Dr. Cristiam Gil

The freight transport sector is a major contributor to greenhouse gas emissions and a central focus of national climate strategies aimed at meeting Nationally Determined Contributions (NDCs) under the Paris Agreement. The transition to electric trucks offers a viable pathway to reduce emissions; however, large-scale deployment depends on substantial planning and investment in charging infrastructure, particularly in emerging economies where freight activity is highly concentrated and public resources are constrained. This paper proposes a data-driven national framework to support policy decisions on the location, sizing, and phased deployment of electric freight charging infrastructure, using Colombia as a case study.
The methodology integrates real-world freight traffic data with network structure analysis, staged implementation planning, and capacity system modelling based on queuing theory. It explicitly considers fixed and variable infrastructure costs, charging power levels, the number of charging points, land requirements, and service reliability. Priority regions and corridors are identified according to freight flow intensity and network connectivity, enabling governments to target investments where utilisation and emissions reduction potential are highest. A phased rollout strategy supports gradual deployment, reducing fiscal risk and electricity grid pressure while maintaining adequate service levels.
Results show that strategically located, high-capacity charging stations along a limited number of high-demand, bidirectional corridors can serve more than half of national light and medium-duty freight activity while significantly reducing land use and system-wide infrastructure costs. The framework provides implementation-oriented guidance for policymakers designing freight electrification roadmaps and infrastructure investment strategies.

The Colombian road freight transport sector is a major contributor to greenhouse gas (GHG) emissions and plays a critical role in achieving the country’s net-zero target by 2050. While technological solutions such as vehicle electrification and alternative fuels are essential, substantial mitigation potential also lies in improving operational efficiency. A persistent source of inefficiency is the prevalence of empty trips, cargo-free freight movements that increase logistics costs, reduce fleet utilisation, and generate avoidable emissions.
This study addresses empty trips from a national, system-level planning perspective by introducing a spatio-temporal synchronisation model that identifies opportunities to coordinate independent freight movements travelling in opposite directions based on temporal and spatial compatibility. By relying on aggregated information rather than firm-level routing optimisation, the model enables scalable coordination across the freight network without requiring centralised control.
Two algorithms were developed to estimate current empty-trip rates from observed trip sequences and the minimum achievable rate under ideal synchronisation. Applied to more than 7.99 million trips from Colombia’s National Freight Registry, the results indicate that synchronisation could reduce empty kilometres by up to 39.34% for light and medium-duty vehicles and 37.37% for heavy-duty vehicles, corresponding to an estimated annual reduction of 130,470 tons of CO2e.
The study also introduces the concept of structural residual empty trip revenue (SRETR) to assess the economic potential of empty trips that remain unavoidable under optimal coordination. These insights support policymakers in prioritising interventions, addressing regional freight imbalances, and integrating operational efficiency measures into national decarbonisation strategies.

This study introduces a simulation-based analysis of the decarbonization options for the road freight transport sector. It focuses on exploring the impact of operational and management measures on fleet renewal strategies aimed at achieving net zero goals by 2050. The proposed approach integrates current and planned future policy changes, operational practices, and technology renewal into the modeling process to offer a macro-level perspective on the decarbonization challenge. Specifically, the proposed modeling approach takes into account the reduction of empty trips, the optimization of cargo consolidation, and the promotion of eco-driving practices based on national freight transport data (i.e. covering more than 7.99 million trips). The proposed approach examines the effect of introducing contemporary vehicle technologies, such as new diesel vehicles (EURO VI or higher), new natural gas vehicles (EURO VI or higher), electric vehicles and hydrogen vehicles, as feasible replacements for aging vehicles powered by conventional fossil fuels. The adoption of these cleaner and newer technologies demonstrates the potential for emission reductions of up to 13% (2,070,000 tons CO2e) by 2030 and 47% (13,232,000 tons CO2e) by 2050. In addition, the results obtained from this research can serve as an exemplary case study for other emerging economies.

This case study addresses the problem of empty container repositioning (ECR) in the Colombian context at a regional scale. The research was motivated by the massive empty container congestion in 2022 in specific nodes of the logistics network. A Mixed methods approach is proposed in this research applying qualitative and quantitative methods that aim to clarify the causes of inefficiency in empty repositioning and to formulate improvement strategies. Street-turn has proven to be a strategy to increase the efficiency in the ECR system. A matching algorithm is developed to pair empty containers in inland destinations with export loads, to achieve a more efficient utilization of trucks in the network. Despite the significant container trade imbalance, the optimization model results confirm significant cost savings and reduction of empty trips of up to 50% for RFT between Colombia's two main ports and their principal hinterland regions. The research also identifies that the actors involved in the ECR system lack incentives to deepen their collaboration, which represents a significant barrier to the implementation of street-turn.

We study the bus synchronization timetabling problem (BST) including bus dwelling times, motivated by the night shift of the transit system operating in Santiago-Chile. The operation at night under a frequency-setting modality could become inefficient, as at night the frequencies must be low under this type of policy, which may result in long waiting times and poor service quality for passengers. To address these inefficiencies, the authorities have required that all of their night services will operate according to fixed timetables considering the possibility of providing coordinated transfers between certain lines to reduce waiting times in night transfers. For this purpose, we propose a mixed integer programming model to define the specific timetables and the duration of the vehicle dwelling periods, taking the line frequencies as given. The model includes all the specifications and operation details of the real system, with a special focus on the synchronization of transfers at stops, pursuing the maximization of successful encounters through the inclusion of dwelling times as optimization variables together with the departure times of each line. The model was strengthened by adding different sets of valid inequalities. The performance is analysed in a real case study of 94 lines comprising 48 services and 14 transfer zones, showing an increase of around 73% in the number of encounters of the most flexible model with respect to the base case operation.