As a major safety intervention, infrastructural facilities such as footbridges, underpasses or signals are provided for pedestrians to remove their direct interactions with vehicles and consequently ensure safe crossing as they attempt to cross roadways. Interestingly, it is evident that even within the proximity of footbridges or underpasses, some individuals are more willing to take the risk of crossing at-grade even where there are no signals or crosswalk markings to separate their movement from vehicles. These crossing alternatives may have different utilization depending on location and road user types. Therefore, sustainable crossing facilities are needed to meet pedestrian needs. This study attempts to investigate the factors that influence pedestrians to avoid provided footbridges and engage in at-grade crossing behaviors. The crossing point preference is an interpersonal behavior which is a multifaceted and complex phenomenon involving conscious (intentions) and subconscious (habits) factors. This study employs Triandis' Theory of Interpersonal Behavior (TIB) as a theoretical framework and structural equation modeling to achieve study objectives. Pedestrians were intercepted randomly and socio-demographics, trip characteristics and perceptions data collected through a stated preference survey. Perceived consequence, affect, and social factors were found as significant antecedents of at-grade crossing intentions. Habits and facilitating conditions significantly moderate the impact of crossing intentions on actual at-grading crossing behavior. Pedestrians' perceived consequence was found to significantly mediate the impact of social factors and affective factors on intention to cross at-grade. Apart from gender, age, satisfaction with footbridge features, work trips, and crossing frequency were all significant determinants of actual crossing at-grade behavior. The study findings can help road safety agencies provide acceptable sustainable facilities that will be used by pedestrians to ensure that the purpose of investments toward pedestrian crossing safety is achieved. Effective road safety education and awareness campaigns on negative consequences of crossing at-grade, while highlighting the benefits of using provided footbridges are suggested to be undertaken by government agencies.

Methods for network leakage estimation include water balance, component analysis and minimum night flow (MNF) methods, the latter of which involves subtracting the customer night use (QCNU) from night leakage and multiplying by the hour day factor (HDF). QCNU and HDF respectively depend on Active Night Population (ANP) and leakage exponent (N1). In most developing countries, these parameters are assumed in the MNF method, thus introducing errors which makes setting realistic leakage reduction targets and key performance indicators (KPI) problematic. In this study, QCNU and HDF were evaluated by determining the relative error associated with ANP and N1 to establish localized rates for accurately estimating leakage in water networks. Between 7 and 11% relative error was associated with every 1% higher or lower ANP while up to 4% relative error was observed for every step considered. A linear relationship exists between the relative error associated with both and ANP although that of ANP is twice as high as This has technical implications for setting water loss reduction targets and investing in the water infrastructure. It is recommended that water utilities must establish localized ANP and values for accurate leakage estimation in water networks.

Tricycles contribute to road crashes globally, especially in Asia where it is a popular means of transport. Safety assessment of tricycles has discovered that associated fatality rate and severity of injuries from tricycle crashes, which has risen steadily, are due to their flawed designs especially the absence of seatbelts and paddings of hard surfaces. Tricycle related crashes were sourced weekly from the regional office of the National Road Safety Authority from January 2012 to June 2022 and modelled with Autoregressive Integrated Moving Average Model. The results showed ARIMA (1,0,3) errors were retained as the best model for estimating road fatalities caused by tricycle crashes. The exogenous factors: tricycle crashes, time of day, season of the year, and road design were tested and found to be statistically significant relative to its contribution to the fatalities. The time series components were deemed to be relevant contributing factors to fatalities for the study period. Further, crash data forecasted to October 2022 largely exhibited a sinusoidal behaviour with spikes at various weeks. It was recommended that tricycle drivers undergo mandatory refresher courses on safe driving techniques and general road safety regulations as a policy intervention. In addition, the licensing regime for tricycles should be strengthened, and government should create separate lanes along some major routes within the metropolitan, municipal, and district assemblies to eliminate or reduce the negative incidents.