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The aim of this study was to develop three roundabout entry capacity models (RECMs) by employing evolutionary-based regression techniques such as genetic programming (GP), age-layered population structure genetic programming (ALPSGP) and grammatical evolution genetic programming (GEGP) in mixed traffic conditions. Necessary data were collected from 27 roundabouts located in eight states in India. The influence area for gap acceptance method was used to determine the critical gap. To assess the significance of the models and select the best, the modified rank index was applied. The results showed that the GEGP model performed better than the GP and ALPSGP models. The GEGP model is also applicable in practice because of its simplicity. Sensitivity analysis revealed that the critical gap is the prime variable in the development of RECMs. The findings of this study should be useful for traffic planners and designers in the capacity estimation of roundabouts in mixed traffic conditions in developing countries with traffic characteristics similar to those in India.

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...

The prime objective of this study is to develop the entry capacity model for roundaboutsby employing Multiple Linear Regression (MLR) analysis under heterogeneous traffic flow conditions. Required data were collected from 27 selected roundabouts spanning across 8 states of India by using high-definition video (HD) cameras. To reflect the driver behaviour, critical gap and follow up time are estimated by using INAGA method. To develop the robust model, data such as speed of vehicles and lateral clearance to Central Island are collected in microscopically way. To ascertain the lateral clearance of vehicles w.r.t. Central Island of roundabouts, path analysis is carried out by employing Cam Shift algorithm in trajectory analysis. The coefficient of determination (R2) and Nash-Sutcliffe model efficiency coefficient (E) are found to be (0.83, 0.84) and (0.9, 0.91) respectively. The p-value in the test of Analysis of variance (ANOVA) is found to be 0.00 (p-value

Pedestrian Level of Service (PLOS) is a multifaceted expression which represents the working condition of walking facilities and satisfaction level of pedestrian experience while using these facilities. In this regard this study aimed at developing PLOS models for three pedestrian facilities such as sidewalk, signalized intersection and un-signalized intersection by using qualitative data sets. Total 2730 pedestrian’s real time sense of satisfaction data were collected from ten cities of India. Behavioral responses were taken in order to assess variation with different age and gender from study areas having miscellaneous activities. Taking sets of questions, different variable scores were estimated to provide a wide variation of independent variable that will be more reliable for model fitting. Before model development degree of dependency has been examined with the help of discriminant factor analysis. Implementing ridge regression technique three different PLOS models have been developed for three pedestrian facilities. PLOS scores found by using the proposed models were classified by applying GA-Fuzzy clustering technique in order to get six PLOS ranges (A-F). At signalized intersection 3% of participants were extremely dissatisfied with the facility and they gave rating of >5 for PLOS score. This high score was because of pedestrian speed was severely restricted at some places due to the left and permissible right turning vehicles. Results also revealed that for un-signalized intersection nearly 23% of participants were having PLOS score of >4 as waiting time was more than expected and also pedestrian had to face conflict with vehicles while crossing.

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

The primary objectives of this study are to develop two signalized-based roundabouts entry capacity model by employing regression-based multiple non-linear regression model (MNLR) and artificial in...

This study proposes bicycle level of service (BLOS) models for the urban road links and signalized intersection approaches operating under heterogeneous traffic flow conditions prevailing in developing countries. Extensive data collection was carried out through field investigations, videography techniques, and perception surveys to inspect which parameters primarily affect the quality of bicycling in the mentioned facilities. As many as 74 road links and 70 intersection approaches of nine different Indian cities were included in the study locations. In addition, the perceived satisfaction ratings of 22,320 on-site bicyclists (i.e., 155 bicyclists from each site) were included in the database. These ratings ranged from 1 to 6 (excellent–worst bicycling conditions), and were used as the array of dependent variable in the model development process. Separate BLOS models were developed for road links and bicycle through movement at signalized intersections through the application of a novel evolutionary artificial intelligence technique, namely multi-gene genetic programming. The link model included eight significant variables in total whereas the intersection model included a total of seven. With compacted structures, both models represented excellent reliabilities in the present context and produced high coefficient of determination ( R2) values of above 0.86 with averaged observations. These models will help traffic planners and engineers to evaluate the service levels of urban bicycle facilities of developing countries. Statistical analysis of modeled parameters has reported that the traffic volume and crossing pedestrians have the highest influences on the quality of bicycle traffic management at urban road links and intersection approaches, respectively.

