Driver speed behavior constitutes a critical aspect in the assessment of the performance of road infrastructure vis-a-vis safety in mountainous terrain. The present study has been conducted to study driver speed behavior on roads in mountainous terrain by analyzing the variability of minimum speed positions on curved and maximum speed positions on tangent road sections. Significant variability is observed to be associated with the minimum speed position on curves and maximum speed position on tangents on four-lane roads in mountainous terrain. Further, the associated variabilities in maximum and minimum speed positions are also characterized with the best-fitted distributions. Higher variability of minimum and maximum speed positions on curves and tangents signifies the necessity of considering the complete length of curves and tangents in the formulation of the operating speed prediction models and the findings also have crucial implications for the safety evaluation of four-lane highways in mountainous terrain.
Activity-based travel demand models have reached a state of maturity but, several challenges remain in their survey design, format, administration technique, sample size determination and constraints faced during field survey. This research is an attempt to explore these issues and suggest way forward towards developing an efficient survey instrument and survey strategy for deployment in emerging countries like India and particularly during the post-pandemic period. A two-step approach of pre-pilot and pilot survey has been adopted to test our survey format and strategy before the conduct of final survey while considering the low literacy rate, qualitative inputs by enumerators, quantitative performance measures; average survey duration, percentage of error and response rate, acceptance rate and cost of survey and impact of the pandemic. A new compact, open-interval based, hybrid activity-travel diary format is proposed along with a hybrid method for data collection and validation and various recommendations for field survey strategies.
Transportation investments are primarily driven by the objective to enhance the accessibility and mobility of people and goods, thereby contributing to the social and economic development of communities. This study examines transportation equity in Bangalore in the context of two types of disturbances on equity: recurring urban floods and fare hikes. While urban floods have a short-term but profound impact, fare hikes result in prolonged economic impacts, both significantly affecting the Quality of Life (QoL) and well-being of citizens. To assess equity, an exploratory survey focusing on parameters of accessibility and affordability was employed. Statistical methods such as cluster analysis and McNemar’s test were carried out to extract patterns and insights from the collected data. The impact of fare hikes assessed using McNemar’s test revealed a 31.5
The extraction of vehicle trajectories is essential for understanding traffic flow characteristics and developing effective road traffic management strategies. For this, the aim of this work is to develop an end-to-end model to extract trajectory data from video footage, which includes vehicle detection and classification. As a first step, vehicle detection and classification models were trained using a combination of two existing datasets, IITM-HeTra and FGVD. This approach achieved a mAP50 score of 0.91, which is the best result compared to those obtained using other dataset combinations or individual datasets. Subsequently, vehicle trajectory was extracted using a model trained with YOLOv8, which involved tracking detected vehicles on the screen and obtaining pixel coordinates. The OpenCV library was utilized to convert the obtained pixel coordinates into latitude and longitude data, which were then mapped using Google Earth Pro. The developed model can be applied to any video data to obtain precise vehicle trajectory information, with Google Maps aiding in accurate mapping.
The underlined study focusses on investigating the rheological properties of bitumen binders VG-30 and bituminous mastic modified with hydrophilic nanoclay (NC). Varied dosages (0
This study investigates the unique traffic dynamics on Indian roads, characterized by a diverse mix of vehicle types and non-lane-based driving behavior, using a section in the Delhi-Panipat route as a case study. Unlike the strictly lane-disciplined traffic in Western countries, Indian traffic involves complex interactions among vehicles, making accurate prediction and modeling challenging. This research leverages high-fidelity trajectory data collected via Unmanned Aerial Vehicle (UAVs), capturing detailed vehicular movements on an eight-lane divided highway. The vehicles in the data set are categorized into six types, providing a rich basis for analyzing lateral positioning, speed, flow, and density distributions. The study employs exploratory data analysis to illustrate the relationship between speed, lateral position, and vehicle density for different vehicle types. One-way ANOVA (ANalysis Of VAriance) tests reveal significant differences in driving behaviors across lanes, while Games-Howell post-hoc tests identify specific lane pair distinctions. Furthermore, multiple linear regression (MLR) models highlight key predictors of lateral positioning, including vehicle speed, local density, flow rate, and vehicle type, with R-squared values of 0.695 for the northbound and 0.612 for the southbound sections. The findings demonstrate that vehicle types like cars, buses, and three-wheelers significantly influence lateral placement, with cars showing the highest lateral placement predictability. Variance Inflation Factors (VIF) indicate low multi-collinearity among most predictors, confirming the robustness of the model. This research enhances the understanding of lateral placement patterns in heterogeneous, non-lane-based traffic environments, providing a foundation for future studies to explore further variables and broader traffic scenarios.
