Search

Trip-based models and activity-based models represent two extreme ends of the spectrum of travel demand models in data granularity requirement and ability to reflect the underlying motivation to travel. Modelling of representative freight activity-travel patterns (RFAPs) has the potential to serve as the bridge between these approaches. RFAP clusters represent homogeneous groups of establishments, where utility maximization models predict the probability that an establishment belongs to a particular cluster. However, it is still an open question how to define, interpret and model activity-travel patterns in the context of freight system. To answer this question, this study conducted a large-scale establishment-based freight survey (EBFS) in seven cities of India and resulted in a sample of 432 establishments and their 1613 shipment records. In the first part, this paper proposes a novel approach for identifying RFAPs based on the notion that "activities" that inspire trip-making for passenger is equivalent to "freight orders" in the case of establishments. The cluster analyses revealed the presence of three well separated main clusters and nine less separated nested clusters. Through interpretation and labelling of these RFAPs, freight travel market is categorized into useful segments. The results suggested that a priori industrial classification systems used in trip-based models are overly simplified representations of the complex structure of the travel patterns. In the last part, freight activity-travel pattern generation (FAPG) models are developed which predicts the probability that an establishment exhibits a particular RFAP. The FAPG models developed using these RFAPs could replace the traditional freight generation (FG) and freight trip generation (FTG) models due to its ability to convert the assigned activity-patterns to trips or tonnage. For example, FAPG model suggest that at an employment level of 120, there is a 56% probability that establishments will exhibit MDV-HFMH (medium duty vehicles - high frequency medium haul) pattern which, in turn, implies that FP = 1630 tons/year; shipment frequency, i.e., FTP = 8 trips/week; length of haul = 240.6 km and commercial vehicle type choice = MDV. Thus, FAPG models can present an enhanced representation of freight flows since both FG and FTG are jointly modeled in this approach. That is, the best features of both commodity-based modelling (i.e., ability to capture the fundamental mechanism that drives freight demand) and vehicle-based modelling (i.e., ability to capture freight traffic implications) are included in FAPG models. The study findings are expected to assist in identifying the variations in establishments' preferences so that it is possible to identify the type of transport supply improvements that the establishments will respond to accurately, and thus prioritize the infrastructure investments. Moreover, the discussions on these findings are expected to improve the behavioral and spatial foundations of traditional freight models. • Defines "freight orders" as logical equivalent of "household activities" that drive trip-making. • Activity based approach is used for modelling freight travel using EBFS data. • Identifies homogeneous segments of representative freight activity-travel patterns (RFAPs). • Propounds freight activity-travel pattern generation (FAPG) models based on RFAPs. • FAPG gives enhanced representations of freight flows (FG, FTG, vehicle type, haulage). [ABSTRACT FROM AUTHOR]

Commercial Vehicle (CV) ownership is a variable of interest in planning interventions and policy instruments for sustainable transportation, but the decision-making process to own a vehicle fleet is not well understood in the context of freight transportation. The necessity to understand the heterogeneity in CV type ownership arises from the need to mitigate externalities associated with them. We argue that the need to own CVs is a derived demand arising out of the demand to move goods and is influenced by freight handling requirements of businesses. To this end, this paper examines the dual role (mediation) of freight generation variables while modelling CV ownership, which is an underexplored area of research in freight transportation planning. Establishment-based freight survey data from seven cities in Kerala, India, are used to develop structural equation models. The CV ownership is analysed through freight generation variables as mediators, apart from the direct influence of business size indicators and commodity characteristics. The study findings confirm the intermediatory role of freight generation and show the consequences of neglecting such a dual role in the overall CV ownership modelling paradigm. The model estimates offer insights into the causal structure of the decision-making process of establishments to own different levels of fleet ownership and composition. Understanding the business managers' decisions to own CVs in their fleet can be used for freight traffic management using urban consolidation centre (UCC). The statistical findings also provide the total effects between fleet and employment size and how freight generation mediates this relationship. The path diagrams representing the CV ownership patterns can be useful to policy makers while implementing policies like low-emission zones. • Heterogeneity in commercial vehicle ownership decisions are examined. • Dual role (mediation) of freight generation in CV ownership is revealed in path diagrams. • Causal structure of fleet ownership and composition decision-making process is revealed in models. • Employment size is having greater positive effects on ownership of medium and heavy-duty vehicles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]