Kingdom of Among the G20 countries, Saudi Arabia (KSA) is facing alarming traffic safety issues compared to other G-20 countries. Mitigating the burden of traffic accidents has been identified as a primary focus as part of vision 20230 goals. Driver distraction is the primary cause of increased severity traffic accidents in KSA. In this study, three different machine learning-based severity prediction models were developed and implemented for accident data from the Qassim Province, KSA. Traffic accident data for January 2017 to December 2019 assessment period were obtained from the Ministry of Transport and Logistics Services. Three classifiers, two of which are ensemble machine learning methods, namely random forest, XGBoost, and logistic regression, were used for crash injury severity classification. A resampling technique was used to deal with the problem of bias due to data imbalance issue. SHapley Additive exPlanations (SHAP) analysis interpreted and ranked the factors contributing to crash injury. Two forms of modeling were adopted: multi and binary classification. Among the three models, XGBoost achieved the highest classification accuracy (71%), precision (70%), recall (71%), F1-scores (70%), and area curve (AUC) (0.87) of receiver operating characteristic (ROC) curve when used for multi-category classifications. While adopting the target as a binary classification, XGBoost again outperformed the other classifiers with an accuracy of 94% and an AUC of 0.98. The SHAP results from both global and local interpretations illustrated that the accidents classified under property damage only were primarily categorized by their consequences and the number of vehicles involved. The type of road and lighting conditions were among the other influential factors affecting injury s severity outcome. The death class was classified with respect to temporal parameters, including month and day of the week, as well as road type. Assessing the factors associated with the severe injuries caused by road traffic accidents will assist policymakers in developing safety mitigation strategies in the Qassim Region and other regions of Saudi Arabia.

Extensive construction augmenting the infrastructure and real estate projects underpin Saudi Arabia’s Vision 2030 of sustainable cities. A part of this struggle involves the transformation of the existing infrastructure together with new construction, which generates a large amount of construction and demolition waste (CDW). In the absence of a structured life cycle assessment (LCA) framework, the waste management companies are planning future scenarios (phased expansions of material recovery facilities to improve the recycling rate) primarily on economic grounds. This study assesses the environmental impacts of the existing and planned CDW management practices of the Saudi Investment Recycling Company in Riyadh City by dint of LCA. Impact 2002+ performs life cycle impact assessment of the base case (45% recycling), four treatments (61, 76, 88, and 100% recycling), and zero waste scenarios. The study demonstrates the benefits of current CDW (mixed soil, concrete blocks, clay bricks, glazed tiles, and asphalt) recycling in terms of avoided impacts of non-renewable energy, global warming, carcinogens, non-carcinogens, and respiratory inorganics potentially generated by landfilling. For the treatment scenario of 100% recycling, CDW conversion into a wide range of aggregates (0–50 mm) can replace 10–100% virgin aggregates in backfilling, precast concrete manufacturing, encasements and beddings of water mains and sewers, manholes construction, non-load bearing walls, and farm-to-market roads. To achieve long-term economic and environmental sustainability, municipalities need to improve source segregation, handling, and storage practices to enhance the existing (45%) recycling rate to 100% in the next five years and approach the zero-waste scenario by 2030. The findings of the present study motivate the generators for source reduction as well as encourage the recycling companies and concerned organizations in the continuous performance improvement of the CDW management systems across Saudi Arabia on environmental grounds, as an addition to the perceived economic benefits.

Limited knowledge on the water–energy–carbon nexus of water supply systems (WSSs) with brackish groundwater sources in arid regions exists to date. In addition, the large amount of fossil-fuel energy utilized by treatment processes generating a significant amount of carbon emissions remains a challenge for the municipalities in Saudi Arabia to meet long-term sustainability goals. To achieve Saudi Arabia Vision 2030’s target of sustainable cities with reduced CO₂ emissions, the present study aimed to analyse the water–energy–carbon nexus for WSSs and propose mitigation measures for reducing energy and carbon footprints from both the water management and treatment technologies perspectives. The detailed energy consumption data for three main components (source extraction, water treatment, and conveyance and distribution) of the main WSS, serving the 600,000 population of Buraydah City (Qassim, Saudi Arabia), was obtained from the concerned municipality. The city water treatment plant removes naturally occurring iron, TDS, and radionuclides in the source water with the help of ion detention, oxidation, sand filtration, ultrafiltration, reverse osmosis, chlorination, and backwash water management. The study found that the treatment facility consumes around half of the total system’s energy (131,122 kWh/day); while, with deep confined aquifer (>600 m) and an average water loss of 8%, conveyance and distribution (34%) and source extraction (18%) are consistent with the reported literature. With oil-driven energy, carbon emissions were found to be 10.26, 27.18, and 19.72 million tons CO₂ eq/year for source extraction, water treatment, and conveyance and distribution, respectively. The reverse osmosis process, with higher energy consumption—1.1 kWh/m³ of treated water—than the global average, consumes most (88%) of the treatment plant’s energy and thus needs effective energy management practices. Moving to renewable (solar and wind-driven) sources, subject to a detailed life cycle analysis, can achieve significant energy and associated carbon emission reductions. To sustainably meet the water demand of the growing population in arid regions, the study also suggests raising the awareness of the public about how water conservation can control CO₂ emissions, proactive maintenance of aging infrastructure, and increasing rainwater and treated wastewater reuse, to enhance the operational life of existing treatment facilities.

