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Purpose This paper aims to study the attenuation and dispersion phenomena of shear waves in anelastic and elastic porous strips. Numerical investigations are performed for the phase and damped velocity profiles of the wave. For numerical computation purposes, water-saturated limestone and kerosene oil saturated sandstone for the first and second porous strips, respectively. Some other peculiarities have been observed and discussed. Design/methodology/approach Dispersion and attenuation characteristic of the shear wave propagations have been studied in an inhomogeneous poro-anelastic strip of finite thickness, which is clamped between an inhomogeneous poroelastic strip of finite thickness and an elastic half-space. Both the strips are initially stressed and the half-space is self-weighted. Analytical methods are used to calculate the interior deformations of the model with the involvement of special functions. The determination of the frequency equation, which includes the Bessel’s and Whittaker functions, has been obtained using the prescribed boundary conditions. Findings Impacts of attenuation coefficient, dissipation factor, inhomogeneities, initial stresses, Biot’s gravity, porosity and thickness ratio parameters on the velocity profile of the wave have been demonstrated through the graphical visuals. These parameters are playing an important role and working as a catalyst in affecting the propagation behaviour of the wave. Originality/value Inclusion of the concept of doubly layered initially stressed inhomogeneous porous structure of elastic and anelastic medium bedded over a self-weighted half-space medium brings a novelty to the existing literature related to the study of shear wave. It may be helpful to geologists, seismologists and structural engineers in the development of theoretical and practical studies.

The focus of this study is to examine the service quality of unsignalized intersections based on the perception of automobile drivers. This was achieved by developing Automobile Level of Service (A...

In this paper gap acceptance model and aggressive clearing model are developed for two uncontrolled 4-legged intersections in the state of Andhra Pradesh, India. Gap duration, type of major and minor road vehicles, speed of major road vehicle and clearing time are the parameters considered in this study which particularly reflects the driver’s clearing behaviour. This analysis showed that the probability of accepting a gap increases with increase in gap/lag value and decreases with increase in clearing time. It also decreases with increase in size of major road vehicle but increases with increase in size of minor road vehicle The level of aggressiveness of right turning vehicles increases with decrease in gap/lag and speed of major road vehicles, and it decreases with decrease in clearing time and size of minor road vehicles. Thus, in order to improve safety at uncontrolled intersections, measures such as minimization of side friction, installation of speed breakers on minor roads etc., should be followed which leads to faster movement of major road vehicles and thus lower the aggressiveness of drivers and increases safety.

This article proposes modelling the service quality offered by signalized intersections, nodal focuses in a transportation network, from automobile users’ perspective in the urban Indian context. Indian traffic is generally heterogeneous in nature, which implies non-motorized vehicles and pedestrians share the same space as the motorized vehicles. All possible geometric, traffic, and built-environmental data were collected from 45 diversified signalized intersections located in one of the metropolitan cities of India (Kolkata). Along with these, responses from around 9000 on-street automobile users were gathered seeking socio-demographic information and overall satisfaction scores (ranging from 6 = excellent to 1 = worst). Accordingly, the parameters exerting significant (p

This paper focusses on modeling automobile drivers’ response patterns to assess urban-street service quality in developing countries. Several quality-of-service attributes affecting drivers’ riding quality were investigated, from 102 urban street segments under widely varying geometric and traffic conditions. Traffic volume, effective road width, travel speed, pavement condition, on-street parking turnover, land use, hindrance due to public transits, non-motorized vehicles, and encounters are found to significantly affect drivers’ comfort levels. Two novel artificial intelligence techniques, that is, artificial neural network (ANN) and functional linked artificial neural network (FLANN) were applied to predict automobile drivers’ level of satisfaction scores ( ALOS_score). The prediction performance of developed models is assessed in terms of various statistical parameters of a modified rank index. Bayesian regularization neural network has given the best fitted model in both training and testing data sets among the ANN models. However, application of the FLANN model shows better prediction performance in the present context, as no hidden layer exists. All input layer neurons are directly linked with output layer neurons with a lesser number of connections, which is advantageous over ANN in reducing accumulated error. The result shows 73% of studied segments are offering service category “C” or below. Sensitivity analyses reported that pavement condition is the most important variable, with relative importance of 26.78%, to influence drivers’ riding quality. Similarly, other parameters were ranked in decreasing order of their relative importance, which will help highway authorities to prioritize budgets of future investments for improving service quality.

