Likelihood Of Crashes Research Articles

Interrupted and uninterrupted lane changes: a microscopic outlook of lane-changing dynamics

Field observations suggest that the Lane-Changing (LC) processes are often interrupted by the surrounding vehicles. Understanding these intermediate interruptions and evasive behavior during an LC is necessary for developing realistic simulation tools, collision-warning systems, and human-like LC in self-driving vehicles. However, identifying and classifying LC into interrupted (ILC) and uninterrupted (ULC) is not straightforward. This study proposes a methodology for the automatic identification and classification of LC events. Further, we highlighted the characteristic differences between ILC and ULC by comparing their duration, crash likelihood, and driver discomfort. The likelihood, duration of exposure, and severity of LC crashes were captured using the surrogate safety measures such as Anticipated Collision Time (ACT), Time-Exposed ACT (TE-ACT), and Time-Integrated ACT (TI-ACT). The driver's discomfort was measured using acceleration noise and the number of sign inversions in the acceleration profile. The Kruskal–Wallis–ANOVA test on the above attributes and the sensitivity analysis of the duration models confirm that ILC and ULC are characteristically different.

The Impact of Stock Price Crash Risk on the Cost of Capital: Empirical Study from China

This study analysed the effect of Sock Price Crash Risk (SPCR) on the cost of capital in Chinese listed firms in the Shenzhen stock exchange and the shanghai Stock Exchange. A sample of 290 firms based on the highest value of assets of each firm was used. The cost of capital consists of two factors; the cost of equity (COE) and the cost of debt (COD). The SPCR is measured by using two statistics, one is NCSKEW means the negative coefficient of skewness of the firm-specific weekly returns and the second is DUVOL that means Down to-Up Volatility used to measure the crash likelihood weekly return of firm-specific and used the Modified PEG ratio model of Eston approach to measuring the cost of equity. We used panel data to run the regression model analyses. SPCR was found to have a significantly positive relationship with the cost of equity and cost of debt. Also, the sample was divided into the State-Owned enterprise (SOEs) and Non-State-Owned enterprises (NSOEs) for comparison. The results show that the impact of SPCR on the COE and COD is stronger in SOEs than NSOEs. The regulators need to improve and strengthen the development of laws and regulations related to company information disclosure, to reduce the cost of capital of listed companies and improve the efficiency of financing the Chinese capital market. Companies need to work together to strengthen internal controls, create a good disclosure environment, and prevent the SPCR.

A random parameters with heterogeneity in means and Lindley approach to analyze crash data with excessive zeros: A case study of head-on heavy vehicle crashes in Queensland

This study performed statistical analyses to identify likely crash contributing factors for Head-on Fatal and Serious Injury (FSI) collisions involving heavy vehicles (HVs) on the Queensland state road network. Head-on HV collisions are associated with the largest number of fatalities compared to other crash types in Queensland. However, there is limited relevant literature regarding this type of crashes. Recent studies on road safety research have focused on variants of random parameters models to capture unobserved heterogeneity that may influence the occurrence of crashes. Among such models, random parameters with heterogeneity in means has recently provided better results and has become popular. However, this study illustrates a potential limitation regarding the use of these models without explicitly factoring for excessive zero crash observations. To address this potential limitation, a random parameters with heterogeneity in means and a Lindley distribution is introduced in this study to factor for the unobserved heterogeneity using additional variables as well as site-specific variation from excessive zero crash observations. Results showed that a Poisson model with random parameters and heterogeneity in means using a Lindley distribution outperformed multiple alternative state-of-the-art specifications in terms of fit as well as overall prediction ability. The analyses using the proposed modelling approach revealed factors likely to affect the likelihood of Head-on FSI crashes involving HVs in Queensland including volume, segment length, period of analysis, terrain type being rolling, curve (moderate/sharp/very sharp) longer than 50% of the corresponding segment length, rural single carriageway with high (>=100 kph) and medium (>=50 and <100 kph) speed limits, and urban single carriageway. Unobserved heterogeneity regarding the parameter for road curvature was explained using rolling terrain type as an explanatory variable. This study has explained variation in the means of random parameters for a road attribute using the effect of a geometric variable, in which several stakeholders are primarily interested.

