scholarly journals Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ying Chen ◽  
Zhongxiang Huang
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Safety Data ◽  
Method Comparison ◽  
Weather Conditions ◽  
Finite Mixture ◽  
Crash Risk ◽  
Crash Data ◽  
Road Crashes ◽  
Inclement Weather

Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that safety researchers only have access to data on sites where a crash has occurred (i.e., zero-truncated data). The conventional crash models are not applicable to zero-truncated safety data. This paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure. The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. The model is capable of capturing the heterogeneity within the sample crash data. In addition, lane width showed mixed effects in different components on wet-road crashes, which are not observed in conventional modeling approaches. Practitioners are encouraged to consider the finite-mixture zero-truncated modeling approach when complete safety dataset is not available.

scholarly journals Urban Road Crashes and Weather Conditions: Untangling the Effects

2019 ◽  
Vol 11 (11) ◽  
pp. 3176 ◽  
Author(s):  
António Lobo ◽  
Sara Ferreira ◽  
Isabel Iglesias ◽  
António Couto
Keyword(s):  
Negative Binomial ◽  
Weather Conditions ◽  
Crash Risk ◽  
Traffic Crash ◽  
Road Crashes ◽  
Road Users ◽  
Poisson Models ◽  
Adverse Weather ◽  
Single Vehicle ◽  
The Impact

Most previous studies show that inclement weather increases the risk of road users being involved in a traffic crash. However, some authors have demonstrated a little or even an opposite effect, observed both on crash frequency and severity. In urban roads, where a greater number of conflict points and heavier traffic represent a higher exposure to risk, the potential increase of crash risk caused by adverse weather deserves a special attention. This study investigates the impact of meteorological conditions on the frequency of road crashes in urban environment, using the city of Porto, Portugal as a case study. The weather effects were analyzed for different types of crashes: single-vehicle, multi-vehicle, property-damage-only, and injury crashes. The methodology is based on negative binomial and Poisson models with random parameters, considering the influence of daily precipitation and mean temperature, as well as the lagged effects of the precipitation accumulated during the previous month. The results show that rainy days are more prone to the occurrence of road crashes, although the past precipitation may attenuate such effect. Temperatures below 10 °C are associated with higher crash frequencies, complying with the impacts of precipitation in the context of the Portuguese climate characteristics.

Freeway Truck Traffic Safety in Wyoming: Crash Characteristics and Prediction Models

2019 ◽  
Vol 2673 (10) ◽  
pp. 333-342 ◽  
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Prediction Models ◽  
Weather Conditions ◽  
Significant Variable ◽  
Truck Traffic ◽  
Crash Data ◽  
Horizontal Curves ◽  
Rate Analysis ◽  
Road Geometry

The State of Wyoming experiences a high percentage of truck traffic along all its highways, especially Interstate 80 (I-80). The increased interactions between trucks and other vehicles have raised many operational and safety concerns. This paper presents a safety analysis and a development of safety performance functions (SPFs) along I-80, with a focus on truck crashes. Nine years of historical crash data in Wyoming (2008–2016) were used to observe the involvement of light, medium, and heavy trucks in crashes. Analysis of the major contributory factors showed that 54% of the total truck-related crashes occurred during icy road conditions and about 46% during snowy weather conditions, and approximately 45% involved driving too fast and driving in improper lane. The analysis also included segments with horizontal curves and vertical grades and their impacts on truck crashes. The crash rate analysis showed higher truck crash rate compared with total crash rate considering equal vehicle miles traveled as exposure. A zero-inflated negative binomial model was applied to develop Wyoming-specific SPFs for various truck crash types. The effects of traffic, road geometry characteristics, and weather parameters influencing different truck-related crashes were quantified from these models. Downgrades and steep upgrade sections were found to increase truck-related crashes. The number of rainy days per year was found to be a significant variable affecting truck-related crashes. On the other hand, the presence of climbing lanes has significant safety benefits.

Does Displaying Safety Messages on Dynamic Message Signs have Measurable Impacts on Crash Risk?

