This study addresses the crucial question of the macroeconomic impact of investing in railroad infrastructure in Portugal. The aim is to shed light on the immediate and long-term effects of such investments on economic output, employment, and private investment, specifically focusing on interindustry variations. We employ a Vector Autoregressive (VAR) model and utilize industry-level data to estimate elasticities and marginal products on these three economic indicators. Our findings reveal a compelling positive long-term spillover effect of these investments. Specifically, every €1 million in capital spending results in a €20.84 million increase in GDP, a €17.78 million boost in private investment, and 72 new net permanent jobs. However, these gains are not immediate, as only 14.5% of the output increase and 38.8% of the investment surge occur in the first year. In contrast, job creation is nearly instantaneous, with 93% of new jobs materializing within the first year. A short-term negative impact on the trade balance is expected as new capital goods are imported. Upon industry-level analysis, the most pronounced output increases are witnessed in the real estate, construction, and wholesale and retail trade industries. The most substantial net job creation occurs in the construction, professional services, and hospitality industries. This study enriches the empirical literature by uncovering industry-specific impacts and temporal macroeconomic effects of railroad infrastructure investments. This underscores their dual advantage in bolstering long-term economic performance and counteracting job losses during downturns, thus offering valuable public policy implications. Notably, these benefits are not evenly distributed across all industries, necessitating strategic sectoral planning and awareness of employment agencies to optimize spending programs and adapt to industry shifts.

The number of accidents at level railway crossings, especially crossings without gate barriers/attendants, is still very high due to technical problems, driving culture, and human error. The aim of this research is to provide road maps application based on ergonomic visual displays design that can increase awareness level for drivers before crossing railway crossings. The double awareness driving (DAD) map information system was built based on the waterfall method, which has 4 steps: defining requirements, system and software design, unit testing, and implementation. User needs to include origin-destination location, geolocation, distance & travel time, directions, crossing information, and crossing notifications. The DAD map application was tested using a usability test to determine the ease of using the application used the System Usability Scale (SUS) questionnaire and an Electroencephalogram (EEG) test to determine the increase in concentration in drivers before and immediately crossing a railway crossing. Periodically, the application provides information on the driving zone being passed; green zone for driving distances > 500 m to the crossing, the yellow zone for distances 500m to 100m, and the red zone for distances < 100 m. The DAD map also provides information on the position and speed of the nearest train that will cross the railway crossing. The usability test for 10 respondents giving SUS score = 97.5 (satisfaction category) with a time-based efficiency value = 0.29 goals/s, error rate = 0%, and a success rate of 93.33%. The cognitive ergonomic testing via Electroencephalogram (EEG) produced a focus level of 21.66%. Based on the results of DAD map testing can be implemented to improve the safety of level railroad crossings in an effort to reduce the number of driving accidents.