Browsing by Author "Mulangi, Raviraj H"

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Design of Operational Strategies for Public Bus Transit System Considering Variations in Passenger Mobility Pattern
(National Institute of Technology Karnataka, Surathkal, 2024) K S, Nithin; Mulangi, Raviraj H
In developing countries, the migration of people to urban areas has led to an increase in the urban population. Growth in urban population directly influences the vehicle population, leading to traffic congestion, pollution and accidents. To overcome this, people should be made to choose public transit over private vehicles by making it more efficient and attractive. The transit system can be made efficient and attractive when the travel time of passengers is reduced. This can be achieved by the implementation of operational strategies including limited-stop services, short-turning, dead-heading and so on. However, before the implementation of these strategies, variability in passenger mobility patterns should be assessed to identify which strategy is best suited for a given corridor. Thus, the present study aimed to design operational strategies for public bus transit system (PBTS) considering variability in passenger mobility patterns. Initially, seasonal variations in passenger mobility patterns were analysed to evaluate the impact of weather on passenger behaviour. Based on the factors influencing passenger flow, passenger demand forecasting models were developed at route and stop levels. Then, the operational strategy was designed for PBTS by integrating limitedstop service with the existing all-stop service. Further, a regression model was developed based on variability in passenger mobility patterns to evaluate the feasibility of limited-stop service before implementing it in any corridor. Based on the outcomes obtained from the analysis, recommendations were given to the transport planners to improve the efficiency of the PBTS. The research work was carried out by considering PBTS from two cities i.e., Davanagere and Udupi of Karnataka state, India. The PBTS of both cities is operated by Karnataka State Road Transport Corporation (KSRTC). The Electronic Ticketing Machine (ETM) is used by the systems to collect ticket data. The ETM data of both cities was procured from KSRTC to obtain passenger flow information. Two cities were considered because of their different weather conditions, making it possible to compare the influence of weather on the variability in passenger mobility patterns. To analyse the impact of different intensities of weather on passenger flow, the weather data of both cities was collected from the Indian Meteorological Department (IMD). Further, land use data was also procured from urban development authorities to evaluate variations in passenger mobility patterns at different spatial regions. The impact of weather on passenger mobility patterns was analysed at different temporal and spatial scales. The change in passenger flow with a change in the intensity of weather was computed to evaluate the impact of different intensities of weather on passenger mobility. The analysis was carried out at system, stop and route levels to have microscopic insights into the relationship between weather and passenger flow. Time-series analysis was carried out at the system and stop levels to evaluate the hourly variations in passenger flow at different intensities of weather. At the system level, the were categorised based on land use and aggregated to evaluate the impact of weather on different spatial regions with different land use. Further, at the route level, the variation in passenger flow between the stops of a route was analysed based on the coefficient of variation (CV). The outcome from the analysis indicated that, at high rainfall, the passenger flow increased in Davanagere and decreased in Udupi. At moderate rainfall, only during weekend variation in passenger flow was observed. The village stops of Davanagere experienced a significant increase in passenger demand during the rainy season. Further, high CV was observed at the routes of Davanagere and Udupi under different weather conditions indicating the concentration of demand among a few stops of a route. These results proved that there is a weather-related impact on passenger flow, but it is region-specific. It was not possible to identify a specific pattern in the relationship between passenger flow and weather. Passenger demand forecasting models were developed with the incorporation of factors influencing passenger flow. The models were developed at the route level and stop level using long short-term memory (LSTM) and graph convolutional neural network (GCN), respectively. Based on the analysis of variability in passenger mobility patterns it was evident that passenger flow is influenced by weather attributes at different temporal and spatial scales. As a result, the LSTM model was developed with the incorporation of weather and temporal attributes and the GCN model was developed with the incorporation of land use as a spatial attribute. In the LSTM model, temporal features such as