2021 ◽  
pp. 036119812110444
Author(s):  
Megat-Usamah Megat-Johari ◽  
Nusayba Megat-Johari ◽  
Peter T. Savolainen ◽  
Timothy J. Gates ◽  
Eva Kassens-Noor
Keyword(s):  
Negative Binomial ◽  
Driver Behavior ◽  
Field Research ◽  
Crash Risk ◽  
Traffic Crashes ◽  
Crash Data ◽  
Safety Issues ◽  
Dynamic Message Signs ◽  
Safety Message ◽  
Message Signs

Transportation agencies have increasingly been using dynamic message signs (DMS) to communicate safety messages in an effort to both increase awareness of important safety issues and to influence driver behavior. Despite their widespread use, evaluations as to potential impacts on driver behavior, and the resultant impacts on traffic crashes, have been very limited. This study addresses this gap in the extant literature and assesses the relationship between traffic crashes and the frequency with which various types of safety messages are displayed. Safety message data were collected from a total of 202 DMS on freeways across the state of Michigan between 2014 and 2018. These data were integrated with traffic volume, roadway geometry, and crash data for segments that were located downstream of each DMS. A series of random parameters negative binomial models were estimated to examine total, speeding-related, and nighttime crashes based on historical messaging data while controlling for other site-specific factors. The results did not show any significant differences with respect to total crashes. Marginal declines in nighttime crashes were observed at locations with more frequent messages related to impaired driving, though these differences were also not statistically significant. Finally, speeding-related crashes were significantly less frequent near DMS that showed higher numbers of messages related to speeding or tailgating. Important issues are highlighted with respect to methodological concerns that arise in the analysis of such data. Field research is warranted to investigate potential impacts on driving behavior at the level of individual drivers.

Evaluating Safety Effectiveness of Truck Climbing Lanes using Cross-Sectional Analysis and Propensity Score Models

2019 ◽  
Vol 2673 (7) ◽  
pp. 662-672 ◽  
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Propensity Score ◽  
Traffic Safety ◽  
Propensity Scores ◽  
Negative Binomial ◽  
Panel Count Data ◽  
Cross Sectional ◽  
Crash Data ◽  
Cross Sectional Analysis ◽  
Safety Effectiveness ◽  
Sectional Analysis

The State of Wyoming is characterized by heavy truck traffic flow, especially along Interstate 80 (I-80). A large portion of I-80 in Wyoming goes through mountainous and rolling terrain, resulting in significant vertical grades. About 9% of I-80 in each direction is within vertical grades of more than 3%, with certain sections reaching grades of close to 7%. Currently, there are 14 miles of climbing lanes in both directions. This study investigates the effects of climbing lanes on traffic safety using sections of I-80 in Wyoming. Cross-sectional analysis and propensity score methods were applied to evaluate the safety effectiveness and calibrate the Crash Modification Factor (CMF) and Relative Risk (RR) for climbing lanes. Data were collected from different sources and Wyoming-specific safety performance functions were developed using crash data from 2008 to 2016 for total crashes and truck-related crashes. All the segments were selected from I-80 in Wyoming with climbing lanes as treatment sites, and segments with similar geometrical characteristics without climbing lanes as comparison sites. Aggregated data were used to develop Negative Binomial and Zero-Inflated Negative Binomial models for performing cross-sectional analysis as they were found to fit better for the crash data. On the other hand, panel count data were used to conduct a propensity scores-potential outcomes framework. The CMFs and RR for climbing lanes from both analyses were found to be effective in reducing total and truck-related crashes. This is a first study that develops CMFs for climbing lanes in Wyoming.

scholarly journals A Surrogate Video-Based Safety Methodology for Diagnosis and Evaluation of Low-Cost Pedestrian-Safety Countermeasures: The Case of Cochabamba, Bolivia

2019 ◽  
Vol 11 (17) ◽  
pp. 4737
Author(s):  
Lynn Scholl ◽  
Mohamed Elagaty ◽  
Bismarck Ledezma-Navarro ◽  
Edgar Zamora ◽  
Luis Miranda-Moreno
Keyword(s):  
Developing Countries ◽  
Latin American ◽  
Traffic Safety ◽  
Road Safety ◽  
Low Cost ◽  
Video Data ◽  
Crash Risk ◽  
Video Analytics ◽  
Crash Data ◽  
Safety Countermeasures

Due to a lack of reliable data collection systems, traffic fatalities and injuries are often under-reported in developing countries. Recent developments in surrogate road safety methods and video analytics tools offer an alternative approach that can be both lower cost and more time efficient when crash data is incomplete or missing. However, very few studies investigating pedestrian road safety in developing countries using these approaches exist. This research uses an automated video analytics tool to develop and analyze surrogate traffic safety measures and to evaluate the effectiveness of temporary low-cost countermeasures at selected pedestrian crossings at risky intersections in the city of Cochabamba, Bolivia. Specialized computer vision software is used to process hundreds of hours of video data and generate data on road users’ speed and trajectories. We find that motorcycles, turning movements, and roundabouts, are among the key factors related to pedestrian crash risk, and that the implemented treatments were effective at four-legged intersections but not at traditional-design roundabouts. This study demonstrates the applicability of the surrogate methodology based on automated video analytics in the Latin American context, where traditional methods are challenging to implement. The methodology could serve as a tool to rapidly evaluate temporary treatments before they are permanently implemented and replicated.

