Advancing proactive crash prediction: A discretized duration approach for predicting crashes and severity.

Driven by advancements in data-driven methods, recent developments in proactive crash prediction models have primarily focused on implementing machine learning and artificial intelligence. However, from a causal perspective, statistical models are preferred for their ability to estimate effect sizes using variable coefficients and elasticity effects. Most statistical framework-based crash prediction models adopt a case-control approach, matching crashes to non-crash events. However, accurately defining the crash-to-non-crash ratio and incorporating crash severities pose challenges. Few studies have ventured beyond the case-control approach to develop proactive crash prediction models, such as the duration-based framework. This study extends the duration-based modeling framework to create a novel framework for predicting crashes and their severity. Addressing the increased computational complexity resulting from incorporating crash severities, we explore a tradeoff between model performance and estimation time. Results indicate that a 15 % sample drawn at the epoch level achieves a balanced approach, reducing data size while maintaining reasonable predictive accuracy. Furthermore, stability analysis of predictor variables across different samples reveals that variables such as Time of day (Early afternoon), Weather condition (Clear), Lighting condition (Daytime), Illumination (Illuminated), and Volume require larger samples for more accurate coefficient estimation. Conversely, Daytime (Early morning, Late morning, Late afternoon), Lighting condition (Dark lighted), Terrain (Flat), Land use (Commercial, Rural), Number of lanes, and Speed converge towards true estimates with small incremental increases in sample size. The validation reveals that the model performs better in highway segments experiencing more frequent crashes (segments where the duration between crashes is less than 100 h, or approximately 4 days).

Assessing driver behavior in work zones: A discretized duration approach to predict speeding.

Higher speeds in work zones have been linked to an increased likelihood of crashes and more severe crash outcomes. To enhance safety, speed limits are often reduced in work zones, aiming to create a steady flow of traffic and safer traffic operations such as merging and flagging. However, this speed reduction can also lead to abrupt speed changes, resulting from sudden braking or acceleration, increasing the risk of crashes. This disruption in speed and flow results increases the likelihood of rear-end crashes. Ensuring driver compliance with the reduced speed limits and traffic flow operations is challenging as work zones may cause frustration and lead to more instances of speeding. Therefore, proactively predicting speeding events in work zones can be crucial for the safety of both workers and road users, as it enables the implementation of speed enforcement measures to maintain and improve driver compliance in advance. In this study, we employ the duration-based prediction framework to forecast speeding occurrences in work zones. The model is used to identify significant predictors of speeding including visibility, number of lanes, posted speed limit, segment length, coefficient of variation in speed, and travel time index. Among these variables, the number of lanes, posted speed limit, and coefficient of variation of speed are positively associated with speeding. On the other hand, visibility, segment length, and travel time index are negatively associated with speeding. Results show the model's predictive accuracy is higher for speeding events with shorter durations between consecutive occurrences. The model predicted speeding within 61% of the actual epoch when speeding events within 5 h of one another were considered for validation. This indicates that the model is more effective for road segments and work zones where speeding occurs more frequently. The prediction framework can be a great asset for agencies to improve work zone safety in real-time by enabling them to proactively implement effective work zone enforcement measures to control speeding and to stay prepared, preventing potential hazards.

Towards smart work zones: Creating safe and efficient work zones in the technology era.

INTRODUCTION: Work Zones (WZs) have long been identified as a source of traffic fatalities and delays. Despite considerable technological advances that have alleviated many operational challenges associated with a WZ, social concerns about safety and mobility near WZs remain. Notably, the concept of a Smart Work Zone (SWZ) emerged from the compelling need to improve the safety and mobility of traffic and other WZ participants. This study reviewed the literature to assimilate studies related to SWZ Systems (SWZSs), report their findings, and ascertain a future path forward. METHOD: To accomplish this, the existing WZ-related literature base was clustered into safety and traffic mobility topics using Latent Dirichlet Allocation (LDA) modeling. A thorough investigation of the pivotal inferences for the research topics was undertaken to comprehend current SWZ technologies and the need for further research. RESULTS: The review uncovered the prominent features of SWZSs reported in the literature and the hindrances to their adoption. The most reported hindrances are the cost and effort associated with development, installation, and relocation. We uncover that Connected Autonomous Vehicles, vehicle-to-vehicle, and vehicle-to-infrastructure communication, along with technology-based worker training are the most promising next frontier for SWZ. CONCLUSION: Significant research gaps exist in the literature regarding developing and implementing SWZS. Additionally, little effort has been directed toward developing workers' skills and competency. Practical approaches such as Virtual Reality (VR)-based training are necessary to bring workers up to pace with the developing SWZ technologies. PRACTICAL APPLICATIONS: Future research should be directed towards interconnecting and implementing available safety technologies to automate WZ safety and management. Workers should be trained using more practical techniques. In this context, using VR will enable the simulation of hazardous events in a safe environment while also improving workers' skill retention.

Identification of high-risk roadway segments for wrong-way driving crash using rare event modeling and data augmentation techniques.

