Modeling temporal correlation and heterogeneity in real-time conflict rates using Bayesian Tobit models for signalized intersections.

The use of real-time traffic conflicts for safety studies provide more insight into how important dynamic signal cycle-related characteristics can affect intersection safety. However, such short-time window for data collection raises a critical issue that the observed conflicts are temporally correlated. As well, there is likely unobserved heterogeneity across different sites that exist in conflict data. The objective of this study is to develop real-time traffic conflict rates models simultaneously accommodating temporal correlation and unobserved heterogeneity across observations. Signal cycle level traffic data, including traffic conflicts, traffic and shock wave characteristics, collected from six signalized intersections were used. Three types of Tobit models: conventional Tobit model, temporal Tobit (T-Tobit) model, and temporal grouped random parameters (TGRP-Tobit) model were developed under full Bayesian framework. The results show that significant temporal correlations are found in T-Tobit models and TGRP-Tobit models, and the inclusion of temporal correlation considerably improves the goodness-of-fit of these Tobit models. The TGRP-Tobit models perform best with the lowest Deviance Information Criteria (DIC), indicating that accounting for the unobserved heterogeneity can further improve the model fit. The parameter estimates show that real-time traffic conflict rates are significantly associated with traffic volume, shock wave area, shock wave speed, queue length, and platoon ratio.

Real-time combined safety-mobility assessment using self-driving vehicles collected data.

The study presents a real-time safety and mobility assessment approach using data generated by autonomous vehicles (AVs). The proposed safety assessment method uses Bayesian hierarchical spatial random parameter extreme value model (BHSRP), which can handle the limited availability and uneven distribution of conflict data and accounts for unobserved spatial heterogeneity. The approach estimates two real-time safety metrics: the risk of crash (RC) and return level (RL), using Time-To-Collision (TTC) as conflict indicator. Additionally, a Risk Exposure (RE) index was developed to reflect the risk of an individual vehicle to travel through a corridor. In parallel, the mobility of corridor were assessed based on the highway Capacity manual methodology using real-time traffic data (Highway Capacity Manual, 2010). The study used a 440-hour AVs' dataset of a corridor in Palo Alto, California. After normalizing for each LOS representation in the dataset, LOS E was identified as the most hazardous operating condition with the highest average crash risk. However, the time spent under different operating condition would affect the safety of individual vehicles traveling through a road facility (i.e., vehicle's exposure time). Accounting for exposure time, the vehicle has the highest chance of encountering an extremely risky driving condition at intersections and segments under LOS D and E, respectively.

An integrated approach of machine learning and Bayesian spatial Poisson model for large-scale real-time traffic conflict prediction.

The use of traffic conflicts in road safety evaluation is gaining considerable popularity as it plays a vital role in developing a proactive safety management strategy and allowing for real-time safety analysis. This study proposes an integrated approach that combines a machine learning (ML) algorithm and a Bayesian spatial Poisson (BSP) model to conduct large-scale real-time traffic conflict prediction by considering traffic states as the explanatory variables. Traffic conflicts are measured by two indicators, the Time to Collision (TTC) and the Post-Encroachment Time (PET). Based on both TTC and PET, traffic conflict severity is classified into five categories. For each conflict severity category, a binary variable (conflict occurrence) and a count variable (conflict frequency) are developed, respectively. In addition to conflict variables, traffic state parameters are extracted from a large-scale high-resolution trajectory dataset. The traffic parameters include volume, density, speed, and the corresponding space-based and space-time-based measures within a 30-second interval. Eight ML-based classifiers are applied to predict conflict occurrence, and the best classifier is selected. A binary logistic regression is developed to explore the potential linkages between traffic states and conflict occurrence. As well, a resampling technique Borderline-SMOTE is used to mitigate the sparsity caused by the predefined short interval. The BSP model is utilized to predict the specific number of conflicts. Further, the BSP model can also explain the relationship between traffic states and conflict frequency, and thus the significant influencing traffic states are identified. The results show that random forest outperforms the other MLs in terms of conflict occurrence prediction accuracy. Further, the proposed integrated approach achieves a high performance of conflict frequency prediction with RMSE values of 0.1384 âˆ¼ 0.1699, MAPE values of 9.25% ∼ 36.99%, and MAE values of 0.0087 âˆ¼ 0.6398. The finding emphasizes the need for separately predicting the occurrence and frequency of conflicts with different severities.