Passengers form the most important stake-holding body in public transit services. This study deals with the complexity of assessing transit service quality by identifying attributes affecting passenger’s satisfaction. An innovative questionnaire was designed by taking passengers’ demographic information and a wide spectrum of attributes related to operating conditions of two bus rapid transit systems (BRTS), i.e., Janmarg (Ahmedabad city) and Sitilink (Surat city) in the state of Gujarat. Total 23 variables are extracted using factor analysis with a Kaiser-Meyer-Olkin (KMO) value of 0.63 to conclude that the sample size is adequate enough for the model. These variables are grouped into six principal components and changed over to fuzzy sets with three membership functions each to map the fuzziness and ambiguity in passengers’ perception. The complications of generating 36 = 729 number of fuzzy rules are solved by introducing a hierarchical fuzzy inference system (FIS) with two lower-level FIS and one higher-level FIS. The first lower FIS consists of “accessibility,” “service provisions,” and “reliability” as fuzzy input variables to get “availability” as an output variable. The second lower FIS contains “safety and security,” “fare,” and “comfort” as input parameters to produce “comfort and convenience” as an output variable. The resulting fuzzy values are used in higher-level FIS and defuzzified to evaluate the satisfaction level of passengers by max-min composition technique. This method will help in improving existing transit facilities and devising strategies for ensuring sustainability.

This paper presents a qualitative study on automobile users’ response pattern to assess the provided transportation service quality under heterogeneous traffic flow conditions. An Automobile Users’ Satisfaction index (AUSi) is established using data sets of questionnaire survey collected from 34 urban street segments of three midsized Indian cities. About 977 respondents with a suitable cross-section of gender, age, driving experience etc. were participated in travellers’ intercept survey. Rasch Model (RM) was applied to identify a set of quantitative measures to analyse the complex process of measuring perceived service quality and degree of drivers’ satisfaction together. The present study comprehends the multidimensional nature of users’ perception to evaluate AUSi with the help of six-dimensional variables such as roadway geometry, traffic facilities, traffic management, pavement condition, safety and aesthetics. RM offers a particular score to each user and each dimensional attribute along with a shared continuum. This way, the attributes those are more demanding to produce satisfaction as well as the variation in response of different modes of transport are evidently identified. The key findings indicate that the participants reported lower satisfaction level mainly due to the absence of separate bike/bus pull-out lanes, improper parking facilities and interruption by non-motorised vehicles/public transit or roadside commercial activities. Fuzzy C-Means (FCM) clustering was applied to classify AUSi scores into six auto Levels Of Service (LOS) categories (A–F) for each street segment. The model was well validated with a significant matching of predicted Automobile users’ LOS (ALOS) service categories with the users’ perceived Overall Satisfaction (OS) scores for fourteen randomly selected segments. This prediction model is new to mixed traffic flow condition, which uses linguistic information and real-life issues of drivers for the current state of services. Hence, the proposed method would be more credible than conventional models to support the decision makers for long term planning and designing road networks on a priority basis

The objective of this research is to develop a model that can be used to assess level of service (LOS) delivered by urban street segments in developing countries. An innovative survey questionnaire was developed taking 33 questions related to the quality of service (QOS) attributes affecting road user’s satisfaction under mixed traffic flow condition. A road user’s satisfaction survey was carried out, in which participants were requested to assess the QOS attributes and overall satisfaction (OS) on a seven-point Likert scale ranging from strongly disagree to strongly agree. The 33 responses were summarized into eight uncorrelated sets of variables using factor analysis. A Kaiser–Meyer–Olkin value of 0.836 indicates that the sample size is suitable for factor analysis. The model was developed using multiple linear regression analysis to predict the OS of urban street taking eight principal components as independent variables. The prediction performance of the developed model and significance of the input data sets were statistically checked. An R2 value of 0.709 and adjusted R2 value of 0.683 signifies the suitability of the model for its universal application. The predicted OS scores are classified into six categories by applying k-means clustering to assign the threshold values for six LOS classes. Most of the road segments were found out with provided service categories “C” or below. Hence necessary actions should be undertaken by the transportation authorities to achieve higher LOS scores.