Wrong-Way Driving (WWD) crashes are relatively rare but more likely to produce fatalities and severe injuries than other crashes. WWD crash segment prediction task is challenging due to its rare nature, and very few roadway segments experience WWD events. WWD crashes involve complex interactions among roadway geometry, vehicle, environment, and drivers, and the effect of these complex interactions is not always observable and measurable. This study applied two advanced Machine Learning (ML) models to overcome the imbalanced dataset problem and identified local and global factors contributing to WWD crash segments. Five years (2015-2019) of WWD crash data from Florida state were used in this study for WWD model development. The first modeling approach applied four different hybrid data augmentation techniques to the training dataset before applying the XGBoost classification algorithm. In the second model, a rare event modeling approach using the Autoencoder-based anomaly detection method was applied to the original data to identify WWD roadway segments. A third model was applied based on the statistical method to compare the performance of ML models in predicting the WWD segments. The performance comparison of the adopted models showed that the XGBoost model with the Adaptive Synthetic Sampling (ADASYN) method performed best in terms of precision and recall values compared to the autoencoder-based anomaly detection method. The best-performing model was used for the feature analysis with an interpretable machine-learning technique. The SHapley Additive exPlanations (SHAP) values showed that high-intensity developed land use, length of roadway, log of Annual Average Daily traffic (AADT), and lane width were positively associated with WWD roadway segments. The results of this study can be used to deploy WWD countermeasures effectively.

COVID-19 impacts on mobility and travel demand.

Since the beginning of the COVID-19 pandemic, many travel restriction policies were implemented to reduce further spread of the virus. These measures significantly affected travel demand to levels which could not have been anticipated by most planners in transportation agencies. As the pandemic has proven to have significant short-term impacts, it is anticipated that some of these impacts may translate to longer-term impacts on overall travel behavior and the movement of people and goods. Beyond the pandemic, the observed travel patterns during this period also provides a great opportunity for planners to assess policies such as work from home and remote learning as strategies to manage travel demand. This study provides a scenario analysis framework to re-evaluate travel demand forecasts under uncertain future conditions using the Maryland Statewide Transportation Model (MSTM). Model parameters associated with working from home, household income, changes in discretionary travel, distance learning, increased e-commerce, vehicle occupancy and mode choice were identified. Parameter values were assigned under the various scenarios using employer surveys on workforce teleworking and observed data on e-commerce growth and shopping behavior. The main findings of this study capture the sensitivities of systemwide vehicle miles travel, and vehicle hours travel under different scenarios and implications on future investment decisions. The study found that future investments under the scenarios remain beneficial to systemwide performance and therefore justified. Although this study focuses on the state of Maryland, the scenario framework and parameter definitions can be used in other states or agencies within a travel demand model environment.

Overcoming challenges in crash prediction modeling using discretized duration approach: An investigation of sampling approaches.

Until recently, statistical approaches used for real-time crash prediction modeling were limited to case-control design and "sampling of alternatives" approaches. A recent study has developed a duration-based real-time crash prediction model capable of incorporating dynamic (time-varying) covariates within its framework. The modeling approach discretizes the duration between crashes into equal time intervals which can be modeled as alternatives in a multinomial logit framework. The approach, however, requires a reformulation of the original crash dataset to fit its modeling framework which results in considerably large data making model estimation computationally demanding. Additionally, validation of the model in the original study is based on crash data from just one interstate, I-405, assuming homogenous highway segments each 5 miles in length. This study improves upon the original study by investigating sampling techniques that can be applied to the reformulated data to reduce computational load using 2019 crash data from two interstates, I-40 and I-55, in Memphis, Tennessee. Furthermore, discretization of inter-crash duration is undertaken following non-homogenous segmentation of the interstates that is based on highway geometry, terrain, and posted speed limit. To accomplish the study objectives, a relatively small future window of 1 h with 15-minute time intervals is used to discretize the inter-crash duration and obtain the reformulated data. Sampling of crashes for model estimation is then done at the crash, epoch, and segment levels to answer the question of which sampling technique (by crash, epoch, or segment) would result in reasonable accuracy when compared with the complete (100%) data. Results show that 25% of samples drawn at the epoch level can result in a considerable reduction of computational load while providing reasonably consistent estimates.

Using worker's naturalistic response to determine and analyze work zone crashes in the presence of work zone intrusion alert systems.

Work zone Intrusion Alert Systems (WZIAS) are alert mechanisms that detect and alert workers of vehicles intruding into a work zone. These systems pre-dominantly employ two components-sensors placed near the work zone perimeter that detect intrusions, and alarms placed closed to or carried by the workers that alerts them. This study investigates the association between layout of these components for three WZIAS on work zone crashes based on worker reaction. Also, the key determinants of work zone crashes in presence of the WZIAS is identified using survival analysis. The ideal deployment strategy and use case scenarios for the three WZIAS is presented based on the findings of the study. The systems were subjected to rigorous testing that emulated intrusions to record worker reaction and determine occurrence of crashes. Analysis of results indicate that the key determinants of work zone crashes are speed of the intruding vehicle, distance between the sensor and worker, and accuracy of a system in detecting intrusions and alerting workers. Results from field experiments suggest that identification of appropriate use cases for WZIAS is necessary to ensure they work effectively. Based on the findings from this study it is suggested that current guidelines on work zones be modified to standardize WZIAS setup.

