Proceedings of the 7th International Conference of Transportation Research Group of India (CTRG 2023), Volume 2

Worldwide growth of cities is unstoppable and a great challenge for urban development and architecture. The existing transportation facilities and buildings do not always provide a suitable level of safety, and level of service. The present study focuses on analyzing the effect of pedestrian count (population), inclusion of buffer space and door widths on the capacity of a doors. The relationship between total times versus door width and capacity versus door width were established. Capacities were estimated for a room with single door and double doors with different pedestrian count. Comparison of capacities for a room with single door and double doors (Door-1 and Door-2 are 1 m apart) has been done for 100 cm width with different pedestrian count. It was observed that capacity increases as the width of the door increases in a room with single door. In the case of room with double doors, the congested flow observed in Door-1 and uncongested flow observed in door-2 due to herding behavior.

Capacity analysis is required for the correct planning, designing, and operation of the transportation system. The main goal of the current study is to estimate the capacity of a two-lane, undivided hilly road under heterogeneous traffic conditions. The gradient is one of the important parameters in a hilly terrain that changes frequently and affects the traffic flow. For this study, four different sections were taken on NH-7 between Devaprayag and Rudraprayag in the hilly state of Uttarakhand. The gradient value of the four sections ranges from 1 to 5%. The PCU value for study sections was calculated considering the heterogeneous traffic environment. Greenshields’ model was used to develop fundamental traffic flow diagrams to estimate the capacity and other macroscopic parameters like jam density and free flow speed. The study examined the impact of gradient on the speed of different classes of vehicles. Since the capacity and operating speed of vehicles are greatly influenced by gradient, this study presented an appropriate approach to develop the relationship between the capacity of the road and gradient. Moreover, the relationship between operating speed and capacity was also developed to gain deeper insights into traffic behavior. The study results will help transportation engineers and planners to develop guidelines for capacity analysis of hilly roads.

The implementation of government initiatives aimed at promoting the adoption of electric vehicles in India has encountered limited success, prompting the need for further research in this area. The present study adds to the existing scant literature by identifying the socio-demographic characteristics that impact the potential purchase decision of electric vehicles among the urban Indian population. To achieve this goal, a one-way ANOVA was conducted to identify sociodemographic variables that have significant differences in means among their various categories. Using a binary logit model, the effect of these variables was then estimated for the potential purchase of an electric vehicle by the population. The results indicate that younger people, retired people, government sector employees, people living in large families, highly educated individuals, and part-time employees are more inclined to purchase an electric vehicle than others. No significant influence of gender, income, or current vehicle ownership was found on the decision at hand. The study's findings provide valuable insights for policymakers, electric vehicle manufacturers, and marketers to target specific subpopulations and increase the efficacy of their initiatives. The identification of key socio-demographic factors in the context of electric vehicle adoption in urban India represents a significant contribution to the current literature in this field.

The COVID-19 pandemic had a significant impact on road transport globally. In this paper, we evaluate the impact of COVID-19 on the decline of roadway Vehicle Miles of Travel (VMT) which led to loss of highway trust fund revenue in the States of Washington and Maryland, USA. We use a time-series analysis to forecast the VMT under no COVID scenario and compare it with the actual observed VMT data. We find that during COVID-19, in 2020, the monthly VMT in Washington was only 55% of the expected VMT in the most critical month. Further analysis showed that 28 out of 38 Counties in Washington experienced a drop of at least 10% in VMT in 2020 compared to 2019. A decline in VMT resulted in a decline of reduced gasoline sales in both States, albeit the decline was higher in Maryland compared to Washington. This, in turn, affected the highway trust fund used for road repairs and other public services. A time-series analysis was performed using Exponential Smoothing Model and SARIMA. These models were trained using historical data, and their accuracy was evaluated by comparing the Mean Absolute Percentage Error (MAPE), Symmetrical Mean Absolute Percentage Error (SMAPE), and Mean Absolute Scaled Error (MASE). The study highlights the importance of monitoring transportation trends during crisis and provides valuable insights that can help policymakers develop effective transportation policies that promote sustainable mobility and resilience in the face of future crisis.

