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The abrupt switch to work from home during the COVID-19 pandemic has not only altered people's commutes but also changed their entire work-life balance. While some workers were quick to adapt and maintain or even increase productivity, others experienced a decrease in productivity. Self-assessed productivity changes after switching from traditional in-person work to work from home is studied using a survey of 3,780 workers (including full-time college students). A probabilistic statistical model is used to estimate the probabilities that workers' self-reported productivity during the pandemic remained the same, decreased, in some ways increased and in other ways decreased, or increased. The model estimation results identify workers who were resilient and adaptable (having a higher probability of increasing their productivity) and those less adaptive workers, who were more likely to experience a decrease in productivity. It was found that race, ethnicity, household income, household size, education, gender, the presence of children in the household, level of life satisfaction, being a student, prior experience with online meetings, and commute distances all play a role in how the workers' productivity changed. This study provides insights for the development of effective policies to improve equity (by targeting vulnerable populations) and sustainability (by retaining the transportation and environmental benefits of telework) in the post COVID-19 reality. [ABSTRACT FROM AUTHOR]

A lower helmet-wearing rate and overspeeding in Pakistan are critical risk behaviors of motorcyclists, causing severe injuries. To explore the differences in the determinants affecting the injury severities among helmeted and non-helmeted motorcyclists in motorcycle crashes caused by overspeeding behavior, single-vehicle motorcycle crash data in Rawalpindi city for 2017–2019 is collected. Considering three possible crash injury severity outcomes of motorcyclists: fatal injury, severe injury and minor injury, the rider, roadway, environmental, and temporal characteristics are estimated. To provide a mathematically simpler framework, the current study introduces parsimonious pooled random parameters logit models. Then, the standard pooled random parameters logit models without considering temporal effects are also simulated for comparison. By comparing the goodness of fit measure and estimation results, the parsimonious pooled random parameters logit model is suitable for capturing the temporal instability. Then, the non-transferability among helmeted and non-helmeted overspeeding motorcycle crashes is illustrated by likelihood ratio tests and out-of-sample prediction, and two types of models provide robust results. The marginal effects are also calculated. Several variables, such as age, cloudy and weekday indicators illustrate temporal instability. Moreover, several variables are observed to only show significance in non-helmeted models, showing non-transferability across helmeted and non-helmeted models. More educational campaigns, regulation and enforcement, and management countermeasures should be organized for non-helmeted motorcyclists and overspeeding behavior. Such findings also provide research reference for the risk-compensating behavior and self-selected group issues under overspeeding riding considering the usage of helmets. [ABSTRACT FROM AUTHOR]

Many states in the United States have passed the primary enforcement seat-belt law. Though there is strong evidence from previous studies that enhanced seat-belt enforcement interventions can substantially increase seat-belt use, thereby reducing fatalities. It is still necessary to evaluate the long-term effects of implementing the primary seat-belt law. In this study, changes in fatalities over time after the primary seat-belt law enactment are investigated using before-and-after study with the comparison group methods for fatality modification factors (FMFs). This study confirms that the number of adult fatalities without seat-belt has significantly decreased by 17.29%. Another key finding is that the fatality rates in states with a higher maximum fine amount are significantly lower than those with a lower one, however, the decrease in fatality trend is not as effective above about $100 fine. Implementing the primary seat-belt law is significantly effective in reducing fatalities without seat-belt in the long-term. Meanwhile, the relationship between fatalities reduction and the maximum fine amount is not positively linear related. It is imperative that states with the secondary seat-belt laws must reform their seat-belt laws to the primary seat-belt law. An appropriate fine amount can be determined to maximize the effectiveness of the primary seat-belt law. [ABSTRACT FROM AUTHOR]