Leading pedestrian intervals - Yay or Nay? A Before-After evaluation of multiple conflict types using an enhanced Non-Stationary framework integrating quantile regression into Bayesian hierarchical extreme value analysis.

A pedestrian was estimated to be killed every 85 min and injured every 7 min on US roads in 2019. Targeted safety treatments are particularly required at urban intersections where pedestrians regularly conflict with turning vehicles. Leading Pedestrian Intervals (LPIs) are an innovative, low-cost treatment where the pedestrian and vehicle usage of the potential conflict area (a crosswalk) is staggered in time to give the pedestrians a head start of a few seconds and reduce the "element of surprise" for right-turning vehicles. The effectiveness of LPI treatment on pedestrian safety is mixed, and most importantly, its effect on vehicle-vehicle conflicts is unknown. This study investigates the before-after effects of LPI treatments on vehicle-pedestrian and vehicle-vehicle crash risk by applying traffic conflict techniques. In particular, this study has developed a quantile regression technique within the extreme value model to estimate and compare crash risks before and after the installation of the LPI treatment. The before-after traffic movement video data (504 h in total) were collected from three signalized intersections in the City of Bellevue, Washington. The recorded movements were analyzed using Microsoft's proprietary computer vision platform, Edge Video Service, and Advanced Mobility Analytics Group's cloud-based SMART SafetyTM platform to automatedly extract traffic conflicts by analyzing road user trajectories. The treatment effect was measured using a Bayesian hierarchical extreme value model with the peak-over threshold approach. For the extreme value model, a Bayesian quantile regression analysis was conducted to estimate the conflict thresholds corresponding to a high (95th) quantile. Odds ratios were estimated for both conflict types using untreated crossing as a control group. Results indicate that the LPI treatment reduces the crash risk of pedestrians as measured by the reduction in extreme vehicle-pedestrian conflicts by about 42%. The LPI treatment has also been found not to negatively affect rear-end conflicts along the approaches leading to the LPI-treated pedestrian crossing at the signalized intersections. The findings of this study further emphasize the effectiveness of video analytics in proactive safety evaluations of engineering treatments.

H2ampa─Versatile Chelator for [203Pb]Pb2+, [213Bi]Bi3+, and [225Ac]Ac3.

A new decadentate chelator, H2ampa, was designed to be a potential radiopharmaceutical chelator component. The chelator involves both amide and picolinate functional groups on a large non-macrocyclic, ether-bridged backbone. With its large scaffold, H2ampa was paired with [nat/203Pb]Pb2+, [nat/213Bi]Bi3+, and natLa3+/[225Ac]Ac3+ ions. Nuclear magnetic resonance spectroscopy and high-resolution mass spectrometry were used to study the non-radioactive metal complexes. A single crystal of [Bi(ampa)](NO3) was obtained; its asymmetric, 10-coordinate complex structure was revealed by X-ray diffraction. Optimal conformations of the metal complexes were assessed by density functional theory studies to provide further structural information. Solution studies providing thermodynamic insights into metal complex formation revealed H2ampa coordinated Bi3+, Pb2+, and La3+ ions to obtain pM values of 26, 14.8, and 15.1, respectively. Preliminary concentration-dependent radiolabeling experiments were carried out between H2ampa and three different radiometals to evaluate their compatibility for radiopharmaceutical applications. The chelator radiolabeled [203Pb]Pb2+, [213Bi]Bi3+, and [225Ac]Ac3+ in short reaction times (7-30 min), at dilute concentrations, and under mild conditions. Thus, H2ampa was proven to be a versatile chelator able to well coordinate a small range of radiometals frequently considered to be alpha therapeutic candidates.

Silver nanoparticles as a viricidal agent to inhibit plant-infecting viruses and disrupt their acquisition and transmission by their aphid vector.