Mobility hubs are revolutionizing urban transportation by offering seamless connections between diverse transit modes. However, maximizing their potential hinges on ensuring high service quality to attract riders and enhance user satisfaction. This study adopts a comprehensive approach, divided into two key parts. Firstly, this study identifies indicators for measuring service quality at the Mobility Hub in Vyttila, Kochi. Through an extensive examination of 19 indicators, the study categorises them into five latent constructs via exploratory factor analysis. Perceived service quality emerges as a second-order latent construct, revealing that service quality is derived from five essential variables: ‘transport services and information systems’, ‘accessibility’, ‘transfer environment’, ‘public utilities and other facilities’, and ‘safety and security’. Confirmatory factor analysis further validates these factors. Secondly, employing Structural Equation Modelling, the study investigates the relationships between service quality attributes and overall service quality. The findings underscore the significance of transfer environment and accessibility, with standard regression weight values of 0.28 and 0.25, respectively. External validation tests confirm the model’s robustness, showing that service quality remains consistent within an acceptable error range. These insights offer valuable guidance for government officials, operators, and transport planners, enabling them to formulate informed policies to enhance service quality at the mobility hub and promote public transport usage.
Pervious concrete (PC) is increasingly adopted in sustainable pavement systems owing to its ability to reduce stormwater runoff, mitigate flooding, and support urban heat management. Despite these advantages, designing mixtures that balance permeability and strength remains challenging because improving one property often reduces the other property. Conventional trial-and-error approaches are inefficient, underscoring the need for systematic methods that can simultaneously optimize multiple performance objectives at the same time. In this study, a combined framework of Response Surface Methodology (RSM), Sobol sensitivity analysis, and symbolic regression was used to investigate the influence of aggregate gradation, cement-to-aggregate ratio (C/A), and water-to-cement ratio (W/C) on the hydrological and mechanical properties of PC. Experimental testing of the designed mixtures enabled the development of predictive models, ranking of factor importance, and discovery of interpretable equations linking the mixing parameters to performance. The results showed that the W/C ratio predominantly governs the porosity, the C/A ratio controls the permeability, and the aggregate gradation strongly affects the compressive strength. Multi-objective optimization identified a mixture that balanced permeability and strength, and validation confirmed its accuracy. The integrated methodology offers a structured approach to PC mix design, reduces the experimental effort, and supports practical decision-making. These findings provide tools for developing efficient and sustainable pavement solutions, particularly in resource-constrained urban contexts.
The initial segment of a journey, specifically the first mile connectivity between a traveler’s starting point (origin) and a primary public transportation hub such as a bus stop, railway station, or metro station, is a crucial element influencing the overall efficiency and attractiveness of multimodal transportation systems. Improving first mile connectivity is necessary to promote sustainable mobility and reduce dependency on customized automobiles in Indian cities where the rapid pace of urbanization and population growth has increased traffic congestion and travel time. By using the multinomial logit model, a discrete choice model, this study aims to develop an access time index and an average accessibility index to enhance first mile connectivity in Indian cities. The authors will assess the first-mile connectivity conditions in the selected Indian cities, ascertain the significance of the factors influencing commuters’ first-mile choices, and offer suggestions for particular actions meant to improve the intermodal integration of feeder, private, and walking modes with the primary public transportation system. It was discovered that the city of Bhopal has an Average Accessibility Index (AAI) of 0.48 and an Access Time Index (ATI) of 0.52. Because an optimum ATI would be closer to 0 (meaning access times would be shorter) and an optimal AAI would be closer to 1 (meaning accessibility would be better), these numbers suggest that Bhopal city’s first mile connectivity can be improved. This research will be useful in developing evidence-based policies and initiatives to enhance first-mile connectivity in Indian cities, which would ultimately result in sustainable urban mobility and improved living circumstances for city dwellers.