recent time intervals (R), daily periodicity (P) and weekly trend (T) were included along with the weather attributes (W) and named RPTW-LSTM. Recent time intervals are the most recent steps in time-series data that account for short-term fluctuations in the data. Periodicity refers to patterns that repeat at regular intervals in time-series data. Daily periodicity specifically involves daily cyclic repetitions at the same hour. The weekly trend is encompassed in the model to account for long-term changes observed in the passenger flow data. The output (predictions) of hourly variation, daily periodicity, weekly trend and weather models are fused in the final model. The fused model uses LSTM for multivariate time series analysis, accounting for multiple factors that change over time. In the GCN model, the bus network is constructed as a graph with bus stops were considered as nodes and link between two nodes specifying the passenger flow between the stops. For each node, the land use around the bus stops within a service range of 500m was extracted and included as a node feature to capture spatial correlations in passenger flow. The developed models were compared with the baseline models to evaluate the impact of different features. The developed model showed better accuracy than the baseline models. This indicates that the performance of the model can be improved by incorporating the factors that influence passenger flow. Thus, the development of the prediction models assists transport planners in identifying the possible passenger flow variations in future, based on which transit schedules and frequencies can be designed. The operational strategies were designed for the PBTS by integrating limitedstop service with the existing all-stop service. The frequency of the existing all-stop service was shifted to the limited-stop service to avoid expansion of the fleet size, thereby avoiding additional investments. An optimisation model was developed to optimise stopping strategy and frequency of limited-stop service by maximising the total cost savings (TCS). The optimisation model included maximisation of the operator cost and in-vehicle travel time of passengers and minimisation of waiting time for passengers not served by limited-stop service. The limited-stop service was designed for PBTS of both cities. Genetic Algorithm (GA) was used to optimise the stopping pattern and frequency of limited-stop service by maximising the TCS. During each iteration, a single-point crossover and bit-flip mutation were applied to the population to introduce genetic diversity. To maintain the quality of the solutions, two elite individuals with the best fitness values were preserved in each generation. From the results, it was observed that the implementation of limited-stop service resulted in significant savings for both PBTS. The model strategically included important stops and skipped stops with low demand. Further, it was observed that the savings and stopping patterns were dependent on the number of stops and demand variability, respectively. As a result, to further identify the impact of different characteristics of corridors on the performance of the limited-stop service, a feasibility analysis was carried out. The feasibility analysis was carried out to identify when and where limited-stop service can be implemented. A regression model was developed to evaluate the influence of different characteristics of corridors on the savings incurred due to the implementation of limited-stop services. Based on the factors identified, the adaptability of limited-stop service can be verified before implementing it in any corridor. The scenarios were generated based on variability in passenger mobility patterns at different weather and temporal conditions. The limited-stop service was optimised for each of these scenarios and TCS was determined. Then, cost percentage savings (CPS), achieved due to the implementation of limited-stop service compared to total cost (TC) from all-stop service, was computed. Employing CPS as the dependent variable and various characteristics of corridors as independent variables, a regression model was calibrated. The model was found to have a better fit with the variables considered. The features like CV, dwell time, operator cost and number of stops had a significant positive impact on the model and wait time cost had a negative impact on the model. The variable CV with a positive impact indicates that the limited-stop service could be feasible when variability in passenger mobility patterns is high and it was observed that variability was high in different weather and temporal conditions. Similarly, limited-stop service could be feasible in the corridors where operator cost, dwell time and number of stops are high. However, care must be taken to reduce the waiting time of passengers whose stops are not served. Thus, the developed methodology can be adopted by transport planners to assess the variability in passenger mobility patterns and based on the observed variations different operational strategies can be designed to make the PBTS efficient and attractive.