Silver nanoparticles (AgNPs) are a potentially effective tool for preventing viral plant diseases. This study was carried out to evaluate the effectiveness of AgNPs for managing bean yellow mosaic virus (BYMV) disease in faba bean plants from the plant-virus-vector interaction side. AgNPs were evaluated as foliar protective and curative agents. In addition, the effect of AgNPs on virus acquisition and transmission by its vector aphid was investigated. The results indicated that AgNPs exhibited curative viricidal activity and were able to inactivate BYMV when applied 48 hours after virus inoculation. The occurrence of disease was prevented using an AgNP concentration as low as 100 mg L-1, whereas virus infection was completely inhibited when plants were preventatively treated with AgNPs at a concentration of to 200 mg L-1 24 h before virus inoculation. AgNPs proved to be highly bio-reactive, binding to viral particles and suppressing their replication and accumulation within plant tissues. Moreover, AgNPs, at all concentrations tested, were found to upregulate the pathogenesis-related gene PR-1 and induce the production of defense-related oxidizing enzymes in treated plants. Exposure of aphids to AgNPs-treated plants before virus acquisition reduced BYMV acquisition and transmission efficiency by 40.65 to 100% at 24 h post-application, depending on the AgNP dosage. At 10 days after treatment, virus acquisition was reduced by 36.82% and 79.64% upon exposure to AgNPs at a concentration of 250 and 300 mg L-1, respectively. These results suggest that AgNPs have curative viricidal activity due to targeting the virus coat protein and affecting virus-vector interactions. Accordingly, AgNPs may contribute to alleviating the natural disease and virus transmission under field conditions. This is the first report on the activity of nanomaterials against plant virus acquisition and transmission by insects.

Real-time signal-vehicle coupled control: An application of connected vehicle data to improve intersection safety.

The proliferation of Connected Vehicles and their ability to collect a large amount of data presents an opportunity for the real-time safety optimization of traffic networks. At intersections, Adaptive Traffic Signal Control (ATSC) systems and dynamic speed advisories are among the proactive real-time safety interventions that can assist in preventing rear-end collisions. This study proposes a Signal-Vehicle Coupled Control (SVCC) system incorporating ATSC and speed advisories to optimize safety in real-time. By applying a rule-based approach in conjunction with a Soft-Actor Critic RL framework, the system assigns speed advisories to platoons of vehicles on each approach and extends the current signal time accordingly. Dynamic traffic parameters are collected in real-time and are used to estimate the current conflict rate at the intersection, which is then used both as an input to the model and to evaluate performance. The system was tested on two different intersections modeled using real-world data through the simulation platform VISSIM. Traffic conflicts were reduced by 41-55%, and vehicle delay was reduced by 21-24%. The results also show that the system functions at lower levels of market penetration, with diminishing returns beyond 50% MPR. The proposed system presents an SVCC framework that is both effective and low in computational intensity to optimize safety at signalized intersections.

Modeling pedestrian behavior in pedestrian-vehicle near misses: A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach.

Using simulation models to conduct safety assessments can have several advantages as it enables the evaluation of the safety of various design and traffic management options before actually making changes. However, limited studies have developed microsimulation models for the safety evaluation of active road users such as pedestrians. This can be attributed to the limited ability of simulation models to capture the heterogeneity in pedestrian behavior and their complex collision avoidance mechanisms. Therefore, the objective of this study is to develop an agent-based framework to realistically model pedestrian behavior in near misses and to improve the understanding of pedestrian evasive action mechanisms in interactions with vehicles. Pedestrian-vehicle conflicts are modeled using the Markov Decision Process (MDP) framework. A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach is implemented to retrieve pedestrians' reward functions and infer their collision avoidance mechanisms in conflict situations. Video data from a congested intersection in Shanghai, China is used as a case study. Trajectories of pedestrians and vehicles involved in traffic conflicts were extracted with computer vision algorithms. A Deep Reinforcement Learning (DRL) model is used to estimate optimal pedestrian policies in traffic conflicts. Results show that the developed model predicted pedestrian trajectories and their evasive action mechanisms (i.e., swerving maneuver and speed changing) in conflict situations with high accuracy. As well, the model provided predictions of the post encroachment time (PET) conflict indicator that strongly correlated with the corresponding values of the field-measured conflicts. This study is a crucial step in developing a safety-oriented microsimulation tool for pedestrians in mixed traffic conditions.

Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning.

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

Multi-type Bayesian hierarchical modeling of traffic conflict extremes for crash estimation.

Most existing Extreme Value Theory (EVT) models were developed based on the total number of conflicts or a single type of traffic conflict to estimate the corresponding frequency of crashes. Using the total number of conflicts to estimate the total number of crashes may make it difficult to diagnose safety problems as countermeasures are usually related to specific conflict/crash types. Single-type EVT models may help to better explain the mechanism of crash occurrence of a certain type, but they only reflect the partial safety of a road entity. Therefore, developing EVT models for multiple types of traffic conflicts would be more representative. However, one important issue in modeling various types of traffic conflicts is that there will be considerable correlation among various conflict types. The modeled crash prediction results would be biased if the conflict type correlation is not accounted for. This study proposes a multi-type Bayesian hierarchical extreme value modeling approach, which has four advantages: 1) integrates multiple types of traffic conflicts; 2) incorporates the influence of several covariates; 3) combines traffic conflicts from different sites; 4) accounts for the unobserved heterogeneity in conflict extremes. The proposed multi-type approach was applied to estimate rear-end crashes and side-impact crashes of left-turning vehicles based on their corresponding traffic conflicts observed from two signalized intersections in the city of Surrey, British Columbia. Both conflict types of left-turning vehicles were characterized by the same indicator time-to-collision (TTC). Overall, the results show that the standard errors of the multi-type model parameters are smaller than those of single-type models. Moreover, the multi-type model produces more accurate crash estimates than its corresponding single-type models. The more accurate crash estimates are probably attributed to the two-type model accounting for the conflict type correlation.

Accounting for seasonal effects on cyclist-vehicle crashes.

Crash data is usually aggregated over time where temporal correlation contributes to the unobserved heterogeneity. Since crashes that occur in temporal proximity share some unobserved characteristics, ignoring these temporal correlations in safety modeling may lead to biased estimates and a loss of model power. Seasonality has several effects on cyclists' travel behavior (e.g., the distribution of holidays, school schedules, weather variations) and consequently cyclist-vehicle crash risk. This study aims to account for the effect of seasonality on cyclist-vehicle crashes by employing two groups of models. The first group, seasonal cyclist-vehicle crash frequency, employs four vectors of the dependent variables for each season. The second group, rainfall involved cyclist-vehicle crash frequency, employs two vectors of the dependent variables for crashes that occurred on rainy days and non-rainy days. The two model groups were investigated using three modeling techniques: Full Bayes crash prediction model with spatial effects (base model), varying intercept and slope model, and First-Order Random Walk model with a spatial-temporal interaction term. Crash and volume data for 134 traffic analysis zones (TAZ's) in the City of Vancouver were used. The results showed that the First-Order Random Walk model with spatial-temporal interaction outperformed the other developed models. Some covariates have different associations with crashes depending on the season and rainfall conditions. For example, the seasonal estimates for the bus stop density are significantly higher for the summer and spring seasons than for the winter and autumn seasons. Also, the intersection density estimate for a rainy day is significantly higher than a non-rainy day. This indicates that on a rainy day each intersection to the network adds more risk to cyclists compared to a non-rainy day.

Validating the Bayesian hierarchical extreme value model for traffic conflict-based crash estimation on freeway segments with site-level factors.

This study validates the Bayesian hierarchical extreme value model that is developed for estimating crashes from traffic conflicts. The model consists of a generalized extreme value distribution that characterizes the behavior of block maxima extremes and a Bayesian hierarchical structure that incorporates the non-stationarity and unobserved heterogeneity into the extreme analysis. In addition to the block-level factors, the site-level factors are also included in the model development for the first time. The model was applied to data of lane change conflicts collected from 11 basic freeway segments in Guangdong Province, China. Block-level factors such as traffic volume per 10 min, number of lane change events per 10 min, and proportion of oversized vehicles per 10 min and site-level factors such as segment length, curvature, and grade were considered. Two types of Bayesian hierarchical extreme value models were developed, including models without site-level factors and models with site-level factors. These models were also compared to at-site models that were developed for 11 segments separately. The results show that Bayesian hierarchical extreme value models significantly outperform the at-site models in terms of crash estimation accuracy and precision. As well, including site-level factors further improves the model performance in terms of goodness-of-fit. This demonstrates the validity of the Bayesian hierarchical extreme value model. The results also show that number of lane change events, segment length, and grade are significant factors which have adverse effect on the safety of lane changes on freeway segments.

