit and captures unobserved heterogeneity to a greater extent.

Effects of emergency escape ramps on crash injury severity reduction on mountain freeways: A case study in China

Emergency escape ramps (EERs) is an infrastructure of mountain freeways to stop runaway vehicles. As the last defence of vehicle occupants’ safety, the performance of EERs in reducing crash injury severity is a concern for stakeholders of road safety. Based on crash records collected on a mountain freeway equipped with five EERs, this study compared the injury severity of crashes on EERs and other road sections, and identified the factors that significantly influence injury severity in the two conditions. Estimations of the parameter coefficients and marginal effects of a random parameters ordered probit model were used to infer EER performance under the impacts of various factors. The results confirm the effectiveness of EERs on the reduction of crash injury severity. The protection function of EERs is weakened by nighttime, the rollover status of crashed vehicle, multi-vehicle collisions, improper design or installation of the roadside infrastructure, drivers’ unfamiliarity with local driving conditions, and crashed vehicle weight. The paper compares the findings with those of previous studies and proposes some recommendations to improve EER performance for occupant and property protection on mountain freeways.

Addressing Driving Actions of At-Fault Older Drivers: Bayesian Bivariate Ordered Probit Analysis

This study aimed to examine the driving actions of at-fault older drivers, and investigate the interrelations between the unobservable factors. To reach the goal, a Bayesian bivariate ordered probit model was proposed, which addressed the driving actions of different drivers simultaneously, and accommodated the interrelations between the unobservables by covariance. The data with 27 arterials from 2014 to 2017 were collected from ArcGIS open data site maintained by Nevada Department of Transportation (NDOT). Compared to individual Bayesian random parameter ordered probit model, the proposed model outperformed according to goodness-of-fit. Results revealed that injury severity and total vehicles were potentially significant factors for actions of at-fault older drivers, while total vehicle and vehicle condition were significant for actions of not-at-fault drivers. The findings can provide potential insights for practitioners to apply the new technology and remind the driving actions of older drivers.

Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model

Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver’s attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads.

The temporal stability of factors affecting driver injury severity in run-off-road crashes: A random parameters ordered probit model with heterogeneity in the means approach

This study presents an effort to investigate the determinants of driver injury severity in run-off-road (ROR) crashes. In order to account for unobserved heterogeneity, a random parameter ordered probit with heterogeneity in the means approach is applied. The police-reported ROR crash data that occurred from 2014 to 2017 in the state of North Carolina is used. Four injury-severity levels are defined: property damage only (PDO), possible injury (PI), non-incapacitating (N), and F/I (by merging fatal and incapacitating). The driver, crash, roadway, environmental characteristics that potentially affect the driver injury severity are explored. Besides, the temporal stability of modeling results among the four-time periods is investigated using a series of likelihood ratio tests. Significant temporal instability is found, indicating underestimating the temporal instability might result in unreliable conclusions. Estimation results demonstrate that the indicators, including male driver, alcohol, and curved roadways, increase the possibility of fatal and incapacitating injuries in the ROR crash in most of the year periods.

Individuals’ willingness to rent their personal vehicle to others: An exploratory assessment of peer-to-peer carsharing

The renting of personal vehicles for monetary compensation (peer-to-peer carsharing or abbreviated as P2P carsharing) has become increasingly popular in the U.S. In applications, the fleet of peer-to-peer carsharing vehicles typically consists of personally owned vehicles identified and coordinated by a third-party company. However, little is known about the attitudes, perceptions, and decision process through which individuals decide to offer their car for rent in such peer-to-peer carsharing. To explore individuals' attitudes and perceptions regarding the act of supplying a personal vehicle to peer-to-peer vehicle fleet, a stated preference survey was designed and disseminated between February and April of 2018 where survey respondents were asked how likely they would be to rent their car (extremely unlikely, unlikely, unsure, likely, extremely likely). The survey questionnaire collected detailed socio-demographic information, as well as data on travel behavior and travel patterns. These data were then used to estimate a random parameters ordered probit model of their likelihood of renting their car. Some of the variables found statistically significant determinants of the willingness to rent a personal vehicle were gender, age, income, household composition, vehicle ownership, living location with respect to a grocery store, and participation in other shared mobility modes. The above findings and especially the gender and income related variables were found to complement prior literature and offered additional layer of understanding of the factors determining the supply side of peer-to-peer carsharing. The findings of this study offer some initial insights into the factors that may determine the success or failure of this novel transportation alternative.

