Proceedings of the 7th International Conference of Transportation Research Group of India (CTRG 2023), Volume 3

A navigation application was developed and tested in Ahmedabad to promote the use of public transportation through the provisioning of auto rickshaws as feeder transportation services. The app provides suitable mode and route for seamless access, not only by integrating information on multiple modes but also a booking and payment function. Based on a questionnaire survey administered to explore the possibility of a modal shift from private to public transportation and its effect on reducing carbon dioxide emissions, a mode choice model with a discrete choice framework was developed. It was found that the booking and payment function only slightly enhanced the probability of selecting public transportation as accessed by feeder transportation. However, the model with this function was applied to the actual behavioral data obtained from the field experiment, and a reduction in carbon dioxide emissions was not achieved through the booking and payment function. This is because the data from the field experiment indicated a high level of convenience in the use of passenger cars, thus it did not lead to a change in the choice of travel modes.

The electrification of public transportation has picked up pace with the worldwide introduction of Battery Electric Buses (BEBs). In India, the BEB operations are now even expanding to hilly regions with extreme climatic and topographical conditions. In this study, we propose an energy consumption estimation model for BEBs operating in hilly regions. The study examined BEB operations on a few selected routes in the hilly terrain of the Leh district of Ladakh, India, to understand the important factors influencing their energy consumption. A hybrid data collection methodology was adopted by employing GPS, dash-board images and readings recorded from various on-board instruments like altimeters and temperature devices. Linear regression modeling was performed to determine the effects of terrain and other factors like passenger weight, ambient temperature, etc., on the energy consumption of the BEBs. We used the average gradients of the route segments between consecutive data readings for the modeling purpose. Results show that the predominant factors influencing the energy consumption of the BEBs are gradient and the passenger loading. BEB charging patterns were also assessed to estimate the expected time to charge various State of Charge (SoC) ranges of the battery. This study will help the stakeholders to critically design and make operational choices regarding BEB operation in the extreme geographical conditions like that of Leh.

Compact development of a region is supported by sustainable transportation, which effectively decreases the travel time and cost between locations and brings in savings in economy and environment. Suburban areas could be facilitated with transport infrastructure development to pave way for enhanced patronage to sustainable modes. Existing travel pattern is examined and estimated the carbon emissions from transportation sector in a suburban region located in the southern state of Kerala in India. Henceforth this paper presents the mode shift behavior toward ecofriendly modes in a proposed transport scenario with good connectivity of sustainable modes. The stated proposed transport scenario comprises of non-motorized transport (NMT) friendly ring road infrastructure enabling good connectivity with reliable public transport and feeder services. Stated preference surveys were carried out at household level to collect information on travelers’ socio-economic and travel characteristics. Traveler’s satisfactory levels with the current travel modes and their willingness to shift to ecofriendly modes were also examined. Binary logistic regression models were formulated to predict the potential mode shift to ecofriendly modes. The predicted mode shifts for work, educational, shopping, social/recreational, and medical purposes was approximately 43.24%, 56.57%, 40.35%, 39.58%, and 34.43% respectively. Based on mode shift analysis, reduction in carbon emissions were estimated to be 40% and 51% for work and educational trips respectively. Development of a ring road infrastructure with reliable public transportation and NMT facilities will help to enhance the patronage for public transportation and thereby reduce the carbon emission from transport sector.

The types of collisions during road crashes and the factors contributing to them are many. Chances of collisions can be reduced by predicting the probability of types of collisions at various locations and providing drivers with real-time details of the risk of collision while they travel. This paper aims to predict the severity of collisions at intersections, specifically two types of collisions—‘Property damage’ and ‘Injury Collision’, through machine learning models. The paper uses a dataset from the Kaggle website which the police department of Seattle regularly updates. It consists of values of different variables such as road condition, weather condition, junction type, and the attention span of the driver, hitting a parked car, light condition, and speeding. Four different conventional machine learning algorithms, namely, Random Forest, K-Nearest Neighbour, Decision tree, and Linear Regression are applied for collision prediction. The Random Forest model is observed to have the highest accuracy (0.7506) followed by the K-Nearest Neighbours algorithm (0.7442), Decision Tree (0.7385) and Linear regression (0.6999). The most featured attributes were weather conditions, road conditions and temperature and predicted more property damage than injury collision. An ensemble of the above models is also tried for crash prediction and observed as with the highest accuracy (0.7519).