The migration behavior of regular passengers in city transport refers to the movement patterns and preferences of individuals who use public transportation on a regular basis. These passengers may have specific routes, travel times, and preferences for certain modes of transportation. Passenger flow prediction is crucial for understanding and managing this behavior. Accurate predictions enable transportation operators to optimize their services by adjusting vehicle frequency and capacity, deploying additional services during peak periods, providing real-time information, identifying high-demand areas, and expanding the network. This improves the efficiency and reliability of public transportation, catering to the needs of regular passengers. Data science and machine-learning methods allow us to extract correlations from historical data, improving the accuracy of passenger flow prediction. The passenger flow on a station is highly affected by various factors such as the day of the week, holiday, rain, available routes from that station, and some uncertain events like COVID-19. In this study, we have successfully reported passenger flow prediction at various stations using ensemble machine-learning algorithms. Comparative analysis of implemented work has been carried out with the help of statistical parameters and visual infographic details. For accurate prediction model, accurate data cleaning, pre-processing and feature selection based on correlation have been performed on real dataset of Thane Municipal Transport (TMT) from April 2021 to May 2022. We have also compared the performance of prediction models based on month, day and individual station for moderately deviated data and highly deviated data resulted due to effect of COVID-19 pandemic and Taukte cyclone. An exhaustive comparative analysis between train set and test set have been reported with necessary parameters. Comparing to benchmark models, the XGB regressor and Random forest model can reach most accurate prediction and computational efficiency on the real-world dataset. The statistical analysis suggests that all models studied have reported excellent results between train and test data.

Due to the growing economy and population, traffic studies in developing countries like India are becoming more complex, which leads to a need for calibrating the developed models with more than one Measure of Effectiveness (MoE). Three MoEs have been used to calibrate the model in VISSIM, such as travel time (\(TT\)), vehicular speed (\(v\)) and acceleration (or) deceleration of each vehicle (\(a\)). Furthermore, minimum headway distribution (\(h\)) and CO₂ emission data were extracted from the field for further validation. The model was calibrated using Genetic Algorithm (GA). The objective function was defined with the p-values obtained from two-sampled Kolmogorov–Smirnov test by comparing the field MoEs and the simulated MoEs. After the 93rd generation, a p-value of greater than 0.05 was obtained, indicating a good similarity between the model and the field conditions. The accuracy of all five characteristics has been verified and a satisfactory resemblance has been obtained.

Traffic problems are a major concern for many cities and towns worldwide. This problem can be overcome with the help of an intelligent transport system by taking the information about the anticipated and time-delay feedback control. This study proposes a car-following model by considering anticipation and delayed time-feedback effects depending on both optimal and local velocity differences to suppress traffic jams. The linear and nonlinear analyses are conducted for the proposed model and it is found that the anticipation effect enhances the stable region of the traffic flow. On the other hand, the unstable region enhances with an increase in the delay time value, indicating that longer delays in the system's response can lead to more congestion and instability. It is also seen that the congestion induced by delay time can be overcome by considering the role of feedback gains. By optimizing feedback control based on anticipated and delayed information, the proposed model effectively regulates traffic flow, reduces congestion, and improves overall flow stability. From a comparison of the current study with the existing one, it is noticed that the proposed model is more efficient in regulating the flow. Numerical simulation shows that the proposed model can improve traffic flow stability and reduce traffic congestion by forecasting future vehicle headway and speed, which is by the theoretical examination. Therefore, the anticipated and delay time feedback effect provides a more efficient solution for traffic management systems that will result in fewer delays, improved safety conditions, and reduced costs for transportation networks.