Random parameters Bayesian hierarchical modeling of traffic conflict extremes for crash estimation.

The use of Extreme Value Theory (EVT) models for traffic conflict-based crash estimation is becoming increasingly popular with considerable recent advances achieved. The latest advances include developing EVT models that combine several conflict indicators and the use of data from several sites to increase the sample size of conflict extremes. Nevertheless, one important issue while developing EVT models is accounting for the unobserved heterogeneity across different conflict observation sites and road user behaviours which can lead to biased and inefficient parameter estimates and erroneous inferences. This study proposes a random parameters (RP) Bayesian hierarchical extreme value modeling approach to account for the unobserved heterogeneity. The proposed approach is applied to estimate rear-end crashes from traffic conflicts collected from four signalized intersections in the city of Surrey, British Columbia. Traffic conflicts were characterized by four indicators: time to collision (TTC), modified TTC (MTTC), post-encroachment time (PET), and deceleration rate to avoid a crash (DRAC). MTTC was used to fit the generalized extreme value distribution, while the other three conflict indicators were treated as covariates. Six covariates including TTC, PET, DRAC, traffic volume, shock wave area, and platoon ratio were considered to account for non-stationarity in conflict extremes. Several RP, random intercepts (RI), and fixed parameters (FP) Bayesian hierarchical univariate extreme value models were developed. The results indicate that the RP model outperforms both the RI model and the FP model in terms of crash estimation accuracy and precision. Such superiority may be due to the ability of the RP model to better account for the unobserved heterogeneity.

Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation.

There is an increased interest in the use of traffic conflicts as a surrogate safety measure and several traffic conflict indicators have been developed. One of these indicators is the deceleration rate to avoid a crash (DRAC). Generally, the greater the DRAC value, the higher the crash risk and a crash would occur when the DRAC exceeds the maximum available deceleration rate (MADR). It is noted that the MADR varies considerably for individual vehicles and depends on many factors such as the pavement conditions, vehicle weight, tire, and the braking system. Previous studies usually either set a specific value for the MADR or randomly sample values from a truncated normal distribution of MADR. However, little is known about which threshold determination approach is better. Therefore, this study aims to compare the threshold determination approaches for DRAC-based crash estimation applying Bayesian hierarchical extreme value modeling. Using traffic conflict and crash data collected from four signalized intersections in the city of Surrey, several Bayesian hierarchical models are developed for five specific values of MADR and values from two truncated normal distributions of MADR. The crash frequencies estimated from these models were compared with observed crashes. The results show that, in terms of DRAC-based crash estimation accuracy, the truncated normal distribution N(8.45, 1.42)I(4.23, 12.68) of MADR outperforms other determination methods of MADR. Moreover, in terms of DRAC-based crash estimation accuracy and precision, the use of multisite Bayesian hierarchical models outperforms the at-site models. The truncated normal distribution N(8.45, 1.42)I(4.23, 12.68) of MADR is therefore recommended for DRAC-based crash estimation.

A systematic mapping review of surrogate safety assessment using traffic conflict techniques.