Stock liquidity, stock price crash risk, and foreign ownership

This study examines the impact of stock market liquidity on a stock price crash, using firm data from Borsa Istanbul for the period 2009–2019. The results show that higher stock liquidity increases the likelihood of stock price crashes, but this positive link is not driven by blockholder ownership. The study also investigates the effects of foreign investors, divided between institutional and individual investors, on the relation between liquidity and crash risk. The findings indicate that higher stock liquidity increases the crash risk as the share of foreign institutional investor rises, but this impact is not present with foreign individual participation. Furthermore, the study explores the effects of high-frequency trading and the removal of short-sale restrictions. The results of these analyses suggest that high-frequency trading increases liquidity, which in turn raises the likelihood of a stock price crash, and the removal of short-sale restrictions generally increases the stock price crash risk.

Macroscopic analysis on the frequency and severity of pedestrian crashes on National Roads in Metro Manila, Philippines

Pedestrians are the most vulnerable road users; thus, understanding the primary factors that lead to pedestrian crashes is a chief concern in road safety. However, owing to the limitations of crash data in developing countries, only a few studies have evaluated the comprehensive characteristics of pedestrian crashes, specifically on different road types. This study attempted to develop pedestrian crash frequency and severity models on national roads by using the road characteristics and built environment parameters, based on the road crash data (2016–2018) that involved pedestrians in Metro Manila, Philippines. Remarkable findings included primary roads, presence of footbridges, road sections with bad surface conditions, and increased fractions of commercial, residential, and industrial roads, which exhibited a greater likelihood of pedestrian crashes. Crashes involving elderly pedestrians, heavier vehicles, late-night hours, fair surface conditions, and open spaces were associated with increased likelihoods of fatal outcomes. Essentially, this study provides a macroscopic perspective in understanding the factors associated with the severity and frequency of pedestrian crashes, and it would aid the authorities in identifying proper countermeasures.

An alternative closed-form crash severity model with the non-identical, heavy-tailed, and asymmetric properties

Crash severity model is a classical topic in road safety research. The multinomial logit (MNL) model, as a basic discrete outcome method, is widely applied to measure the association between crash severity and possible risk factors. However, the MNL model has several assumptions and properties that are possibly not consistent with the actual crash mechanism, and therefore with the association measure for crash severity. One significant attribute is the variation in drivers’ safety perception. Risk-taking drivers tends to drive at a higher speed, which increases the likelihood of severe crashes. However, the variations in speed and other driving performance lead to the error in the utility function more profound. This violates the assumption of identical error distributions between different crash severity outcomes. In this paper, we propose a multinomial multiplicative (MNM) model, as an alternative for crash severity model. There are two possible formulations for the proposed MNM model: (1) Weibull and (2) Fréchet, according to the distributions of random propensities and subject to the signs of the systematic parts of the regression equation. The two heavy-tailed distributions can capture the effect of unobserved contributory factors on crash injury severity. Additionally, the MNM model can incorporate the effects of the non-identical, heavy-tailed, and asymmetric properties of the distribution, whereas the conventional MNL model cannot. Several operational considerations are also attempted in this study, including the specifications of the systematic parts and the interpretations of the parameters. The MNM model is further extended to the mixed MNM (MMNM) model by considering unobserved heterogeneities using random coefficients, while the mixed MNL (MMNL) model is used as the benchmark model. The proposed MMNM model is calibrated using the crash dataset obtained from the Guangdong Province, China. Results indicated that the proposed MMNM model outperformed the MMNL model in this case. Also, the results of parameter estimates are indicative to impact factors on crash severity as well as the design and implementation of policies. This justified the use of MMNM model as an alternative for crash severity model in practice. This is the first application of MMNM model in the traffic safety literature, it is worth exploring the application of other advanced multiplicative models for safety analysis in the future.