Experimental Investigations on Alkali Activated Concrete Developed By Incorporating Marginal Materials for Rigid Pavement
(National Institute of Technology Karnataka, Surathkal, 2022) B J, Panditharadhya; Mulangi, Raviraj H; A U, Ravi Shankar
Road infrastructure projects are very much important in the progress of any country and involves large budget. Concrete roads are being considered over bituminous pavements due to its longer design life and lesser maintenance costs. The higher demand for concrete roads and other infrastructure developments has resulted in the increased production of Ordinary Portland Cement (OPC), which is one of the basic constituents required for concrete production. However, the production of OPC is associated with emissions of large amounts of CO2, with the cement industry accounting for about 5 to 8 % of worldwide CO2 emissions. In addition to CO2 emissions, the production of OPC requires considerable amounts of natural raw materials and energy. The present research community is focused on the development of alternative binders, with the aim of minimization of production of OPC. Alkali Activated Binders (AAB) such as Alkali Activated Slag (AAS), Alkali Activated Slag Fly Ash (AASF), Geopolymers, etc. can be considered as potential alternatives to OPC. Also, there is a huge scarcity of natural aggregates to be used in road projects. Industrial marginal materials can be taken as fine aggregates in pavement quality concrete for sustainable growth. In the present study, Processed Iron Slag (PIS), an industrial by-product obtained from iron and steel industry is identified as an alternative to natural aggregates for concrete production, since there is an acute shortage of natural aggregates for concrete production. The present study is mainly focussed on evaluating the performance of PIS as fine aggregate in Alkali Activated Slag Concrete (AASC) and Alkali Activated Slag Fly Ash Concrete (AASFC) by replacing river sand. AASC and AASFC mixes are designed to attain a minimum strength of M40 grade and compared with conventional Ordinary Portland Cement Concrete (OPCC) mix of similar grade. AASC mixes were prepared with 100% Ground Granulated Blast furnace Slag (GGBS) as sole binder, while AASFC mixes were prepared by mixing GGBS and Fly Ash (FA) in different proportions, i.e., 75:25, 50:50 and 25:75. Along with slag and fly ash, Aluminium dross – a by-product from aluminium refinery industry was considered as a partial replacement for binder in this study. However, it could not be a suitable binder in AAS and AASF based concrete because of the swelling observed at 5 % replacement itself. It was not considered in further stages of development of AASC and AASFC. ii Preliminary tests were carried out to identify the optimal activator modulus and dosage of alkaline activators for each of the AASC and AASFC mixes. PIS as fine aggregates were incorporated in the AASC and AASFC mixes by replacing the river sand by weight replacement method at different levels of replacement, i.e., 0, 25, 50, 75 and 100%. The fresh and hardened properties such as workability, compressive strength, split tensile strength, flexural strength, and modulus of elasticity of concrete mixes were evaluated as per the standard test procedures. The durability of concrete mixes in terms of resistance to sulphuric acid attack, magnesium sulphate attack, water absorption and Volume of Permeable Voids (VPV) were investigated. Flexural fatigue behaviour of various concrete mixes was determined by carrying out repeated load tests on beam specimens. The fatigue life data obtained were analyzed using S-N curves to establish fatigue equations. Probabilistic analysis of fatigue data was carried out using two parameter Weibull distribution method. Further, goodness-of-fit test was done to ascertain the statistical relevance of the fatigue data using Weibull distribution model. Survival probability analysis to predict the fatigue lives of concrete mixes with required probability of failure was carried out. The economic benefits of AASC and AASFC mixes in comparison with conventional OPC concrete were analyzed. The results indicated that incorporation of PIS in AASC and AASFC mixes resulted in slight reduction in mechanical strength. The inclusion of PIS aggregates slightly reduced the durability performance of AASC and AAFC mixes. Water absorption and subsequent VPV were increased with inclusion of PIS in both AASC and AASFC mixes which may be attributed to higher water absorption of PIS as compared to normal aggregates. Alkali Activated Concrete (AAC) mixes with natural aggregates exhibited better resistance to sulphuric acid and magnesium sulphate environments as compared to OPCC, which may be attributed to properties/structure of binders. The acid and sulphate resistance of AAC mixes slightly decreased with replacement of natural aggregates with PIS aggregates. Reduction in number of cycles for fatigue failure was observed in AASC and AASFC mixes containing PIS as compared to river sand. Two parameter Weibull distribution was used for statistical analysis of fatigue data and it was observed that the fatigue data of concrete mixes can be approximately modelled using Weibull distribution. iii Cost comparison was done to compare the costs of all the materials per cubic meter of concrete with respect to OPCC, AASC and AASFC mixes. AASC and AASFC mixes showed a slight reduction in cost when compared to conventional OPCC. Incorporation of PIS aggregates in AAC mixes led to further reduction in cost as compared to OPCC. Overall, PIS aggregates reported acceptable performance in AASC and AASFC mixes for its use in pavement quality concrete. AASC and AASFC mixes satisfying the requirements of M30 and M40 grades of concrete are recommended for low and high-volume concrete pavement construction.
