Quasi-induced exposure (QIE) is an effective technique for estimating the exposure of a specific driving or vehicle population when real exposure data are not available. Typically crash prediction models are carried out at the site level, that is, segment or intersection. Driving population characteristics are generally not available at this level, however, and thus are omitted from count models. Because of the sparsity of traffic crashes, estimating driving population distributions at the site level using crash data at individual sites is challenging. This study proposes a technique to obtain demographic proportions to incorporate in the count models as an exposure at each site by aggregating similar adjacent sites until significant demographic proportions are obtained. Information on driver gender, age, and vehicle type are obtained by QIE using five years (2010–2014) of crash data; and road inventories are obtained for 1,264 urban four-lane divided highway segments in California. Count models including only site level factors were compared with models including both crash level and site level factors. The latter outperformed the former in relation to mean prediction bias and mean absolute deviation statistics on holdout sample predictions. Results indicate that teen drivers are more crash prone in total and in fatal plus injury severity crashes. For senior drivers, crash risk increases with the increase in severity level. The presence of vehicles other than passenger cars and trucks reduces total and property damage only crash counts. Female drivers are associated with higher total and fatal plus injury crash counts.

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Crash risk depends on several factors, driver factors being the most significant of all. Previous studies have tended to use roadway and driver demographic information to explain crash risk, overlooking driver psychological characteristics, which are also important for crash risk estimation. Crash data from police reports are available only for reportable crashes and do not detail driver characteristics or comparable information on driving activities. Naturalistic driving studies (NDSs) offer unique opportunities to obtain information about driver attributes, behavior, or other precrash factors for predicting crash occurrence. This study estimated NDS event-oriented models to evaluate the interaction between driver attributes and roadway environmental factors for predicting safety critical events. A latent class clustering approach was used to uncover categories of drivers by psychological, perceptual, and cognitive characteristics, and by driving experience. The results revealed four driver types: risk-taker, careful-impaired, careful-unimpaired, and distractible. These types were incorporated in mixed-effects binary logistic models, with roadway, traffic, and environmental variables to estimate and predict crash risk. The models that included driver factors more successfully predicted crash risk than those without. Risk-takers showed the highest probability of being in crashes. However, careful-impaired drivers—those whose impairments made it difficult to identify the location of another vehicle, visualize missing information, who had difficulties with visual–spatial perception and executive functioning—posed a higher crash risk in roadway conditions such as snow, lack of lane markings, and certain traffic operating conditions. The results point to novel avenues for educational and behavioral interventions to improve road safety.

Purpose: The adoption and use of mHealth is considered as an effective intervention to improve health sector performance in developing countries across the world. Yet mHealth is in the early stage of its implementation in many countries. This study aims to identify the patterns, extent, motivations, and problems of mHealth applications in Bangladesh. Methods: The study was done in Dhamrai Upazila of Dhaka district where 250 mobile-phone users were interviewed to identify their extent and reasons of using mHealth services. Based on literature review and focus group discussion in the study area, the research confined five dimensions or reasons for using mHealth services, six issues for motivational and discouraging factors each. Both descriptive and inferential statistics were used to analyze data using statistical software STATA. Findings: Calling doctor’s private office is top ranked mHealth application. In this connection, 38 percent respondents used at least four types of mHealth applications among five dimensions and 30 percent respondents used all types of mHealth applications. On an average, respondents used near about four types of application where the mean application is3.82 out of 5. Hypothesis testing result shows that male’s mHealth application is higher than that of female. Again, average mHealth application of extended family is higher than that of nuclear family. Both findings are statistically significant at 1 percent level. Regarding the motivational factor of using mHealth application, employed group has ranked time saving’ and unemployed group has ranked ‘accessibility from remote area’ as the most motivational factor. On the other hand, both groups have ranked ‘not reaching focal person timely’ as the most discouraging factor of adopting mHealth. Spearman’s rank correlation coefficient reports that, between employed and unemployed group, there exist77 percent resemblance in benefit rank and 94 percent resemblance in problem rank. Proper regulation is essential to have proper coordination among health service providers, seekers and telecommunication service providers.