The safety performance of connected vehicles on slippery horizontal curves through enhancing truck drivers’ situational awareness: A driving simulator experiment

Although drivers can adequately adjust their operating speed according to the road curvature, they show a lack of recognition regarding the pavement friction conditions. In this regard, inappropriate speed selection on Horizontal Curves (HCs) with reduced surface friction can lead to a remarkable rate of run-off-road, sideswipe, head-on, and rollover crashes, especially on rural highways. Aligned with the Connected Vehicle (CV) Pilot Program on Interstate-80 in Wyoming, this study scrutinizes how CV advisory/warning messages can enhance traffic safety on slippery HCs. To this aim, a roadway consists of two HCs with regular and slippery pavement conditions was designed in a high-fidelity driving simulator experiment. A total of 24 professional truck drivers were recruited to drive the simulated roadway under CV and non-CV environments. In the CV scenario, drivers were informed about the pavement conditions and the advisory speeds before entering HCs. In contrast, no messages were given to non-CV drivers. Truck drivers' behaviors in both scenarios were quantified using four Kinematic-based Surrogate Measures of Safety (K-SMoS), including deviation from the pathway, instantaneous acceleration, lateral speed, and steering angle. CVs’ trajectories were statistically compared to non-CVs in terms of the central tendency and dispersion using the Wilcoxon Signed-Rank Test (WSRT) and Median Absolute Deviation (MAD), respectively. The results of WSRT depicted, under the effect of CV advisory/warning messages and throughout the slippery HC, the central tendency of four K-SMoS could be shifted toward zero by 23% up to 99%. This shifting is associated with a significant safety enhancement that potentially can reduce the likelihood of curve-related crashes on slippery HCs. It was revealed that the variation in drivers’ behavior on the slippery HC could be minimized in the CV environment, where 54% up to 95% reduction in the dispersions of four K-SMoS were observed, leading to more certainty in drivers’ behavior.

Evaluating the impact of Road Rangers in preventing secondary crashes

Many transportation agencies utilize freeway service patrols (FSPs) to quickly identify and respond to incidents. The objectives of FSP are to minimize the incident duration and increase safety at the incident scene. The current research explored the safety benefits of Florida’s FSP program known as Road Rangers – harnessed from lowering the likelihood of secondary crashes (SCs) – compared to other responding agencies. The analysis was done on 6088 incidents that occurred on freeways in Jacksonville, Florida, from 2015 through 2017. Since SCs were not explicitly identified in the SunGuide® incident database, the study adopted a data-driven technique that used BlueToad® speed data to identify them. Once SCs were identified, a model was developed to identify factors influencing their occurrence. Factors such as an increase in equivalent hourly traffic volume, incident impact duration, and the percent of lanes closed significantly increased the likelihood of a SC. Besides, moderate/severe incidents, crash events, weekdays, peak hours, shoulder blockage, and incidents involving towing showed a high likelihood of resulting in a SC. Of practical importance, the model results revealed that a minute increase in incident impact duration increased the SC probability by 1.2 percent, with other factors held constant. Based on a 16-minutes decrease in incident impact duration, the Road Rangers program could lessen the probability of SCs by 21 percent, compared to other agencies. These findings could be beneficial to incident managers, responders, and researchers in evaluating the program’s performance.

Sleep disorders and risk of traffic crashes: A naturalistic driving study analysis

Sleep disorders and various acute and chronic medical conditions affect the quality and quantity of an individual’s sleep causing excessive day time sleepiness. This study examined the relationship between clinical sleep disorders (i.e., shift work sleep disorder (SWSD), sleep apnea and insomnia) and crashes using naturalistic driving study data. Using data from 1892 events that occurred in six different US states, random effect binary logistic regression models were developed to estimate crash risk associated with SWSD, sleep apnea and insomnia. The study found that sleep disorders elevate crash risk in drivers. Drivers with SWSD exhibited the highest crash risk (OR = 2.96). Older drivers with SWSD were found to be more vulnerable and prone to crashes than other drivers. The analysis showed that drivers with sleep apnea and insomnia were 29% and 33% more likely, respectively, to be involved in a crash or a near-crash. For drivers with insomnia, factors such as age and sleep quality can elevate the associated crash risk. Decrease in sleep quality increases the likelihood of crash or near crash. The study also found that individuals with a sleep disorder are 29% more likely to be inattentive while driving as compared to drivers without a sleep disorder. The OR values found in this study were lower than those reported in studies using self-reported data and driver simulator experiments. In addition to medical diagnosis and treatment, transportation agencies could also consider alleviation strategies such as providing sufficient rest areas on highways and using technology such as in-vehicle and personal technologies that monitor drowsy in-vehicle behavior to increase driver attention. The use of transportation modes such as taxi, transportation network companies (e.g. Uber), or public transit, can be an alternative for shift workers returning home after completing their shift.