Examining Safety on Two-Lane and Multilane Highways in Consideration of Access Spacing

2019 ◽  
Vol 2673 (11) ◽  
pp. 388-397
Author(s):  
Raha Hamzeie ◽  
Megat-Usamah Megat-Johari ◽  
Iftin Thompson ◽  
Timothy P. Barrette ◽  
Trevor Kirsch ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Random Effect ◽  
Management Strategies ◽  
Negative Binomial Regression ◽  
Access Point ◽  
Management Policy ◽  
Crash Risk ◽  
Access Management ◽  
Crash Data ◽  
Crash Rates

Access management strategies, such as the introduction of minimum access point spacing criteria and turning movement restrictions, have been shown to be important elements in optimizing the operational and safety performance of roadway segments. The relationship between safety and these types of access policies is a complex issue, and the impacts of such features on traffic crashes is critical to the development of appropriate access management strategies. The purpose of this study was to provide a quantitative evaluation of how crash risk on multilane and two-lane highways varies with respect to access spacing in support of the development of a revised access management policy. Data were obtained for approximately 1,247 and 5,795 mi of segments across multilane and two-lane highways, respectively. Crash data were obtained for a five-year period from 2012 to 2016 and a series of random effect negative binomial regression models were estimated for each facility to examine the association between crash frequency, access point spacing, and traffic volume. For both facility types, crashes were found to increase consistently as the average spacing of access points along road segments decreased. Crash rates were highest when consecutive accesses were within 150 ft of one another and the frequency of crashes decreased substantively as spacing was increased to 300 ft and, particularly, 600 ft. With spacing beyond 600 ft, crash rates continued to decrease, although these improvements were less pronounced than at the lower range of values. These findings were generally consistent on multilane and two-lane highways.

Examining the factors affecting the severity of run-off-road crashes in Abu Dhabi

2016 ◽  
Vol 43 (2) ◽  
pp. 132-138 ◽  
Author(s):  
Mohamed Shawky ◽  
Hany M. Hassan ◽  
Atef M. Garib ◽  
Hussain A. Al-Harthei
Keyword(s):  
Traffic Safety ◽  
Surface Condition ◽  
Probit Model ◽  
Abu Dhabi ◽  
Factors Affecting ◽  
Environment Factors ◽  
Crash Data ◽  
Road Crashes ◽  
Run Off ◽  
Road Type

Recently, the severity of injuries resulting from traffic crashes has been extensively investigated in numerous studies. However, the number of studies that addressed the severity of the run-off-road (ROR) crashes is relatively low. In the Emirate of Abu Dhabi (AD), approximately 22% of the total serious crashes and fatalities that occurred from 2007 to 2013 were ROR crashes. Despite these facts and the uniqueness of the composition of licensed drivers in AD (approximately 87% of them are non-Emiratis), the factors affecting the occurrence and severity of ROR crashes in AD have not been explicitly addressed in any prior studies. Therefore, this study aims to investigate the characteristics of at-fault drivers involved in ROR crashes in AD, the nature and main causes of those crashes. In this regard, conditional distribution and two-way contingency tables were developed. In addition, this study aims to identify and quantify the factors affecting the severity of ROR crashes such as driver, road, vehicle and environment factors. To achieve this goal, ordered probit model approach was employed. Crash data for a total of 3819 ROR crashes that occurred in AD were employed in the analysis. The results indicated that driver factors (carelessness, speeding, and nationality), vehicle characteristics (vehicle type), and road and environment factors (road type, crash location and road surface condition) were the significant factors influencing the severity of ROR crashes in AD. Countermeasures to improve traffic safety and reduce numbers and severity of ROR crashes in AD were discussed.

scholarly journals Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Changle Sun ◽  
Hongyan Gao
Keyword(s):  
Traffic Safety ◽  
Weather Conditions ◽  
Ramp Metering ◽  
Traffic Model ◽  
Crash Risk ◽  
Traffic Density ◽  
Percentage Error ◽  
S Model ◽  
Takagi Sugeno ◽  
Curve Radius