Performance Analysis of Hublidharwad Bus Rapid Transit System
(National Institute of Technology Karnataka, Surathkal, 2023) Halyal, Shivaraj; Mulangi, Raviraj H
The concept of ‘Smart Mobility’ is one of the innovative solutions to tackle many urban transportation-related issues; that will connect various elements of technology and mobility, and Intelligent Transport System (ITS) is a step toward implementing it. The ITS integrates transportation system users with vehicles and infrastructure using information and communication technology. Bus Rapid Transit System (BRTS) is a state-of-the-art smart mobility system and is a boon for urbanized areas, which are affected by numerous transportation-connected glitches. The role of BRTS has now been recognized as essential for physically active, economically sound, and energyefficient cities. The BRTS has a combined structure of various exclusive features with a strong identity and distinctiveness. A dedicated lane of bus operation is the critical parameter of any BRTS, which will enhance its performance from all the perspectives. The interference of mixed traffic with the operation of BRTS buses, although it only occurs on a few road segments, can compromise the end-to-end travel time of the whole system due to congestion and contribute to reliability-related problems. Many internal and external factors will also influence the Travel Time Reliability (TTR) and Travel Time Variability (TTV) temporally as well as spatially and finally cause an impact on whole system performance. The main motivation behind this research is to study the impact of such nondedicated, and dedicated lanes of BRTS bus operation on its overall system performance from multiple perspectives by identifying bus stations, routes, and segments that are critical in nature. The current study used Automatic Vehicle Location (AVL) data and Automatic Passenger Count (APC) data from the recently implemented Hubli-Dharwad Bus Rapid Transit System (HDBRTS) as a case study. HDBRTS buses operate as express and non-express routes along the single linear corridor between twin cities Hubli and Dharwad. Express route buses serve the limited bus stations, whereas non-express route buses serve all the bus stations. Most of the buses of both environments will run from terminal to terminal, such as the terminal ii at the Hubli side to the terminal at the Dharwad side, which is named as UP direction, and the terminal at the Dharwad side to the terminal at the Hubli side which is named as a DOWN direction in the current study. Most of the length of this corridor has a dedicated nature for the bus operation, and a small part of it has non-dedicated nature, too; hence HDBRTS is considered as a hybrid-based BRT system. The BRT corridor from Hosur Circle of Hubli City to the Jubilee Circle of Dharwad is dedicated in nature, in UP and DOWN directions and the corridor from Hosur Circle Hubli to CBT Hubli is completely non-dedicated in nature. For the current research work, express routes and non-express routes were considered for the route level analysis, and one dedicated segment at the Dharwad side, one dedicated segment, and one non-dedicated segment towards Hubli were considered for the segment level analysis. From the preliminary study carried out for the HDBRTS, it was understood that, higher dwell time, bus bunching at the stations, signal delays at intersections, peak, and off-peak traffic hours of the day were few of the general incidences that were actually influencing the travel time variability of the buses and further leading to the less travel time reliability of the system. Keeping all those points in observance, in the first part of the current study, systematic smart data-based end-to-end travel time variability and reliability analysis have been carried out for the HDBRTS. Analysis has been done for two routes (express and non-express) and three segments exclusively (Two dedicated and one non-dedicated) in two stages. Travel time data points have been extracted for all the days of the week and different hours of the days as different aggregations. In the first stage, descriptive statistics and TTR analysis of the selected data points were done, whereas, in the second stage of the study, probability distribution fitting was carried out for both the routes and selected segments separately with seven potential continuous distributions to characterize the travel time. In the analysis, distribution parameters were extracted using the Maximum Likelihood Estimations (MLE) method. Kolmogorov-Smirnov (K-S) test was used to extract the distribution parameters and check for the goodness of the fit of each distribution. Hence based on the K-S p-value, the robustness of best-fit distribution was selected and iii ranked amongst all the choices, for describing the travel time data points under different conditions considered. In conclusion, as per the total number of cases passed by each selected distribution model, distribution performance was established at different ratios for all routes and segments. At the end of the probability distribution fitting with the travel time data points, the best fit distribution parameters were tried to compare with the passenger demand of that particular time stamp. From the analysis, it was found that peak and off-peak hours have a direct influence on the change in the characteristics of route and segment travel time and subsequent reliability indices. Except for the higher values of reliability indices during peak hours, the performance of the express routes seems to be more reliable. From the distribution study, the