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.

The primary objectives of this study are to develop two roundabout entry capacity models using a regression-based multiple non-linear regression model (MNLR) and artificial intelligence (AI)-based ANFIS (adaptive neuro-fuzzy inference system) model under heterogeneous traffic conditions. ANFIS is the latest technique in the field of AI that integrates both neural networks and fuzzy logic principles in a single framework. Required data have been collected from 27 roundabouts in eight states of India. To assess the significance of these models and select the best model among them, modified rank index is applied in this study. The coefficient of determination ( R2) and Nash–Sutcliffe model efficiency coefficient ‘ E’ values are found to be 0.92, 0.91 and 0.98, 0.98 for the MNLR and ANFIS model, respectively. The ANFIS model is found to be the best model in this study. However, from a practical point of view, the MNLR model is recommended for determining roundabout entry capacity under heterogeneous traffic conditions. Sensitivity analysis reports that critical gap is the prime variable and shares 18.43% for the development of roundabout entry capacity. As compared with the Girabase formula (France), Brilon wu formula (Germany), and HCM 2010 models, the proposed MNLR model is quite reliable under low to medium ranges of traffic volumes.

This article presents the operational analysis of urban signalized segments, the most fundamental entity of road networks, operating under heterogeneous traffic conditions. Geometric, traffic, and built-environmental data were collected from mid-block sections and downstream intersections of 45 well-diversified segments. Besides, the socio-demographic details, travel characteristics, and perceived satisfaction scores (varying from 1 $$=$$ excellent to 6 $$=$$ worst) were collected from 9000 on-street automobile drivers. Subsequently, the variables having significant ( $$p < 0.001$$ ) influences on the perceived satisfaction scores were identified by Spearman’s correlation analysis. As observed, the array of significant variables included six quantitative road attributes and the age group of motorists. By incorporating these variables, highly reliable but less complex automobile level of service (ALOS) models were developed with the help of two novel artificial intelligence techniques namely, multi-gene genetic programming (MGGP) and functional network (FN). Both models exhibited excellent prediction efficiencies in the present context and produced high coefficient of determination ( $$R^{2}$$ ) values of above 0.86 under the prevailing site conditions. The model comparison showed that the MGGP model is more reliable and easier for field implementations as compared to the FN model. The sensitivity analyses of modeled attributes revealed that traffic volume, travel speed, and automobile stop rate have by far the most significant influences on the ALOS of urban streets. The crucial outcomes of this study would largely help the transportation planners and engineers in quantifying the operational efficiencies of urban roadways and in taking efficient decisions for the better management of automobile traffic.

This study is intended to define the Free Flow Speed (FFS) ranges of urban street classes and speed ranges of Level of Service (LOS) categories. In order to accomplish the study FFS data and average travel speed data were collected on five urban road corridors in the city of Mumbai, India. Mid-sized vehicle (car) mounted with Global Positioning System (GPS) device was used for the collection of large number of speed data. Self-Organizing Tree Algorithm (SOTA) clustering method and five cluster validation measures were used to classify the urban streets and LOS categories. The above study divulges that the speed ranges for different LOS categories are lower than that suggested by Highway Capacity Manual (HCM) 2000. Also it has been observed that average travel speed of LOS categories expressed in percentage of free flow speeds closely resembles the percentages mentioned in HCM 2010.