Investigation of factors contributing to injury severity in single vehicle motorcycle crashes in India

Single-vehicle crashes are of major concern in both developed as well as in Low Middle Income Countries due to the severity of injuries, particularly fatal accidents. In India, a significant proportion of crashes are single-vehicle crashes. The vehicles which are involved in accidents due to causes such as self skidding, hitting stationary objects, trees that are simply contributed by the drivers themselves are referred to as out-of-control single-vehicle crashes. The main objective of this study is to evaluate the risk factors associated with single-vehicle motorcycle collisions in Tamilnadu, India and identifies the unique characteristics and injury outcomes associated with these collisions. Crash dataset for the present study was prepared from the police-reported crashes for the past nine years that occurred within the state of Tamilnadu between 2009 and 2017. The identified contributory factors which influence injury severity include driver characteristics, crash-related factors, traffic-related factors, vehicle and environment-related factors. In this study, injury severity is classified into three categories, i.e. fatal, serious, and minor injuries. Since the outcome of the injury severity could be measured on an ordinal scale, a discrete ordered outcome model, an ordered logit model is applied. To summarise the results, thirteen of the studied factors are found to have a significant influence on the injury severity of drivers. Results show that the likelihood of fatal injuries increases in crashes where motorcyclists hit stationary fixed objects, hit trees, ran-off road, inclement weather conditions, urban areas. It is also found that winter season, north districts of Tamilnadu, single and two-lane roads, highways, village roads and, other district roads, daylight conditions, drivers who are younger and working-age group, overtaking from left, taking u-turn are associated with less likelihood of fatal crashes. To increase the overall safety of the roads, targeted countermeasures may be designed in light of injury severity of the drivers with respect to single-vehicle crashes also. This study provides useful insights for reducing injury severity in single-vehicle motorcycle crashes.

Trajectory data based freeway high-risk events prediction and its influencing factors analyses

The frequent crash occurrences have caused massive loss of lives and properties all over the world. In order to improve traffic safety, it is vital to understand the relationships between traffic operation conditions and crash risk, and further implement safety countermeasures. Emerging studies have conducted the crash risk analyses using discrete and aggregated traffic data (e.g., loop detector data, probe vehicle data), where crash events were selected as the prediction target. However, traditional traffic sensing data obtained at segment level cannot describe the detailed operation conditions for the vehicle platoons near crash locations. Thus, more microscopic and high-resolution traffic sensing data are needed. In addition, considering the random occurrence feature of crashes, high-risk events should be paid more attentions given their higher occurrence probability and consistent causations with crashes, which could proactively reduce crash likelihood. In this study, HighD Dataset from German highways was utilized for the empirical analyses. First, high-risk events were obtained using safety surrogate measures with Modified Time to Collision (MTTC) less than 2 s. Traffic operation characteristics within 5 s prior to event occurrence were extracted based on vehicle trajectory data. Then, a total of three different logistic regression models were established, which are standard logistic regression model, random-effects logistic regression (RELR) model, and random-parameter logistic regression (RPLR) model. Among which, the RPLR model was showed to have the best fitness and prediction accuracy. The results showed that the disturbed traffic flows in both longitudinal and lateral directions have positive impacts on high-risk events occurrence. Besides, too close following distance between vehicles would lead to high-risk events. Moreover, RPLR models could provide a high prediction accuracy of 97 % for 2 s ahead of the high-risk events. Finally, potential safety improvement countermeasures and future application scenarios were also discussed.