Recent years have seen a sharp rise in Kolkata's motor vehicle fleet, which has increased energy consumption and transportation-related pollution. Adoption of environment-friendly energy-efficient technologies like Electric Vehicles (EVs) is urgently required in the city. EVs are a practical and workable technology that could reduce fuel use and pollution. EV is a suitable option for both public and private transport. However, like other new technologies, EVs require the encouragement and assistance of multiple drivers. However, the drivers are different for public and private modes of transport. Within private modes, drivers are different in the HIG, MIG and LIG categories of people. The study aims to investigate the various elements that contribute to the quicker adoption of EVs in Kolkata's public and private transportation systems. A survey of two hundred stakeholders from ten relevant entities is carried out. The technique of Principal Component Analysis (PCA) is employed to ascertain the key components and their contributing drivers for faster EV adoption in public and private modes. The research results show that: (i) establishing an EV accelerator cell and coordinating with stakeholders; (ii) developing a smart EV infrastructure facility; (iii) innovating in technology; (iv) raising awareness of environmental issues and building awareness; (v) consumer support centres; and (vi) financial instruments are main contributing drivers to promote EVs in public and private transport.

This study presents a comprehensive methodological framework to identify and explore the factors associated with road traffic injury frequency at urban midblock sections in Hyderabad, India. To explore the relationship between crash frequency at different levels of severity (fatal, non-fatal, and total) and roadway and traffic and geometric attributes, three sets of exclusive Safety Performance Functions (SPF) are formulated. For SPF model calibration, (a) historical crash data (2015–2019) collected from Hyderabad police and (b) primary geometric, traffic, and roadway-built environment-specific information collected through detailed road inventory survey across Hyderabad were utilized. Subsequently, Negative Binomial Regression models, an extensively adopted technique for count-data-based modelling are used to develop three sets of SPFs (i.e. Total Crash-SPF, ii. Fatal-Crash-SPF and iii. Non-Fatal-Crash-SPF) for midblock sections in Hyderabad. Results clearly indicate that speed, median width, and midblock segment length are found to significantly influence the fatal/non-fatal/total crash frequency at midblock roadway segments. The identification of potential influencing factors that are relevant to the frequency of crashes could help in developing necessary mitigation measures.

Automated vehicles (AVs) are a promising solution for the future of transportation. As human factors are responsible for 90–95% of accidents, the integration of Advanced Driver Assistance Technology (ADAS) in vehicles has the potential to significantly reduce crashes. Furthermore, numerous studies have confirmed the benefits of these vehicles in improving traffic dynamics, including their impact on traffic stream capacity and stability. However, the full deployment of AVs on roads will require a transitional phase where they will be sharing the road with regular vehicles (RVs). In this context, this paper provides a review of studies that have assessed the impacts of AVs on traffic dynamics in a mixed-traffic environment. A detailed discussion reveals that the Market Penetration Rate (MPR), vehicle category, vehicle's operational settings, and traffic volume are critical factors that impact the improved traffic dynamics. Moreover, in a country such as India, where traffic conditions are heterogeneous and unique, it is of particular interest to investigate the impact of these factors on traffic characteristics.

In an attempt to understand how different roadways, driving situations, and other external factors contribute to stressful driving behavior, the current study develops a latent factor analysis model using questionnaire data collected from car drivers. Results of the exploratory factor analysis obtained from a 14-item questionnaire study revealed a 4-factored construct model, which was further evaluated by confirmatory factor analysis. Additionally, the study developed a second-order latent variable model to express drivers’ emotional states while driving under stressed conditions. A gradual decrease in factors’ scores was observed as the age of drivers increased. However, a positive relationship between self-reported accident frequency and stressful driving behavior was obtained, which justifies the impending risks and exposure to dangerous situations that would be associated with stressed drivers. Finally, the study revealed the most influencing driving situations that would contribute to stressful driving depending on the item-wise analysis of each factor.