Roadways in developing countries usually carry heterogeneous traffic where on-street bicyclists encounter a very complex interaction with various categories of vehicles. In order to quantify the operational conditions of bicyclists under such conditions, a reliable bicycle level of service (BLOS) model is yet to be devised. This study intends to partially fill this gap by proposing a BLOS model suitable for urban road segments in mid-sized cities carrying heterogeneous traffic. A recently introduced artificial intelligence technique namely, associativity functional network (FN) is implemented for the development of this model. FN is a problem-driven approach that overcomes several limitations of the artificial neural network (ANN) technique. The urban bicycling environments persisting on 74 street segments are analyzed, and 8 influencing variables (geometric, traffic and built environmental, etc.) are identified. Of these variables, interruptions caused by frequent stoppages of intermittent public transits and frequency of driveways carrying high volume of traffic are newly introduced. In the modeling process, a strong relationship has been established between the identified variables and perceived BLOS scores collected through perception surveys. The resulting BLOS model has shown a high reliability for its applications in the mid-sized cities and has reported a high correlation coefficient (R) of 0.94 with the average observations. Besides, a sensitivity analysis is also carried out to identify the relative importance of input variables based on their contribution in the BLOS estimation. As observed, effective width of the outside lane, traffic volume, and on-street parking activity are by far the most important variables, which contribute 38.3%, 21.8%, and 12.7% respectively in the prediction of facility BLOS. Thus, these three attributes should be largely prioritized while making any plan of actions for the betterment of bicyclists.

The main objective of the study is to develop a roundabout entry capacity model by employing Influence Area for Gap Acceptance (INAGA) method under heterogeneous traffic flow conditions. In this regard, data collection has been carried out to extract the traffic flow and gap acceptance parameters which were significantly contributing to modelling the roundabout entry capacity. The traffic flow data were found to be heterogeneous in nature because of the percentage of traffic share in entry leg varying (0–55.8)%, (10–79.5)%, (18–76.5)%, (0–25.5)% and (0–7)% of heavy vehicles, light motor vehicles, motorcycles/scooters, bicycles and human-drawn vehicles, respectively. The critical gap (\(t_\mathrm{c}\)) and follow-up time (\(t_\mathrm{f}\)) were found by (INAGA) method ranging between (0.88–2.68) s and (0.86–2.65) s, respectively, via comprised all sites together. Then, multivariate regression analysis was carried out by utilizing M1 distribution function. The analysis of variance demonstrates that the model was statistically significant at 95% confidence level with measured R square is 0.78. To assess the prediction performance of the model, data splitting method was employed in model validation process and revealed that field capacity varies 2.5% w.r.t model capacity in which the measured R square is 0.9758. The newly developed model was compared with different existing models to judge the appropriateness of the model. HCM 2000 and HCM 2010 models showed nearly same results as the developed model. While the Brilon–Wu formula (Germany) and GIRABASE formula (France) overestimated and underestimated the capacity for all flow ranges, respectively.

The gap acceptance behavior of drivers at U-turn median openings is studied by considering the critical gap which cannot be obtained directly by field measurements. A thorough investigation on critical gap estimation is carried out by collecting the data sets from median openings at 4-, 6-, and 8-lane roads of Hyderabad City, India. Wide difference (10–42%) among the critical gap values estimated by the application of existing methods shows the limitation to consider for the mixed traffic situations. To address this issue, recently developed method Influence Area for Gap Acceptance (INAFOGA) which is based on clearing behavior of drivers at unsignalised intersections is modified and applied considering the merging behaviour of U-turn vehicles at median openings and named ‘Modified INAFOGA’ method. Modified INAFOGA method is compared with probability equilibrium method through paired-sample hypothesis (t test), and result revealed that difference in mean values 0.009 signifies that both methods are comparable. Difference in critical gap values obtained from the box plots and radar charts indicates that probability equilibrium method is not suitable to address the behavior of U-turn vehicles at median openings under mixed traffic conditions. These observations coupled with higher critical gap values validate the fact that ‘modified INAFOGA’ method is indeed appropriate under mixed traffic conditions.