Generalized Extreme Value (GEV) distribution stood first on the best performance distributions list for the routes, dedicated segments, and even a non-dedicated segment. Hence it shows the robustness of GEV in explaining the heterogenous Travel Time (TT) characteristics. Based on TTR analysis with GEV distribution, it was inferenced that passenger demand and Buffer Time Index (BTI) have a direct correlation with the variations in the GEV shape parameter ‘k’. In the second part of the study, travel time reliability modelling was carried out with observed and unobserved independent variables obtained from HDBRTS operations. The travel time data points have been extracted according to the selected segments. Modelling was carried out with the Multiple Linear Regression (MLR) technique. Average travel time (ATT) and buffer time (BT) were the two dependent variables chosen from the operator’s and passengers’ point of view. Independent variables were selected based on permutation and combination of multiple covariables. Length of the segment, passenger demand, bus stop density, intersection density, peak and off-peak periods, and land use type were the finalized independent variables. Finally, two MLR models were developed in relation to the two dependent and eight independent variables. The performance of both models was examined with the adjusted R square values and t-statistics and significance values of individual covariables of both the developed models. With the higher adjusted R square values of iv 0.795 and 0.804, respectively, ATT and BT as dependent variables have shown superior explanatory power in describing the system's reliability. In the third part of the current study, as passenger demand forecast for the public transit system is a crucial and inevitable step in keeping the public transit system in the direction of continuous upgrading mode in their performance; hence forecasting of passenger demand was done with Long Short-Term Memory (LSTM) using the three months Automatic Passenger Counter (APC). Then the forecasting of passenger demand was also done with Seasonal Autoregressive Integrated Moving Average (SARIMA) models, and the comparison of the forecasting accuracy of both methods was made using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Furthermore, to validate the results, a novel approach has been adopted for the process, by following some more time series resampled with different time intervals. The study shows that LSTMs will be used satisfactorily in the traffic conditions present between Hubli- Dharwad, for forecasting passenger demand using APC data. As the last objective, the travel time reliability-based Level of Service (LOS) of the HDBRTS has been established for three operating conditions, such as route, dedicated segment, and non-dedicated segment. Planning Time Index (PTI), Buffer Time Index (BTI), and Travel Time Index (TTI) were the three reliability indices used to establish LOS. K-mean clustering method was used to develop clusters, and silhouette analysis was carried out to validate the quality of the clusters. Most of the clusters were found to be reasonable and opt with an average silhouette coefficient of more than 0.5. Hence LOS development in the current study better suits with selected data points of travel time reliability indices. Based on the analysis and obtained results of current research work, finally elaborated, three stages of recommendations were made to the operator for improving the performance of HDBRTS.
Study on The Factors Governing The Travel Time Reliability of Public Bus Transport System
(National Institute of Technology Karnataka, Surathkal, 2022) M M, Harsha; Mulangi, Raviraj H
Travel time reliability is the key aspect that indicates the quality of urban public transit service. The reliability is the most preferred parameter by the passengers to decide whether or not to choose the public transit mode of transport. Several factors can affect travel time reliability of the public transit system and it is necessary to understand the impact of these factors on travel time reliability of public transit system. Hence, the present research work aims to study the factors governing travel time reliability of the public bus transport system. Mysore is one of the largest cities in Karnataka state whose transit system has been considered in the present research work, since it generates the Automatic Vehicle Location (AVL) data through the Intelligent Transport System (ITS) infrastructure for public transit. Data collected for the research work comprises of AVL data from Mysore ITS and side friction data collected from study sections using videography method. Mysore city transit vehicles are equipped with the GPS units which provide the Automatic Vehicle Location (AVL) and other trip details of the respective buses. This AVL data has been used to extract travel time of bus routes and segments. The field data extracted from videography includes, side friction elements, traffic volume, and travel time of public bus transit at two different road sections (divided and undivided) during weekdays and weekends. This data is utilised in studying the impact of side friction on travel time reliability of the public transit system. Roadside friction is one of the critical factors which hinders the movement of traffic. The impact of different types of friction elements on travel time depends on their static and dynamic characteristics, as well as the position of friction elements on the carriageway. The data collected at two side friction locations of Mysore city has been used to analyse the impact of