Future Transportation

Validation of Advanced Driver Assistance Systems (ADAS) relies on standardized pedestrian soft targets to ensure comparable safety assessment. However, real-world pedestrians differ from standardized targets in clothing and carried objects, potentially altering radar-based sensing. This exploratory study investigates how such variations affect the sensor-reported apparent radar cross section (RCS) of a pedestrian soft target. Eight configurations were evaluated on a closed proving ground. For each configuration, one 30 s stationary measurement at approximately 5 m and three vehicle-approach runs ranging from 30 to 5 m were performed using a vehicle-mounted automotive radar. Dynamic runs were evaluated in three distance bands using run-level median apparent RCS values, differences from the reference, and run-to-run standard deviation. The backpack and notebook bag produced the largest observed positive deviations, reaching +9.17 and +7.25 dB at 20–30 m. The visibility vest showed a moderate, consistent positive shift, whereas the winter coat and jacket remained closer to the reference in most bands. The umbrella configuration exhibited the greatest run-to-run variability. Among the investigated configurations, the largest observed differences were associated with the backpack and notebook bag, both of which substantially altered the external shape of the pedestrian surrogate. Because only three dynamic repetitions, one radar sensor, one pedestrian surrogate, and one side-profile orientation were investigated, the findings should be interpreted as an exploratory, sensor-specific sensitivity analysis rather than as a definitive characterization of pedestrian RCS or ADAS performance.

This study develops a unified optimization framework to evaluate and compare the performance of electric, conventional, and hybrid vehicle routing strategies under consistent operational conditions. The proposed model integrates routing decisions, energy consumption, infrastructure constraints, and emission considerations within a single mathematical formulation, enabling a comprehensive assessment of both economic and environmental performance. Using a benchmark urban transportation network, the study analyzes route configurations, operational costs, financial feasibility, and carbon emissions across the three vehicle technologies. The results indicate that electric vehicles offer lower and more stable operational costs, with zero direct emissions. In contrast, conventional vehicles offer greater operational flexibility, albeit at the expense of higher costs and a greater environmental impact. The hybrid model, which allows endogenous technology selection at the route level, achieves the most balanced performance by assigning vehicle types based on route-specific characteristics. This leads to significant reductions in both operational costs and emissions compared to single-technology approaches. The findings underscore the importance of integrated and flexible routing strategies in supporting sustainable transportation planning, offering practical insights for policymakers and transport operators in designing efficient and environmentally responsible systems.

Public transit agencies are increasingly expected to support smart city goals, but the existing assessment tools often examine transit technology, urban design, environmental performance, or service quality separately. This paper develops a multidisciplinary framework for assessing how ready a public transit system is for integration into a smart city environment. The framework organizes readiness into eight dimensions: technological integration, accessibility, mobility, sustainability and environmental impact, data security and privacy, resilience and adaptability, community engagement and feedback, and safety, security, and rider comfort. These dimensions are further divided into operational subcategories that can be examined using quantitative indicators, public documents, and expert ratings. The framework is applied to Austin, Texas, using CapMetro service information, static GTFS data, census and ACS data, public agency documents, and a short expert rating exercise. The Austin case indicates relatively stronger performance in sustainability and technology adoption, supported by electric and hybrid fleet investments, mobile fare payment, and real-time vehicle information. Lower scores are observed in resilience, community feedback, mobility, and spatial accessibility, particularly in areas where peripheral neighborhoods have limited stop coverage or long scheduled headways. The paper contributes a practical screening tool that can help planners identify where public transit systems are aligned with smart city objectives and where additional investment, governance, or data transparency is needed.