Do High Visibility Enforcement programs affect aggressive driving behavior? An empirical analysis using Naturalistic Driving Study data

This paper investigates the effect of High Visibility Enforcement (HVE) programs on different types of aggressive driving behavior, namely, speeding, tailgating, unsafe lane changes and ‘other’ aggressive driving behavior types (occurrence of not-yielding right-of-way and red light or stop signs violations). For this purpose, the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data are used, which include forward-facing videos and time series information with regard to trips conducted at or near the locations of HVE implementation. To capture the intensity and duration of speeding and tailgating, scaled metrics are developed. These metrics can capture varying levels of aggressive driving behavior enabling, thus, a direct comparison of the various behavioral aspects over time and among different drivers. To identify the effect of HVE and other trip, driver, vehicle or environmental factors on speeding and tailgating, while accounting for possible interrelationship among the behavior-specific scaled metrics, Seeming Unrelated Regression Equation (SURE) models were developed. To analyze the likelihood of occurrence of unsafe lane changes and ‘other’ aggressive driving behavior types, a grouped random parameters ordered probit model with heterogeneity in means and a correlated grouped random parameters binary logit model were estimated, respectively. The results showed that drivers’ awareness of HVE implementation has the potential to decrease aggressive driving behavior patterns, especially unsafe lane changes and ‘other’ aggressive driving behaviors.

Exploring Risk Factors Contributing to the Severity of Hazardous Material Transportation Accidents in China.

With the increasing demand of hazardous material (Hazmat), traffic accidents occurred frequently during Hazmat transportation, which had caused widespread concern in communities. Therefore, a good understanding of Hazmat transportation accident characteristics and contributing factors is of practical importance. In this study, 1721 Hazmat accidents that have occurred during road transportation for the period 2014–2017 in China were examined, and a random-parameters ordered probit model was established to explore the influence of contributing factors on the severity of accidents by accounting for unobserved heterogeneity in the data. Both the injuries and the number of people evacuated were considered as the indicator of accident severity and investigated, respectively. Results show that higher injury severity is likely to be associated with type of Hazmat (compressed gas, explosive, and poison), misoperation, driver fatigue, speeding, tunnel, slope, county road, dry road surface, winter, dark, more than two vehicles, rear end crash, and explosion. As for the correlation between risk factors and the severity of evacuation, type of Hazmat (compressed gas, explosive, and poison), quantity of Hazmat (10–39 t), misoperation, county road, dry road surface, weekdays, dusk, explosion significantly contribute to increasing the severity of evacuation of Hazmat accidents.

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

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.

Investigation of life satisfaction, travel, built environment and attitudes

This study explores the relationships subjective well-being has with travel behaviour, the built environment and attitudes toward transportation. With data from a survey of residents of Nova Scotia, Canada, it develops a random-parameters ordered probit model with life satisfaction as the dependent variable. The approach extends current research by including built environment and attitudinal variables in the model along with daily travel behaviour, and by observing heterogeneous effects of variables at the individual level. The study results reinforce the evidence that daily out-of-home travel has a positive impact on life satisfaction by facilitating access to fulfilling daily activities such as work and social events, and that being physically active positively affects life satisfaction. The results suggest that having greater access to varied modes of transportation generally improves life satisfaction. Results on the built environment and attitudes indicate that living closer to a park or sports field is positively related to life satisfaction. Being community-minded is also generally related to higher life satisfaction. The results reveal that heterogeneity exists in the sample; for instance, attitudes toward being community-minded showed a statistically significant standard deviation in the model. Insights from this study will guide effective transportation policymaking for healthy communities.

An ordered-probit analysis of enforcement of road speed limits

The effectiveness of speed limit enforcement (SLE) is a critical factor in reducing the global burden of fatalities and injuries due to road crashes. A random-parameter ordered-probit model was developed to explore the relationship between the effectiveness of SLE and different explanatory variables using data from a 2013 World Health Organization global road safety report. An attempt was made to understand the complex interactions between SLE, socio-economic factors, other critical key risk factors and their enforcement levels. Socio-economic status, public policies and road safety legislation and its enforcement were found to be significantly associated with different level(s) of SLE (very low, low, medium and high enforcement levels). Out of seven significant variables, two variables – effectiveness of seatbelt enforcement and road safety audits of new roads – were found to be normally distributed random parameters, suggesting that their effects vary in different countries. The model findings provide preliminary insights into how socio-economic characteristics, road safety policies and traffic laws can be associated with SLE.