Foggy weather seriously deteriorates the performance of freeway systems, particularly regarding traffic safety and efficiency. General macroscopic traffic models have difficulty reflecting the characteristics of a freeway under foggy weather conditions. In the present study, a macroscopic traffic model using a correction factor under foggy weather conditions is therefore proposed, which is regulated according to the different levels of visibility and curve radius of the freeway using the Takagi–Sugeno (T-S) model. Based on the proposed traffic model, a local ramp metering strategy with density correction under foggy weather conditions is proposed to improve traffic safety. The proposed local ramp metering strategy regulates the on-ramp flow using the T-S model according to the mainstream density, speed, and visibility. The correction factors are determined based on the parameters of the consequent part in the T-S model, which are optimized using the particle swarm optimization algorithm. The sum of the mean absolute percentage error of the mainstream traffic density and speed is used to evaluate the proposed traffic model. The real-time crash-risk prediction model, which reflects the degree of traffic safety, is used to evaluate the proposed local ramp metering strategy. Simulations using VISSIM and MATLAB show that the proposed traffic model is suitable under foggy weather conditions and that the proposed local ramp metering strategy achieves a better performance in reducing fog-related crashes.

Evaluation of Roundabout-Related Single-Vehicle Crashes

2017 ◽  
Vol 2637 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Beau Burdett ◽  
Andrea R. Bill ◽  
David A. Noyce
Keyword(s):  
Negative Binomial ◽  
Vehicle Crashes ◽  
Impaired Driving ◽  
Crash Data ◽  
Road Crashes ◽  
Run Off ◽  
Traffic Volumes ◽  
Single Vehicle ◽  
Total Proportion ◽  
Crash Types

Roundabouts reduce fatal and injury crashes at intersections when converted from other intersection control types. In Wisconsin, roundabouts have been linked to a 38% decrease in fatal and injury crashes. Part of this reduction can be attributed to crash types that result in the mitigation of more serious injuries. However, the reduction comes at a cost because other crash types, such as single-vehicle collisions, may increase. Six years of crash data on 53 roundabouts in Wisconsin were examined for crash causes and geometric characteristics that affected single-vehicle crashes. Weather and impaired driving, particularly by younger drivers, were primary causes for more than half of all single-vehicle crashes at the study roundabouts. Younger drivers (18 to 24 years of age) were involved in a significantly higher proportion of single-vehicle crashes than the total proportion of licensed drivers in that age group. Younger drivers were involved in approximately one-third of all crashes that involved impaired driving and in two-thirds of all speed-related single-vehicle crashes. A negative binomial model was constructed to estimate run-off-road crashes at approaches. It was found that roundabouts with higher approach speeds and higher traffic volumes experienced more run-off-road crashes. Landscaped central islands experienced significantly lower frequencies of run-off-road crashes.

Traffic Safety Region Estimation Based on SFS–PCA–LSSVM: An Application to Highway Crash Risk Evaluation

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1555-1570
Author(s):  
Yanfang Yang ◽  
Yong Qin ◽  
Limin Jia ◽  
Honghui Dong
Keyword(s):  
State Space ◽  
Real Time ◽  
Traffic Safety ◽  
Risk Evaluation ◽  
Crash Risk ◽  
Support Vector ◽  
State Variables ◽  
Crash Data ◽  
Traffic State ◽  
Safe Condition

Accurate real-time crash risk evaluation is essential for making prevention strategy in order to proactively improve traffic safety. Quite a number of models have been developed to evaluate traffic crash risk by using real-time surveillance data. In this paper, the basic idea of traffic safety region is introduced into highway crash risk evaluation. Sequential forward selection (SFS), principal components analysis (PCA) and least squares support vector machine (LSSVM) are used to estimate the traffic safety region and classify the traffic states (safe condition and unsafe condition). The proposed method works by first extracting state variables from the observed traffic variables. Two statistics [Formula: see text] and squared prediction error (SPE) are calculated by SFS–PCA and used as the final state variables for traffic state space. Next, LSSVM is used to estimate the boundary of traffic safety region and identify the traffic states in the traffic state space. To demonstrate the advantage of the proposed method, this study develops two crash risk evaluation models, namely SFS–LSSVM model and PCA–LSSVM model, based on crash data and non-crash data collected on freeway I-880N in Alameda. Validation results show that the method is of reasonably high accuracy for identifying traffic states.

Sign in / Sign up

Export Citation Format

Share Document