A growing proportion of privately owned vehicles is increasing congestion in our cities today. To reduce the dependency on private vehicles there is a need to study the inertia effects of shifting to public transport despite good accessibility. This research focused on identifying the reasons behind the reluctance to use public transport among the residents of Chennai in areas with high public transport accessibility. Areas with high accessibility to public transport were identified using the Public Transport Accessibility Level (PTAL) analysis. The questionnaire was created using travel behavior variables such as primary mode, residence location, age, employment type, gender, number of family members, presence of children, income, household tenancy, vehicle ownership, travel expense, trip length, travel time, distance to bus stop, safety and comfort. Revealed preference surveys were conducted across five wards in Chennai. Multinomial Logistic regression was further used to identify mode-wise inertia effects. The variables such as income, gender, trip length, vehicle ownership, travel time, presence of children, and travel expense have a significant impact in using public transport. The analysis of the study was further used to provide policy recommendations and develop a demand-responsive transit model to provide doorstep services and enhance the efficiency of public transport.

Micro mobility, such as electric mopeds (e-mopeds), is gaining acceptance and popularity in urban areas, particularly for last-mile connectivity. However, there is no mechanism to assess the level of service (LOS) offered to them. With the growing popularity of e-mopeds, the LOS required by them should be taken into consideration when designing transportation infrastructure. Therefore, this study has been done to address this gap by developing a novel LOS framework for e-mopeds and assessing the current LOS offered to them. The developed framework is applied to a case study of Delhi. The findings of this study reveal that the interaction between the e-moped and a faster vehicle, which can impact the ease of driving the e-moped, is 1.5 veh/sec. This indicates that the LOS situation for the specified length is normal. Further, the developed LOS framework can be utilized to evaluate the LOS of e-mopeds in other cities.

Despite the availability of various pedestrian crossing facilities, most of them are often disliked by pedestrians and are underutilized. The current study provides a systematic review of the various literatures available regarding the underutilization of pedestrian facilities. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines serve as the foundation for the review. About 32 papers are reviewed giving emphasis on the methods of data collection, analysis techniques, factors considered, and models developed in the studies. Attempt is also made to present the year-wise and country-wise distribution of the studies conducted so far. The study found that the years 2017, 2019, 2020, and 2021 have witnessed the maximum number of studies pertaining to pedestrian crossing underutilization. About 50% of the studies were conducted in India. Majority of the studies utilized questionnaire survey as the data collection method. Logit regression and Chi-square are the most frequently used data analysis techniques. Age and traffic parameters are the most commonly considered parameters in most of the studies. The study has helped in identifying the parameters which are considered in the literatures so far for analyzing the underutilization of pedestrian facilities. It has also provided a summary of the factors leading to the unsafe pedestrian behavior. The findings of this study provide an insight to the importance of a multifactored approach to address the issue of unused crosswalk and improve pedestrian safety in urban environments.

High motorization and growing population have exacerbated traffic congestion in metropolitan areas, which has had a detrimental influence on the functioning of the road network and resulted in longer delay, pollution, decreased productivity, increased fuel usage, and other significant socioeconomic repercussions. Urban road networks are frequently congested by traffic incidents, which reduce capacity and pose risks to the driver (s) of subject vehicle (s), road users around the place of incident, and the incident response team. Each traffic incident may have an impact ranging in terms of road network area and time duration. To determine this, existing studies have been reviewed systematically. This investigation was carried out using a thorough evaluation of previous peer-reviewed literature, standards, and formal guidelines. During this review process, the causes of incident as well as the impact have been studied with respect to traffic incident duration, traffic incident frequency, travel time reliability, and network vulnerability analysis aspects. The primary goal of this study is to pinpoint research gaps concerning vehicle incidents. One such gap pertains to the necessity for a more detailed analysis of incidents, focusing on disaggregation. Particularly, more study is needed to delve deeper into the most prevalent incident, vehicle breakdowns (due to mechanical problem and/or as a result of an accident) are to be further studied regarding the location of incident, i.e., mid-block, intersection, flyover/freeways, and the position of vehicle within the width of the road.

