Economía de los vehículos vacíos de carga y logística colaborativa
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Date
2024
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Abstract
Los viajes vacíos representan una parte considerable del flujo de camiones interurbano, fluctuando del 15 % al 40 %, y afectan significativamente la eficiencia y sostenibilidad de las operaciones logísticas. Aunque algunos de estos viajes vac´ıos se deben a desequili brios en los flujos de carga, es comun encontrarlos en ambas direcciones de una ruta. Este fenómeno, conocido como el problema de la carga de retorno, contribuye a aumentar las tasas de congestion vial, accidentes y emisiones a nivel agregado, además de incrementar los costos para productores y despachadores, sin generar ganancias adicionales. Comprender y modelar los factores que influyen en el flujo de vehículos vacíos es crucial para una amplia variedad de decisiones, desde políticas públicas hasta la planificación de infraestructuras y la regulacion del transporte. Sin embargo, los modelos tradicionales no permiten abordar profundamente el problema de la carga de retorno. Este trabajo profundiza la comprension del flujo de vehículos vacíos mas allá de los desequilibrios de carga. Examina como la competencia, la tecnología, la estructura de la demanda y la diferenciacion de servicios de transporte afectan los flujos de carga, la presencia de vehículos vacíos, los precios de transporte y la cantidad de transportistas en equilibrio. El problema se aborda inicialmente mediante el desarrollo de un modelo microeconómico para un circuito cíclico simple. En este modelo, los transportistas conectan dos mercados y ofrecen servicios diferenciados, utilizando la misma tecnología. Los resultados del modelo muestran que el transporte de carga no solo produce vehículos vacíos debido al desbalance en el transporte de cargas entre los pares de origen y destino, sino tambien a causa de los patrones estocasticos de la demanda. Cuando un nuevo transportista entra al mercado, contribuye a aumentar el flujo de vehículos vacíos debido al efecto de robo de negocios. El modelo propuesto permite mostrar que los viajes vacíos perturban el equilibrio de precios y transportistas, creando una brecha entre el equilibrio de y los valores socialmente optimos. La coordinación de las operaciones de los transportistas, permite minimizar el flujo de vehículos vacíos, acercando el equilibrio del sistema al resultado socialmente óptimo. En segundo lugar, ampliamos el modelo anterior para analizar el equilibrio de míltiples mercados geograficamente separados e interconectados mediante un sistema que integra diferentes modos de transporte, como carreteras y ferrocarriles. Este modelo captura las decisiones estrategicas de despachadores y transportistas, incluyendo la selecció de rutas, modos de transporte y negociacion de tarifas, ofreciendo una visión integral del equilibrio en el mercado de transporte. Examina como el movimiento de vehículos vacíos influye en los flujos de carga, los costos del transporte y la dinamica competitiva entre operadores, aspecto clave para comprender las implicaciones de las decisiones relacionadas con la capacidad y la asignacion de recursos en diversos entornos multimodales. El modelo puede validarse experimentalmente estimando los parametros de demanda a través de encuestas y calibrando los flujos de vehículos y carga con datos obtenidos en estacionesde pesaje. Esto permite considerar factores como el peso, volumen, tipo de mercancía y requisitos específicos de transporte. Ademas, permite simular el impacto de regulaciones o cambios en la infraestructura, asistiendo a los tomadores de decisiones en la mitigacion de riesgos y la explotacion de oportunidades económicas. Finalmente, la aplicacion del modelo requiere calibrar con precisión los flujos proyectados para replicar los observados, lo que implica estimar los flujos de vehículos, con y sin carga, en diversas ubicaciones. Ante la falta de mediciones sistematicas, se desarrollo un enfoque basado en datos de Estaciones de Pesaje en Movimiento (WIM) y aprendiza je supervisado para estimar el Trafico Promedio Diario Anual (AADT) y el peso de los vehículos. Este metodo mejora la eficiencia, proporcionando predicciones precisas con menos datos, lo que amplía su utilidad en diversas ubicaciones estrategicas.