Injury severity analysis of commercially-licensed drivers in single-vehicle crashes: Accounting for unobserved heterogeneity and age group differences.

This study analyzes the injury severity of commercially-licensed drivers involved in single-vehicle crashes. Considering the discrete ordinal nature of injury severity data, the ordered response modeling framework was adopted. The moderating effect of driver's age on all other factors was examined by segmenting the parameters by driver's age group. Additional effects of the different drivers' age groups are taken into consideration through interaction terms. Unobserved heterogeneity of the different covariates was investigated using the Mixed Generalized Ordered Response Probit (MGORP) model. The empirical analysis was conducted using four years of the Highway Safety Information System (HSIS) data that included 6247 commercially-licensed drivers involved in single-vehicle crashes in the state of Minnesota. The MGORP model elasticity effects indicate that key factors that increase the likelihood of severe crashes for commercially-licensed drivers across all age groups include: lack of seatbelt usage, collision with a fixed object, speeding, vehicle age of 11 years or more, wind, night time, weekday, and female drivers. Also, the effects of several covariates were found to vary across different age groups.

Analysis of passenger-car crash injury severity in different work zone configurations.

Work zone safety remains a priority to the Federal Highway Administration, State Highway Departments, highway engineers, and the traveling public. Work zones create a hospitable environment for crashes; an issue that gained tremendous share of attention in recent years. Therefore, every effort should be sought out to reduce the injury severity of crashes in work zones. In this paper we attempt to investigate factors contributing to the injury severity of passenger-car crashes in different work zone configurations. Considering the discrete ordinal nature of injury severity categories, a Mixed Generalized Ordered Response Probit (MGORP) modeling framework was developed. The model estimation was undertaken by compiling a database consisting of 10 years of crashes that involved at least one passenger car, and occurred in a work zone. Revealing the underlying factors contributing to injury severity levels for different work zone configurations will allow for distinguishing mitigation methods for higher severity outcomes that best suit each of the depicted work zone layouts. This can be accomplished through the implementation of specific safety measures based on the specific configuration of a work zone as a potential crash location. Elasticity analysis suggests that partial control of access, roadways classified as rural, crashes during evening times, crashes during weekends, and curved roadways are key factors that increase the likelihood of severe outcomes. Also, the effects of several covariates were found to vary across the different work zone configurations.

Prediction of secondary crash frequency on highway networks.

Secondary crash (SC) occurrences are major contributors to traffic delay and reduced safety, particularly in urban areas. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, determining the primary contributing factors for their occurrence is crucial. The non-recurring nature of SCs makes it imperative to predict their occurrences for effective incident management. In this context, the objective of this study is to develop prediction models to better understand causal factors inducing SCs. Given the count nature of secondary crash frequency data, the authors used count modeling methods including the standard Poisson and Negative Binomial (NB) models and their generalized variants to analyze secondary crash occurrences. Specifically, Generalized Ordered Response Probit (GORP) framework that subsumes standard count models as special cases and provides additional flexibility thus improving predictive accuracy were used in this study. The models developed account for possible effects of geometric design features, traffic composition and exposure, land use and other segment related attributes on frequency of SCs on freeways. The models were estimated using data from Shelby County, TN and results show that annual average daily traffic (AADT), traffic composition, land use, number of lanes, right side shoulder width, posted speed limits and ramp indicator are among key variables that effect SC occurrences. Also, the elasticity effects of these different factors were also computed to quantify their magnitude of impact.

Analysis of injury severity of large truck crashes in work zones.

Work zones are critical parts of the transportation infrastructure renewal process consisting of rehabilitation of roadways, maintenance, and utility work. Given the specific nature of a work zone (complex arrangements of traffic control devices and signs, narrow lanes, duration) a number of crashes occur with varying severities involving different vehicle sizes. In this paper we attempt to investigate the causal factors contributing to injury severity of large truck crashes in work zones. Considering the discrete nature of injury severity categories, a number of comparable econometric models were developed including multinomial logit (MNL), nested logit (NL), ordered logit (ORL), and generalized ordered logit (GORL) models. The MNL and NL models belong to the class of unordered discrete choice models and do not recognize the intrinsic ordinal nature of the injury severity data. The ORL and GORL models, on the other hand, belong to the ordered response framework that was specifically developed for handling ordinal dependent variables. Past literature did not find conclusive evidence in support of either framework. This study compared these alternate modeling frameworks for analyzing injury severity of crashes involving large trucks in work zones. The model estimation was undertaken by compiling a database of crashes that (1) involved large trucks and (2) occurred in work zones in the past 10 years in Minnesota. Empirical findings indicate that the GORL model provided superior data fit as compared to all the other models. Also, elasticity analysis was undertaken to quantify the magnitude of impact of different factors on work zone safety and the results of this analysis suggest the factors that increase the risk propensity of sustaining severe crashes in a work zone include crashes in the daytime, no control of access, higher speed limits, and crashes occurring on rural principal arterials.