The present study aims at the divination of the speed ranges of level of service (LOS) categories of urban traffic facilities. Free flow speed, congested travel speed, geometric, and surrounding environmental conditions are considered to define LOS criteria for urban street in Indian context. Cluster analysis is found to be a powerful tool to delineate LOS criteria. Hard competitive learning method is used to classify large number of speed data obtained using global positioning system. Six cluster validation parameters are used to classify the urban streets as well as the LOS categories. It can be confirmed from the above research work that the LOS categories for different urban street class are lower than that of the values proposed by HCM 2000 and the average travel speed of LOS categories expressed in percentage of the free-flow speed is lower than the values mentioned in HCM 2010.

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

Introduction The perceived comfort levels of on-street bicyclists are affected by both road characteristics and environmental healthiness. A thorough knowledge of these factors helps to encourage bicycle use and improve human health. This study thus aims to incorporate the parameters describing environmental healthiness in the evaluation of urban street performance. Methods For analysis purpose, extensive data are collected from sixty street segments of three Indian mid-sized cities. Variables having significant influences on bicycling comfort are identified using Spearman's correlation technique and a “Bicycle Comfort Level Rating” (BCLR) model is developed using the step-wise regression technique. A service scale is also defined using the Genetic Programming (GP) cluster technique to convert model outputs to letter-graded bicycling comfort levels A–F (excellent–worst). Results As observed, the bicycling comfort is influenced by total eight attributes. Of all, air quality index (AQI) is the most significant one (Spearman's correlation coefficient = 0.645). The BCLR model developed using all identified parameters has produced a high coefficient of determination (R2) value of 0.87 with overall observations. Results have also shown that around 97% segments are offering average–worst levels of bicycling comfort (C–F) at their present scenario. Conclusion An unhealthy environment largely discourages the use of bicycles as a choice mode of transport (as the users are more likely to be exposed to environmental hazards). Hence, the improvement in factors like air quality is essential to encourage the bicycling activity. The roadway parameters like traffic volume, road width and roadside commercial activities, etc. should also be prioritized in the planning process to provide better bicycling comfort. The developed BCLR model is highly reliable for its applications in mid-sized cities of India and other developing countries. This model along with other outcomes of this study would be helpful to enhance the quality of bicycling and public health.

This study aims at developing a model for pedestrian to evaluate service measure at the roadside walking environment from the qualitative and quantitative analysis in developing countries. The potential primary factors influencing service measure of pedestrian were summarized taking consideration to heterogeneous traffic flow condition and perception of road users. Total 735 real-time sense of participant's satisfaction when they are using the sidewalks, road geometric as well as operational characteristics of the road segments were collected from the 73 sites. The selected sites belong to midsized cities with varying geometric features. Different statistical investigations like factor analysis, Wilks’ lambda test and stepwise regression analysis were carried out to develop PLOS model for sidewalk facilities. From the results it has been observed that the variables taken such as width of sidewalk, vehicle volume, pedestrian volume etc. are significantly influencing the pedestrian service measure and the model was validated with a significance value of R2= 0.972. The PLOS scores got from the model are then classified using Genetic Programming clustering to find the six PLOS ranges (A-F) which show score for PLOS A ≤1.8 with best condition and PLOS F > 5.1 having worst condition of service.

It is crucial to measure the performance of existing roundabouts in terms of its ability to handle the present traffic scenario. The capacity of an entry flow is found to vary immensely with the changing geometrics of the roundabout. A meticulous study was conducted to gather data from roundabouts with varying geometry and traffic characteristics. A model was developed using empirical analysis based on five such geometric parameters, which proved to be significant for estimating the capacity. The parameters used in this equation had a multiplicative relation with the conflicting flow. Every geometric has been tested individually for its influence on capacity, keeping the flow conditions constant. The developed model satisfied the practicality when validated with the site data. A comparative study with some existing empirical models indicated that the capacities of roundabouts are underestimated/overestimated for Indian scenario.