Disaggregated traffic conditions and road crashes in urban signalized intersections

Introduction: Road safety studies in signalized intersections have been performed extensively using annually aggregated traffic variables and crash frequencies. However, this type of aggregation reduces the strength of the results if variables that oscillate over the course of the day are considered (speed, traffic flow, signal cycle length) because average indicators are not able to describe the traffic conditions preceding the crash occurrence. This study aims to explore the relationship between traffic conditions aggregated in 15-min intervals and road crashes in urban signalized intersections. Method: First, an investigation of the reported crash times in the database was conducted to obtain the association between crashes and their precursor conditions. Then, 4.1 M traffic condition intervals were consolidated and grouped using a hierarchical clustering technique. Finally, charts of the frequency of crashes per cluster were explored. Results: The main findings suggest that high vehicular demand conditions are related to an increase in property damage only (PDO) crashes, and an increase in the number of lanes is linked to more PDO and injury crashes. Injury crashes occurred in a wide range of traffic conditions, indicating that a portion of these crashes were due to speeding, while the other fraction was associated with the vulnerability of road users. Traffic conditions with: (a) low vehicular demand and a long cycle length and (b) high vehicular demand and a short cycle length were critical in terms of PDO and injury crashes. Practical Applications: The use of disaggregated data allowed for a stronger evaluation of the relationship between road crashes and variables that oscillate over the course of the day. This approach also permits the development of real-time risk management strategies to mitigate the frequency of critical traffic conditions and reduce the likelihood of crashes.

Bicycle Rider Behavior and Crash Involvement in Australia.

This research investigated how behaviours and attitudes of bicycle riders influence crash frequency and severity. The study recruited 1102 Australian bicycle riders for an online survey. The survey comprised questions on demographics, frequency of riding and the number and severity of traffic crashes during the last five years. The survey included the Cycling Behaviour Questionnaire and the Cyclist Risk Perception and Regulation Scale. Overall, there were low levels of errors and violations reported by participants indicating that these behaviours were on average never or rarely exhibited while riding a bicycle. Conversely, participants reported high levels of engagement in positive behaviours and reported high levels of traffic rule knowledge and risk perception. Higher rates of violations and errors were associated with increased crash likelihood, while higher rates of positive behaviours were associated with reduced rates of crash involvement in a period of 5 years. The findings highlight the relationship between errors, total crashes and crash severity Further promotion of positive behaviours amongst riders may also help to reduce the risk of crashes.

Assessing the likelihood of secondary crashes on freeways with Adaptive Signal Control System deployed on alternate routes

Introduction: Reducing the likelihood of freeway secondary crashes will provide significant safety, operational and environmental benefits. This paper presents a method for assessing the likelihood of freeway secondary crashes with Adaptive Signal Control Systems (ASCS) deployed on alternate routes that are typically used by diverted freeway traffic to avoid any delay or congestion due to a freeway primary crash. Method: The method includes four steps: (1) identification of secondary crashes, (2) verification of alternate routes, (3) assessment of the likelihood of secondary crashes for freeways with ASCS deployed on alternate routes and non-ASCS (i.e. pre-timed, semi- or fully-actuated) alternate routes, and (4) investigation of unobserved heterogeneity of the likelihood of freeway secondary crashes. Four freeway sections (i.e., two with ASCS deployed on alternate routes and two non-ASCS alternate routes) in South Carolina are considered. Results and Conclusions: Findings from the logistic regression modeling reveal significant reduction in the likelihood of secondary crashes for one freeway section (i.e., Charleston I-26 E) with ASCS deployed on alternate route. Other factors such as rear-end crash, dark or limited light, peak period, and annual average daily traffic contribute to the likelihood of freeway secondary crashes. Furthermore, random-parameter logistic regression model results for Charleston I-26 E reveal that unobserved heterogeneity of ASCS effect exists across the observations and ASCS are associated with the reduction of the likelihood of freeway secondary crashes for 84% of the observations (i.e., primary crashes). Location of the primary crash on the freeway is observed to affect the benefit of ASCS toward freeway secondary crash reduction as the primary crash’s location determines how many upstream freeway vehicles will be able to take the alternate route. Practical Applications: Based on the findings, it is recommended that the South Carolina Department of Transportation (SCDOT) considers deploying ASCS on alternate routes parallel to freeway sections where high percentages of secondary crashes are found.

