Traffic Management, Operation, Safety and Emerging Technology

Equity in health and healthcare is widely accepted as an essential function of public policy. Healthcare sector has seen the advantage of spatial analysis supported by GIS technologies and road networks. In rural regions, people experience distance barriers to healthcare facilities. Previous studies have emphasized physical reach as the primary determinant of access; however, scholars have overlooked the hierarchy of healthcare facilities that influence travel characteristics in the region. A comparison between user travel behavior and the benchmarking public guidelines is presented. A 2SFCA method is used as a general accessibility model using GIS to highlight hierarchy-wise coverage gaps in the region. The user survey was conducted at each hierarchy of facility in the district and the geographic location of all facilities was obtained by physically visiting all the facilities. The best- and least-performing blocks of Hapur district in terms of health accessibility are identified based on the spatial analysis. It is proposed to increase the number of facilities in the region or to increase the capacity of the existing facilities for better health outcomes.

Reduced speed zones and infrastructural modifications are carried out to lessen accidents and increase user mobility in areas with large pedestrian traffic. These measures may have the opposite impact in case of faulty design or improper location. Recently, vandalism of speed bumps was noticed on the streets of Chandigarh city, where either the public or the concerned authority had partially removed them. This study examines road users’ behavioral aspects regarding community perception towards traffic calming and identifies the key factors influencing their credibility and compliance. An online questionnaire was distributed among the road users of Tier-1 and Tier-2 cities of northern India, wherein 303 valid responses were analysed. Exploratory Factor Analysis (EFA) and a Second-Order Confirmatory Factor Analysis (SCFA) were employed to prove the hypothesized model. The analysis revealed that familiarity, need, and comfort with these measures significantly contribute to the credibility and lead to a positive perception by the road user. In contrast, compliance was affected by factors such as traffic congestion, sudden speed reduction, and unrecognizable signage on traffic-calmed streets, which incorporated a negative perception. By considering the community’s perception, policymakers can improve the effectiveness of speed-reducing measures that meet the requirements of all road users.

Unprecedented urbanization and increased private vehicle ownership in India have led to the issues of traffic congestion and pollution. To meet the global emission targets set for the transportation sector towards sustainability, developing nations like India are in desperate need of a preferred sustainable public mode of transport. The paper discusses potential influencing factors considered by the users in their affiliation towards sustainable Inland Water Transport (IWT) systems within an urban context. An extensive literature review was carried out to identify the probable leveraging attributes of the public. A total of 20 qualitative and quantitative metrics from eclectic published resources were selected for the user perspective survey. Both stated and revealed preference queries were included in the questionnaire. The urban city of Kochi in the state of Kerala was selected for the study. The hypothetical situations required were analyzed using stated preference questions. The multi-criteria decision-making (MCDM) technique of the Analytic Hierarchy Process (AHP) was carried out to reckon the pairwise weights of different characteristics on the final decision. Safety and comfort were the aspects given the highest priority contrary to the intuitive belief of the economy being the dominating one. This study is unique in its exploration of the under-researched area of IWT in India and the application of MCDM technique to this issue. The methodology and criteria employed in the study can be replicated in any other cities of similar terrain and demographics, albeit the conclusions arrived at may not be considered universal.

Segmentation architectures are essential in computer vision, enabling the precise extraction of meaningful information from images and videos by breaking them down into understandable parts. This paper explores six key segmentation architectures: Fully Convolutional Network (FCN), U-Net, DeepLab, YOLO (You Only Look Once) and Mask R-CNN, focusing on their unique methods, strengths, and applications, especially in autonomous navigation. In autonomous vehicles, tasks like lane detection, road segmentation, and identifying objects such as pedestrians and other vehicles rely heavily on semantic segmentation, instance segmentation, and object detection. FCN and U-Net are great at semantic segmentation, making them suitable for understanding the overall scene. DeepLab stands out in tasks that need high-resolution and fine detail. YOLO is preferred when for dynamic environments. This review aims to help researchers and practitioners choose the right architecture based on their specific needs, such as available computational resources, required precision, and the need for real-time processing. By doing so, it seeks to advance the development of smarter transportation systems and better autonomous navigation technologies.