To proactively enhance pedestrian safety using traffic conflict technique, it is necessary first to identify traffic conflicts among traffic interactions. Conflict indicators such as PET are used to quantify the proximity of traffic interaction to actual crash. These indicators are then compared to a threshold value to determine whether the interaction qualifies as a conflict. Three common approaches are used to determine threshold values for conflict indicators: select a predetermined threshold from the existing literature or use techniques such as crash conflict relationship or extreme value analysis. Selecting a predetermined threshold may not yield the best results, especially for heterogeneous traffic situations, and may require a more nuanced approach. The crash conflict relationship approach has the disadvantage of utilizing crash data, which sometimes can be unreliable, particularly in developing countries. The extreme value approach demands data to follow certain types of distribution, which may not be true in every case. This study addresses the limitations of existing methods by considering traffic conflicts as anomalies and employs Isolation Forest, a machine learning technique, to identify threshold value. An algorithm for automated extraction of PET values from video data is also used and validated. The proposed methodology was applied to an unsignalized intersection on an intercity highway with heterogeneous traffic. It established the PET threshold necessary to identify pedestrian-vehicle conflicts to be 2 s. The automated way of PET extraction has demonstrated excellent performance, as evidenced by its high R-squared value of 0.9756.

In order to manage urban traffic and assist people in making wise travel decisions, timely and accurate traffic speed forecasts have become extremely important. In this study, a new Neural Network (NN) structure with a Chaotic Particle Swarm Optimization (CPSO) technique is introduced into the two-layer bidirectional long short-term memory network, termed CPSO-DBLSTM, with the aim of improving forecast accuracy of traffic speed. To accelerate the convergence of the Particle Swarm Optimization (PSO) algorithm, CPSO method was applied, which is an optimization strategy based on chaotic theory and PSO. Chaotic theory was applied to the PSO technique to balance the exploration and exploitation phases. Evidently, it was found that the CPSO algorithm is more effective than the PSO algorithm by rapidly deviating from local optima due to the incredible behavior of chaos and its good capability. In comparison to Long Short-Term Memory (LSTM) NN and single-layer bidirectional LSTM (Bi-LSTM) NN, it was found that Bi-LSTM neural networks with two layers perform better. To improve the prediction accuracy of two-layered Bi-LSTM model, it was combined with the chaotic PSO technique which reduces the impact of a random selection of hyperparameters on the prediction performance. The proposed model has been tested on the real traffic data which were collected from selected locations in Delhi. According to the study results analysis, proposed model was found superior as compared to other baseline models in terms of evaluation metrics.

Transit oriented development (TOD) is the development approach that integrates land use with public transit. TOD gives a systematic approach that reduces the dependency of private motorized vehicles and makes people shift toward public transport which can be achieved by providing accessibility to diverse destinations within walkable distance. This paper explores the cases of TOD for Ahmadabad which is having one of the best BRTS systems in India and Curitiba which is one of the best BRTS systems in the world. By doing this the study is attempted to know what India is lacking in the process of implementing TOD. From the international experiences of TOD learn to improve the quality and better understanding of TOD. This paper also covers the process of implementation of BRTS based on TOD to Vijayawada city where we tried to suggest a framework that India is lacking in the process of implementing TOD.