Real-time traffic movements at intersections is vital for transportation and traffic engineering. It helps in providing intersection traffic data and optimizing signal control plans. This study attempts to extend the data coverage by developing algorithms to estimate through and left-turn movements in real-time at signalized intersections. This study is the first attempt to estimate short-term traffic movement counts at signalized intersections at the cycle-level for signal control. Real-time data were collected from 19 intersections along two main corridors in Orange county, Florida. A framework was proposed to identify different variables considering the characteristics of the cycle-level traffic movement estimation. To develop generic algorithms, a proposed approach was utilized to generalize the traffic movement estimation at the corridor level. Signal timing and movement counts at the upstream and downstream intersections were utilized to estimate through and left-turn movements at target intersections. An extensive comparison study was carried out based on the processed data. The experimental results show that the proposed Gradient Boosting model outperformed the baseline models with Mean Absolute Percentage Error 9.53% and 4.6% for the through and left-turn movements, respectively. In addition, the transferability of the developed models for the abnormal traffic conditions was validated and the estimated model proved that it could instantaneously capture the traffic change due to an accident. It is expected that the proposed method could reduce the sensor cost and extend the movement data coverage, which could help in developing efficient signal control plans at signalized intersections. [ABSTRACT FROM AUTHOR]

Interchanges are important highway facilities by connecting two or more freeways/expressways; however, it has been considered more dangerous than basic segments because of drivers' decision-making to stay or exit, weaving, variations in speeds, etc. In this study, we aim at contributing to the literature by using a quasi-induced exposure method and logistic regression modeling approach to identify contributing factors associated with the risk of causing an interchange crash. To this end, the 2014 traffic crash data, roadway and drivers' characteristics were collected from Florida. The modeling results indicate that drivers' age, gender, distraction, alcohol, and other factors have statistically significant effects. In addition, the finding suggests that drivers are more likely to cause crashes at cloverleaf and direct connection interchanges than at diamond interchanges. Furthermore, a support vector machine (SVM) model was applied to compare its predictability with the logistic regression model, and a sensitivity analysis was conducted. From the comparison of the areas under the receiver operating characteristic curve (AUC) of the two approaches, it shows that the SVM model outperforms the logistic regression model. It is expected that the findings would help establish effective strategies to reduce traffic crashes at interchanges by targeted education, engineering, and enforcement. [ABSTRACT FROM AUTHOR]

Recently, traffic agencies have implemented Bluetooth detection systems (BDS) on arterials to collect traffic data and purchased data from private sector companies such as HERE, INRIX, and TomTom for real-time traffic monitoring and long-term traffic system management and performance evaluation. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. This study utilized high-resolution GPS trajectory to evaluate data from private sector, HERE and BDS of a principal arterial corridor in Orlando, FL, USA. The results showed that the accuracy and reliability of BDS data are better than private sector data on the study corridor, which might be credited to a better presentation of the bimodal traffic flow pattern on signalized arterials. In order to improve the quality of private sector data, information about bimodal traffic flow was extracted by a finite mixture model from historical BDS data and incorporated with the real-time private sector data by a Bayesian inference framework. The evaluation of the augmented data showed that the augmentation framework is effective for the most part of the corridor except for segments highly influenced by traffic from or to the expressway ramps where evaluation datasets might have some bias. [ABSTRACT FROM AUTHOR]

This study focuses on fully investigating the effects of bicycle facilities and other contributing features on bicycle crashes at intersections. Although multiple studies have been conducted to analyze bicycle crashes and volumes, no study to date has attempted to integrate bicycle crashes and volumes at the modeling stage. Thus, a novel methodological framework for simultaneously analyzing bicycle crashes and volumes is proposed to better identify the effects of bicycle facilities and other contributing features. Kruskal–Wallis test is adopted to classify different bicycle lanes and shared paths for the modeling analysis. The modeling results reveal the significant effects of bicycle facility-related factors such as different types of bicycle lanes, the width of shared path, and the distance between the shared path and the edge of roadway on bicycle crashes. Other factors such as vehicle volume, bicycle volume, number of lanes, percentage of government and institutional land use, and population density are also found to significantly affect bicycle crashes at intersections. Based on the estimated model, the safety-in-numbers effect was identified when bicycle crash rates decrease as bicycle volumes increase. Findings in this study could assist traffic engineers to select appropriate designs to improve bicycle safety at intersections. [ABSTRACT FROM AUTHOR]

