Locating carbon neutral mobility hubs using artificial intelligence techniques.

This research proposes a novel, three-tier AI-based scheme for the allocation of carbon-neutral mobility hubs. Initially, it identified optimal sites using a genetic algorithm, which optimized travel times and achieved a high fitness value of 77,000,000. Second, it involved an Ensemble-based suitability analysis of the pinpointed locations, using factors such as land use mix, densities of population and employment, and proximities of parking, biking, and transit. Each factor is weighted by its carbon emissions contribution, then incorporated into a suitability analysis model, generating scores that guide the final selection of the most suitable mobility hub sites. The final step employs a traffic assignment model to evaluate these sites' environmental and economic impacts. This includes measuring reductions in vehicle kilometers traveled and calculating other cost savings. Focusing on addressing sustainable development goals 11 and 9, this study leverages advanced techniques to enhance transportation planning policies. The Ensemble model demonstrated strong predictive accuracy, achieving an R-squared of 95% in training and 53% in testing. The identified hubs' sites reduced daily vehicle travel by 771,074 km, leading to annual savings of 225.5 million USD. This comprehensive approach integrates carbon-focused analyses and post-assessment evaluations, thereby offering a comprehensive framework for sustainable mobility hub planning.

Mining groups of factors influencing bus/minibus crash severities on poor pavement condition roads considering different lighting status.

OBJECTIVE: This study employs a data mining approach to discover hidden groups of crash-risk factors leading to each bus/minibus crash severity level on pothole-ridden/poor roads categorized under different lighting conditions namely daylight, night with streetlights turned on, and night with streetlights turned off/no streetlights. METHODS: The bus/minibus data employed contained 2,832 crashes observed on poor roads between 2011 and 2015, with variables such as the weather, driver, vehicle, roadway, and temporal characteristics. The data was grouped into three based on lighting condition, and the association rule data mining approach was applied. RESULTS: Overall, most rules pointing to fatal crashes included the hit-pedestrian variable, and these crashes were more frequent on straight/flat roads at night. While median presence was highly associated with severe bus/minibus crashes on dark-and-unlighted roads, median absence was correlated with severe crashes on dark-but-lighted roads. On-street parking was identified as a leading contributor to property-damage-only crashes in daylight conditions. CONCLUSIONS: The study proposed relevant countermeasures to provide practical guidance to safety engineers regarding the mitigation of bus/minibus crashes in Ghana.