Research on intersection safety has been mostly focused on regions with car-dominated traffic and proper lane discipline. However, with improper design and control measures, two-wheeler dominated traffic and deficiency in driver awareness, illegal traffic movements are seen at many intersections in India. An illegal movement is a movement which is not accepted as per the prevailing rules of the road. As drivers may not expect such movements, the chances of crash may increase. An analysis of the potential for additional crashes due to illegal movements is necessary to develop and justify measures to prevent illegal movements. Since crashes are rare events and there is no sufficient crash data available, in this study, the authors analyse traffic conflicts as a surrogate to crashes. They aim to explore how the type and severity of conflicts involving illegal movements differ from those involving only legal movements. Further, they aim to identify whether the intersection records a higher rate of conflicts as the proportion of illegal movements increases. Considering the video-graphic traffic data from 2015 at a suburban intersection on NH 163 in Warangal, they analyse the traffic conflicts happening due to legal and illegal traffic movements. They find that nearly 30% of all local turns during the study period are illegal turns. The data also shows that illegal turns contribute to higher rate of conflicts and more severe conflicts than legal turns. This study helps to develop and justify measures to prevent illegal movements and the resultant crashes at intersections.

Traffic is the main reason for noise pollution at intersections in mid-sized cities. This study identifies nineteen sample intersection locations in Kanpur to analyse the traffic noise level concerning environmental noise capacity. The study utilizes an intersection-specific traffic noise model developed by Yadav et al. (Yadav et al. in India. Noise Mapping, 2023) to calculate equivalent noise levels (L_cal) at intersections. The L_cal varies significantly at these intersections due to variety in the input variables. The noise level has crossed the environmental noise capacity at all locations except LML chauraha. The poorest acoustical climate is reported at Sachan chauraha regarding environmental noise capacity. The noise level should not exceed the environmental noise capacity at any location. Immediate actions are required to reduce the noise level if it crosses the environmental noise capacity. This study can be useful for policymakers to plan strategies to control and mitigate traffic noise at intersections in mid-sized cities considering environmental noise capacity.

Freight transportation has skyrocketed in recent decades due to the globalization-driven increase in demand for transportation. This growth surge would necessitate last-mile delivery, particularly in urban logistics. The increase in urban freight movement contributes to congestion and greenhouse gas emissions. Keeping sudden growth of transportation and its associated negative externalities, different nations have set goals to reduce carbon emissions. This requires the prioritization of determinants of sustainable freight transportation that would contribute to zero carbon emissions. This paper aims to provide insight into sustainable freight transport planning from the perspective of stakeholders. To address this, we have investigated literature-proposed determinants that are significant in achieving net zero carbon emission. In addition, the extracted determinants are discussed with an expert-led focus group. Furthermore, we utilized the Bayesian Best Worst method to establish the importance-based priority. On the basis of the analysis, the significant determinants for sustainable freight transportation have been prioritized. The findings would provide a systematic approach to achieving the nation's goal of carbon neutrality in the coming decades.

Increased urban population and road traffic has resulted in traffic congestion. Thus, traffic state estimation is the process of determining the current state of traffic, such as traffic volume, speed, and congestion levels, using sensor data and mathematical models. Probe vehicles equipped with Global Navigation Satellite System (GNSS) receivers, and other sensors are now frequently used to collect data on traffic condition such as speed, vehicle to vehicle spacing etc. The data collected by probe vehicles can be used to detect traffic congestion and other traffic-related issues in real-time. The probe vehicles function as the moving traffic detectors, which are not restricted to selected or fixed locations. With advent of new algorithms, the probe vehicle data can be utilized to predict traffic congestion using machine learning algorithm. In this presented research work the authors have implemented the Artificial Isolation Forest algorithm to predict the congested locations using instantaneous vehicle to vehicle space headways derived from ultrasonic sensors and speed derived from the positional data acquired using GPS sensor on the road segments subjected to traffic congestion. From the analysis of the results, it can be posited that Isolation Forest algorithm can be suitably used to detect the traffic congestion using speed and instanteneous space headway data.

The paper reviews the different geometric variables needed for developing an SPF and the sources or survey methods for collecting them. In this process, the relationship between individual road geometry variables (qualitative and quantitative) and crashes is realized to exhibit mixed nature (i.e., increase or decrease). The findings of the review on road geometric variables indicated the need for further study on the interrelation between variables, understanding the influence of different variables on urban and rural roads of less importance, the inclusion of variables qualitative, which are difficult to collect, etc. From the data sources or survey methods and the type of data, we can imply that every method employed is either not able to collect all the possible geometric variables influencing crashes and their condition or expensive or timeconsuming process. Many of the techniques are also difficult to extract for larger study regions. This causes restricted study sites.