The choice set and the associated differences in choice behavior are often captured by assuming the choice set to be latent. However, such model structures are of limited use for practitioners and policymakers and might not help frame choice set specific policies. In the absence of empirical application/calibration of model structures that capture behavioral differences among commuters whose choice set vary, choice set specific behavioral investigations and more meaningful choice set specific policy proposals might be challenging to achieve. Under such circumstances, a deterministic choice set model which classifies commuters based on choice set differences would aid in analyzing the behavioral difference among these segments. In this regard, the paper attempts to develop a mode choice model which captures the differential choice behavior across various choice set groups – captive commuters, commuters who do not have access to either personal vehicle or public transport, and those who have access to both. It also attempts to propose target segment (choice set group) based policies to promote sustainable modes. Evident behavioral differences in the choice of work mode were observed among these choice set groups. It was found that non-captive commuters could be attracted to company bus/cab by subsidizing its monthly fare Commuters who have access to both personal vehicle(s) and public transport were the responsive target segment that might shift to bus and metro on providing new routes and free metro cards, respectively.
Uncertainty is a fundamental feature of traffic and transportation systems. It arises from fluctuating travel demand, varied traveler behavior, and gaps or inconsistencies in available data. Individual decisions, such as departure time, route choice, or mode choice, are often based on personal perception and qualitative information rather than precise numerical data. Traditional mathematical methods face difficulties in modeling human perception and decision-making. Fuzzy logic (Zadeh, [388–395]), through fuzzy sets and linguistic variables, provides a mathematical framework for representing imprecision, human perceptions, and approximate reasoning. Pappis and Mamdani [271] were the first to apply fuzzy logic to a traffic engineering problem, developing a fuzzy controller for an isolated intersection. Its success stimulated broader use of fuzzy logic in transportation. Over the past fifty years, fuzzy logic has been applied to traffic flow modeling, network dynamics, signal control, ramp metering, incident detection, public transport operations, vehicle routing and scheduling, air traffic control, airport surface operations, railways, and inland water and maritime transportation. This paper reviews fuzzy logic applications in transportation over five decades, examines approaches across modes, discusses the evolution from rule-based models to hybrid and Type-2 fuzzy systems, and outlines limitations and future research directions.
Wrong-way driving (WWD) and related crashes have been studied in the regions where WWD is rare. Despite being prevalent and a safety concern in India, detailed field investigations of WWD are limited. Using six years of WWD-related crash data, this study identified high-risk road sections in Vellore, Tamil Nadu and then performed a detailed field investigation along these sections. The study classified the sites into four distinct road configuration scenarios to examine how road design, access conditions and traffic operations influence WWD behaviour. The study documented and classified common WWD manoeuvres, entry points, travel paths, and conflict situations. Most WWD movements were intentional and primarily involved motorized two- and three-wheelers. Drivers often chose the wrong direction to save travel distance. The absence of proper signs, pavement markings, and enforcement made this behaviour common among the drivers. Many drivers were unaware of or disregarded one-way restrictions, and even authorities informally permitted bidirectional flow on service roads. Conflicts with regular traffic arose when wrong-way vehicles encroached on the carriageway due to narrow or obstructed shoulders. Additional risks stemmed from higher speed, overtaking within WWD traffic, poor lighting, glare, and sight obstructions. While emphasizing basic signs, speed control and visibility improvements, the study proposes scenario-specific signs and pavement markings to discourage WWD and alert regular drivers. It further recommends coordinated efforts from police, driving schools and communities to raise awareness and strengthen enforcement. The study highlights the need for India-specific engineering interventions to mitigate WWD risk, ultimately contributing to safer road environments.
