Transportation Research Record: Journal of the Transportation Research Board

The two-echelon delivery structure is a strategy that can be implemented in urban areas to lower delivery costs by reducing the movement of heavy goods vehicles. In a two-echelon delivery structure, large trucks deliver shipments from a consolidation center to several terminals, where packages are transferred to smaller trucks for last-mile deliveries. This paper formulates a model that solves the two-echelon delivery structure by the use of approximation techniques. Several potential terminal locations and demand areas were identified, and the optimal number and locations of the terminals were examined, as the model evaluated the most cost-effective routes between the consolidation center, potential terminals, and demand areas. Downtown Toronto, Ontario, Canada, was chosen as the case study area to assess the model, and a cost analysis of the number and locations of the terminals was performed. The experiments showed that the number and the locations of the terminals were greatly influenced by the opening cost of the terminals and the transportation cost of the delivery trucks. It was also discovered that the likelihood of selection of terminals that were positioned near both the consolidation center and the center of the service area was higher than the likelihood of selection of terminals at any other location.

Events that disable parts of the highway transportation network, ranging from weather conditions to construction closures, may affect freight travel times and ultimately degrade economic productivity. Although previous studies of criticality typically focused on the impacts of natural disasters or terrorist attacks on systemwide travel times, these studies did not quantify the costs associated with disruptions to the economy because of disruptions to the freight transportation system. This paper quantifies the economic criticality of the highway infrastructure in Ontario, Canada, with the use of a new measure of criticality that determines the cost of highway closures (in dollars) on the basis of the value of goods, the time delayed, and the associated value of time. When criticality is measured in this way, it has some correlation with truck volumes, but the correlation differs when the values of shipments and the physical redundancy in the network are considered, and results in new insights into critical freight infrastructure. For example, the highway network within the greater Toronto, Ontario, Canada, area has a high degree of redundancy, but highways farther away from this metropolitan area have less redundancy and are thus more critical. Moreover, sections of Highway 401 located west of the greater Toronto area were found to be more critical—even though it carries lower truck volumes—than those located east of the greater Toronto area because of the lower redundancy in the western portion of the network. This measure has many potential applications in freight transportation planning, operations, and maintenance. Finally, with the cost of these disruptions quantified in dollars, one can then calculate the monetary benefits of potential transportation improvements for comparison (i.e., perform a cost–benefit analysis).

The freight transport industry is growing rapidly. There is great interest in improving processes and operations to make them more efficient and customer friendly. Freight transport research traditionally focuses on demand modeling, mode choice, and operational optimization. In contrast, this paper addresses a new field: development of a customer-oriented measurement system for freight and logistics companies. The measurement system was developed as part of research carried out for the Swiss National Railways Cargo Division and is based on professional expertise and data from stakeholders in all parts of the logistics chain. The proposed method is an indicator-based system built on a theoretical foundation, informed with the help of a workshop with experts, and validated with real-world data. The method can be used to measure the customer focus of logistics service providers over time and thus enable them to improve and benchmark a pool of logistics companies. The proposed method can also be customized to evaluate other specific customer needs by adjusting weights and indicators within its computational structure.

A real-world container drayage problem in which containers are transported between an intermodal terminal, a container terminal, and customer locations is considered. The problem was modeled as a multi-resource routing problem (MRRP) that included trucks, trailers, and containers. Given a fleet of trucks and trailers, the goal is to use these resources most efficiently to complete a number of given orders. Orders consisted of several tasks with time windows, such as picking up a container at the terminal, delivering it to a customer, and bringing the processed container back. A challenging aspect of this problem is the management of trailers, which are required to transport the containers. Here, the compatibility between container types and trailer types must be considered. Thus, the decision of which trailer should be attached to which truck depends on the containers that must be transported, the day of availability of trailers, and the toll costs of the truck and trailer combination on the highways. This paper presents an efficient way to model this problem and proposes a metaheuristic approach based on a variable neighborhood search. It uses a compact solution representation and tailored neighborhood structures to reduce the search space. Classical MRRP neighborhood structures, as well as problem-specific ones, were used in combination and contributed to the overall success. The results show that the given real-world problem can be solved efficiently, and it can be shown that with proper planning, the utilization of resources can be increased.