Speed of vehicles is one of the major reasons for road crashes. Implementations of traffic calming measures can reduce these kinds of crashes up to an extent. In this study we try to analyse the effects of installing tubular marker on an accident-prone study stretch. The stretch selected is a curve with overtaking behaviour leading to crashes. The pre- and post-conditions prevailing relating to vehicle speed and accident data have been taken into account for the analysis. The Infra-Red Traffic Logger (TIRTL) has been used to determine the vehicle speed and count. A sharp reduction in the average speed was observed—in case of the vehicles like Light Motor Vehicle (LMV), Medium Commercial Vehicle (MCV) and Multi Axle Vehicle (MAV)—after installing tubular marker. There was a decline in the accident rate also since over-taking was restricted. Speed reduction was found to be more effective on larger vehicles since that class would be more affected by the overtaking hindrance in-stalled. No statistical significance in mean speeds was observed for smaller vehicles like two-wheeler and three-wheeler due to their better maneuverability.

This manuscript is dedicated to investigating the crucial connection between driver vision and road crashes, with the primary objective of identifying factors that influence visual performance and proposing effective strategies to enhance road safety. To achieve this, the study begins with an extensive literature review, delving into the significance of visual perception in driving safety. The review explores the essential visual components necessary for safe driving, such as visual acuity, field of view, depth perception, and visual attention. To gain deeper insights, the research conducts an analysis of accident data and case studies. The focus is on understanding how impaired vision, including refractive errors, visual impairments, and age-related changes, impacts road crashes. Additionally, the study examines external factors like adverse weather conditions, nighttime driving, glare, and distractions, as well as the influence of modern technologies like in-vehicle displays on visual attention and driving performance. The study particularly concentrates on heavy truck drivers, considering the potential implications of their visual issues on situational awareness and traffic safety. The findings highlight a notable prevalence of visual issues among heavy vehicle drivers, significantly affecting their driving efficiency, situational awareness, and overall traffic safety. As a crucial recommendation, the study suggests a review of the current Driving Licensing System in India. It proposes the implementation of mandatory eye testing for all applicants to obtain or renew a driving license. This measure aims to identify and eliminate drivers with vision problems, ultimately leading to improved road safety. Furthermore, the manuscript emphasizes the importance of regular vision screening and access to corrective measures for heavy vehicle drivers. Such measures can play a significant role in improving their visual function and overall driving performance, consequently enhancing road safety. In conclusion, the manuscript underscores the urgent need for effective policies and interventions to address vision-related challenges among heavy vehicle drivers. By doing so, road safety can be substantially improved, reducing the number of accidents caused by impaired vision.

The present study focuses on assessing the safety scenario of a two-lane highway network in India and subsequently identifying cost-effective countermeasures to improve the safety situation. For this purpose, five state highways consisting of a total length of 1384 km were selected from the state of West Bengal. Geo-referenced video data capturing the road inventory of the entire network is processed to develop Star Ratings using International Road Assessment Program (iRAP) for four road user groups. The results show that most of the network is rated below three stars. Five major safety issues are also identified: Deficiencies in road delineation, Unprotected roadside hazards, Untreated built-up areas, Improper horizontal alignments, and Safety issues at intersections. Subsequently, the countermeasures most effectively reducing fatalities and serious injuries (FSI) are identified from Safer Road Investment Plan (SRIP) based on the benefit–cost ratio. The outcomes of the present study would be helpful to the stakeholders and policymakers in allocating funds and resources to implement the most-effective countermeasures. Moreover, in developing countries, with limited resources, the results would guide the road-owning agencies to prioritize their areas of investment for enhancing road safety on two-lane highways proactively.