Urban roads in developing countries often experience diverse traffic conditions and lax regulatory frameworks, leading to significant side friction activities that impact road capacity and operational speeds. Managing these elements is crucial for optimizing transportation systems. This study focuses on mixed traffic conditions, where side friction components are exceptionally high and often interact with one another. The primary aim is to develop a methodology for quantifying the impact of multiple friction elements into a single metric, termed the Side Friction Score (SFS). The relative importance of each friction variable is determined by assigning weights to them. Two techniques, namely the multiple regression method and relative weight analysis, were used to obtain the relative weight factors. Additionally, the study investigates the sensitivity of speed reduction to SFS levels at different traffic volumes.

This study investigated the relationship between mobile usage, travel habits, and perceived crash risk. A survey conducted in Surat and Ahmedabad used home interviews, face-to-face surveys, and online questionnaires. Starting with a pilot survey of 40 responses, the final survey collected 246 responses, of which 235 were valid for analysis. The survey mainly used Likert scales to assess demographics, driving profiles, risk perception, travel behavior, and mobile usage. SPSS and SEM modeling were applied to analyze the data, revealing a strong correlation between Mobile Travel Behavior (MTB) and risky riding behavior, which significantly impacts crash confidence. MTB was also linked to variables such as Wrong Driving Habits (WDH) and Disobedience of Traffic Rules (DoTR). The study highlights the increased perceived crash risk from mobile use, especially video watching while driving, emphasizing the need for stricter regulations on mobile usage during driving to improve safety policies and compliance.

Most of the pedestrian facilities with pedestrian flow is not specifically governed by traffic rules but is more based on the social and psychological aspects of individual pedestrians. The pedestrian crowds generally consist of social groups and the group interactions greatly affect crowd behavior thus, it is necessary to understand the effect of groups on pedestrian flow. However, most of the research in this area has not considered group dynamics of pedestrian flow on pedestrian facilities like staircase of foot-over bridge of railway station. The characteristics of pedestrian flow while moving up and down stairs of foot-over bridge at railway stations in Villupuram, India, have been studied. Using the video graphic method, data is collected during morning and evening peak periods. The movement trajectories were extracted using a semi-automated pedestrian trajectory extractor. The individual and group characteristics were further compared to study the effect of social groups on walking behavior of pedestrian flow. The inferences from this study can be further used to understand crowd dynamics and improve pedestrian facilities. The design requirements may be amended to meet the demands of pedestrians and to provide effective management of pedestrian flows at the train station’s staircase of foot-over bridge.

Estimating capacity in non-lane based mixed traffic streams has always been a challenge due to the haphazard behaviour of vehicles. The present study aims at developing a saturation flow estimation methodology for non-lane-based mixed traffic streams, which is necessary for the capacity estimation and signal design of signalized intersections. The methodology involves formulating a base saturation flow model for mixed traffic streams and adjusting it to the site conditions using various adjustment factors. Base saturation flow is described as a function of width using the concept of unit saturation flow. Further, the traffic characteristics affecting this base saturation flow are developed as adjustment factors. The proposed model is calibrated using field data collected from 16 signalized intersections across 5 cities in India. Here, saturation flow from the field is determined using an optimization technique, which estimates the Passenger Car Unit (PCU) value of vehicles in addition to the saturation flow values. These saturation flow values from the field are used to calibrate the width-based base saturation flow model. Further, adjustment factors for bus blockage, blockage by right-turning vehicles and initial surge are developed considering the discharge behaviour of vehicles in mixed traffic conditions. Agreement of the model with the field-observed saturation flow value indicates the reliability of the developed saturation flow model in mixed traffic streams. The developed model is specifically helpful for engineers and planners dealing with non-lane based mixed traffic conditions wherein the discharge pattern in the field does not comply with the ideal discharge curve.