Signalized intersections have the second-highest number of accidents after un-signalized intersections according to the Road Accidents in India (2022) report by Ministry of Road Transport and Highways of India. Safety analysis using accident data is a reactive approach. Traffic conflict techniques on the other hand are proactive approaches that identify observable critical vehicle interactions (conflict) that could have led to a crash. In this study, the traffic conflicts are identified by using a methodology that incorporates the heterogeneity and the disordered nature of the Indian traffic condition. The conflicts are estimated by modifying conventional Modified Time to Collision (MTTC) and Deceleration to Avoid Crash (DRAC) by incorporating vehicle heading direction, relative position, speed, and acceleration between vehicles to estimate rear-end and side-swipe conflicts. A threshold value suitable for disordered traffic conditions is estimated to segregate the conflicts and non-conflicting interactions. The variation in MTTC value with respect to the DRAC of vehicles showed that the lower MTTC values are obtained for motorized two-wheelers and motorized three-wheelers indicating that smaller vehicle types contribute to more critical vehicle interactions. A conflict Severity Index (SI) was developed with values closer to zero indicating a non-risk event and closer to 1 indicating a high-risk event. The temporal variation in SI was above 0.9 during the first one-third of red time for rear-end conflict and the first half of green time for side-swipe conflict.

The inclination toward vehicle-centric infrastructure is greatly impacting the safety of vulnerable road users, such as pedestrians, in many developing nations. It is evident from past literature that built-environment characteristics near vulnerable locations, such as bus stops, play a crucial role in the frequency and overall percentage of pedestrian deaths and injuries on the road. The present study, therefore, aims to evaluate the relative crash risk associated with pedestrians near high-risk bus stops based on traffic characteristics and built environment and suggest policy recommendations to counter such incidents in Delhi. The study was conducted in two broad segments, namely day-time and night-time, with 78 day-time and 98 night-time bus stops to better compare crash-influencing factors around these high-risk locations. Binary logistic regression was applied to determine the best-fit models. Built-environment features such as the width of sidewalks, presence of bus bays, traffic volume and the number of bus commuters are major impacting factors that cause pedestrian crashes during the day, while the way of crossing, cleanliness of sidewalks, disability-friendliness of sidewalks, lighting conditions, as well as the number of bus commuters, affects the safety of pedestrians at night.

The central and state governments in India aim to promote sustainable transportation systems by encouraging electric mobility. However, the acceptance of electric vehicles (EVs) depends on public perceptions and attitudes and understanding of various EV features. Since EVs are expected to change the transportation sector significantly, it is essential to study how people's attitudes toward various features of EVs would influence the adoption rate. The main objective of this study is to analyze the attitudes of Indians toward various EV features by utilizing EV-related tweets. This study provides a detailed conceptual framework for extracting public sentiments toward EVs and their attributes using natural language processing techniques and machine learning algorithms. The Random Forest classifier with TF-IDF vectorizer performed better compared to the other machine learning classifiers. Also, we have used the Latent Dirichlet Allocation (LDA) approach to extract the most discussed features of EVs. The results indicate that the sentiments of the Indian public are, in general, positive toward EVs, and sentiment variations are observed on several features of EVs. Based on the research findings, some policy-related insights are presented regarding price, charging infrastructure, technology, environmental concerns, and driving range to promote EVs. The results will assist policymakers, vehicle manufacturers, and private enterprises in better understanding key EV features and consumer attitudes to boost EV adoption rates.

Median U-Turn Intersection (MUTI) is an alternative to conventional signalized intersections that replace the direct right turns and/or the through movements at the intersection by facilitating these movements indirectly using U-turns in a wide median. This reduces the number of potential conflict points at the main intersection and can result in reduced number of traffic signal phases and improved intersection operations and safety. Realizing this advantage, the Hyderabad traffic police has replaced several signalized intersections in the city with MUTIs. However, the case-specific potential of such MUTIs in the context of lane-free, heterogeneous traffic is yet to be explored and a proper set of guidelines for implementation of such unconventional intersections is lacking. This paper aims to understand the gap in detail and provides a direction for assessing the applicability of MUTI along with implementation guidelines with reference to a case study. The traffic impacts using certain key performance indicators are highlighted for different types of MUTIs and some recommendations and future scope of study are provided.

