Independent Retailer Restocking Choices in Urban Goods Movement and Interaction Effects with Traditional Markets

Traditional markets play a key role in local supply chains in many countries, often influencing retailer decisions due to their inherent attractiveness. In contrast to restocking choices for retailers as part of large chains, choices of independent retailers driven by local traditional markets have not been widely researched and are not well understood. This paper analyses the factors influencing independent retailer restocking choices and investigates the interplay between the presence of traditional markets and retailer choices. Bandung city in Indonesia is chosen for the study where independent retailers are prevalent, and where a number of traditional markets are thriving. A retrospective questionnaire was used to capture independent retailer restocking behaviour and generation models were calibrated to arrive at the trip propensity. Discrete choice models were estimated to explain the retailer preferences for supplier location and transport service choice. Results indicate that trips generated by independent retailers are explained by the presence of traditional markets and retailers’ vehicle ownership, in addition to the standard variables such as number of persons employed, weekly goods demand and average shipment weight. As for restocking location choice, retailers are more likely to choose suppliers within a traditional market where the number of wholesaler units is larger. Furthermore, the choice of traditional markets has a positive influence on whether retailers choose to use their own vehicle to restock their shops.

Predicting Commercial Vehicle Parking Duration using Generative Adversarial Multiple Imputation Networks

As the world rapidly urbanizes in pace with economic growth, the rising demand for products and services in cities is putting a strain on the existing road infrastructure, leading to traffic congestion and other negative externalities. To mitigate the impacts of freight movement within commercial areas, city planners have begun focusing their attention on the parking behaviors of commercial vehicles. Unfortunately, there is a general lack of information on such activities because of the heterogeneity of practices and the complex nature of urban goods movement. Furthermore, field surveys and observations of truck parking behavior are often faced with significant challenges, resulting in the collection of sparse and incomplete data. The objective of this study is to develop a regression model to predict the parking duration of commercial vehicles at the loading bays of retail malls and identify significant factors that contribute to this dwell time. The dataset used in this study originates from a truck parking and observation survey conducted at the loading bays of nine retail malls in Singapore, containing information about the trucks’ and drivers’ activities. However, because of the presence of incomplete fields found in the dataset, the authors propose the use of a generative adversarial multiple imputation networks algorithm to impute the incomplete fields before developing the regression model using the imputed dataset. Through the parking duration model, the activity type, parking location, and volume of goods delivered (or picked up) were identified as significant features influencing vehicle dwell time, corroborating with findings in the literature.

Delivery Process for an Office Building in the Seattle Central Business District

Movement of goods within a central business district can be very constraining with high levels of congestion and insufficient curb spaces. Pick-up and delivery activities encompass a significant portion of urban goods movement, and inefficient operations can negatively impact the already highly congested areas and truck dwell times. Identifying and quantifying the delivery processes within the building is often difficult. This paper introduces a systematic approach to examine freight movement, using a process flow map with quantitative delivery times measured during the final segment of the delivery process. This paper focuses on vertical movements such as unloading/loading activities, taking freight elevators, and performing pick-up/delivery operations. This approach allows visualization of the components of the delivery process and identification of the processes that consume the most time and have greatest variability. Using this method, the delivery process for an office building in downtown Seattle was observed, grouped into three major activities (or steps): 1. Entering, 2. Delivering, 3. Exiting. This visualization tool provides researchers and planners with a better understanding of the current practices in the urban freight system, and helps identify the non-value-added activities and time that can unnecessarily increase the overall delivery time.