As connections between customers and suppliers become increasingly complex, the design of transport networks is under growing pressure to achieve efficiency. In response to this challenge, this paper proposes a service network design that takes into account both the forward flows from a production facility to a customer and the returning flows from customers to the production facility. The design allows for products to be shipped directly or to be consolidated and sorted at a depot. To assess the benefits of optimising both the forward and returning flows simultaneously in terms of network costs, the paper formulates an arc-based model and applies it to a real-life case. The results indicate that the network costs can be reduced by optimising simultaneously, as depots are built in the optimal locations and with the right capacity. This contrasts with the sequential method, which does not account for the return flows and can lead to suboptimal depot placement.

Understanding the logistic determinants of freight trips is an important goal in freight transport modeling. Freight shipments move between nodes in the supply chain for different logistic purposes, including production, storage, transshipment, and consumption. A key problem with data availability is that databases generally do not identify these purposes, given the commercial sensitivity of the data. In addition, including information on senders and receivers of the shipments is often prohibitively costly. Therefore, one of the challenges of transport data analysis is to identify freight trip purposes using data fusion, linking information about the main function of logistics nodes to trips in existing databases. This paper proposes a data fusion approach to enrich big truck shipment databases with firm registry data. We use the national freight shipment micro-database from the Netherlands which includes shipment, vehicle, and tour information. Although our presentation here uses formats and methods of accounting for freight data used in the Netherlands, it can be readily replicated for conditions in other countries, as long as similar data sets on shipment data and firm registry are available. The enriched, new database contains transport and firm data for more than 2 million observed trips with information on the vehicle used, shipments carried, and sender/receiver firm. An initial descriptive analysis provides unique empirical insights into the logistic determinants of freight trips. These include the share of national trips that use intermediate nodes, typical changes in shipment sizes, and the role of distribution centers for (de)consolidation of shipments.

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Crowdsourced shipping or crowdshipping is a promising solution to sustainable parcel delivery, owing to the potential to consolidate freight trips with preexisting passenger trips. Previous literature focuses on these consolidation benefits but does not address the possibility of new activity generation in crowdshipping. In this study, we investigate the willingness of private persons to accept shipments based on a newly generated home-based trip. We compare this to the choices of occasional carriers who build on the daily home–work commute to deliver parcels. Two stated preference experiments are conducted and a multinomial logit choice model and a latent class choice model are employed. These allow us to provide values of time of the occasional carriers, as an original contribution to the literature. The results show that commute-based carrier values of time are higher than those of home-based carriers. Concerning the trip generating power of crowdshipping, we find that low-income groups have a relatively high propensity to generate a home-based pickup and delivery trip. Finally, parcel lockers as delivery points positively influence acceptance of crowdshipping requests, as they allow for more flexibility in delivery times. Together, these results support the notion that crowdshipping can act as a potential trip generator in households.@en

Understanding preferences and behaviours in road freight transport is valuable for planning and analysis. This paper proposes a data-driven vehicle routing and scheduling approach for use as a descriptive tool to study road freight transport activities. The model developed seeks to capture planners’ or drivers’ preferences in order to reproduce observed road freight activities. The model is based on a parametrized time-dependent vehicle routing problem whose parameters can be estimated from a set of observed planned tours. We propose a Bayesian optimization technique for parameter estimation of the model. Empirical results show that the model can fit real-world data accurately and synthesize tour flows close to reality.

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A rapid increase in international trade volumes during recent years has led to truck congestion at container terminals’ gates during peak hours. This has consequences on the costs of trucking companies waiting long times behind the gates. Furthermore, it leads to containers arriving too late at the hinterland, a lower port attractiveness and high emissions of idling trucks. One possible policy to deal with this issue is to steer trucking companies towards night-time transport. However, this requires hubs with extended opening hours. The objective of this research is to investigate the implication of such policy further by evaluating and simulating the actors and their (inter)actions in the container transport system. This research uses a combination of qualitative (semi-structured interviews with actors) and quantitative (agent-based modelling, discrete event simulation, queueing theory and discrete choice modelling) methods to assess the impact of hubs with extended opening hours on freight transport under various scenarios. These scenarios compare possibilities for a chassis-exchange or a container hub and investigate the choice of trucking companies to use such hubs. Results determine that if a container hub would be introduced, other actors need to partly pay for trucking companies using the hub so that the hub capacity is fully used. This study reveals concrete recommendations on the type and location of the hubs to mitigate waiting times at terminals' gates and transport more containers per day. For example, a chassis-exchange hub can reduce the total waiting time at four container terminals by 420 hours per day. If trucking companies would invest in a chassis-exchange scenario, the profit is estimated to be around two to four thousand euros per day.