In the transition era towards connected autonomous vehicles (CAVs), the sharing of the roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected to pose safety and flow efficiency concerns even though CAVs may tend to adopt rather conservative maneuvering policies. Unfortunately, this will likely cause HDV drivers to unduly exploit such conservativeness by driving in ways that imperil safety. A context of this situation is lane-changing by the CAV, a potential major source of traffic disturbance at multi-lane highways that could impair their traffic flow efficiency. In dense, high-speed traffic conditions, it will be extremely unsafe for the CAV to change lanes without cooperation from neighboring vehicles in the traffic stream. To help address this issue, this paper developed a framework through which connected HDVs (CHDVs) could cooperate to facilitate safe and efficient lane-changing by the CAV. A numerical experiment was carried out to demonstrate the efficacy of the framework. The results indicated the CAVs’ lane-changing feasibility and the overall duration of the lane-changing if the CAV carries out that maneuver. It was observed that throughout the lane-changing process, the safety of not only the CAV but also of all neighboring vehicles, was promoted through the framework’s collision avoidance mechanism. The overall traffic flow efficiency was analyzed in terms of the ambient level of CHDV–CAV cooperation. Overall, the results of the study present evidence of how CHDV–CAV cooperation can help enhance the overall system efficiency.

People with disabilities face significant barriers in using public transport, limiting their access to healthcare, employment, education, and daily life activities. A comprehensive review of existing research critically identifies these barriers, analyzes efficient interventions, and uses modern technology to create inclusive public transport systems that empower people with disabilities and foster a more equitable society. The current review systematically identifies the key barriers and prospects of disability-inclusive public transportation. It also highlights the policy shortcomings, accessibility gaps, and current technological advancements to foster inclusivity and improve mobility needs for disabled individuals. Searching scientific databases Scopus and Web of Science yielded 1100 articles; 35 met the inclusion criteria. The selected studies were conducted in various countries, emphasizing the topic’s geographical importance. Several studies in the last five years indicated the topic’s growing interest and potential impact. The present study categorized the key barriers into five types: inaccessible infrastructure, information and communication, attitudinal, economic, and safety and security. Case studies from diverse geographical and social settings correlated the barriers with the satisfaction levels of people with disabilities. The case studies highlighted the critical problems disabled people face that should be considered while designing successful transportation systems and travel chains. The study also illustrates the key features of a successful transportation system for disabled people based on best practices adopted worldwide. The prospects of using advanced technology, such as machine learning and modern imagery techniques, are also discussed.

In addition to a wide range of socio-economic impacts, traffic congestion during the era of the COVID-19 pandemic has been identified as a critical issue to be addressed. In urban neighborhoods, the timespan of traffic congestion hazard (H_TC) after the curfew lift is subjected to the commuters’ decisions about home-to-shopping center departures. The decision for departing early or late for shopping depends on both the internal (commuter related) and external (shopping center related) factors. The present study developed a practical methodology to assess the H_TC period after the curfew timings. An online questionnaire survey was conducted to appraise the commuters’ perception about departure time and to assess the impact of eight internal (family size, involvement in other activities, nature of job, education level, age, number of vehicles, number of children, and availability of personal driver) and three external (availability of shopping center of choice in near vicinity, distance to shopping center, and size of the city) factors on their decision. With an acceptable 20% response rate, Chi-square and Cramer’s V tests ascertained family size and involvement in other activities as the most significant internal factors and availability of shopping center of choice as the primary external factor. Age, number of children, and size of the city influenced to some extent the commuters’ decisions about early or delayed departure. Large associations were found for most of the factors, except education level and availability of drivers in a household. Fuzzy synthetic evaluation (FSE) first segregated the commuters’ responses over a four level-rating system: no delay (0), short delay (1), moderate delay (3), and long delay (5). Subsequently, the hierarchical bottom-up aggregation effectively determined the period of highest traffic congestion. Logical study findings revealed that most (about 65%) of the commuters depart for shopping within 15 min after the curfew lift, so H_TC in the early part (the first one hour) of the no curfew period needs attention. The traffic regulatory agencies can use the proposed approach with basic socio-demographic data of an urban neighborhood’s residents to identify the H_TC period and implement effective traffic management strategies accordingly.