Ride Sharing offerings are becoming more famous each day. In Bangladesh, younger human beings are the usage of this provider. But the helmet great in Bangladesh is not so precise. And more often than not the cost is simply too high than public transport in Bangladesh. Government should take right steps to experience sharing offerings. Contact base ride-sharing services isn't secured in Bangladesh.

As Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) technologies become more prevalent, evaluating their safety performance in complex, real-world environments has emerged as a critical research priority. This study presents a systematic literature review of crashes involving vehicles equipped with ADAS and ADS. Using the PRISMA methodology, this review analyzed 51 peer-reviewed articles and books published between 2018 and 2025. Articles were identified through comprehensive searches of ScienceDirect, TRID, and the Transportation Research Record. The selected studies were organized into five thematic categories: safety and crash characteristics, human interaction and user behavior, testing tools and simulations, cybersecurity and perception risks, and policy and governance. Findings reveal that while AV technologies show promise in reducing crash severity and frequency, performance is highly context-dependent, affected by roadway geometry, weather, system limitations, and user behavior. ADAS-related crashes are more common on highways and often involve human error, whereas ADS crashes are more likely in urban settings and linked to system disengagements. The review also highlights significant gaps in data availability, standardized evaluation protocols, and human-machine interaction research. Despite growing literature, many studies rely heavily on simulations and legacy datasets, limiting generalizability. To support safer AV deployment, future research must prioritize real-world testing and user trust frameworks deployment strategies. This review offers a knowledge base to guide policymakers, researchers, and industry stakeholders in advancing the development and regulation of vehicle automation technologies.

Background: Pedestrian fatalities remain a critical urban transportation safety concern, with crash outcomes potentially influenced by temporal conditions, vehicle characteristics, and contributing factors. This study investigates the associations between these characteristics and pedestrian fatal-crash outcomes in New York City (NYC) from 2021 to 2025, with particular attention to nighttime occurrence and solar darkness.Methods: Police-reported collision data from NYC Open Data were analyzed. Of 486,548 collision records, 42,911 pedestrian casualty crashes were identified, including 606 crashes involving at least one pedestrian fatality. Geographic information system (GIS) techniques were used to recover missing borough information. Descriptive statistics, chi-square tests, and multivariable logistic regression models were employed to examine temporal patterns, first-recorded vehicle types, and police-reported contributing factors. Solar darkness was classified using crash-specific sunrise and sunset times for 40,578 crashes. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated across five regression specifications.Results: The fatal-crash proportion was higher during nighttime (2.62%) than daytime (1.03%). In the primary regression model, nighttime was associated with increased odds of pedestrian fatal-crash occurrence (aOR = 2.55, 95% CI: 2.16–3.00), and the association persisted after additional adjustment for vehicle types and police-reported contributing factors (aOR = 2.72, 95% CI: 2.30–3.23). Exploratory analyses identified elevated fatal-crash odds for crashes involving first-recorded heavy/commercial vehicles, buses, pickup trucks, and SUVs/station wagons. Solar darkness was also associated with higher fatal-crash odds (aOR = 2.59, 95% CI: 2.16–3.09), and the association remained significant after adjustment for calendar month and five time-of-day categories (aOR = 2.32, 95% CI: 1.67–3.22). Weekend associations were less consistent across model specifications.Conclusions: Nighttime was consistently associated with increased odds of fatal outcomes among reported pedestrian casualty crashes, while vehicle characteristics and police-reported contributing factors revealed additional differences in crash outcomes. Solar darkness provided complementary information beyond conventional clock-based nighttime definitions. These findings support integrating temporal, vehicle-related, and environmental characteristics into urban pedestrian safety assessments to better identify conditions associated with fatal crashes.

Road traffic injury remains a persistent public-health and transport-planning challenge, sitting at the intersection of transportation safety, urban policy, and intelligent transportation systems (ITS). This study reanalyzes open, country-level panel data from World Health Organization Sustainable Development Goal (SDG) indicator 3.6.1, distributed by Our World in Data, to characterize the evolution of road traffic death rates across 182 countries at five time points, 174 of which carry an assigned United Nations SDG region. The analysis combines descriptive statistics, a paired-samples t-test and Wilcoxon signed-rank test, a linear mixed-effects model with a Year × Region interaction and a random intercept for country (re-estimated with an additional random slope for Year as a robustness check), OLS trend estimation, three dispersion indices (Gini coefficient, interquartile range, and coefficient of variation), and sensitivity analyses excluding zero-valued, extreme, and small-island observations. The world-aggregate death rate declined 12.1%, and country-level rates fell significantly (paired t(181) = −3.67, p < 0.001, Cohen’s dz = 0.27), unchanged in sensitivity analysis (dz = 0.31). The improvement was highly uneven, however: a mixed-effects model with a Year × Region interaction fit significantly better than a main-effects-only model (likelihood-ratio χ²(6) = 103.98, p < 0.001), and the interaction remained significant when country-specific slopes were also modeled (χ²(6) = 41.62, p < 0.001). In population-weighted regional aggregates, Europe and Northern America and Australia and New Zealand recorded reductions exceeding 44%, whereas Sub-Saharan Africa, Central and Southern Asia, and Latin America and the Caribbean declined by less than 9% and Northern Africa and Western Asia rose slightly (+1.2%). Global dispersion widened on every measure: the Gini coefficient rose from 0.241 to 0.353. The observed regional and temporal patterns are interpreted, in conjunction with the existing transportation literature, in relation to public transit, vulnerable road users, and the potential roles of ITS and connected and autonomous vehicles in addressing persistent road-safety disparities.

Urban food deserts are a serious systemic vulnerability at the nexus of data-driven mobility planning and public health. In order to maximize supply chain logistics and spatial accessibility for community food infrastructure, this article presents an urban intelligence framework. We examine a spatial development project in Baltimore, Maryland, using a multi-method computational framework that combines descriptive statistics, multi-variable regressions, travel demand modeling, and Monte Carlo simulations. Predictive systems measure multi-modal transit results, estimate network traffic generation, and assess accessibility gaps. According to empirical results, the suggested smart logistics node increases transit-accessible food coverage from 31% to 74%, creates 1,240 daily trips, and shortens home journey times by 14.7 minutes. Using an 8% discount rate, system-level transportation benefits result in a Net Present Value of $3.82 million over a five-year period. In the end, this research offers a highly reproducible, data-driven approach for utilizing smart city engineering and predictive analytics to improve urban food security.

This study evaluated the feasibility and impact of establishing a proposed primary school in Arjuna Union of Bhuapur Upazila, Tangail District, Bangladesh, through an integrated transportation accessibility and educational infrastructure planning framework. Arjuna Union, with a population of 26,888 and a literacy rate of 41.1%, currently has only one government primary school, indicating significant service gaps. The research combined demographic demand assessment, GIS-based transportation accessibility analysis, engineering design evaluation, financial feasibility analysis, and regression modeling to assess how improved spatial access influences educational outcomes. Travel time modeling identified underserved residential clusters, while cost-efficiency metrics evaluated investments relative to the proposed 300-student capacity. A quantitative OLS regression framework estimated the relationship between reduced travel time and increased enrollment and attendance rates. Findings suggest that integrating transportation planning with school infrastructure development can significantly reduce accessibility barriers, improve literacy rates, and enhance educational equity in rural Bangladesh. The study offers a replicable planning model that supports evidence-based infrastructure investment aligned with national education goals.