Introduction Bicycling reduces the risk of various health problems associated with sedentary lifestyles. Hence, it is important to encourage bicycle commuting by enhancing the bikeability of transportation facilities. To support this process, this article proposes efficient bicycle level of service (BLOS) models for the assessment of signalized intersections under heterogeneous traffic conditions. Here, BLOS denotes bicyclists' perceived level of satisfaction. Method Extensive data sets (geometrical, traffic operational and built-environmental) are collected from 70 well-diversified intersection approaches of India. All approaches are also rated by 200 on-site bicyclists based on their perceived satisfaction levels on a Likert scale of 1–6 (excellent–worst). The attributes having significant influences on these ratings are then identified through Spearman's correlation analysis. Subsequently, three highly efficient techniques namely, associativity functional network (FN), genetic programming (GP) and step-wise regression are utilized to develop reliable BLOS models. Results As observed, the intersection BLOS is significantly (p Conclusion The important measures of BLOS improvement at signalized intersections include the efficient management of crossing pedestrians, restrictions on nearby parking activities, and minimization of bicycle delay. The deficiencies in these aspects have perhaps made the intersection approaches in India to offer BLOS C–F. The BLOS models and transportation engineering solutions proposed in this study for the improvement of public health through bicycling are highly efficient for developing countries.

In this study, an attempt has been made to study the gap acceptance behaviour of pedestrians to establish safety margins while crossing at unsignalized intersections. To examine the gap acceptance behaviour of the pedestrians, road geometric and traffic data were collected from 42 unsignalized intersections of seven Indian cities. Equilibrium probability method and maximum likelihood method were adopted for the estimation of critical gap of pedestrians on the basis of different demographic factors. Comparison between these two methods shows that equilibrium probability method is more suitable for estimating pedestrian critical gap at unsignalized intersections in mixed traffic condition. Analysis shows that the average critical gap for a young pedestrian is (5.31 s) less than that for a middle-aged (6.49 s) or old pedestrian (6.09 s), whereas regarding gender, the average critical gap for male is (5.7 s) less than that for female (6.1 s). It has been noted that with increase in critical gap, the crossing time increases logarithmically. Pedestrian safety margin model has been developed with the help of ridge regression technique. It was found that critical gap and crossing time had significant effect on the safety margin of the pedestrian. The model was validated with R2 value of 0.75 and the model output showed that 7% of pedestrians took high risk while crossing, as their safety margin was observed to be nearly zero.

This article proposes a bicycle level of service (BLOS) model for the assessment of urban roadway segments in mid-sized cities carrying heterogeneous traffic. The bicycling environments persisting on as many as 74 segments of four Indian cities are thoroughly analyzed. On-street bicyclists with varied demographics have rated these segments using a Likert scale ranging from ‘1’ (excellent) to ‘6’ (worst). The influences of various road attributes (geometric, traffic, and built-environmental) and bicyclists’ characteristics (socio-demographic and travel characteristics) on the perceived ratings are assessed using Spearman’s correlation analysis. Subsequently, eight significant variables are identified and used to develop a Levenberg–Marquardt neural network-based BLOS model. The most efficient but less complex model consisted of one hidden layer, three hidden neurons, and hyperbolic tangent activation function. This model produced very high values of correlation coefficient between the actual and predicted perceived ratings (i.e., 0.93 and 0.92 in the training and testing phases, respectively). The applications of Garson’s algorithm and connection-weight approaches explored that the effective width of outermost lane has the highest influence on urban street BLOS. The BLOS criteria are classified into six categories A–F (representing excellent–worst) using the self-organizing map in artificial neural network cluster technique. It was observed that most of the studied segments are offering average to worst kind of services at their present-day conditions. Thus, the influencing variables should be largely prioritized in the planning process to achieve better service levels efficiently.

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.