Crash injuries are major concerns in the transportation area due to the severe social and economic impacts. In this study, instead of focusing on the total crashes, crash injury severities caused by out-of-state drivers (drivers whose licenses were issued by other states or countries) were targeted. The different influencing factors between crashes caused by in-state and out-of-state drivers were investigated, and the impacts of drivers' residency on the injury severity levels were identified. Besides, to simultaneously considering the unobserved heterogeneity issue of crash observations and relaxing the parallel lines assumption (constant parameter estimates across different severity levels), a multilevel thresholds of change modeling (MTCM) approach was proposed. Then, empirical analyses were conducted based on the data from state of Florida, where crashes occurred on roadway segments and intersections were analyzed separately. The analyses results showed that MTCMs provided better model goodness of fits comparing to the traditional ordered logit models and random effects ordered logit models. In addition, interesting findings have been concluded through comparing the variables affecting the two driver population, and suggestions were made to improve the crash injury severity from the perspectives of police enforcement, driver education, and roadway management. [ABSTRACT FROM AUTHOR]

Fog is a weather condition that reduces visibility of the driving scene, while slow traffic may be formed due to the bottleneck on freeways. This phenomenon may lead to higher rear-end crash risk when vehicles approach slow traffic, since drivers might not observe the speed reduction ahead of them timely with the reduced visibility and could not have enough time to respond. This study aims to develop a variable speed limit (VSL) control strategy to reduce the rear-end crash risk at freeway bottlenecks under fog conditions. A VSL control algorithm was developed with consideration of the different relationships between the gap and visibility distance. The VSL strategy was also tested in the fully connected vehicles (CV) environment. A feedback control framework was developed to combine the VSL and CV control. The proposed VSL strategy was implemented and tested for a freeway section with a bottleneck through the micro-simulation software VISSIM and the intelligent driver model (IDM) was employed to account for car following in the CV environment. Finally, two measurements, which include time-to-collision at braking (TTCbrake) and total travel time (TTT), were employed to evaluate the effectiveness of the proposed control strategy. The results demonstrated that the VSL control played an important role in reducing rear-end crash risk and the effects of the VSL control could be affected by compliance rates. In addition, it was found that the CV environment could further enhance the safety benefits of VSL control and improve the traffic efficiency. [ABSTRACT FROM AUTHOR]

The evident conflict in upgrading policies between several entities necessitates the crucial need to develop a reliable urban appraisal method to ensure successful interventions. The Egyptian initiative, aims at categorizing them into 'unsafe' versus 'unplanned' areas. Yet, it has ignored some basic socio-spatial and psychological quality of life indicators. Accordingly, the study aims at strengthening the local-level data collection through testing the reliability of the 'ISDF' index versus the two other sets of indicators statistically, by conducting an urban socio-economic survey on 30 different Ashwa'iyyat areas in Egypt. [ABSTRACT FROM AUTHOR]

In the recent decade, walking has been encouraged as an active mode of transportation, which could reduce congestion and air pollution and also improve community health. However, pedestrians are more vulnerable to traffic crashes compared with other road users, especially in developing countries such as Saudi Arabia. This paper examines the association among traffic volume, land-use, socio-demographic and roadway characteristics factors, and the frequency of pedestrian crashes based on macro-level safety analysis using data from Riyadh, the Capital of Saudi Arabia. Two Bayesian spatial Poisson-lognormal models for total and severe pedestrian crashes are developed in this study. The results show that the factors that affect total pedestrian crash occurrence are different from those affecting severe pedestrian crash. Several implications for pedestrian safety policies in Riyadh are suggested based on the results. [ABSTRACT FROM AUTHOR]

