Freight traffic fulfils not only the business needs of a region to move goods between producers, manufacturers, and end consumers, but also creates a host of unintended environmental, social, and economic impacts. Despite its importance, freight traffic impacts and associated logistic inefficiencies are largely overlooked in the urban transport discussions in developing economies like India. This paper addresses this research gap by outlining the research progress related to freight transport in India and discusses the key problems related to freight system performance. The published literature in the last three decades (1990–2020), policy briefs and institutional reports are explored to summarize key findings and uncover thematic linkages. We categorize the inefficiencies in the freight system into four aspects: (i) long-haul trucking, (ii) last-mile logistics, (iii) freight distribution (inventory level), and (iv) policies and regulations. Apart from identifying the limitations in policy discourse, this paper also explores the possible solution concepts to improve efficiency in freight transport and mitigate the unintended negative externalities in urban areas. The overall conclusion is that increasing and improving infrastructure and equipment, technology and operations, and policy and regulations will go some way towards making freight more efficient in India and reducing congestion and emissions of air pollutants and GHG. The present paper can be expected to promote further freight research and effective policy instrument design in India.

Freight demand models typically employ a priori classification systems for dividing establishments into hypothetically complementary groups with homogeneous patterns in freight production (FP) and freight trip production (FTP). Although an attractive and popular notion, the assumption of homogeneity within these a priori industrial classes is reductive in nature and is not yet tested in literature. This research examines this hypothesis and explores the possibility of a data-driven segmentation by examining the relationships between FP/FTP patterns and prevalent a priori classes; subsequently, it creates homogeneous ensembles of a posteriori segments through aggregation. This research labels, explains, and interprets these novel segments using commodity value density of industrial classes. The alternate segmentation schemes are compared in their ability to predict FP and FTP and it is found that: (i) industrial classification systems (NAICS, ISIC) perform significantly better than product classification systems (ASICC); (ii) a considerable portion of variability in FTP does not depend on employment predictor due to the underlying influence of shipment size; (iii) an a posteriori segmentation scheme considering shipment size may represent an effective middle ground for developing both FP and FTP models in freight demand model systems. Adoption of these novel segments of the freight travel market has the potential to reduce the sample size requirements of freight demand model systems and minimize the financial necessities for future freight surveys.