The safety of physically separated cycle tracks compared to marked cycle lanes and mixed traffic conditions in Amsterdam

Bicycle infrastructure is utilised to improve cycling safety and encourage bicycle use as a sustainable and healthy transport mode. This study sets out to assess whether providing physically separated cycle tracks along distributor roads, as prescribed in Dutch design guidelines and the Sustainable Safety vision, yields the expected safety benefits for cyclists. Therefore the safety of physically separated cycle tracks is compared to marked or painted cycle lanes and to mixed traffic conditions at distributor roads with a speed limit of 50 km/h in Amsterdam in the Netherlands. The study also includes the presence of the risk factors curbside parking and trams. Since police records are known to underreport single bicycle crashes and other crashes without a motor vehicle involved, ambulance records are used in this study instead. Also, both motor vehicle volumes as well as cyclists counts are taken into account in the crash analysis. By doing so, this study aims to address two weaknesses of previous research, i.e. the lack of control for exposure of cyclists and the use of police recorded crashes which miss the majority of bicycle crashes without motor vehicles. Results show that, controlled for kilometres travelled by bicycle and by motor vehicle, 50-60% less bicycle crashes occur on distributor roads with cycle tracks compared to those with cycle lanes. Curbside parking and trams are related to an increased likelihood of bicycle crashes, a difference of a factor 2 and 1.7-2 respectively. The authors therefore recommend to favour physically separated cycle tracks over cycle lanes and to take out curbside parking from the cross section as this presents the possibility to introduce cycle tracks in existing cross sections and mitigate an important risk factor concurrently.

High-order Markov model for prediction of secondary crash likelihood considering incident duration

Traffic incidents can create major non-recurring congestion and have the potential to be fatal. Traffic Engineers and researchers have worked vigorously to reduce and prevent traffic crashes and ma...

Crash Risk Estimation Due to Lane Changing: A Data-Driven Approach Using Naturalistic Data

Road crashes are one of the critical issues in the transportation sector. Crash studies aim to establish the relationship of crash occurrences with driver, environment and traffic factors. Lack of a large disaggregate driving or accident data constrains the study of driver factors and driving maneuvers. Moreover, the usual outcome of these studies is the crash likelihood without accounting for the impact of a potential crash, thus leading to an incomplete interpretation of crash risk. New and innovative ways of data collection, such as drone videography and floating car data, are a promising candidate for a disaggregated analysis. Our study proposes a method for estimation of rear-end crash risk during a specific traffic state, using separate formulations of likelihood and severity. We quantify rear-end crash risk by weighing the likelihood by the potential severity of a collision, wherein we introduce a severity indicator for the rear-end crash. The methodology is applied to the highD dataset, a large naturalistic traffic dataset collected on German freeways. The proposed methodology allows for the analysis of risk with traffic state and lane-changing maneuver. The findings show that speed-drop is associated with increased crash risk. Also, lane changing is associated with higher rear-end crash risk, as compared to lane-keeping, during the free-flow as well as congestion traffic. The understanding of the evolution of the crash risk, due to the driving maneuvers under different traffic conditions, can be useful for real-time crash prediction and for devising traffic management strategies.