Road traffic crashes are a major cause of concern in Saudi Arabia. Although the general view of the design and quality of Saudi Arabia's road network meets global standards, there is not enough in-depth analysis of crash locations and contributing factors and their relationship with user characteristics or behaviors. This study aims to investigate, analyze, and examine the safety problems on a main corridor in the Eastern Province and then suggest treatments to enhance the safety and explore the crash-risk factors associated with crashes on that corridor. A multiple survey approach was used to collect data from road users and experts. The survey addresses traffic safety issues and user behaviors on the corridor. To achieve the study aims, one-way analysis, two-way contingency table analysis, and structural equation modeling were applied. The results showed a need for adequate shoulders along the corridor, and the markings, signs, and signal timing should be reevaluated. In addition, greater restrictions on the use of mobile phones while driving are needed. Also television and radio programs are recommended for traffic awareness as reported by participants. Lastly, it showed that the multiple survey approach can be very useful when crash and geometric data are not available or incomplete. [ABSTRACT FROM AUTHOR]

Over the last decade, bicycle ridership has been encouraged as a sustainable mode of transportation as it is economic and has less impact on the environment. Still, higher crash risk for bicyclists remains a deterrent for people to choose bicycling as their major mode of travel. As a first step in investigating bicycle safety, it is essential to identify not only the characteristics of the areas with the excessive number of bicycle crashes; but also those of the areas where crash-prone bicyclists reside. Therefore, this study aims to identify contributing factors for two subjects: (1) the number of bicycle crashes in the crash location's ZIP code and (2) the number of crash-involved bicyclists in their residence's ZIP. In order to achieve these objectives, a multivariate Bayesian Poisson lognormal CAR (conditional autoregressive) model was developed to identify the contributing factors for each subject. Regarding the model performance, the multivariate model outperformed its univariate counterpart in terms of DIC (deviance information criterion). Subsequently, hot zones for the two target zones were identified based on the modeling results. It is expected that practitioners are able to understand the contributing factors for bicycle crashes and identify hotspots from the results suggested in this study. In addition, they could implement safety countermeasures for the identified problematic locations to effectively reduce bicycle crashes. [ABSTRACT FROM AUTHOR]

Objective: Emergency medical services (EMS) play a vital role in the postcrash effort to reduce fatalities by providing first aid treatment and transportation to medical facilities. This study aims to analyze the time required for crash reporting and EMS arrival in fatal traffic crashes and to explore the effects of EMS time on the traffic injury severity.Methods: The time required for EMS reporting, arrival, and transport to hospital was calculated by location type and roadway functional classification using 2016 Fatality Analysis Reporting System (FARS) data. Subsequently, an ordered probit model was developed to identify factors contributing to injury severity considering EMS time.Results: The average time for the crash-reporting duration is 5.38 min, the reporting-scene arrival interval is 10.52 min, and the scene-hospital interval is 34.72 min. The average crash reporting and reporting-scene arrival intervals were the longest on conventional roads in rural areas and the shortest on conventional roads in urban areas. The average scene-hospital interval was longest in conventional rural areas and the shortest on freeways/expressways in urban areas. The developed random effects ordered probit model shows that prolonged reporting-scene arrival and scene-hospital intervals result in more severe injuries. The result also presents that crash type, violation, age, location, lighting condition, and alcohol/drug involvement have significant effects on injury severity.Conclusions: The key findings from this study indicate that EMS times differ according to urban/rural location and road functional classification and that reporting-scene arrival and scene-hospital intervals have significant effects on injury severity along with various factors. It is expected that the findings from this study can be used to develop effective and practical strategic plans to minimize EMS reporting, arrival time, and transport to hospital and therefore decrease the traffic injury severity. [ABSTRACT FROM AUTHOR]