Drive cycle (DC) is a speed vs time relation, which represents localised data of driving pattern which is mostly depends on local traffic, individual behaviour, type of highway, geometry of highway and other factors such as seasonal variation and environmental factors. DCs are used for estimation of mechanical requirements, Mileage and emission in two-, three- and four-wheeler vehicles. This paper mainly focused on derivation of power requirement, total tractive effort (TTE) and torque requirement from real-world two-wheeler DC and comparing the output to synthetic DC, i.e. MoRTH DC. Each output is derived by developing a MATLAB/SIMULINK model for each DC. The results indicates that there is a significant variation in TTE, power requirement and torque between real-world DC from four cities and synthetic DC. MoRTH DC which has lowest parametric and output values when comparing them with real-world DC from other mentioned cities. Power required for Hyderabad DC was 288.19%, torque requirement was 448.84%, and total tractive effort required was 622.7% more than MoRTH DC output. Hence, for two-wheeler designing and testing, it is concluded that real-world DC gives us more practical and realistic output. Thus, real-world DC should also be used to measure other parameters, such as emissions, which are often underestimated when using synthetic DC. Therefore, to effectively limit emissions and formulate benchmark emission standards for policy frameworks, adopting real-world DC is the way forward.

Pedestrians are a crucial component of urban transportation but are vulnerable at unprotected mid-block locations, especially under mixed traffic conditions. At these locations, some vehicles may yield to pedestrians at crosswalks, but others may use forced gaps to cross the road. This behaviour can reduce vehicle flow and affect traffic conditions. This study aims to investigate the impact of pedestrian crossings on traffic flow characteristics, including speed, capacity and level of service. To analyse these effects, six-lane divided mid-block sections in urban areas were chosen. Videos were recorded at seven sites, including base and non-base sections, during peak and off-peak hours to collect data on traffic volume and speeds. The analysis shows that as traffic flow increases, speed reduces. The most pronounced speed reductions are at ECIL (18.04%) section, followed by Raj Mahal (17.02%), Rama Devi (RD) College (14.68%) and Unit-I (13.32%). Capacity reduction is greatest at Unit-I (56.06%) due to frequent stops and high pedestrian activity, followed by ECIL (32.56%), Raj Mahal (22.29%) and RD College (19.05%). Also, a model has been developed for the number of pedestrians crossing the road and capacity reduction. The level of service (LOS) also degrades from LOS C to LOS D. Based on speed and capacity reductions and the level of service degradation, it is recommended to improve pedestrian crossing infrastructure and facilities.

This study investigates the safety of powered two-wheelers (PTWs) during “on the fly” and “oblique maneuver”-type overtaking behaviors in urban mixed and weak-lane-disciplined traffic using the proactive approach. A two-dimensional conflict indicator, Anticipated Collision Time (ACT), was employed to detect conflicts involving PTWs during overtaking. The crash risk for each type of PTW overtaking maneuver was then estimated using the extreme value theory (EVT) approach. The study findings reveal that PTWs predominantly perform “on the fly” overtaking maneuvers compared to “oblique maneuver”-type overtaking and show a preference for left-side overtaking in urban traffic, although right-side overtaking also occurs frequently. PTWs maintain smaller lateral gaps when overtaking from the right side compared to the left, regardless of the overtaking maneuver type. Importantly, the crash risk associated with “oblique maneuver”-type overtaking was found to be more than twice that of “on the fly” maneuvers. These findings highlight the need for targeted safety measures to mitigate the increased crash risk associated with specific overtaking maneuvers and lateral gap management. Public awareness campaigns can further educate PTW riders about high-risk overtaking maneuvers and promote safer riding practices.