This paper presents the findings of a study recently conducted in Florida to quantify freight users’ willingness to pay (WTP) for the improvement of transportation-related attributes, particularly reliability. A stated preference survey was developed and administered between January and May 2016. The survey collected responses from 150 shippers, carriers, and forwarders. Econometric models, including mixed and multinomial logit models, were developed to estimate the users’ WTP and to investigate the presence of user heterogeneity. The value of time and the value of reliability were estimated separately for the various user groups. The results indicated that carriers showed the lowest WTP when their WTP was compared with that of other freight users. Shippers without transportation—that is, shippers who contracted out their shipping— exhibited more interest in reducing travel time savings, whereas shippers with transportation showed more sensitivity to reliability. Preference heterogeneity was also explored by commodity group and product type. The results confirmed the findings from past studies and showed significant differences in WTP values when the sources of heterogeneity were considered. This paper contributes to the literature by providing empirical evidence of the quantification of the value of reliability in road freight transportation and the impacts of user heterogeneity. The study results will help advance understanding of the impacts of the performance of transportation systems on the freight industry.

This paper analyzes the concentration of warehouses and distribution centers (W&DCs) in five counties in Southern California between 1998 and 2014, and it explores spatial relationships between W&DCs and other industry sectors through centrographic and econometric modeling techniques. Furthermore, the authors estimate factors that explain the concentration of W&DCs in the area. The analyses used aggregate establishment, employment, and other socioeconomic data for different industries, complemented with transportation-related variables. The results confirm the existence of logistics sprawl, although the analyses indicate that this trend did not continue to increase after 2007. Additional results follow: (a) W&DCs showed a lower spatial correlation compared with other industries, (b) the locations of the weighted geometric center shifted slightly differently for the W&DC industry and within its subindustries, (c) concentration levels for some subindustries were much lower than for the aggregated W&DC industry, and (d) the number of W&DCs could be explained by the number of establishments in the manufacturing and transportation service industries, proximity to highways and intermodal facilities, the number of W&DCs and accommodation and food services in neighboring zip codes, population, the number of adults using public transit, and per capita income.

This research outlined and demonstrated an approach and methodology for assessing domestic truck traffic suitable for diversion to intermodal truck to rail shipping. The research was spurred by the following factors: transportation-related findings from the Wisconsin Economic Development Corporation's Wisconsin Economic Future Study: Statewide and Regional Analysis; a general interest in intermodal diversion from federal, state, and local transportation agencies (to alleviate roadway congestion, reduce road deterioration, and reduce transportation emissions and pollutants); and seemingly constant commentary from the state's private-sector freight stakeholders seeking increased inter-modal services. Estimates of diverted loads from trucking to truck-to-rail intermodal vary, depending on the associated cost savings, the commodity being shipped, and the distance between origin and destination. Model results suggest that nonmonetary incentives would be necessary for large volumes of freight to divert.

Crowdsourced shipping presents an innovative shipping alternative that is expected to improve shipping efficiency, increase service, and decrease cost to the customer, and such shipping promises to enhance the sustainability of the transportation system. This study collected data on behavioral responses to choose from available crowdsourced shipping jobs. The goal of the study was to measure the potential willingness of individuals to change status from pure commuters to traveler–shippers. In particular, the study quantified potential crowdsourced shippers’ value of free time, or willingness to work (WTW) in a hypothetical scenario in which crowdsourced shipping jobs were available in a variety of settings. This WTW calculation is unique compared with the traditional willingness to pay (WTP) in that it measured the trade-off of making a profit and giving up time instead of spending money to save time. This work provides a foundation to analyze the application and effectiveness of crowdsourced shipping by exploring the WTW propensity of ordinary travelers. The analysis was based on a newly developed stated preference survey and analyzed choice across three potential shipping jobs and the option to choose none of the three (i.e., the status quo). Results showed that the experiment was successful in recovering reasonable WTW values that are higher than the normal WTP metrics. The results also identified many significant sociodemographic variables that could help crowdsourced shipping companies better target potential part-time drivers.