The current work attempts to investigate how curbside bus stops can influence the lateral movement characteristics and speed-maintaining behavior of different vehicles on an urban area. Using detailed vehicle trajectory data obtained from a six-lane divided road in Delhi with a curbside stop, the variations in vehicle movement patterns in the presence and absence of buses at the stops are examined. The results indicated a significant reduction in vehicle speeds and lateral separation when the bus stop was occupied, indicating the influence of bus stops in the vehicle movement patterns. Results on lateral placement further depicted a higher reduction in speed when the vehicle travels closer to the bus lane, as compared to when it travels toward the inner lane. Furthermore, the effect of bus dwell time on vehicle speed and lateral placement indicated a decreasing relationship for speed; however, as dwell time increases, vehicles prefer to travel toward the rightmost lane (inner lane). The findings can assist the policy makers to implement rational policies for improving traffic operations near curbside stops.

Many Machine Learning (ML)-based approaches have been developed for travel-time prediction, including ensemble-based, neural network, and others. However, these approaches often rely solely on historical data and lack the capability to adapt to real-time traffic conditions. They are unable to extract live traffic stream and develop a more realistic travel-time prediction between desired Origin–Destination (O-D) pairs in a dynamic manner. In this paper, we develop a bi-layered ML model to predict travel time along congested traffic networks using real-time traffic information from Google Maps. The first ML layer uses typical traffic conditions from Google Maps using historical data. The second ML layer uses a Random Forest Classifier to predict travel time at various peak times of the weekday at 5-min intervals. An example using a 7.7-mile congested section of I-495 outer loop in the USA is presented. We first use simulated data to gain insight into the effect of different variables on prediction. We then use the actual congestion level from the first layer using Google Maps to perform the prediction. The results show about 89% match with travel-time congestion under typical conditions. A plot of the resulting travel time shows up to a twofold increase in the travel time during some time periods. Additional test scenarios, including a further decomposition of the congestion segment, can be studied in future works. In addition, the first ML layer can be directly integrated into the second ML layer via a computer vision algorithm in future works.

The traffic conditions in urban area consist of different vehicle types, which results in varying traffic dynamics for each vehicle class. The smaller vehicles can overtake larger vehicles and also move through the gaps between larger vehicles to traverse faster. The distinct dynamics of smaller and larger vehicles is a challenge to the traditional traffic assignment models which lack class-specific behavior. The routing of vehicles based on the travel time should capture these class-specific features to have a holistic view regarding typical urban traffic conditions. Traditional traffic assignment methods fail to reproduce conditions such as congestion, queue spill back, and bottleneck regions, thereby resulting in underestimation of real traffic scenarios. This study proposes a multi-class dynamic traffic assignment framework for disordered traffic to overcome the limitations of traditional traffic assignment. The framework is tested for different traffic conditions to deduce the class-specific behavior in multi-class traffic conditions. The results from the dynamic traffic assignment are compared to the traditional traffic assignment to account for the difference in travel time computations. The travel time plots obtained using dynamic traffic assignment shows that the vehicles can overcome low to mild levels of congestion by exhibiting overtaking and creeping behavior. This stands close to real traffic conditions where there is no much change in travel time unless heavy congestion. Thus, study justifies the necessity of class-specific features in travel time computation.