Volume Delay Functions (VDFs) are essential for predicting congestion levels and are crucial for infrastructure planning and traffic management. They help quantify the impact of traffic volume on travel times, allowing planners to make informed decisions about road network improvements and traffic management interventions. This study calibrates VDFs using crowd-sourced data with videography data, focusing on urban mid-block sections in Delhi. Unlike traditional approaches, this research determines free-flow speeds using real-time crowd-sourced data and establishes road capacity from field data using 5-minute volume observations. The methodology is applied to 8 different locations in Delhi. The results indicate that among the tested VDFs, which include the Bureau of Public Roads (BPR), conical, and Akcelik models, the calibrated BPR model provided the best fit. This model reduced the mean absolute percentage error (MAPE) for all locations by approximately 60%. Additionally, the BPR model was validated at a separate location, confirming its accuracy. By incorporating real-time crowd-sourced data, this research provides insights for more accurate traffic modeling.

Globally, traffic-related deaths amount to 1.37 million per year, or nearly 3,800 per day. In India, the severity of accidents has increased by 32.5% over the past decade, with 38.3 fatalities per 150 accidents. In 2023, there were 512,432 reported accidents in India, resulting in approximately 522 deaths daily and 163,992 fatalities on Indian highways. The safety performance of National Highways is crucial in developing countries like India. This research paper focuses on the four-lane NH-30 (Mohania to Bakhtiyarpur Road, a national highway approximately 230-km-long that surrounds the city of Patna, India. Safety performance of four-lane highway is a primary concern for developing countries such as India. This paper examines the advantages of the Mohania to Bakhtiyarpur Road, highlighting improvements in commuter travel time, goods transportation, accident reduction, and economic growth. Additionally, it underscores the importance of monitoring road conditions to ensure a safe, efficient, and adaptable transportation system. This process includes collecting data on road safety and performance and pinpointing areas needing improvement. So a study on the identification of risk factor on such as highways is of immense interest in mitigating road accidents. The paper highlights the role of trained professionals and community stakeholders in conducting road observations and ensuring that transportation infrastructure meets the needs of local communities. This case study presents crucial observations made at specific locations on the Mohania to Bakhtiyarpur Road. The study aims to analyze traffic flow, identify potential bottlenecks, and propose solutions to improve the road's efficiency and safety.

The potential of roundabouts to improve safety and operational efficiency has led to their widespread adoption throughout India. Even with their acknowledged advantages, roundabouts are still viewed critically by the general public, which makes it difficult to evaluate how they affect driving behavior and overall safety. The present study investigated the safety perceptions at roundabouts among its users in India, emphasizing how socio-demographic factors, vehicle types, and roundabout usage patterns influence perceived safety. A structured questionnaire survey was conducted to examine the safety perceptions of a sample of 1,250 participants by analyzing user demographics, driving experience, and varying roundabout geometric configurations. Multiple Correspondence Analysis (MCA) was employed to identify patterns among respondent categories, and an ordered probit model was developed to assess factors influencing perceived roundabout safety. Key findings revealed that male users aged 26–35 years with greater roundabout familiarity perceived roundabouts to be safer, while females, motorcyclists, and bicyclists perceived roundabouts as dangerous. Roundabout entry and complex lane configurations were perceived as hazardous, with users reporting higher risk perceptions in double- and multi-lane configurations. The ordered probit model achieved an overall prediction accuracy of 72.64%, highlighting significant relationships between demographic factors, roundabout design, and safety perception. The results of the study underscored the need for user-centered design improvements and robust safety education to foster greater roundabout usability and safety in low- and middle-income countries.

Driver gaze estimation is important for different driving applications such as driver gaze behavior understanding, visual distraction detection, and making gaze-based advanced driving assistance systems (ADAS). This study aims to build a driver gaze estimation model based on gaze zone classification using a deep neural network. The driver’s visual field is divided into pre-defined gaze zones, where the driver frequently looks during driving. A benchmark driver gaze dataset named Driver Gaze in Wild (DGW) was used for this study. A pre-trained convolutional neural network (CNN) model, EfficentNet-B7 model was fine tuning for the driver gaze zone classification. A classification report and confusion matrix were plotted to show the result of model training and performance. Finally, we checked the gaze classification model on the driver face video driving data to track the driver gaze.