This study has taken an attempt to model the quality of services offered to bicycle through movement at urban signalized intersections carrying heterogeneous traffic. An extensive set of quantitative attributes (geometric, traffic and built-environmental) are collected from 35 intersection approaches of four Indian mid-sized cities and are thoroughly analyzed from a bicyclist’s perspective. Perceived satisfactions of approximately 160 bicycle users on each intersection approach are assessed through an extensive perception survey. By using the survey outcomes as the dependent variable, six attributes of intersection approaches having significant influences on the bicycle service quality are identified with the help of Pearson’s correlation analysis. Further, a step-wise regression analysis is carried out to establish a strong relationship between these influencing variables and perceived satisfactions of bicyclists. As observed, the resulting model is highly reliable in the present context and has a very high coefficient of determination (R 2) value of 0.86 with averaged observations. A sensitivity analysis is carried out to recognize the percentage contributions of modeled variables in service quality prediction. As per the results obtained, traffic volume (38%), width (17.6%), and pavement condition (15.9%) of an intersection approach have by far the most significant influences on its service quality. Affinity Propagation (AP) clustering is used to define the ranges of bicycle service categories (A–F). It is observed that approximately 69% of all studied intersection approaches are offering above average quality of services (A–C) and remaining are offering below average (D–F) kind of services at their present scenario.

Levels Of Service (LOS) evaluation criteria for off-street pedestrian facilities are not well defined in urban Indian context; hence an in-depth research is carried out in this regard. Defining Pedestrian Level of Service (PLOS) criteria is basically a classification problem; therefore a comparative study is made using three methods of clustering i.e. Affinity Propagation (AP), Self-Organizing Map (SOM) in Artificial Neural Network (ANN) and Genetic AlgorithmFuzzy (GA-Fuzzy) clustering. Pedestrian data are used on validation measure of clustering method to obtain optimal number of cluster used in defining PLOS categories. To decide the most suitable algorithm applicable in defining PLOS criteria for urban off-street facilities in Indian context, Wilk’s Lambda is used on results of the three clustering methods. It is observed from the analysis that GA-Fuzzy is the most suitable clustering analysis among the three methods. With the help of GA-Fuzzy clustering analysis the ranges of the four measuring parameters (average pedestrian space, flow rate, speed of pedestrian and volume to capacity ratio) are defined by using the data collected from two mid-sized cities located in the state of Odisha, India. It is also observed that at >16.53m2/ped average space, ≤0.061 ped/sec/m flow rate, >1.21speed and ≤0.34v/c ratio pedestrians can move in their desired path at LOS ‘A’ without changing movements and it is the best condition for off-street facilities. But in the pedestrian facility having ≤4.48m2/ped average space, >0.146ped/sec/m flow rate, ≤0.62 average speed and >1.00v/c ratio, pedestrian movement is severely restricted and frequent collision among users occurs. The ranges of the parameters used for LOS categories found in this study for Indian cities are different from that mentioned in HCM (Highway Capacity Manual 2010) because of differences in population density, traffic flow condition, geometric structure and some other factors. First published online:10 Sep 2014

To know how well roadways accommodate pedestrian travel or how they are pedestrian friendly it becomes necessary to assess the walking conditions. Estimation of PLOS (pedestrian level of service) criteria is very important as this is the first step of LOS (level of service) analysis but this is not well defined in Indian context. The analysis followed in India is basically an adaptation of the Highway Capacity Manual (HCM) 2010 methodology. However, the defined ranges of various measuring parameters mentioned in HCM 2010 are more suitable for developed countries. In this research, an in- depth study has been carried out to define PLOS criteria of urban off-streets facilities in developing countries having heterogeneous traffic flow conditions. Defining LOS is basically a classification problem and to solve it Affinity Propagation (AP), a very recently developed cluster algorithm is used. Inventory details and speed data are collected from two important cities (Bhubaneswar and Rourkela) of Odisha state, India having less than a million populations, through the application of video camera. After the collection of data, based upon average pedestrian space, flow rate, speed and volume to capacity ratio (v/c), six categories of PLOS are estimated. PLOS ranges found from this study for six categories (A-F) using average pedestrian space are (in m2/pedestrian) >15.67, >(13.84-15.67), >(10.67-13.84), >(7.63-10.67), >(5.08-7.63), =5.08, flow rate are (in pedestrian/sec/m) =0.065, >(0.065-0.081), >(0.081-0.096), >(0.096-0.116), >(0.116-0.141), > 0.141, speed are (in m/pedestrian) >1.51, >(1.32-1.51), >(1.13-1.32), >(0.91-1.13), >(0.69-0.91), =0.69 and volume to capacity ratio (v/c) are =0.4, >(0.4-0.57), >(0.57-0.76), >(0.76-0.9), >(0.9-1), >1 for PLOS categories A, B, C, D, E and F respectively. The PLOS ranges defined in this study are proportionately lower than that mentioned in HCM (2010) because of highly heterogeneous traffic flow and low volume of pedestrian movements in less than a million population cities in Indian context.