Safety assessment of road sections and networks have historically relied on police-reported crash data. These data have several noteworthy and significant shortcomings, including under-reporting, subjectivism, post hoc assessment of crash causes and contributing factors, limited behavioural information, and omitted potential important crash-related factors resulting in an omitted variable bias. Moreover, crashes are relatively rare events and require long observation periods to justify expenditures. The rarity of crashes leads to a moral dilemma-we must wait for sufficient crashes to accrue at a site-some involving injuries and even death-to then justify improvements to prevent crashes. The more quickly the profession can end its reliance on crashes to assess road safety, the better. Surrogate safety assessment methodologies, in contrast, are proactive in design, do not rely on crashes, and require shorter observation timeframes in which to formulate reliable safety assessments. Although surrogate safety assessment methodologies have been developed and assessed over the past 50 years, an overarching and unifying framework does not exist to date. A unifying framework will help to contextualize the role of various methodological developments and begin a productive discussion in the literature about how the various pieces do or should fit together to understand road user risk better. This paper aims to fill this gap by thoroughly mapping traffic conflicts and surrogate safety methodologies. A total of 549 studies were meticulously reviewed to achieve this aim of developing a unifying framework. The resulting framework provides a consolidated and up-to-date summary of surrogate safety assessment methodologies and conflict measures and metrics. Further work is needed to advance surrogate safety methodologies. Critical research needs to include identifying a comprehensive and reliable set of surrogate measures for risk assessment, establishing rigorous relationships between conflicts and crashes, developing ways to capture road user behaviours into surrogate-based safety assessment, and integrating crash severity measures into risk estimation.

A hierarchical bayesian peak over threshold approach for conflict-based before-after safety evaluation of leading pedestrian intervals.

A hierarchical Bayesian peak over threshold (POT) approach is proposed for conflict-based before-after safety evaluation of Leading Pedestrian Intervals (LPI). The approach combines traffic conflicts of different sites and periods to develop a uniform generalized Pareto distribution (GPD) model for the treatment effect estimation. The hierarchical structure has three levels, a data level that consists of modeling the traffic conflict extremes through the POT approach, a latent process level that relates GPD parameters of the data level to certain covariates, and a prior level with prior distributions to characterize the latent process. The approach was applied to a before-after (BA) safety evaluation of leading pedestrian interval (LPI) in Vancouver, BC. Pedestrian-vehicle traffic conflicts were collected from treatment and control sites during the before and after periods using an automated computer vision analysis technique. The treatment effect was measured by the best fitted GPD model with the calculation of the odds ratio (OR). The overall treatment effect varies from 18.1%-20.9% in terms of reduction in the estimated extreme-serious conflicts. The treatment effect indicates a considerable improvement in pedestrian safety after the implementation of the LPI, and the consistent results demonstrate a reliable BA safety evaluation. As such, the proposed approach is recommended as a promising tool for BA safety studies, particularly in cases where collision data is limited.

Self-learning adaptive traffic signal control for real-time safety optimization.

Adaptive traffic signal control (ATSC) is a promising technique to improve the efficiency of signalized intersections, especially in the era of connected vehicles (CVs) when real-time information on vehicle positions and trajectories is available. Numerous ATSC algorithms have been proposed to accommodate real-time traffic conditions and optimize traffic efficiency. The common objective of these algorithms is to minimize total delay, decrease queue length, or maximize vehicle throughput. Despite their positive impacts on traffic mobility, the existing ATSC algorithms do not consider optimizing traffic safety. This is most likely due to the lack of tools to evaluate safety in real time. However, recent research has developed various real-time safety models for signalized intersections. These models can be used to evaluate safety in real time using dynamic traffic parameters, such as traffic volume, shock wave characteristics, and platoon ratio. Evaluating safety in real time can enable developing ATSC strategies for real-time safety optimization. In this paper, we present a novel self-learning ATSC algorithm to optimize the safety of signalized intersections. The algorithm was developed using the Reinforcement Learning (RL) approach and was trained using the simulation platform VISSIM. The trained algorithm was then validated using real-world traffic data obtained from two signalized intersections in the city of Surrey, British Columbia. Compared to the traditional actuated signal control system, the proposed algorithm reduces traffic conflicts by approximately 40 %. Moreover, the proposed ATSC algorithm was tested under various market penetration rates (MPRs) of CVs. The results showed that 90 % and 50 % of the algorithm's safety benefits can be achieved at MPR values of 50 % and 30 %, respectively. To the best of the authors' knowledge, this is the first self-learning ATSC algorithm that optimizes traffic safety in real time.

Real-time conflict-based Bayesian Tobit models for safety evaluation of signalized intersections.