Driver inattention and improper driving behavior are key causes of traffic incidents, including rear-end collisions and poor lane discipline in Indian traffic. This study explores the impact of Advanced Driver Assistance Systems (ADAS) in addressing these issues. ADAS features, such as Lane Departure Warning (LDW), Forward Collision Warning (FCW), and Pedestrian Collision Warning (PCW), provide timely alerts to improve driver performance. This study utilized ADAS warning data and kinematic variables such as speed, acceleration, and response time to evaluate the driver performance for lower levels of ADAS. Field Operational Tests (FOT) were conducted on a 65-km stretch in Guwahati, India, using an instrumented vehicle equipped with ADAS, V-boxes, and cameras. Data on speed, acceleration, and braking characteristics were collected across diverse driving environments in urban, hilly, and rural regions. Driver performance was evaluated in active (ADAS-enabled) and stealth (ADAS-disabled) modes. The results showed significant improvements in active mode, with fewer warnings generated (64, 132, 70, 54) compared to stealth mode (114, 190, 112, 111). Drivers demonstrated smoother braking (–0.25 to –3.75 m/s²) in active mode, compared to harsher braking (–4.5 m/s²) in stealth mode. Response times were shorter during rear-end events (0.5–2.2 s) and lane departure incidents (up to 3 s) in active mode, compared to longer times in stealth mode (up to 3 s and 7 s, respectively). Findings highlight ADAS’s potential to reduce road fatalities and enhance driving conditions in India, emphasizing its role in fostering safer driver behavior.

India has one of the highest road fatality rates globally, making the identification and treatment of high-risk accident locations (blackspots) essential to improving road safety. This study focuses on identifying and prioritizing blackspot treatment in Surat, India. Using GIS, Kernel Density Estimation (KDE), and Spatial Density Function, sixteen blackspots were identified according to Ministry of Road Transport and Highways (MoRTH) standards. Each blackspot was assigned a Total Severity Score (TSS), allowing for prioritized treatment based on crash severity and frequency. The results underscore the importance of targeted interventions at high-risk locations and offer a practical framework for identifying and ranking blackspots, which can be applied to improve safety in similar urban settings. A case study of Khatushyam Intersection improvement is presented as a case study highlighting the use of crash data for focused interventions. The study highlights a data-driven approach for prioritizing accident blackspots and reducing crashes and injuries in rapidly urbanizing areas.

India's highways and urban streets have a highly diverse traffic pattern, with noticeable variations in vehicle dimensions and speed characteristics. It is common for drivers to disregard traffic movement priority laws and breach lane discipline at unsignalized intersections. Drivers often become more impatient and ignore competing traffic as they pass unsignalized crossings; pedestrian crossing and bicycle movement pose additional anxieties. These considerations significantly affect the efficiency and capacity of traffic navigation, rendering the situation at unsignalized intersections exceedingly intricate. Data were gathered at prominent three-legged and four-legged unsignalized intersections in tier-II cities, likewise Ranchi, Dhanbad city in Jharkhand state, and Patna city in Bihar state, during peak hours on five consecutive weekdays in India using the video-graphic technique. The vehicles being evaluated were classified into seven distinct categories: two-wheelers (2W), three-wheelers (3W), slow-moving three-wheelers (SM3W), standard cars (SC), large cars (LC), mini commercial vans (MCV), and heavy vehicles (HV). Field video recordings provide the percentage distribution of different maneuvers executed by different cars at an unsignalized crossroads. In the present study, the fundamental equations for evaluating the conflicting volumes for different movements in unsignalized intersections were certainly revised for India's tier-II cities. The modified equations obtained in the study can be implemented by traffic engineers and practitioners to assess the capacity of unsignalized intersections in heterogeneous traffic scenarios.