The Bengaluru Metro, also known as Namma Metro, plays a crucial role in the transportation infrastructure of Bengaluru, India. The success and sustainability of the Bengaluru Metro significantly depend on its commuters’ satisfaction and loyalty. Therefore, integration of these two latent variables into the metro transportation system is crucial for enhancing ridership and ensuring that satisfied commuters become loyal advocates who actively promote metro transit. Nevertheless, there is a glaring deficiency of these satisfaction and loyalty models in the literature in the context of Indian metro systems, given the fact that most of the Indian metro systems are relatively new to the country’s transportation. This paucity of information has led this study to focus on assessing the satisfaction and loyalty intention of Bengaluru metro users by analyzing their perceptions of the service quality rendered in the metro by employing the structural equation modeling approach. Through a structured questionnaire, 700 metro users’ responses were collected by random sampling method. The findings extract four factors, such as Smooth Transition (ST), Amenities (AMT), Service Availability (SA), and Anxiety (ANX), with amenities and service availability being the most influential factor. Factor anxiety is having a detrimental impact on user satisfaction and loyalty. The developed model provides insight into the perception of metro users and provides useful information that can aid government institutions, Bangalore Metro Rail Corporation Limited (BMRCL), and other transit agencies to postulate policies and implement strategies. This empirical relationship, based on various factors extracted, can be tested for other metro systems in India.

Pedestrian activity on hilly roads introduces a unique set of challenges and considerations for both vehicular traffic and overall road capacity. The presence of pedestrians can lead to alterations in vehicular speed, traffic flow, and road capacity due to several interrelated factors. The present study focuses on examining the effects of pedestrians on vehicular speed and capacity of two-lane hilly road. The study utilized the video-graphic method to collect field data on four different sections of NH-7 in Srinagar city of Uttarakhand. The sites were selected with varying magnitude of gradient, i.e., two sites with a gradient less than 2% and the remaining two sites with a gradient greater than 2%. The primary goal of this study is to examine the effect of pedestrian movements on the capacity and speed of the road for different gradient ranges. The findings indicated that locations with pedestrian movement had lower vehicular speed and roadway capacity compared to sites without pedestrian activity. Moreover, the traffic speed and roadway capacity were observed to be decreasing with an increase in the longitudinal gradient of the road. The study results can be used by transportation engineers and planners to develop a comprehensive understanding of the traffic on hill roads. The results of studying the impact of pedestrians on vehicular speed and road capacity provide valuable insights that can inform policies, improve infrastructure, enhance traffic management, and promote safer and more sustainable transportation systems in urban environments.

Vehicular trajectory data plays a crucial role in multiple studies related to driving behavior, automated driving, and safety analysis, which includes developing models for car-following, lane-changing, overtaking, and other measures for safety assessment. Despite the method used to collect data, there will always be some degree of error present. Although recent improvements in data collection have been made, errors in microscopic traffic analyses may still exceed acceptable limits. To address this issue, smoothing and filtering techniques are applied to eliminate noise and outliers in the extracted data. This study proposes a novel approach that enables researchers to select the best smoothing technique for their data and choose the optimal smoothing window. The contribution of this chapter is twofold: firstly, it provides a detailed description of the data collection, extraction, and processing of vehicular trajectories; secondly, it evaluates smoothing techniques with a focus on improving the accuracy of the data. The study’s data is collected using unmanned aerial vehicles (UAV) at 4 K quality and 30 fps in Delhi, India. Trajectory data extraction from drone videos was outsourced to DataFromSky, a cloud-based platform that uses artificial intelligence and machine learning methods to fully automate traffic analysis from videos. Graphical and numerical evaluations are made to the most widely adopted smoothing techniques, including Moving Average, Symmetric Exponential Moving Average (sEMA), Kalman, and Savitzky–Golay (S-G) for different smoothing windows to evaluate their effectiveness.

Residential mobility of a household is an important driver for change in urban form, accessibility, and travel demand. The reasons to move, urgency, and household characteristics trigger a household to change its location and influence the subsequent decisions in residential relocation process such as tenure choice, search time, and in some cases where to search. Given the residential mobility studies done in Indian context, there is still a lack of work exploring the characteristics of movers and for what reasons they move. The present chapter aims to explore this gap. A retrospective survey is carried out in Bidhannagar Municipal Corporation and Newtown (India), and a data sample of 459 households is collected. Initially, a typology of moves is developed based on the urgency in move and the type of reasons. Thereafter, a multinomial logit model is developed to identify the significant household characteristics (socio-economic, socio-demographic, commute behavior, dwelling, and attitude) for each developed typology. The result indicates the significance of household head (HHH) age, household size, monthly income, HHH employment type, housing tenure, mode of travel, car ownership, and attitudinal variable (locational sensitive) on the typology of move. The present study is the part of major research which aims to explore the overall residential relocation process through decision-trajectory approach where the type of move initiates the decision trajectory.