The proportion of rear-end crashes is the highest for expressways. An effective ways to reduce the rear-end crash risk is Active Traffic Management (ATM), and knowing the mechanism of how contributing factors affect crash risk in space and time is the foundation of ATM. However, the existing studies are mainly based on highly aggregated traffic data. It is hard to capture the evolution mechanisms of crash risk. Meanwhile, crash risk mechanisms might be heterogeneous in smooth and congestion states. This study explored the crash mechanisms in different traffic states with high-resolution trajectory data. First, an ordered clustering method is used to divide a four km expressway section into several segments as the spatial unit. Second, the spatial–temporal ranges are decided by the spatial–temporal correlations analyses between crash risk and potential contributing factors. Thirdly, three types of time-series models are established to quantitatively obtain the impacts of contributing factors on the crash risks. The results showed that crash risk is mainly decided by the upstream contributing factors under smooth states, and determined by the downstream factors under congestion conditions. [ABSTRACT FROM AUTHOR]

Minority status and low-income are two important criteria on which environmental-justice areas are being identified. These unique areal characteristics have been of particular interest to traffic safety analysts in investigating pedestrian safety planning factors in deprived areas. In this study, a pedestrian crash model was developed for these areas explicitly accounting for the spatial autocorrelations of different zonal factors commonly used in the traditional transportation planning models. It was found that the Bayesian Poisson-lognormal model with a spatial effect term performed the best compared to the models without accounting for spatial autocorrelations. [ABSTRACT FROM AUTHOR]

Visibility is a critical component to the task of driving on all types of roads. The visibility detection and warning systems provide real-time, automated detection as well as appropriate responses to counteract reduced visibility conditions due to fog, heavy rain, snow, smoke, dust, or haze by informing drivers of present conditions and lowering the speed limits to match the reduced visibility condition. The objective of this research is to provide a synthesis of visibility detection systems and traffic control techniques that are developed and/or implemented in the United States and around the world. This article provides an overview of the best practices of fixed visibility systems at areas of recurrent dense fog and mobile systems for seasonal visibility reduction for areas of predicted seasonal fog or smoke from wildfires. Ongoing research efforts of developing new camera-based visibility detection systems are also discussed. [ABSTRACT FROM AUTHOR]

This article investigates the factors affecting drivers' stated behavior in adverse visibility conditions and examines whether drivers rely on and follow advisory or warning messages displayed on portable changeable message signs (CMS) and/or variable speed limit (VSL) signs in different visibility and traffic conditions, and on two types of roadways: freeways and two-lane roads. A multiple-approach survey was designed to collect opinions and stated data from Central Florida drivers. Categorical data analysis techniques such as conditional distribution, odds ratio, and chi-squared tests were applied. In addition, two modeling approaches, bivariate and multivariate probit models, were estimated. The results revealed that gender, age, road type, visibility condition, and familiarity with VSL sign were the significant factors affecting the stated likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this study were to determine the content of messages that would achieve the best-perceived safety and drivers' compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that respondents thought that "Caution--fog ahead--reduce speed" was the best message and that using that CMS and VSL signs together was the best way to improve safety during such inclement weather situations. Based on the findings of the present study, several recommendations are suggested as guidelines to improve safety in reduced visibility conditions. [ABSTRACT FROM AUTHOR]