The school going children are one of the most vulnerable groups of road users due to their lesser developed cognitive skills and small physical structure. During morning school drop-off and afternoon pickup times, a large volume of pedestrians and vehicles interact with each other within a shorter time frame. Thus, the children are often exposed to very risky situations. In developing countries like India, other factors like heterogeneous traffic conditions, lane indiscipline coupled with poor road infrastructure and enforcement aggravate the situation further. This paper presents a case study conducted in an Indian city for a typical school zone. To circumvent the limitation of data collection using a fixed camera setup, an Unmanned Aerial Vehicle, (UAV) was used for video data collection. The video data processing was done using advanced image processing techniques and computer vision. As the availability of crash data for safety assessment is an intricate issue for developing countries, proactive means of safety assessment have been utilized to evaluate the safety of children. Different surrogate safety indicators such as time-to-collision, post-encroachment time and instances of heavy braking were extracted to identify the conflict points within the selected school zone. Thresholds for mild and serious conflicts were verified using 85^th percentile data. Further conflict maps and heatmaps were developed to get insights to the exact conflict locations. Significant differences between the morning and afternoon situations were observed. The findings emphasize on the utility of UAV technology in providing a comprehensive overview of traffic behavior and critical interactions in school zones. This study offers a detailed understanding of crash causal scenario in the school zone where child pedestrian crashes are prevalent. Further, it provides a methodology for an extensive safety assessment method, which can serve as an invaluable tool for traffic engineers, policymakers, and transport planners aiming to implement effective safety improvements around schools.

The performance of transportation networks depends on efficient and safe traffic management and control at the crossings located on National Highways (NH). At these crossroads, travelers are likely to experience higher delays, traffic congestion, and vehicular emissions. The present study evaluates vehicular emission levels at NH crossings under prevailing heterogeneous traffic conditions using the available emission modeling tools such as the microsimulation technique. The research also evaluates the effect of traffic movement restrictions and applies designed traffic control in addition to geometric enhancements to improve traffic operations, and hence reduce emissions significantly. The PTV-VISSIM microsimulation tools were used to simulate traffic at selected intersections. Model calibration and validation using spot speeds, traffic volume counts, and continuous unmanned aerial vehicle (UAV) video data for a selected duration. The Wiedemann-74 car-following model was calibrated to correctly mimic the varying traffic conditions. As one of the important outcomes, the authors assessed the solutions' efficacy of proposed geometric interventions, including traffic operations and control. It was found that Intersections with an existing Level-of-service (LOS-F) imply heavy congestion in the case of a business-as-usual scenario. Additionally, using a validated simulation model, it was also found that the suggested improvements reduce the delays significantly, improving the LOS-F to LOS-C. Further, with an assumed growth rate of 6%, it is established that the achieved LOS-C (using proposed interventions) is likely to be sustained through for a substantial number of years before it could deteriorate to LOS-E till 2031. Further, emission modeling revealed that there could be a reduction by 50–70% in CO emissions due to proposed interventions. This considerable drop indicates the proposed measures' additional benefits in terms of improving traffic operations as well as vehicular emission levels. The study demonstrates the approach of evaluating the suggested improvements related to roadway geometry and traffic control prior to its real-life implementation.

Road safety is a global concern, with traffic crashes posing significant threats to human life and well-being. This study conducted an exploratory analysis of crash data, obtained from the District Crime Records Bureau Kollam, to identify factors contributing to crashes on National Highway 66 (NH 66) in Kollam district of Kerala in India and employed a dynamic method, namely, the Sliding Window Method (SWM) for black spot determination. The conventional method of black spot determination uses the definition by The Ministry of Road Transport and Highways (MoRTH) where a road crash black spot on National Highways is defined as a road stretch of about 500 m where either five accidents (involving fatalities/grievous injuries) occurred in the last three calendar years or ten fatalities took place during the same period. This method divides the road stretch into static 500 m segments, thereby missing out on the black spot that may be hidden within these static segments. Thus, this study aims to compare the conventional method of black spot determination by MoRTH with a sliding window method based on MoRTH guidelines, thereby evaluating the efficiency of the sliding window method to capture all potential black spots in the selected study stretch. The study results revealed that the sliding window method identified 162 black spots compared to 87 identified by the conventional method, showcasing the enhanced sensitivity of SWM.