At present, a suitable methodology is not available for assessment of the quality of services offered by the transportation facilities like shared use roadway segments for pedestrian use under the influence of heterogeneous traffic flow. Hence an initiative has been taken in present study to develop a new method that can be well applicable for mid-sized cities. Required data sets has been collected from a total of 60 segments of three Indian cities namely; Bhubaneswar (Odisha), Rourkela (Odisha) and Kottayam (Kerala). These segments widely vary in their traffic flow and geometric conditions. Also, both kinds of segments, i.e. with and without provision of sidewalk facilities have been considered. The quality of walking environments offered by these segments has been examined. Approximately 700 respondents differing in their age, sex, education level and driving experiences participated in perception surveys to share their satisfaction level on these segments. Ten different variables affecting the service quality significantly (p < 0.001) has been found out and stepwise multi-variable regression analysis has been carried out to develop a new model. Primary factors included are namely: total available width of the road, paved shoulder and sidewalk facilities in through direction; volume of pedestrians and vehicles in peak hours of traffic flow; percentage of heavy vehicles; 85th percentile speed of running motor vehicles; and interruptions created by nearby commercial activities and available public transits. Also, influences of two secondary factors namely; availability of driveways and type of separation of sidewalk facilities from the main carriageway has been examined under prevailing conditions. These influences have been reflected in the developed model by using adjustment factors. This regression model reported high reliability in its universal application with a high correlation coefficient (R2) of = 0.75. Also, its parameter estimates satisfied required significance criteria through several tests including t test, collinearity statistics etc. Fuzzy C-Means clustering technique has been used to define the ranges of service categories (A–F). In order to evaluate off-street pedestrian facilities, average pedestrian space (m2/ped) has been used as the measuring parameter. The service categories predicted by the developed model for fifteen randomly selected segments has been compared with the observed ones. The high value found for exact matching (86.7 %) denotes well applicability of this developed model.

Perception based Level of service methodology depicts how well a transportation facility satisfy its road users. There are several engineering factors affecting driver’s perception of service quality on urban arterials, which vary largely by individual user and not easy to be distinguished. Therefore, user-perceived service quality appraisals don’t relate to the LOS assessed using Highway Capacity Manual (HCM) method. The main objective of this study is to speak to the variability of human discernment and to characterize level of satisfaction of road users using hierarchical fuzzy inference system (HFIS). Around 500 responses of road users were gathered from two emerging cities of India i.e. Bhubaneswar and Vishakhapatnam, where traffic facility is portrayed by distinctive sorts of roadway features and variation in number of pedestrians, motorists and each category of vehicles. So that our survey can include every circle of transportation and people belonging to all classes can be taken into account. The present approach, contains fuzzy reasoning experiences to combine important factors which reflect road user’s perception of service quality. In order to decrease the intricacy of multivariable, the fuzzy inference system (FIS) is partitioned into two subsystems, together conducing to a hierarchical fuzzy inference system. First the four input variables namely roadway geometry, traffic facilities, pavement condition and safety are fuzzified. The resulting fuzzy values are entered into the fuzzy inference system, where number of fuzzy rules are generated by applying fuzzy operators. Applying max–min composition method all the outputs are aggregated and defuzzified to evaluate composite LOS of various road segments of both the cities. This methodology offers unique and significant approach to define service quality provided by the transportation infrastructure and may defeat the constraints of customary delay-based methods to some extent.

Aluminium (Al) was evaluated for induction of oxidative stress and DNA damage employing the growing roots of Allium cepa L. as the assay system. Intact roots of A. cepa were treated with different concentrations, 0, 1, 10, 50, 100, or 200 microM of aluminium chloride, at pH 4.5 for 4 h (or 2 h for comet assay) at room temperature, 25+/-1 degrees C. Following treatment the parameters investigated in root tissue were Al-uptake, cell death, extra cellular generation of reactive oxygen intermediates (ROI), viz. O(2)(*-), H(2)O(2) and (*)OH, lipid peroxidation, protein oxidation, activities of antioxidant enzymes namely catalase (CAT), superoxide dismutase (SOD), guaiacol peroxidase (GPX), ascorbate peroxidase (APX); and DNA damage, assessed by comet assay. The findings indicated that Al triggered generation of extra-cellular ROI following a dose-response. Through application of specific enzyme inhibitors it was demonstrated that extra-cellular generation of ROI was primarily due to the activity of cell wall bound NADH-PX. Generation of ROI in root tissue as well as cell death was better correlated to the levels of root Al-uptake rather than to the concentrations of Al in ambient experimental solutions. Induction of lipid peroxidation and protein oxidation by Al were statistically significant. Whereas Al inhibited CAT activity, enhanced SOD, GPX and APX activities significantly; that followed dose-response. Comet assay provided evidence that Al induced DNA damage in a range of concentrations 50-200 microM, which was comparable to that induced by ethylmethane sulfonate (EMS), an alkylating mutagen served as the positive control. The findings provided evidence that Al comparable to biotic stress induced oxidative burst at the cell surface through up- or down-regulation of some of the key enzymes of oxidative metabolism ultimately resulting in oxidative stress leading to DNA damage and cell death in root cells of A. cepa.