The study developed microsimulation (VISSIM) models and utilized the Surrogate Safety Assessment Model (SSAM) to assess traffic safety in the case of a three-lane unsignalized roundabout under mixed traffic conditions. The roundabout network was constructed in VISSIM using drone images, and traffic characteristics extracted from the aerial videos were used as input in the simulation model. Subsequently, vehicular trajectories exported from VISSIM were analyzed in SSAM to identify conflict points (setting TTC = 1.5s and PET = 1.5s). A total of 1194 conflicts were identified, of which 77.5
The present study examines the spatial development of transport infrastructure in India and its impact on socio-economic status using a GIS-based spatial analysis approach. A Composite Transport Infrastructure Index (CTII) and a Composite Socio-Economic Development Index (CSEDI) are constructed using key indicators such as road, rail and air connectivity, alongside income, unemployment, poverty, urbanization, education and healthcare accessibility. The study employs correlation and spatial regression models to assess the relationship between CTII and CSEDI. The results reveal a strong positive correlation (r = 0.738) between transport infrastructure and socio-economic development, alongside pronounced regional disparities. GWR results indicate that transport investments have stronger developmental impacts in southern and western India, while eastern and northeastern states show weaker effects due to structural and institutional constraints. The findings highlight the need for region-specific transport planning integrated with human development policies to reduce spatial inequalities and promote inclusive growth.
A feasible and sustainable mode of freight transportation is crucial for facilitating trade and logistics operations for a new developing port like Matarbari port in Bangladesh. Therefore, the study aims to identify the factors which influence users, including shippers, consignees and forwarders to choose the preferred transport mode. Based on the identified factors, the study also investigates the best mode of transportation for transporting containerized cargo from the Matarbari deep seaport to its hinterland. The research adopts two operations research models and one econometric model. The Best Worst Model (BWM) has been applied to identify the weights or relative importance of the factors influencing the choice of freight transport mode. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) model has been used to outrank the three freight transport alternatives: rail, road, and inland waterway transport (IWT). Furthermore, the Conditional Logit Model (CLM) has been used to identify the freight transport alternative(s) based on the negative and positive influential factors. The research findings indicate that transportation cost, customs clearance and documentation, and cargo damage and accident risk are key factors influencing the movement of containerized cargo from Matarbari to its hinterland. The results further suggest that rail is the only feasible mode for transporting containerized cargo from Matarbari to Dhaka and its surrounding areas, as supported by both PROMETHEE and CLM analyses. The findings will help Bangladeshi policymakers to draft the freight transport policy relevant to Matarbari port and other similar port-hinterland connectivity problems.
This study examines the factors influencing the adoption of electric vehicles (EVs) in the context of an emerging market, specifically Türkiye. Structural equation modeling was employed to estimate 641 consumer questionnaires. Findings show that economic incentives and subsidies in purchase significantly impact consumer behavior, whereas consumers’ future expectations for EV technology and infrastructure also positively influence adoption. Unlike widespread assumptions, environmental benefits, social image, and individual innovativeness do not impact the adoption behavior of Turkish consumers. These results contradict widespread theoretical concepts that emphasize environmental consciousness and social influence as major determinants of EV adoption in developed and emerging countries. This study contributes to the extant literature by broadening the scope of consumer adoption behavior beyond developed economies. Practically, the findings provide valuable insights for policymakers and market participants seeking to design effective incentive schemes and competitive advertising strategies tailored to the unique demands of evolving economies.
The growth of demand for public transport in developing countries has led to the use of diesel fuelled buses for BRT in cities. However, the diesel buses are potential emitters of GHGs and Criteria Air Pollutants (CAPs) which have disastrous effect on climate change and human health. Studies reveal that the use of electric powered buses for BRT in cities can reduce and/or eliminate GHGs and CAPs from the road transport sector. Nonetheless, most transit agencies are still sceptical on the investment of E-buses for BRT due to the envisaged high investment cost and business risk. This study therefore proposes the Financial Evaluation Model (FEM) to be applied by a transit agency to determine the feasibility of the investment in E-buses for BRT operations. The study uses the Dar es Salaam based bus rapid transit agency (UDART) as a case study. The secondary data used by the FEM were collected from various sources including the local transit agency, electric bus manufacturer and published sources of similar studies conducted in developing countries. The numerical results reveal that the net cashflows computed by the FEM is negative which implies that the investment of E-buses for BRT in Tanzania is currently not a financially viable option. Further, the sensitivity analysis reveals that capital costs, ridership, interest rates, and average investment life are the key parameters when considering the investment of E-buses for BRT operations.