Electrically assisted bicycles (e-bikes) are gaining popularity around the world due to their several mobility, health, and environmental benefits, particularly during and post COVID-19 pandemic. Despite the increasing global recognition, market acceptance of e-bikes is still low in some developing economies like India. To promote e-bike use, it is essential to study the factors influencing the intention to adopt e-bikes among users and non-users. Majority of the past research has investigated the e-bike use related factors from the experiences of e-bike users; while little is known about the non-user’s perceptions. This study identifies and prioritizes the factors related to the perceived usefulness of e-bike influencing e-bike adoption through Principal Component Analysis integrated with Meta Ranking. Results identified e-bike use for last-mile connectivity, for better health, for fun, and for reducing traffic congestion as the most important motivators. While risk of theft, range anxiety, and inadequate charging and cycling infrastructure were found to be the important barriers to e-bike adoption. Purchase cost was observed to be the influencing deterrent among low and middle-income groups. People with greater age and high-income levels preferred using an e-bike for fun and leisure over last-mile connectivity. These findings offer important insights for designing the effective e-bike promoting programs by emphasizing the most influential motivators and deterrents to e-bike adoption.

According to the different characteristics of roads, each road type has different speed limits from the safety point of view, which are posted along the roads at regular intervals. Yet, it is observed that over-speeding is one of the main reasons for fatal road accidents. Therefore, the question arises that whether the posted speed limits are appropriate, whether the drivers comply with the posted speed limits, and if not complied, then whether the violations of posted speed limits are by chance or not. The appropriateness of the speed limits requires a separate research, but this paper reports the findings from an investigation carried out along two arterials in Kolkata, West Bengal, to address the other two questions. The drivers’ tendency to violate the posted speed limit has been modeled using Binary Logistic Regression technique to reveal the site-specific factors that affect the posted speed limit violation. It is observed from the model that with increase in time headway between vehicles, the violation of speed limit increases. More violations are expected along roads with higher number of lanes due to the availability of more road space free from the influence of leading vehicles. Bus and truck drivers have higher tendency to violate the posted speed limits than the passenger cars and motorized two-wheelers.

Road fatalities in the present era are increasing at an alarming rate. As per the World Health Organization (WHO) report (2021), deaths from road traffic crashes have increased to 1.35 million a year which implies that on average about 3700 people die on the world’s roads everyday (World Health Organization, Global Safety Report on Road Safety [1]). Statistics show that developing countries have 90% of the casualties are from these countries and 11% alone from India. So, data- driven initiatives to safeguard our roads should be taken to minimize accidents. One of the methods is the identification of blackspot sections on the roads and ranking them. Applying cost-effective methods in road safety is also a major challenge that is being faced by various developing countries. Suitable tools like International Road Assessment Programme (iRAP) can be used to analyze the road sections and in identification of the root cause of the problem and implementation of suitable countermeasures. Case studies have shown that applying newer technologies in doing road safety audits has given new insights and better solutions as discussing iRAP, which is an open-source software used for the rating and analysis of sections. Attributes are filled for every 100 m of section for analysis of 1 chainage length, i.e. 1 km of road. In this study 30 hotspot locations are identified, and their star ratings are generated using the ViDA tool.

Signalized intersections act as an integral component of urban traffic management and control systems by providing maximum control over the movement of conflicting traffic streams at different time separations, reducing the conflict risk between the vehicles. This study analyzes the conflicts probabilities at different states, namely, Free flow state (A), Jam state (B), and Capacity state (C) of the signalized intersection by using the concept of leader–follower pair. The Time to Collision (TTC) threshold of 0–2 s has been considered critical, and data on conflicts is analyzed using this threshold. The data on the traffic flow at the signalized intersection was collected in the city of Ahmedabad, India using Unmanned Aerial Vehicles (UAVs) for two locations. The data is collected for 12 cycles at each intersection. The queue lengths are determined by the UAV trajectories in mixed traffic conditions. The result shows that the average probability of conflicts is highest at the Jam state in every cycle at all locations. The average percentage of the conflicts for the Leader–Follower pair was found to be 78.77% for the Jam state (B).