In India, the escalating motorized transportation sector has led to a concerning rise in road traffic accidents and fatalities, imposing severe social and economic repercussions. Addressing this issue, we present a novel approach leveraging smartphone technology to develop a speed limit warning mobile application aimed at enhancing road safety. The application computes the real-time speed of vehicles and compares it with the government-posted speed limit, issuing warnings to drivers upon exceeding the limit. Field data collection of speed limits on major roads in Pune city was conducted. The application is written in Flutter and uses a location plugin for GPS functionality. The application is also integrated with Google Maps for navigation purposes. Audio and visual alerts promptly notify drivers of speed limit violations, supplemented by advance warnings. Our application offers a cost-effective and accessible solution to curb excessive speeding, contrasting with existing road safety systems in India. The proposed system serves as a crucial tool to bolster road safety efforts, ensuring the well-being of Indian road users. Its implementation demonstrates a direct and positive impact on community road safety, underscoring its significance in mitigating the adverse effects of road traffic accidents. Through this paper, we contribute to advancing road safety initiatives in India and beyond, fostering safer transportation environments for all.

Urban intersections in Hyderabad face severe traffic delays, adversely affecting economic viability, fuel consumption, and urban mobility. This study focuses on mode-wise delay estimation for various vehicle types, analyzing the impact of different signal timings and assessing the potential benefits of grade-separated intersections like underpasses. Using the PTV Vissim simulation tool, vehicle delays are quantified under heterogeneous traffic conditions, and the effectiveness of various traffic management strategies is evaluated. The methodology includes detailed data collection through videographic surveys, calibration of the simulation model with field data, optimization of signal timings, and comparative analysis of cycle durations and intersection configurations. Key findings reveal that a 240 s signal cycle reduces average delays by 57.21% and improves average speeds significantly compared to 180 and 120 s cycles. Annually, approximately ₹4.54 million is wasted due to idling fuel consumption for Leg 1, underscoring the critical need for implementing measures to optimize fuel efficiency and reduce operational waste. Additionally, grade-separated intersections achieve the least delays and highest average speeds, underscoring their efficacy as long-term solutions for mitigating congestion. This research provides actionable recommendations for traffic management strategies to enhance intersection performance and urban mobility in Hyderabad.

Unsignalized intersections are critical locations in terms of the risk and vulnerability involved in incorrect assessment of gaps by drivers on minor roads and higher speeds of vehicles on major roads. The speed profile of minor road and major road vehicles with subject movements under consideration is analyzed. Right-turning movement from a minor road is a critical maneuver with several conflict points. This paper examines the performance level and risk associated with right-turn movements from minor roads by estimating the critical gap and capacity with due consideration to the vehicle types. Analyzing drivers’ gap acceptance behavior is essential for designing safer and more efficient intersections in terms of performance. The model is formulated for gap acceptance of right-turn movements from minor roads by logistic regression. The critical gap, the minimum time interval deemed safe by drivers, is crucial for estimating intersection capacity and associated risk toward planning prospective traffic management measures. By analyzing critical gaps for different vehicle types, estimating the probability of gap acceptance, and developing predictive models, this research aims to enhance traffic flow and safety at unsignalized intersections. The findings will inform urban traffic planners, contributing to enhancing the design and overall traffic efficiency of an unsignalized intersection.

The need for the selection of diesel fuel alternatives for compression ignition engines arises from the ongoing exploration of more sustainable and environmentally friendly options to traditional diesel fuels. The current work focuses on the selection of an optimum diesel fuel alternative among vegetable oil, biodiesel, hydrotreated vegetable oil, emulsion, and alcohol. The comparison criteria among these alternatives are based on performance (brake specific fuel consumption, and brake thermal efficiency) and emissions (NOx, CO, HC, and smoke). Multi-criteria decision making (MCDM) techniques like AHP, ARAS, TOPSIS, and VIKOR were used for the choice of best optimum fuel alternative among these five alternatives. AHP was applied for the calculation of criteria weights, while ARAS, TOPSIS, and VIKOR were utilized to assess and rank the alternatives. Hydrotreated vegetable oil has better performance and lower emissions in comparison to diesel, and hence was ranked as the best diesel fuel alternative in all the ranking methods. Vegetable oil, on the contrary, leads to a significant drop in efficiency, along with an increase in fuel consumption, and hence ranked last. This evaluation is crucial to identify the most suitable alternative that can offer improved efficiency, lower emissions, and overall better performance in compression ignition engines.