The safety of signalized intersections has traditionally been evaluated at an aggregate level by relating historical collision records for several years to the annual traffic volume and the geometric characteristics of the intersection. This is a reactive and macroscopic approach that gives little insight into how important dynamic signal cycle-related variables can affect intersection safety such as the arrival type and the shock wave characteristics. The objective of this study is to develop traffic conflict-based real-time safety models for signalized intersections using several state-of-the-art techniques. Traffic conflicts were measured by multiple indicators including time-to-collision (TTC), modified time-to-collision (MTTC), and deceleration rate to avoid collision (DRAC). Traffic conflict rate was employed as independent variable while traffic volume, queue length, shock wave area, shock wave speed, and platoon ratio of each cycle were used as covariates in the safety models. Four candidate Tobit models were developed and compared under the Bayesian framework: conventional Tobit model, grouped random parameters Tobit (GRP-Tobit) model, random intercept Tobit (RI-Tobit) model, and random parameters Tobit (RP-Tobit) model. The results showed that the GRP-Tobit model performs best with lowest Deviance Information Criteria (DIC), indicating that accounting for the unobserved heterogeneity across sites can significantly improve the model fit. The model estimation results showed that higher conflict rates were associated with various shock wave characteristics (positive sign for shock wave area, shock wave speed, and queue length) and higher traffic volume. Lower conflict rates were related with higher platoon ratio (favorable arrival patterns). The developed models can have potential applications in real-time safety evaluation, real-time optimization of signal control, and connected and autonomous vehicles (CAV) trajectories planning.

Cyclist-vehicle crash modeling with measurement error in traffic exposure.

Exposure measures are always among the explanatory variables of any crash model. Regardless of the technique used to model crash, the mean crash frequency will increase with an increase in exposure since more crashes are likely to occur at higher exposure. For cyclist-vehicle crash models, bike and vehicle exposure measures are essential for an accurate and reliable estimate of the cyclist crash risk. However, traffic exposure measures are an example of variables that are measured with error. Generally, measurement error in regression estimates has three effects: 1) produce bias in parameter estimation for statistical models, 2) lead to a loss of explanation power, 3) mask important features of the data. This study proposes a full Bayesian Poisson Lognormal crash models that account for measurement error in traffic exposure measures (i.e., Vehicle Kilometers Travelled and Bike Kilometers Travelled). The underlying approach is to adjust the traffic exposure measures for measurement error to improve the accuracy of the crash model and crash model estimates. The full Bayesian models are developed using data for 134 traffic analysis zones (TAZs) in the city of Vancouver, Canada. The results show that Poisson Lognormal models that account for measurement error have a better fit for the modeled cyclist-vehicle crash data compared to traditional Poisson Lognormal models. The estimates of the Poisson Lognormal model that accounts for measurement error are consistent, with traditional Poisson Lognormal models' estimates except for the BKT and VKT estimates. Estimates of the BKT and VKT increased after introducing measurement error, which indicates an underestimation (downward bias) to BKT and VKT estimates in case of overlooking measurement error.

Investigating safety effects of wider longitudinal pavement markings.

Many road authorities in Canada have been contemplating the use of wider longitudinal pavement markings (LPMs) to enhance road safety and driver comfort. However, conclusive evidence on the safety impacts of wider LPMs has not been available. To address this gap in the literature, this study was conducted to investigate the safety impacts of wider LPMs. The study adopted the Full Bayes approach to conduct a before and after safety evaluation, using data collected from 38 treatment sites (highway segments) from three Canadian jurisdictions (i.e., British Columbia, Alberta, and Quebec). Collision and traffic data were obtained from the 38 sites over a period of eight years (2008-2015). The widths of LPMs at all sites were increased between 2012 and 2013, which enables a before and after safety evaluation to investigate the impact of the wider markings on the collision frequency. The results showed an overall significant reduction in both total collisions and target collisions (i.e., run-off-the-road collisions) by 12.3 % and 19.0 %, respectively, after implementing the wider LPMs. Total collisions were reduced by 11.1 %, 27.5 %, and 1.1 % in Alberta, British Columbia, and Quebec, respectively. Similarly, a reduction in the run-off-the-road collisions that ranged between 22.7 % and 28.9 % were observed in the three jurisdictions. The results suggest that wider longitudinal pavement markings can reduce collisions and improve safety on Canadian highways. As such, road authorities should consider using this intervention to enhance road safety, particularly, at locations that experience a high frequency of run-off-the-road collisions.