Manoranjan Parida

Evaluation and analysis of traffic noise along bus rapid transit system corridor.

Due to increasing motorization, construction of flyovers and growth in transport network, the noise level has exceeded the prescribed limits in many Indian cities. The health implications of high noise levels are being identified as hypertension, sleeplessness, mental stress, etc. Due to this adverse effect of noise level, it is essential to assess the impact of traffic noise on residents and road users. This research is an effort to quantify and analyze the traffic noise emissions along bus rapid transit corridor in Delhi. Field measurements were carried out to understand and assess various aspects of the impact of bus rapid transit system corridor on land use and social lives of residents and road users. The present analysis presents the comparison between observed and predicted noise level at selected corridors and also describes the mitigatory measures to overcome such type of traffic noise pollution through design of noise barrier along the road and motivate people towards the use of public transport system.

Evaluation of prioritization methods for effective pavement maintenance of urban roads

Priority analysis is a multi-criteria process that determines the best ranking list of candidate sections for maintenance based on several factors. In this paper, two methods for priority ranking of road maintenance, viz. (a) ranking based on subjective rating and (b) ranking based on economic indicator, are evaluated. The subjective ranking was done using maintenance priority index which is a function of road condition index, traffic volume factor, special factor and drainage factor. The second ranking method was based on economic indicator in which NPV/Cost ratio was calculated for each pavement section using the HDM-4 software.

Short term traffic flow prediction in heterogeneous condition using artificial neural network

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.

Performance of FHWA model for predicting traffic noise: A case study of Metropolitan city, Lucknow (India)

Abstract Industrial and transport activities are the two major sources of noise pollution in any metropolitan city. Lucknow city, the capital of the largest populated state Uttar Pradesh in India has an area of 310 sq. km and is rapidly growing as a commercial, industrial and trading centre of northern India. The population of Lucknow city as per census 2001 is 22.45 Lacs. It is expected that by the year 2021 it will make 45 Lacs. The total vehicle population in Lucknow city on 31 March 2008, was nearly 1 million with almost 80% two wheelers, 12% cars, 1.36% three wheelers, 0.45% buses etc. A study was carried out to assess the existing status of noise levels and its impacts on the environment with a possibility of further expansion of the city. Ambient noise levels were measured at different locations selected on the basis of land use such as silence, heavy traffic and residential and commercial zones. It was found that noise levels at all selected locations were much higher (75–90 dB) than the prescribe...

A comparative analysis of heterogeneity in road accident data using data mining techniques

Road accidents are one of the most imperative factors that affect the untimely death among people and economic loss of public and private property. Road safety is a term associated with the planning and implementing certain strategy to overcome the road and traffic accidents. Road accident data analysis is a very important means to identify various factors associated with road accidents and can help in reducing the accident rate. The heterogeneity of road accident data is a big challenge in road safety analysis. In this study, we are making use of latent class clustering (LCC) and k-modes clustering technique on a new road accident data from Haridwar, Uttarakhand, India. The main focus to use both the techniques is to identify which technique performs better. Initially, we applied LCC and k-modes clutering technique on road accident data to form different clusters. Further, Frequent Pattern (FP) growth technique is applied on the clusters formed and entire data set (EDS). The rules generated for each clusters do not prove any cluster analysis technique superior over other. However, it is certain that both techniques are well suited to remove heterogeneity of road accident data. The rules generated for each cluster and EDS proves that heterogeneity exists in the entire data set and clustering prior to analysis certainly reduces heterogeneity from the data set and provides better solutions. The rules for Haridwar district reveals some important information which can used to develop policies to prevent and overcome the accident rate.

A New Approach to 3D Dense LiDAR Data Classification in Urban Environment

Classification of Mobile Mapping LiDAR (Light Detection and Ranging) data is a challenge in the research community since the day when laser scanner system were integrated and mounted on vehicles for collection of 3D data in urban environment. The approach proposed here for classifying LiDAR data is analogous to the process followed for classifying data from satellite images. Pixel based and segmentation based methods have been employed in past for classifying images obtained from satellites. These methods were based on spectral properties of objects present in the images. But for Mobile mapping LiDAR data this approach has been applied and tested for the first time. The properties of this data are completely different from that of satellite images. So even if the basic approach remains the same, many changes have to be made in the entire classification process. The paper here aims to propose the basic procedure of using pixel-wise classification on dense 3D LiDAR data.

Analysis of antibody response (IgM, IgG, IgG3) to Chikungunya virus using panel of peptides derived from envelope protein for serodiagnosis.