Currently, the transportation planners are experiencing more challenges when identifying appropriate places for allowing on-street parking spaces to ensure efficient operation. In this study, on-street parking spaces and their effects on traffic speed are studied. The interaction between approaching vehicles and vehicles parked in on-street parking locations on urban roads can greatly influence the performance of traffic networks. Parked vehicles contribute to traffic congestion in urban areas, leading to delays for approaching vehicles. The average speed of approaching vehicles at the site of on-street parking is also studied lane wise. Analysis of the traffic speed in different lanes at on-street parking locations shows that occurrence of parking maneuvers (both in and out) has resulted in a decrease in the speed of various types of vehicles including Two Wheelers (TW), Auto rickshaws (Auto), Standard Cars/Small Cars (SC), Big Cars/Vans (BC), Buses, Light Commercial Vehicles (LCV). At the locations, lane 3 (near median side) consistently has highest speed across all flow levels because of less interference with maneuverability. Lane 2 (middle lane) shows a gradual decrease in speed as flow level increases, indicating moderate congestion because this is closer in connection with both lane 3 and lane 1 (parking side lane). Lane 1, which experiences the most significant reduction in speed, decreases from 27.95 km/h at the lowest flow level to 14.69 km/h at the maximum flow level due to more maneuverable operations.

Motorized two-wheelers are on the rise in Kerala and across India. This mode offers flexibility with door-to-door access, making it a popular choice among people for its ability to navigate through congested roads. However, their involvement in road accidents is a significant concern due to their vulnerability compared to other vehicles. As an engineering intervention, providing dedicated two-wheeler lanes can be a practical and effective solution to reduce the interaction between two-wheelers and heavy vehicles, thereby enhancing the safety of two-wheeler riders. Kerala in its initial step towards it, has implemented an inclusive motorised two-wheeler (MTW) lanes on State Highway 8 between Muvattupuzha and Vengalloor. While traffic segregation has been piloted in various locations worldwide, its potential benefits have not yet been sufficiently studied. This work is a case study of the selected corridor where inclusive MTW lanes are implemented which attempts to understand the traffic flow characteristics of the roadway to understand the effect of inclusive MTW lanes. The study primarily focuses on investigating the traffic variables like traffic flow, density, and speed on this road with inclusive MTW lanes. It has been found that implementing dedicated MTW lanes can improve traffic flow, but careful consideration of roadside conditions is crucial for optimal performance. The results and findings in this research can help in understanding operational performance of a road with MTW lanes and also provide insights to improve any shortcomings.

School zones are areas around schools where children are present, and due to their smaller size and lack of traffic awareness, children are more vulnerable to traffic accidents than adults. Therefore, extra precautions must be taken to ensure their safety in these zones. Children’s unpredictability, such as running into the street without looking or crossing at undesignated crosswalks, makes them particularly susceptible to accidents. Additionally, their smaller stature makes them less visible to drivers, and they often cannot judge the speed and distance of approaching vehicles or react quickly enough to avoid danger. To protect children, school zones typically have lower speed limits and other traffic calming measures. However, studies show that a significant number of children still suffer injuries or fatalities near school zones, prompting research into improving these measures. This research will use a statistical and dilemma zone approach, including road inventory surveys of school sites, and speed and volume surveys, to identify factors contributing to road safety near schools. These factors include road design, traffic speed, driver and child behavior, and traffic law enforcement. The research aims to propose effective strategies for improving road safety in school zones, thereby protecting children from traffic accidents and raising awareness about the importance of road safety in these areas.