This study investigates the effects of the traffic flow parameters related to individual drivers' lane-changing and car-following behavior on the occurrence of sideswipe and rear-end crashes on freeways. A total of 184 sideswipe and 605 rear-end crashes on I-4 freeway in Orlando, Florida, with the loop detector data were used for the analysis. A set of the Bayesian logistic regression models were developed to estimate the likelihood of crashes in a specific lane compared to the adjacent lanes using the 5 min average and lane-by-lane traffic flow parameters. The analysis results showed that the significant traffic flow parameters affecting crash occurrence in a specific lane are distinctively different between sideswipe and rear-end crashes. The flow-related variables were significant in the sideswipe crash models whereas the speed-related variables were significant in the rear-end crash models. The results suggest that the lane-by-lane traffic flow parameters are considered as surrogate measures of lane-changing and car-following behavior that are associated with the occurrence of sideswipe and rear-end crashes. These parameters can potentially be used for real-time monitoring of the likelihood of crash occurrence by lane on instrumented freeways. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems-based lane management technologies were introduced to work zones in an attempt to reduce congestion and diminish queue lengths. Two forms of lane merging-the early merge and the late merge-were designed to advise drivers on definite merging locations. This study suggests two SDLMS-early merge and late merge-to supplement the current Florida Maintenance of Traffic (MOT) plans. Data were collected in work zones on I-95, Florida using three different traffic maintenance treatments. The first MOT plan treatment was the standard MOT used by the Florida Department of Transportation. The second MOT plan was the early SDLMS, and the third MOT was the late SDLMS. Results showed that the maximum queue discharge rate (or capacity) of the work zone was significantly higher for the early SDLMS compared to the conventional Florida Department of Transportation MOT plans. The late SDLMS did not result in significant increase in the work zone capacity. Moreover, results showed that early merging rate was the highest for the early SDLMS and the lowest for the late SDLMS, which suggests that some drivers were complying with the messages displayed by the system. [ABSTRACT FROM AUTHOR]

A large-scale injury severity analysis for arterial roads in Florida was conducted using roadway and crash information. The objective was to compare the reliability of different arterial road crash involvement modeling frameworks (combining crash locations vs. traditional analysis by location) and to find new contributing factors using newly available data. In the first phase of the investigation, logistic regression models of all driver involvements by roadway locations (i.e., segments and intersections) were developed and compared. The second phase involved an extension of these models, controlling by crash types, including rear-end, angle, and left turn. The results suggest a method for arterial road systematic analysis by modeling all crashes combined complemented by two models of involvements at signalized intersections and on segments combined with access points (nonsignalized intersections). The crash type models showed some possible safety strategies not clearly shown by the road entity models alone. Important contributing factors were significant, including driver age, gender, speeding, aggressive driving, seat belt use, alcohol or drug use, driver ejection, type of collision, type of vehicle, and point of impact. Significant road-related factors included speed limit, ADT per lane, access management class, land use, lane and sidewalk width, roadway lighting, curves, and friction characteristics. Suggested applications for future crash analysis and roadway design guidelines are discussed. [ABSTRACT FROM AUTHOR]

Crashes that occur on freeway ramps and in the vicinity of ramps were analyzed using a variety of statistical methods. The data used in this study are the 6 year records of crashes that occurred on 98 ramps of a 25 mile section of Interstate-4 freeway in the Orlando, Florida, metro area. Log linear models were used to identify the relationship among the frequency of ramp crashes, ramp geometry, and ramp traffic volume. Binary logit models and bivariate probit models were developed to observe the propensity of specific type of ramp crashes in terms of curvature of ramps, number of vehicles involved in crashes, and collision type. The binary logit model was also used to identify the factors correlated with injury severity of drivers involved in ramp crashes. The results of model estimates showed that crash characteristics differ by the location of crashes with respect to ramps: ramp related and ramp intersection related crashes. It was found that the presence of advisory speed signs potentially reduces the likelihood of the occurrence of crashes on off ramps and rear-end crashes on ramps. Finally, it was found that female drivers, single vehicle crashes, and shorter ramp length are correlated with higher probability of fatal and severe crashes on ramps. The findings suggest some important factors closely related to ramp crash risk and hidden relationships that have not been identified in the past. [ABSTRACT FROM AUTHOR]