Cellular Automata (CA) modelling has emerged as a promising approach to address the computational challenges associated with microscopic traffic flow models, especially under mixed traffic conditions. CA models offer advantages in terms of flexible evolution rules and high computational efficiency. The inherent discreteness and localized cell computations provide a unique capability to connect micro-level dynamics to macro-level traffic behaviour. This paper presents a comprehensive review of existing CA models and identifies potential directions for advancing current practice to capture the features of heterogeneous and mixed-traffic environments. The paper outlines the fundamental principles of CA, emphasizing its relevance in mixed traffic flow modelling. It explores how CA models can be customized to address the complexity of mixed traffic by modifying parameters such as cell size, cell structure, and randomization rules, adopting ideas from physics and biology literature. It reviews lane-change modelling, focusing on the nuances of lane-changing behaviours in both homogeneous and heterogeneous scenarios. It also explores the representation of mixed traffic within CA models through various dimensions, including cell representation, vehicle representation, and driver behaviour representation. Each of these aspects is critically evaluated, highlighting the strengths and limitations of existing models and proposing potential enhancements to better replicate scenarios marked by a diverse range of vehicle types and driver behaviours. Thus, this comprehensive review highlights the state-of-the-art research and practice regarding the cellular automata models, their limitations, and underscores future directions that hold potential for more accurate replication of mixed traffic environments.
This research investigates the lateral safety of vehicles navigating roundabouts by utilizing surrogate safety measures and extreme value theory. To capture vehicle interactions at the Kasna Gol Chakkar roundabout in Greater Noida, India, we employed drone technology to gather high-resolution aerial footage. Our main goal was to quantify conflict probabilities and set safety thresholds under different traffic conditions using metrics like Time to Collision (TTC) and Post Encroachment Time (PET). Traditional safety analysis methods often overlook near-misses and other critical incidents because they primarily focus on collision data. To address this, we used advanced data collection and processing techniques for a more proactive safety assessment. The data was processed using Data from Sky (DFS) software, which excels at extracting vehicle trajectories and identifying conflict points. DFS’s advanced trajectory analysis features, including TTC and PET calculations, enabled a thorough evaluation of conflict severity and frequency. We identified distinct interaction patterns during on-peak and off-peak hours, with a significant rise in rear-end conflicts during on-peak periods due to increased traffic density. Additionally, we applied the Generalized Pareto Distribution (GPD) to model TTC values and estimate critical conflict probabilities, providing a statistical framework for assessing high-risk scenarios. The study’s findings emphasize the importance of implementing targeted safety measures, such as enhanced lane markings, signage, and intelligent traffic management systems, to mitigate risks and improve overall roundabout safety.
The most commonly used analytical delay models for signalised intersections, namely Webster’s or Ackelick’s, are based on the assumptions of random arrivals and deterministic discharge with one queue per lane. This may not be true under different traffic conditions. This study starts with checking these assumptions. Analysis of traffic patterns from a selected study site reveals that both arrivals and departures are random, prompting the use of an M/M/n queueing system for delay formulation. Based on this, a new analytical delay model rooted in queueing theory, designed to address the unique characteristics of multi-class and lane-free (MCLF) traffic, including multiple vehicle classes, lane-free movement, multiple number of servers, and arrival and service processes, is developed. The uniform delay is derived from the cumulative arrivals and departure curves, while the random delay term is by using the properties of the M/M/n queueing system. Empirical evaluations indicate that the proposed model significantly improves delay estimation, highlighting the critical need for developing delay models that are specifically adapted to traffic conditions.