Behavioral freight transportation modeling is an approach to enhance the quality of freight and logistics policy assessments. Outsourcing of freight activities is one of the essential decisions that firms make. Such decisions are influenced by factors such as economic conditions, competition, the industry, firm strategies, and firm characteristics. In this paper, the authors introduce a set of discrete choice models—binary and multinomial logit—that quantify the effect of some of these factors on outsourcing of the freight-related activities of goods production and logistics for Canadian manufacturers. The models were estimated with the use of data from the Survey of Innovation and Business Strategy obtained from Statistics Canada. The models consider firm characteristics (e.g., employment, number of products or services, and supplier locations), economic conditions, and local and international competition. The models also show the influence of the use of innovation and advanced technologies and measure the impact of government support programs for businesses (e.g., government grants) on outsourcing decisions. Models that explain international freight outsourcing are also presented. Model results highlight that freight outsourcing culture differs from one industry to another. Firms that use government training programs are more likely to outsource freight operations locally, whereas those that use government grants outsource their freight activities internationally. Model validation indicated acceptable predictive capabilities. Simplified models that were based on industry classification, location, and firm size were estimated and are to be used for future microsimulation purposes.

A major issue in freight modeling is the interrelationship between logistics choices that can be seen as a learning process that shippers or carriers undertake to optimize their logistics process, with the aim of minimizing their cost, maximizing their level of service, or both. This study looked at the interrelated decisions of vehicle type choice and shipment size in urban freight transportation by formulating a model that considered (a) the nature of these two dependent variables via a joint discrete–continuous model, (b) the correlation between the two decisions via a copula-based approach, (c) the differences in decision making between carriers and shippers via the estimation of two models, and (d) the relaxation of the assumption of pure utility maximization via a hybrid utility–regret specification. Results show differences between shippers’ and carriers’ preferences. These differences were logical because many urban shippers owned an efficient fleet of commercial vehicles, whereas carriers evaluated alternatives to maximize their aggregated utility and minimize their direct costs. Results also show the importance of considering jointly the two decisions as well as the relevance of using a hybrid utility–regret formulation for the cost. Practical findings emerge from the model: (a) when faced with night delivery and intercity trips, carriers were more likely to use heavier vehicles and more voluminous shipments, whereas smaller shipments were preferred during the afternoon peak hour; (b) urban shippers tended to deliver larger shipments during night by light trucks but preferred trailers for longer distances; and (c) commodity types played a role in these joint decisions because some commodities were more likely to be transported by for-hire carriers and others were more likely to be transported by shippers.

Understanding truck activity is an essential component of strategic freight planning and programming. However, recent studies have revealed a significant void in the availability of detailed truck activity data. Although some existing detectors are capable of providing truck counts by axle configuration, higher-resolution data that indicate truck body configuration, industry served, and commodity carried cannot be obtained from existing sensors. This paper presents the newly developed Truck Activity Monitoring System, which leverages existing in-pavement traffic sensors to provide truck activity data in California. Existing inductive loop detector sites were updated with inductive signature technology and advanced truck classification models were implemented to provide detailed truck count data with more than 40 truck body configurations. The system has been deployed to more than 90 detector locations in California to provide coverage at state borders, regional cordons, and significant metropolitan truck corridors. An interactive geographic information system website provides users with advanced visual analytics and access to archived data across all deployed locations. The case studies presented in this paper demonstrate the potential of the data obtained from this system in analyzing and understanding current and historical industry-specific truck activity.