The accurate prediction of travel time in India is essential for efficient transportation planning and management, especially in busy cities with heavy traffic. Recent research in transportation engineering, computer science, and data analytics has demonstrated the effectiveness of data-driven approaches in predicting travel times more accurately. Accurate road travel time prediction can benefit both transportation service providers and travelers by improving trip planning, reducing travel times, and increasing operational efficiency. Driven by a vision to make a significant contribution to this expansive realm of research, this paper suggests a methodology for forecasting taxi travel times for Bangalore city using data from Uber Movement to increase the effectiveness and dependability of urban transport networks. The model combines regression approaches with spatiotemporal data analysis to capture complicated geographical and temporal trends. The model adds pertinent data, such as weather from Wunderground, and is trained using Uber Movement’s data on cab trips. This report provides a thorough trend analysis of Bangalore city’s commuters. With a Mean Absolute Error (MAE) of 103 s on the Uber data, the ExtraTree regressor outperforms the other regression methods, according to a comparison of their findings. Based on Uber Movement data, the suggested algorithm can accurately predict the time needed to travel between places in Bangalore city. Furthermore, a comprehensive evaluation is undertaken to analyze the suitability of eXplainable Artificial Intelligence (XAI) in effectively addressing the interpretability of black box machine learning models for predicting travel time. The XAI methods, SHAP and LIME are used to evaluate the interpretability of the trained models. As part of future work, this model can be applied to other cities and improve traffic management by taking proactive measures in advance.

Urban traffic comprises of a major proportion of two-wheelers which have greater maneuverability to traverse through congested conditions in disordered traffic. These peculiar features are generally not accounted for in the traffic flow models used in dynamic traffic assignment. The applications pertaining to planning and control operations are highly dependent on the class-specific features of the traffic. Two different applications of dynamic traffic assignment are presented—dynamic wireless charging lane implementation and combined dynamic traffic assignment and control for two-class traffic stream. The results of combined dynamic traffic assignment and control model show that higher split of green times is required for shorter routes with more traffic based on the queue length and clearance time. The proportion of electric two-wheelers was more on the shorter routes and the number of links suitable for dynamic wireless charging lanes was computed for different proportions of electric two-wheelers as thresholds.

Covid-19 was a pandemic that impacted many sectors of the world, including the transportation industry. Some implications for this sector were the shift from public to private modes, reduced traffic volume, and increased fatalities and speeds. Lockdown and unlock measures were announced globally to manage fluctuating Covid-19 cases. The scope of the current manuscript was to understand the implications of vehicular speeds in NCT Delhi during these phases. The data was collected using Google API from over fifty OD pairs spread geographically across Delhi. The speeds were extracted from travel times and were segregated into waves I (2020) and II (2021). The analysis included statistically evaluating the average rates during these waves and comparing them with the speeds during the odd–even policy implemented earlier. During the Covid waves, when the cases were at their highest, the average speeds observed were 40 km/h (wave I) and 36 km/h (wave II). Spatially, the average speeds spread across Delhi districts did not show much variation between waves I and II. Both waves’ speeds in the lockdown periods were higher than in the unlock periods. The speeds recorded during the Covid-19 waves exceeded the odd–even policy phase period speeds. The study gave mixed results and aimed to understand the changes a pandemic can bring for better future preparation.