The paper presents a classification and analysis of the results achieved using various tools for the estimation of level of service (LOS) of urban streets. The basic premise of urban streets and LOS are discussed. LOS is analyzed quantitatively and qualitatively. Average travel speed (ATS) on street segments is considered as the measure of effectiveness in defining LOS criteria of an urban street using quantitative methods. The travel speed data collection procedure has been changing over time from the traditional followed moving observer method to a distance measuring instrument and now global positioning system is being extensively used worldwide. Classifying urban streets into number of classes and ATSs on street segments into number of LOS categories are essential components of LOS analysis. Emphasis is put on application of soft computing techniques such as fuzzy set theory, genetic algorithm, neural network, cluster analysis and modeling and simulation for the LOS analysis of urban streets both quantitatively and qualitatively. Quality of service of urban streets is analyzed using the satisfaction level that the road user perceived while using the urban road infrastructure. Possibilities are shown regarding the further improvement in research methodology.

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.

Aggregates obtained from natural stone resources are the main source of infrastructure, including road-pavement construction. In keeping pace with economic development, rapid industrialization, and urbanization, natural stone resources are depleting at an alarming rate, which is a major concern for future construction activities. Keeping this issue in mind, the use of locally available materials as a substitute for aggregates, particularly in road construction, is a major research interest. Because sand is available abundantly in some places such as coastal deltaic plains and desert areas, it is necessary to evaluate the potential of sand as an alternative to the stone aggregates. But because of its low stability, sand-bituminous mixes were not found to be suitable for pavement construction. The advantageous effects of using sulfur with construction materials motivated the authors to develop sand-bitumen mixes modified by locally available commercial sulfur. This research addressed the procedure to develop the sulfur modified sand bituminous mixes. The influences of mixing parameters such as mixing sequences, mixing temperature, and mixing time on performance characteristics of the mix were evaluated. Marshall properties of the mixes with varying proportions of sand, sulfur, and bitumen at different levels of compaction were studied, and the results were analyzed for drawing conclusions.

Defining Level Of Service (LOS) criteria is very important as this is the first step of LOS analysis but this is not well defined in Indian context. The analysis followed in India is basically adaptation of Highway Capacity Manual (HCM 2000) methodology which is more suitable for developed countries having homogenous traffic pattern. An attempt has been made in this study to define LOS criteria of urban streets for developing countries having heterogeneous traffic flow condition. Defining LOS is basically a classification problem and to solve it Affinity Propagation (AP), a very recently developed cluster algorithm is used. Inventory details and the required speed data are collected from five major street corridors of Greater Mumbai Region in India through the application of Trimble GeoXT Global Positioning System (GPS) receiver. Six validation parameters are used on Free Flow Speed (FFS) data to find the optimal number of clusters, which is required for the classification of street segments into number of classes. After that speed data collected during both peak and off-peak hours are averaged over street segments and clustered into six groups to get the speed ranges of different LOS categories. Using validation parameters, considering the physical and surrounding environmental characteristics it is found that street segments can be classified into four classes in Indian context as mentioned in Highway Capacity Manual 2000. However, the FFS range for urban street class IV (urban design category) is significantly lower because of varying road geometric characteristics. The speed ranges of LOS categories under urban street classes are proportionately lower to that values mentioned in HCM 2000 because of highly heterogeneous traffic flow on urban Indian roads. The travel speed data collection procedure using GPS is simple and accurate. In addition, AP clustering is highly efficient in terms of time saving and provides a very accurate solution to classification problems. Hence, both GPS and AP techniques can be applied in other countries to define the speed ranges of LOS categories considering the local conditions. First Published Online:16 Dec 2014