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Highway proximity is essential to the economic growth of establishments and productivity of freight operations. The present study is motivated by the discernible gap in knowledge about the potential impacts of highway proximity on freight flow patterns of establishments. This knowledge gap impedes accurately estimating the potential benefits associated with infrastructure investments, especially related to freight-specific facility planning in developing countries. This paper investigates highway proximity impacts on freight establishments’ travel and shipment patterns using a comparative experiment approach. A total of 17 explanatory variables reflecting the freight flow pattern of an establishment have been considered. Proximity impact is evaluated by comparing the means of these explanatory variables among establishments closer to the highway, termed the “Influence” group, and those farther from the highway, defined as the “Control” group. Spatial and kernel analyses are carried out to determine the cut-off distance for demarcating samples into Influence and Control groups. The propensity score matching technique is subsequently used to create matched samples for a one-to-one comparison between the Influence group and Control group establishments. Results suggest that the Influence group typically consists of older establishments with larger business areas, generates many small-sized trips, and is associated with lower tonnage generation, expenditure, and shipment size than the Control group. The study findings are expected to have important implications on the infrastructure investment decision process and planning of freight operations.

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.

AbstractThis article discusses the concept of facility center development by identifying locations for colocating multiple facility types so as to access several facilities at a single location. This concept maximizes accessibility and reduces travel costs for individuals with multiple trip purposes. The appropriateness of facility center locations are evaluated with two parameters: (1) habitation accessibility to facility centers using the two-step-floating-catchment-area (2SFCA), method, and (2) facility center crowdedness using the inverted two-step-floating-catchment-area (i2SFCA) method. This study uses both 2SFCA and i2SFCA methods concurrently to get perception of quality of service provided and received. Considering two facilities such as a health care center and a high school for spatial planning of facility centers, the proposed methodology is analyzed for two cases. In the first case, it is assumed that no facility exists in the study area and in the second case, the existing locations of health care centers and high schools are considered for analysis. The proposed methodology is applied in Jhunjhunu—a district in Rajasthan, India—as a case study. The discussed policy strategy would help with resource allocation in a phased manner for new facility development or capacity augmentation of existing facilities, and thus would improve the quality of rural citizens’ lives.

Electric bicycles (e-bikes) are an emerging mode of sustainable transportation well-known for their individual and environmental benefits. Past research suggests factors for e-bike adoption from new and experienced e-bike users, but little is known about prospective users’ attitudes. Understanding the standpoint of non-users would reveal practical barriers impeding e-bike adoption in developing markets. We identify important drivers and barriers from a representative city-level sample of prospective e-bike users in India. The study employs exploratory factor analysis integrated with a multi-criteria decision-making model to identify latent components and prioritize their variables. The results revealed five factors: user-perceived benefit-specific motivators, travel quality-specific motivators, e-bike mobility-specific motivators, perceived social and economy-specific barriers, and e-bike infrastructure-specific barriers. Attributes such as monetary savings, reduced congestion, and last-mile connectivity were identified as the most important benefits, while fear of battery explosion and lack of cycling and charging infrastructure were perceived as the key barriers. Comparison by age shows “purchase cost” as the most influencing perceived social and economy-specific barrieramong young male commuters. Comparison by income underlines the diminishing importance of “purchase cost” with increasing income among males. Regardless of age, income, and trip length, females prioritized “range anxiety” over “purchase cost.” The trip length-based comparison reveals the significance of “risk of theft” for males with longer trip lengths. In general, males of all groups preferred using e-bikes for “short non-commuting trips” substituting motorized transport, while females preferred using them for “last-mile connectivity.” These findings offer insights for designing effective e-bike promotion campaigns for the mass adoption of e-bikes.

Autonomous vehicle technologies are anticipated to transform road transportation systems, promising enhanced traffic safety and efficiency across different modes, including public buses (PB) and ride-pooling services (RPS). However, in India, there is a growing security concern/fear of crime concerning conventional PB and RPS because of the recent rise in crimes committed on them. Moreover, the introduction of driverless modes of PB and RPS may further heighten commuters’ crime concerns on such services because of the absence of a driver. Thus, this study investigates the acceptance of autonomous public buses (APB) and autonomous ride-pooling services (ARPS), as well as how commuters’ characteristics influence the perceived risks of crime and victimization and their willingness to use the modes. To achieve this, a stated preference survey was designed and conducted across India. The survey resulted in 732 complete responses. The results show that socioeconomic attributes, vehicle automation, and security-related measures significantly influence commuters’ perceived fear of crime and willingness to use APB and ARPS in India. More specifically, young commuters demonstrate higher willingness to use APB and ARPS, while females exhibit lower willingness to use APB and ARPS. In addition, the presence of a security officer on these modes decreases commuters’ concerns about crime. Moreover, travel distance is positively associated with commuters’ perceived level of crime and victimization, while it has a negative relationship with their unwillingness to use APB and ARPS. APB and ARPS are yet to be introduced in India, and Indian commuters have not experienced the security concerns associated with them; thus, the results of this study can serve as the base for guideline formulation for security concerns in India. Based on the results of this study, a set of policy implications, such as female-only transit units, enhancing security measures on the automated modes, and design framework and infrastructure, were proposed. These policy implications can be instrumental in increasing the acceptability of APB and ARPS in India and other countries with similar characteristics.

Increase in vehicle ownership (VO) as a result of rapid economic and population growth and its negative environmental effects have gradually become a potential threat to the physical and social environment. A wide range of factors influences the propensity for VO by individuals or firms; however, the existing body of literature lacks a critical review which provides an understanding of their underlying associations and potential implications for policymaking in one holistic frame. To fill this research gap, this study presents a systematic review of factors affecting private and commercial VO using the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). The factors influencing private VO decisions are extracted from the reviewed articles; subsequently, the identified factors are topologically divided into seven categories. The findings show that most studies identified the factors contributing to VO as related to socio-economic, demographic, and built environmental characteristics. A lack of research has been identified for attitudinal, technological, and parking-related variables. It is also concluded that there are gaps in the knowledge base on factors contributing to the commercial VO of a business establishment. Designing informed policy measures based on such factors that can induce a sustainable transition in the mobility sector is important to mitigate transportation-related negative externalities on the environment. The overall review findings offer actionable guidance for low-carbon mobility transition and outline a future research agenda for academic researchers and decision makers. The quantifiable factors revealed in this study are expected to assist policy makers in discouraging VO propensity among individuals or firms.

Large urban freight traffic generators (LTGs) are large specialized buildings or landmarks housing multiple establishments and generate a significant truck trips at both disaggregate and aggregate levels. Identification of LTGs and quantifying their relationship with freight travel characteristics helps policymakers formulate necessary logistical interventions and reduce externalities from freight activity. Hence, this study proposes a methodology for identifying LTGs and exploring their interactions on freight travel, expenditure pattern, shipment pattern, and other establishment characteristics. A decision-tree approach called chi-squared automatic interaction detector (CHAID) algorithm is used to map these interactions. Results suggest that LTGs are distinctly associated with multiple variables such as shipment size, shipper expenditure, commodity classification, and business age characteristics. Business age is the best predictor across all models. These associations vary based on LTG definitions. Implications of this study would augment the efforts on interlinking LTGs with urban freight demand modeling systems and enable sustainable city logistics initiatives and last mile delivery management.