Advanced driving simulators have a potential to be utilized to assess crash risks of driving behaviors, traffic surroundings, and highway designs. This study focused on investigating if drivers' stop/go decisions due to signal change in the simulator's virtual reality can be used to assess rear-end crash risk at the signalized intersection. A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. Using the virtual signalized intersection, a driving simulator experiment was conducted to test driver's stop/go decisions at two approaches of the intersection that respectively showed higher and lower rear-end crash risks in the crash history analysis. The experiment results showed that the variability in drivers' stop/go decisions at the higher crash-risk location is higher than that at the lower crash-risk location. Further, through modeling rear-end conflicts based on driver's stop/go probability as a function of potential time to intersection, it was found that the rear-end crash tendency at the two locations displayed by the driver's stop/go behavior in driving simulator is consistent with the finding from the trend in crash history analysis. This research supported that driver's stop/go behavior in a driving simulator can be utilized to assess rear-end crash risk at signalized intersections to seek effective engineering countermeasures to lower crash rates for the high-risk locations. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive overview of the novel idea of real-time traffic safety improvement on freeways. Crash prone conditions on the freeway mainline and ramps were identified using loop detector data, then intelligent transportation systems (ITS) strategies to reduce the crash risk in real-time are proposed. Separate logistic regression models for assessing the risk of crashes occurring under two speed regimes were estimated. The results show that the variables in the two models are consistent with probable mechanisms of crashes under the respective regimes (high-to-moderate and low speed). This study also discusses the analysis of parameters and conditions that affect crash occurrence on freeway ramps by type (on-/off-ramp) and configurations (diamond, loop, etc.) using five-minute traffic flow data obtained from the loop detectors upstream and downstream of ramps to reflect actual traffic conditions prior to the time of crashes. Finally, several traffic management strategies are evaluated for the resulting traffic safety improvement in real-time using PARAMICS microscopic traffic simulation and the measures of crash potential determined through the logistic regression models. The results show that, while variable speed limit strategies reduced the crash potential under moderate-to-high speed conditions, ramp metering strategies were effective in reducing the crash potential during the low-speed conditions. [ABSTRACT FROM AUTHOR]

Reactive traffic management strategies such as incident detection are becoming less relevant with the advancement of mobile phone usage. Freeway management in the 21st century needs to shift focus toward proactive strategies that include anticipating incidents such as crashes. A simple approach to identify freeway locations with high probability of crashes through real-time traffic surveillance data is presented here. The crash and loop detector data for the study was collected from 36-mile corridor of Interstate-4 in Orlando, Florida. The analysis is based on simple (one covariate) logistic regression models developed under a matched study design. Individual traffic parameters obtained one at a time from series of loop detectors have been examined as potential covariates to these models. Hazard ratio for each individual covariate is the output from the models. Based on the hazard ratio and its statistical significance it was found that the log of coefficient of temporal variation in speed, standard deviation of volume, and average occupancy expressed as percentage are the parameters that are most critically associated with potential occurrence of multivehicle crashes. The univariate logistic regression models were validated based on their classification performance on an independent set of crash data. Using the relative location of loop detectors measuring these parameters with respect to the crash location contour maps depicting spatial-temporal distribution of crash risk was generated. Using the model outputs, a generic strategy to assess crash risk in real-time is also proposed. With this strategy one can identify the segment of freeway having high potential for crash occurrence within next 15–20 minutes. The crash mitigation and law enforcement set up can be prepared for dispatch to such locations based on the real-time assessment of crash potential. [ABSTRACT FROM AUTHOR]

Many housing projects suffer from striking dissonance between the physical built environment and their social life. Accordingly, there is a significant need to deduce socio-spatial assessment tools that can predict the resulting community satisfaction based on realizing their psychological and social needs (nonphysical aspects) in terms of the components forming the built environment (physical aspects; architectural, urban design, and planning) to provide an acceptable level of social life quality. From this view, this paper presents a statistical methodology for predicting these qualitative aspects through conducting a questionnaire on four neighborhoods of intermediate social housing level, in Egypt. [ABSTRACT FROM AUTHOR]