Rapid urbanization combined with limited attention to walk-facilities has resulted in a substantial increase in overall pedestrian injuries and deaths in India. Developing policies based on user perception to improve existing pedestrian infrastructure is key to promoting safe and walkable pedestrian movement in urban areas. This paper demonstrates a pedestrian satisfaction-based methodology to prioritize an essential set of attributes influencing safety and walkability at sidewalks and crosswalks. Initially, an exhaustive set of 15 sidewalk- and 11 crosswalk-related attributes influencing safety and walkability were identified from a literature review. Subsequently, pedestrian perceptions of associated importance and satisfaction toward the identified attributes were collected using a five-point Likert scale. Face-to-face interviews were conducted at selected pedestrian crash-prone locations in Hyderabad, India. The data was analyzed using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), a widely used multi-attribute decision-making (MADM) method to rank the attributes based on user priority. Importance–satisfaction analysis (ISA) was then used to group the attributes in a set of quadrants based on associated standardized satisfaction and importance, respectively. The relative strength and weakness of different sidewalk and crosswalk attributes were measured and compared simultaneously using TOPSIS and ISA. Both TOPSIS and ISA were simultaneously used to estimate and compare various sidewalk and crosswalk attribute relative strengths and weaknesses. Results indicated that physical separation from traffic, sidewalk amenities, sidewalk width, sidewalk continuity, and sidewalk cleanliness were the most important sidewalk attributes, but were rated as less than satisfactory, requiring immediate intervention. The crosswalk-specific priority attributes requiring urgent attention were vehicle stop-line behavior, conflicts with turning traffic, zebra crossing, pedestrian holding space. These research findings are expected to be critical inputs for rational decision-making regarding pedestrian infrastructure improvement in developing countries in general and Indian cities in particular.

This study has identified a comprehensive set of motivators and deterrents influencing bicycling in typical Indian context based on literature review. A travel intercept survey was conducted to collect user perceptions on the identified motivators and deterrents from Kharagpur and Asansol, two typical Indian cities. The collected data were analyzed for prioritization of the identified factors using two established Multi-Attribute-Decision-Making (MADM) approaches, namely, RIDIT and GRA. The rankings of the factors obtained are found to be consistent for both cities. Results reveal that among deterrents, ‘safety concern’ and ‘route visibility’ are perceived as strong deterrents to bicycling, whereas ‘parking fee’ is not perceived as a significant deterrent. Motivators such as ‘physical fitness,’ ‘environmental awareness,’ and ‘reliability’ are perceived to be more important than the low cost of bicycle travel, which contradicts a wildly held perception that the relatively lower cost of bicycle travel is the primary motivator for bicycling.

A bicycle is one of the key modes of transport in small Indian cities, where two-thirds of all trips are made by either bicycle or by walking. Despite having a substantial bicycle share, none of these cities have safe bicycle infrastructures. As a result, bicyclists share the road space with various types of motorized traffic, putting them at higher risk. It is thus necessary to evaluate the quality of bicycle travel from the users’ perspective and develop appropriate mitigation measures. To this end, the main objective of this paper is to develop a tool to evaluate the quality of bicycling at roadway segments with respect to several roadway-related physical and functional characteristics and land use. For the stated purpose, user perception on bicycle suitability was collected using a six-point Likert scale in several homogeneous roadway segments in Kharagpur, a small Indian city. Subsequently, bicyclist perceived level of service (LOS) criteria were developed by formulating Ordered-Probit (OP) models, which can capture the ordered nature of the dependent variable (perceived bicycle LOS of a segment). Results indicated that among the variables measured in continuous scale, on-street parking (ONP) proportion has the strongest influence on bicyclist-perceived LOS followed by motorized volume and the 85th percentile speed of motor vehicles. Among the categorical independent variables, both pavement condition score and land-use type influence the bicyclist-perceived LOS significantly. Subsequently, bicyclist-perceived LOS criteria were also applied to assess bicycle suitability of a set of roadway segments in Kharagpur.

This paper contributes to the emerging literature on freight studies by identifying the optimal freight traffic analysis zone (FTAZ) system at which to conduct macro-level freight travel analysis. To arrive at the optimal scale, we develop alternate zone systems by grouping census wards with similar freight-related characteristics (industrial characteristics, commercial land use characteristics, locational characteristics and socio-demographic characteristics). The resultant zone systems are analysed at multiple geographic scales and the optimal scale of each zone system is determined by performing the Brown-Forsythe test. Results suggest that a 1:3 aggregation ratio (24-28 zones) is the optimal scale for Metropolitan FTAZs, whereas the publicly available ad-hoc zone system and prior literature on National FTAZs follow 1:10 aggregation. The study findings suggest that Metropolitan planning organizations need to reconsider their existing data collection strategy, consider a larger aggregation ratio and, by extension, adopt smaller zones to ensure that both local and global freight travel characteristics are captured in freight travel analyses.

The traditional parametric modeling approaches used to predict freight generation (FG), such as ordinary least squares (OLS), suffer from limitations because of the realistic possibility of violating basic assumptions such as linearity or data distribution. This problem is multidimensional because of the need to model numerous industry sectors in the freight system and the possibility of using different explanatory variables; little guidance is currently available on which modeling methodology is suitable for a particular case. The non-parametric models that could solve several limitations with traditional FG models are rarely examined in their predictive ability. This paper offers insights into this frequent research question by comparing the performance of various modeling methodologies that can be used for predicting FG: OLS, weighted least squares, robust regression (RR), seemingly unrelated regression (SUR), multiple classification analysis (MCA), and support vector regression (SVR). To this effect, the research carried out in this study uses a freight dataset of 432 establishments across Kerala State, India. The model outputs are validated using resubstitution and cross-validation methods, and the prediction errors are quantified using root-mean-square error and mean absolute error. The validation results show that the non-parametric SVR models are better alternatives in developing state-, regional- and industrial segment-level models. The MCA models are more precise in predicting FG for suburban models. RR models provide a better predictive ability for modeling FG in some industrial segments. Overall comparison and result interpretations suggest that the non-parametric models are superior in relation to predicting FG. At the same time, RR seems to be the only parametric modeling approach that can provide comparable model performance to non-parametric models.