Exploring the influential factors of roadway departure crashes on rural two-lane highways with logit model and association rules mining

• Simultaneous presence of multiple factors results in roadway departure crashes. • Animal, no streetlight, drug, alcohol influence rural two-lane road departure. • Associated factors are – animal presence & no streetlight, male driver & 12am – 6am. • Other associations are – male driver & no passenger, use of both drug & alcohol. • Association rule mining facilitates strategic countermeasure development. Roadway departure (RwD) crashes are a major contributor of rural two-lane (R2L) highway crashes and fatalities. For targeted reduction of crashes and fatalities due to roadway departure, a thorough understanding of factors associated with RwD crashes is necessary. This study quantitatively assessed the available pre-crash factors that might influence the RwD crashes by developing a logit model comparing roadway, crash environment, and the vehicle and driver-related characteristics of 122,978 crashes that occurred in Louisiana over thirteen years. With a high prediction accuracy (81.9% area under the receiver operating characteristics curve), the model presented significant individual associations across crash characteristics with RwD crashes on R2L highways, for example – animals on roadways, snow/sleet/hail, 50–55 mph speed limit, AADT of 1,001 to 5,000 vehicles per day, drug intoxication, motorcycles, driving during 12 am to 6 am, curve radius of 501–1,000 ft., absence of streetlight, alcohol intoxication. Investigation on these top factors using association rules mining reveals findings such as – a higher likelihood of RwD crashes can be strongly associated animal presence coupled with the absence of streetlights, male drivers during the early morning (12 am to 6 am), male drivers driving with no passengers, drivers being intoxicated by both drugs and alcohol, etc. Findings from this study are expected to help highway safety specialists not only in identifying and predicting RwD crashes but also in an improved understanding of associated contributing factors leading to the application of proper countermeasures for the strategic reduction of RwD crashes.

A random parameters regional quantile analysis for the varying effect of road-level risk factors on crash rates

Many studies have been devoted to investigate the spatial variations (heterogeneities) in the effects of risk factors on crash likelihood. However, investigations mainly target the safety effects on the mean of the crash data (distribution). Less attention was paid to investigate the spatial nonstationary effects on the different quantiles of the crash data distribution. In this study, a conditional quantile-based Bayesian hierarchical random parameter Tobit model is proposed to investigate the regional varying effects of road-related factors on crash rate at different quantiles of the crash rate distribution. A specific roadway facility type, urban two-lane two-way roadway segments in Florida, with crash and road related data for a three-year period is used for a case study. The results show that: 1) the regression coefficients of all of the selected risk factors vary over a wide range among 34 counties on every investigated quantiles of crash rate distribution; 2) in each county, the regression coefficients of all of the factors vary over investigated quantiles of the crash rate distribution, and for the same factor, the coefficients present different ranges of the variation in different counties; 3) the 50th-quantile conditional quantile-based Bayesian hierarchical random parameter Tobit model outperforms the conditional mean-based Bayesian hierarchical random parameter Tobit model, Bayesian quantile Tobit model and Bayesian Tobit model in terms of the prediction accuracy measured by the MAE, and 75th-quantile conditional quantile-based Bayesian hierarchical random parameter Tobit model is outstanding in terms of the goodness-of-fit measured by the DIC. These findings suggest the importance of investigating the regional nonstationary effects of risk factors for different quantiles of the crash rate distribution. The practical implications of the proposed conditional quantile-based Bayesian hierarchical random parameter Tobit model in terms of data prediction, parameters interpretation and safety effects explanation are highlighted at the end of this paper.

Risky behaviors of older taxi drivers and suggested requirements for renewing their professional driver’s licenses

This paper investigates how specific risk factors affect the severity of injuries in crashes involving male taxi drivers 60 years of age and older in Taiwan. The heteroscedastic logit model is used to identify these significant risk factors and variations that affect the severity of crashes. The marginal effects show that older taxi drivers with risky behaviors of driving while fatigued and driving while drunk significantly increase the probability of crashes with injuries by 16.7% and 7.9%, respectively. Driving at night from 8:00 pm to midnight and from midnight to 6:00 am also significantly increase the probability of crashes with injuries by 4.0% and 2.6%, respectively. Furthermore, significant variations are found in notwearing a seat belt in older taxi drivers involved in crashes causing injuries. To reduce the likelihood of such crashes, in addition to the mandatory physical examinations, a sleep apnea test, an alcohol assessment, and a cognitive impairment assessment are recommended for renewing a driver’s license for taxis drivers age 60 older.