side friction on travel time reliability of public transit system. The data have been categorized as static and dynamic side frictions. An approach has been proposed to represent different types of side friction elements with a single index called Side Friction Index (SFI) using relative weight analysis. Travel time reliability is represented using measures such as Buffer Time Index (BTI), Planning Time Index (PTI), Travel Time Index (TTI) and Reliable Buffer Index (RBI). The impact of side friction on travel time reliability was found to be sensitive to traffic volume, and hence the thresholds for different traffic volume levels have been ii determined using K-means clustering method. The impact of side friction on reliability measures at different traffic volume levels has been studied and found to have a non- linear (exponential) relationship. The impact of SFI has been observed to be higher on TTI and PTI in comparison with BTI. The outcomes from this study show that the impact of side friction on TTI and PTI is sensitive to traffic volume, especially at higher traffic volume level and impact of side friction on BTI is least at medium traffic volume level. The inference from this research work shows that the impact of side friction elements varies with respect to the type of road (divided and undivided), traffic volume levels, different days of week (weekday and weekend), and different time periods of day. Travel time variability (TTV) plays a significant role in analysing the reliability of the public transit system. Therefore, this study attempts to analyse travel time variability of the public transit system with the help of AVL data of buses collected from the Mysore ITS. The travel time data are analysed at different temporal aggregation levels corresponding to different Departure Time Windows for peak and off-peak periods. Travel time variability is also influenced by the presence of intersections, bus stops and other geometric and traffic characteristics. Hence, the segment level analysis has been carried out taking into consider the presence of bus stops, intersections and land-use type. AVL data collected from Mysore ITS are used to evaluate travel time distributions with respect to temporal aggregations (peak period, off-peak period, 60 minutes, 30 minutes and 15 minutes) and spatial aggregations (route level and segment level). The distribution fitting process has been carried out using EasyFit software, which estimates the distribution parameters using maximum likelihood estimation (MLE) method. The Kolmogorov-Smirnov (KS) test for goodness of fit has been used to evaluate the fitting of each distribution. The performance of each selected distribution has been evaluated in terms of accuracy and robustness. The results of both route and segment level analysis show that the Generalised Extreme Value (GEV) distribution is superior in describing travel time variability of public transit. The accuracy and robustness of GEV distribution are higher than that of other distributions and also the performance of GEV distribution in the case of signalised intersections and land use type shows the fitting ability and versatility of GEV distribution. Hence, GEV iii distribution has been considered as the descriptor of travel time variability of the public transit system. Travel time reliability measures, TTI, PTI and BTI of four bus routes are determined using GEV distribution and reliability of these routes have been evaluated. The reliability measures of the study routes indicate that the reliability of public transit is lower during peak hours. Understanding the factors causing unreliability of the public transit system is necessary for the improvement of system’s reliability. In this study, the reliability of the system has been modelled considering three travel time reliability measures. The Multiple Linear Regression (MLR) method has been adopted to model the three travel time reliability measures (Average Travel Time (ATT), Planning Time (PT) and Buffer Time (BT)) as the dependent variables and independent variables selected are corresponding to five important factors affecting the measures related to travel time: segment length, bus stops, intersections, land-use and peak/off-peak time period. The results of this study show that length of the segment has a higher impact on all the three reliability measures. The average delay has a higher standardised coefficient value than standard deviation (SD) of delay in the case of ATT and PT. In BT model, SD of delay is more than average delay, which shows that variation in bus stop delay leads to a higher buffer time. The presence of intersection in the segments and Central Business District (CBD)/commercial land-use segments are found to have lesser travel time reliability. Level of service (LOS) is a quantitative stratification of a performance measure or a measure that represents the quality of service. The LOS of bus routes are determined based on travel time reliability such as TTI, PTI and BTI. K-means clustering method has been applied to the segment level travel time data of four bus routes to determine LOS thresholds. Initially, globally accepted six clusters for LOS (A to F) have been considered and cluster validation has been conducted using silhouette analysis. The results of cluster validation show that clusters have reasonable structures and six clusters can be used to determine the LOS thresholds based on these reliability measures. Finally, recommendations have been put forward based on the outcomes of the research work to improve the reliability of the public transit system.