Abstract Background: Many epidemic outbreaks of Chikungunya fever (CHIKF) have been reported throughout the world including India after its reemergence in 2005. The immuno protective role of envelope proteins during Chikungunya virus (CHIKV) infection has been reported. With the aim of identifying the immunodominant epitopes within the envelope protein we investigated the detailed analysis of fine specificity of antibody response in different individuals during CHIKV infection. Methods: The peptides corresponding to the full length of E1, E2 and E3 proteins of S27 strain of CHIKV were synthesized and their seroreactivity with CHIKV positive patients’ sera collected from different epidemic regions of India was determined using indirect ELISA. Results: The data analysis reveals many potent epitopes throughout the length of envelope E2 protein thus displaying it as the most promising antigen for diagnostic purpose. We found that the main IgG isotype response to envelope protein was predominantly of subclass IgG3. Interestingly, most of the epitopes were found to be conserved for detecting IgM, IgG and IgG3 antibody response. Conclusions: Peptides E2P3, E2P7, E2P16 and E2P17 were revealed as the most immunodominant peptides that together can form the basis for designing an accurate, economical and easy to synthesize a peptide-based immunodiagnostic for CHIKV. This study provides new and important insight into the humoral response generated by CHIKV S27 strain during the early phase of infection.

Artificial neural network modeling for road traffic noise prediction

Several attempts have been made by the researchers to predict and model urban road traffic noise mathematically and statistically. There has been a lot of interest in the new techniques for analyzing data. Neural networks offer a new strategy with enormous potential for many tasks in the domain of geospatial planning. ANN technique for modeling provides smaller errors in comparison to other classical methods. Neural networks have been applied to many interesting problems in various areas including road traffic noise prediction. In the present study an attempt has been made to explore the application of neural networks to road traffic noise prediction in Lucknow city, capital of Uttar Pradesh, India. Traffic volume, speed and noise level data were collected at ten selected locations. For development of model, classified traffic volume (Car/Jeep/Van, Scooter/ Motorcycle, LCV/ Minibus, Bus, Truck and 3-Wheeler), traffic speed on both sides of the road were taken as input data. Output was estimated as Leq. Performance of the model was tested by root mean square error (RMSE), mean absolute error (MAE) and coefficient of correlation (R). It was observed that there is no significant difference between observed and predicted noise levels in the present case, indicating the accuracy of model.

Identifying Safety Factors Associated with Crash Frequency and Severity on Non Urban Four Lane Highway Stretch in India

ABSTRACT Crash scenario in India is quite alarming and presents an urgent need to understand and mitigate the risk contributing factors leading to these crashes. This in turn depends on the reliable quality data pertaining to crashes and its injury severity correlated with other contributing factors available for scientific analysis and modelling of crashes. Here an attempt has been made to develop a scientific database with in-depth crash details required for modelling of crashes for divided four lane nonurban highway. This article describes the effect of safety factors including highway geometric parameters, traffic parameters, temporal parameters, environment parameters, and different land use types on monthly crashes and crash severity model for 3 years (2011–2014) National Highway crash data. The results of the analysis present the critical safety parameters that need to be considered for highway development projects in future. This article also highlights the need and significance of detailed crash information in analyzing the crashes and understanding the effect of different engineering, temporal, and environmental parameters on crashes occurring in India. Crash frequency was predicted using Poisson-gamma model and crash severity using ordered probit model using Bayesian inference. The study results are applied to rank hazardous crash locations and to develop crash modification factors.

Comparative Appraisal of Metro Stations in Delhi Using Data Envelopment Analysis in a Multimodal Context

Urban public transit is a critical component for sustainable urban development and is crucial to multisector expansion of a developing economy. Continuous monitoring of infrastructure performance and assessment of its effectiveness are required to continually improve service quality. The urban agglomeration of Delhi, India, was studied for the efficacy of its multimodal urban public transit system. The toolkit used was Data Envelopment Analysis (DEA), a linear optimization technique that estimates relative efficiencies of its decision making units (DMUs) for a multitude of inputs and outputs. The study area includes the Red and Yellow lines of the Delhi Metro network. Commuter-based questionnaires were used to collect 1,328 valid responses about demographic, travel time, and quality perception parameters, which were analyzed, and relative rankings of the DMUs were evaluated. The efficiency was analyzed according to the Red and Yellow lines divided into seven corridor segments and individual stations. Results revealed efficiency scores and inefficiency slacks for which improvement strategies are proposed.