Manoranjan Parida

This study evaluates the impact of road traffic noise on roadside urban schools by adopting a dual methodological approach that combines quantitative noise monitoring and mapping with a socio-acoustical survey. 10 schools located near busy roads with varying noise exposure levels were examined. Noise levels were measured both inside classrooms and along adjacent roadways, and key acoustic indices including Traffic Noise Index (TNI), Noise Climate (NC), and Noise Pollution Level (L _np ) were computed. Noise propagation maps were generated to visualize extent and spatial distribution of noise exposure around each school. Results show that observed noise levels frequently exceeded the World Health Organization (WHO) recommended limits, with exceedances ranging from 15 % in S5 to 107 % in S6. The socio-acoustical survey further highlighted the perceptual and psychological impacts of noise, including reduced speech intelligibility and increased emotional stress. Approximately 80 % of students reported frequent disturbance from external noise, 65 % experienced loss of concentration, and 50 % reported feelings of annoyance or fatigue. While the findings provide substantial evidence of the adverse effects of traffic noise on roadside schools, further research is needed to deepen understanding and explore long-term implications.

Nighttime driving presents unique challenges and risks compared to daytime driving. This study analyzed rear-end conflicts on expressways and identified thresholds for various conflict indicators under both day and night conditions. Utilizing cost-effective 3D LiDAR technology, renowned for its robustness in low-light environments, this study elucidates the multifaceted influence of various factors on traffic safety dynamics across day and night conditions. Extreme value theory was applied to evaluate safety, incorporating factors like traffic environment and driver characteristics that are often overlooked in naturalistic studies. The analysis also included the effect of percentage oblique width on safety-critical events. Interestingly, drivers experienced about three times higher crash risks during the day compared to night, likely due to increased vigilance and caution at night. These findings offer valuable recommendations for setting headway requirements based on lighting conditions and can help improve advanced driver assistance systems to detect and respond more effectively to unsafe following distances.

The integration of autonomous vehicles into the mainstream transportation system requires advanced perception technologies to accurately detect and navigate road boundaries. This study addresses the challenge by utilizing a cost-effective 16-beam light detection and ranging (LiDAR) sensor combined with robust data processing techniques. The experiments were conducted in various environments using an instrumented vehicle. Ground and non-ground points were segregated using the random sample consensus (RANSAC) algorithm. The road boundary detection algorithm utilized the shortest-beam method and median filtering. This approach was used to enhance detection accuracy even in complex road and environmental conditions, such as different types of intersection, adverse weather, and varying terrain and lighting conditions. The RANSAC polynomial fitting method was used to fit the road boundaries. This methodology demonstrates significant improvements in identifying road boundaries, proving to be a viable alternative to more expensive, high-resolution LiDAR systems. Experimental results highlight the stability of the proposed approach, showing its potential to facilitate the broader adoption of autonomous driving technology by making it more economically accessible and scalable.

Identification of potential locations constitutes a crucial aspect of park-and-ride facility planning and successful implementation. The present study prioritizes the planning criteria and identifies potential park-and-ride facility locations in Delhi, India. The survey instrument comprised a pairwise comparison between seven park-and-ride location planning criteria finalized for the present study. A pen-and-paper personal interview method was adopted to record responses related to pairwise comparison between criteria from 20 experts of related domains. The present study employed an enhanced fuzzy analytical hierarchy process to obtain relative weights of the planning criteria influencing park-and-ride location decisions. Subsequently, criteria levels and scores corresponding to each level of the identified criteria were determined. The candidate locations received scores based on the levels of the criteria corresponding to the locations. Finally, cumulative weighted scores were obtained for all selected candidate locations. The research findings indicated “Location of park-and-ride at interchange transit station” was the most important criterion, followed by “Transfer time from park-and-ride lot to transit station,” and “Access time to park-and-ride lot from trip origin.” The present study also identified the top 10 potential park-and-ride facility locations in Delhi. The study outcomes can be used as a prescription tool to assist the urban planners and policymakers in the rational location planning and implementation of park-and-ride facilities for sustainable urban mobility.

The transportation sector is a major source of environmental air pollution, contributing 25% of total greenhouse gas (GHG) emissions globally. Among India’s transport sector, the road sector alone is responsible for 80%–90% of total GHG emissions. Besides emissions from construction and maintenance and rehabilitation (M&R) activities of road infrastructure and vehicular tailpipe emissions (TPEs) are responsible for about 80%–90% of total emissions in case of an urban environment. TPEs are directly correlated to vehicular fuel consumption (FC), and hence, influenced by the pavement performance and application of different maintenance strategies. This paper presents the development of a road asset management system (RAMS) to manage urban roads in India sustainably. The developed framework implements Highway Development and Management (HDM‐4) software to study the urban road network under condition‐based maintenance scenario in three aspects: technical (pavement performance), economic and environmental (emissions estimation). Three additional scenarios were defined to analyse the effectiveness of maintenance strategies: do nothing (DN), routine and scheduled‐based. This study was conducted on urban roads of Pune Smart City over a network of 116.16 km in length, consisting of both bituminous (BT) and cement concrete (CC) pavements. The case study results showed that condition‐based is the most economical scenario and estimates the maximum reduction in emissions. Over Indian Rupees (INR) 285.76 million of funds and 6028.73 tonnes of carbon dioxide (CO ₂ ) can be saved in 10 years by opting for a condition‐based over scheduled‐based scenario. The decline in GHG emissions was even more significant compared to other scenarios.