In response to the inefficiencies in road freight transport, this research proposes an innovative collaboration index for carriers. The framework involves a collaboration decision tree that determines four collaboration modes at the trip level, which are then aggregated into a carrier-level evaluation using heuristic local optimization. This results in a collaboration index that quantitatively assesses the potential for collaboration among carriers based on operational data, filling a gap in partner selection processes. The study reveals that participants in horizontal collaboration achieved an average cost-saving rate of 14.4%, with the top 25% reaching 22.12%. While geographical similarity moderately correlates with the index, vehicle type does not show a linear relationship.
The significance of this work lies in its unique collaboration index formulated from operational data, which not only facilitates the identification of collaboration potentials but also provides insights for collaborative partner selection strategies.

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.

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.