The primary objective of this study is to examine various types of speeds on hill roads in mixed traffic conditions and critically review NH-7 at various road sections with different geometrical features. This study aims to identify different speeds such as design speed, lower speed, upper speed, median speed, and model speed. Additionally, a speed model is developed using multiple linear regression (MLR) to determine the relationship between vehicle speed and factors that affect vehicle speed. The study investigates the variance, examines the influencing factors, and develops a model for vehicle speed on hill roads in mixed traffic conditions. The data was collected using a videography methodology at four study sites on NH-7 in Srinagar Garhwal, Uttarakhand. The findings indicate that the upper speed limit (85th percentile value) is 50.64 km/h, while NHAI’s recommended speed limit is 40 km/h, which may result in traffic congestion. The speed model developed in this study can be used to establish speed limits, design and evaluate highways, and implement other enforcement measures.

Highway traffic characteristics are shaped by the presence and dynamics of various vehicle classes, ranging from light vehicles to multi-class vehicles, sharing the roadway under diverse traffic conditions. Vehicle movement in heterogeneous traffic is affected by the interaction between different vehicle classes. Traffic density is typically used to determine the level of service provided to drivers. However, the concept of occupancy has been identified as a more suitable measure for describing traffic concentration when vehicles in a traffic stream exhibit significant variation in length and speed. This is because occupancy calculations take into account differences in vehicle lengths and speeds. Managing congestion in heterogeneous traffic situations characterized by mixed vehicle types and weak lane discipline remains a challenging task. This literature review aims to explore the effects of the fluctuating proportion of light vehicles to multi-class vehicles on highway speed, passenger car unit (PCU), capacity, and level of service (LOS). The paper first introduces the challenges arising in heterogeneous traffic and discusses the concept of area occupancy and its determination methods. Subsequently, the paper presents an estimation of traffic volume (PCU/h) using the area occupancy concept. The paper concludes by recommending the calculation of the correlation coefficient (R) to evaluate the variability of area occupancy and PCU per hour for vehicles.

Nonlinear hyperbolic partial differential equations govern continuum traffic flow models. Higher-order traffic flow models consisting of continuum equations and velocity dynamics were introduced to address the limitations of the Lighthill, Whitham, and Richards (LWR) model. However, these models are ineffective in incorporating road heterogeneity. This paper integrates an extended AW–Rascle higher-order model with the source terms in the continuum equation to predict the traffic states in heterogeneous road conditions. The system of the equations was solved numerically with the central dispersion (CD) method incorporated into the standard McCormack scheme. Smoothing is applied to take care of the numerical oscillation of the higher-order model. Different combinations of initial conditions with source terms showed that the proposed model with the numerical methods could produce a stable solution and eliminate oscillation of the McCormack scheme.

Intelligent transportation systems (ITS) rely on automated sensor data from roads/vehicles to provide adequate traffic management solutions. Radio frequency identification (RFID) technology for detecting vehicles with RFID tags is currently being used for data collection. There are many challenges in adopting this technology in mixed traffic conditions, where the traffic stream is composed of vehicles of different categories that move without any lane discipline. The effectiveness of RFID sensors for data collection under mixed traffic conditions in Kerala, India, is assessed in this study. Data from RFID sensors were used to develop real-time prediction models for estimating queues at a signalized intersection for different traffic scenarios. The estimated queues were compared with actual queues, and the results were promising. Thus, the study devised techniques for constructing real-time estimation models that could be applied in intelligent transportation systems (ITS) operating in mixed traffic settings.

The primary step in reducing traffic noise is to build a model for traffic noise prediction. The suitability and applicability of calculation of road traffic noise (CoRTN) model were checked in Indian city road traffic condition and national highways. Both roads were different from each other in many ways. Previously CoRTN model was successfully applied in homogeneous traffic condition, but its applicability in heterogeneous traffic flow requires much attention. Four locations were selected, and two locations were on homogenous traffic flow condition (national highways) and another two location on heterogeneous traffic flow (city roads). The range of noise level (L₁₀) in national highways were 76–80 dBA and in city roads were 80–84 dBA. The result of the study showed that CoRTN model predicted well for national highways with R² value 0.817. While for city roads, model fits the data really poorly.