Making decisions regarding delayed line haul transport is a very demanding and complex process in E-commerce distribution centres. Automating this process can decrease decision-maker discussion and assessment time and, as a result, allow decision-makers to spend more time on other demanding tasks like sorting and distributing. Automation in this domain increases on-time deliveries and strengthens the E-commerce firms’ competitive position. However, such decisions involve experts’ knowledge, discussions among planners, and complex thought processes. Therefore, it is necessary to involve planners’ inclinations and preferences to automate their decisions. This paper proposes the Behavioural Artificial Intelligence Technology (BAIT) to automate expert decisions in delayed line haul deliveries. BAIT uses fundamental Discrete Choice theory under the hood to capture expert preferences effectively, and incorporates these into a decision-making tool. We use this method in a case study to replicate expert decisions in delayed line haul deliveries at DHL Express. The case study results show that BAIT can accurately replicate expert decisions.

This research is carried out to determine the effect of truck platooning on traffic flow using empirical data. This research contains two parts, data fusion, and statistical analysis. For data fusion, loop detector data, infrastructure information and weather data will be added to the original data set. For statistical analysis, the time gap distributions under different categories are analyzed to determine the performance of the truck platoon. Additionally, an analysis of the lane change behavior is included.

This thesis proposes a truck arrival shift (TAS) policy to control truck arrivals at seaport terminals. The aim is to reduce congestion at terminal gates which is caused by a lack of port-hinterland alignment. We proposed, developed, and applied a modeling framework to assesses the impact of the TAS policy for the use case of the Port of Rotterdam. This policy is designed for the implementation of a Time Slot Management System (TSMS) and takes the behavioural aspect of Truck Operating Companies (TOC) into account. The time of day preferences of TOC for container pick-ups are inferred from the exchange of information between port and hinterland stakeholders using discrete choice modelling (DCM). These preferences are used to shift truck arrivals and consequently reduce the high waiting time of trucks at terminals gates. To evaluate the effectiveness of the designed TAS policy, we developed a simulation platform that resembles terminal operations using discrete-event simulation (DES). For the allocation of trucks to a certain time period, a choice-based heuristic is designed to approximate the optimum configuration of the TAS policy. The optimum TAS policy design shows that significant gain can be obtained at a low shift rate. Moreover, a measurable amount of waiting time gain can be achieved by the application of the designed TAS policy.

Short-term traffic prediction is an important component of traffic management systems. Around logistics hubs such as seaports, truck flows can have a major impact on the surrounding motorways. Hence, their prediction is important to help manage traffic operations. However, The link between short-term dynamics of logistics activities and the generation of truck traffic has not yet been properly explored. This paper aims to develop a model that predicts short-term changes in truck volumes, generated from major container terminals in maritime ports. We develop, test, and demonstrate the model for the port of Rotterdam. Our input data are derived from exchanges of operational logistics messages between terminal operators, carriers and shippers, via the local Port Community System. We propose a feed-forward neural network to predict the next one hour of outbound truck traffic. To extract hidden features from the input data and select a model with appropriate features, we employ an evolutionary algorithm in accordance with the neural network model. Our model predicts outbound truck volumes with high accuracy. We formulate 2 scenarios to evaluate the forecasting abilities of the model. The model predicts lag and non-proportional responses of truck flows to changes in container turnover at terminals. The findings are relevant for traffic management agencies to help improve the efficiency and reliability of transport networks, in particular around major freight hubs.

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