Empty trips account for a significant portion of interurban truck flows, ranging from 15 % to 40 %, and substantially impact the efficiency and sustainability of logistics operations. While some of these empty trips result from imbalances in freight flows, they are commonly observed in both directions of a route. This phenomenon, known as the backhaul problem, contributes to increased road congestion, accidents, and aggregate emissions while driving up costs for producers and shippers without generating additional revenue. Understanding and modeling the factors influencing empty vehicle flows is critical for many decisions, from public policy to infrastructure planning and transport regulation. However, traditional models fail to address the complexities of the backhaul problem adequately. This study advances the understanding of empty vehicle flows beyond simple freight imbalances. It explores how competition, technology, demand structure, and differentiatedtransport services affect freight flows, the prevalence of empty vehicles, transport costs, and the equilibrium number of carriers. The problem is initially addressed by developing a microeconomic model for a simple cyclic circuit, where carriers connect two markets and offer differentiated services using the same technology. Model results indicate that freight transport generates empty vehicles due to imbalances in origin-destination freight flows and stochastic demand patterns. When a new carrier enters the market, it increases the flow of empty vehicles through a business-stealing effect. The proposed model demonstrates that empty trips disrupt price and carrier equilibria, creating a gap between free-entry equilibrium and socially optimal values. Coordinating carrier operations minimizes empty vehicle flows, moving the system equilibrium closer to the socially optimal outcome. Subsequently, the model is extended to analyze the equilibrium across multiple geographically separated markets interconnected through a system integrating different trans port modes, such as roadways and railways. This extended model captures the strategic decisions of shippers and carriers, including route selection, mode choice, and rate nego tiations, providing a comprehensive view of equilibrium in the transport market. It examines how empty vehicle movements influence freight flows, transport costs, and com petitive dynamics among operators, a crucial aspect for understanding the implications of capacity and resource allocation decisions in multimodal environments. The model can be validated experimentally by estimating demand parameters through surveys and cali brating vehicle and freight flows using data from weigh stations. This approach accounts for factors such as weight, volume, commodity type, and specific transport requirements, enabling the simulation of the impacts of regulations or infrastructure changes to support decision-makers in mitigating risks and seizing economic opportunities.Finally, the model’s application requires precise calibration of projected flows to repli cate observed patterns, involving the estimation of loaded and empty vehicle flows across various locations. Given the lack of systematic traffic measurements, an approach was de veloped using data from Weigh-in-Motion (WIM) stations and supervised learning techni ques to estimate Annual Average Daily Traffic (AADT) and vehicle weights. This method enhances efficiency, delivering accurate predictions with fewer data inputs, thereby expan ding its applicability to various strategic locations.
Empty trips account for a significant portion of interurban truck flows, ranging from 15 % to 40 %, and substantially impact the efficiency and sustainability of logistics operations. While some of these empty trips result from imbalances in freight flows, they are commonly observed in both directions of a route. This phenomenon, known as the backhaul problem, contributes to increased road congestion, accidents, and aggregate emissions while driving up costs for producers and shippers without generating additional revenue. Understanding and modeling the factors influencing empty vehicle flows is critical for many decisions, from public policy to infrastructure planning and transport regulation. However, traditional models fail to address the complexities of the backhaul problem adequately. This study advances the understanding of empty vehicle flows beyond simple freight imbalances. It explores how competition, technology, demand structure, and differentiatedtransport services affect freight flows, the prevalence of empty vehicles, transport costs, and the equilibrium number of carriers. The problem is initially addressed by developing a microeconomic model for a simple cyclic circuit, where carriers connect two markets and offer differentiated services using the same technology. Model results indicate that freight transport generates empty vehicles due to imbalances in origin-destination freight flows and stochastic demand patterns. When a new carrier enters the market, it increases the flow of empty vehicles through a business-stealing effect. The proposed model demonstrates that empty trips disrupt price and carrier equilibria, creating a gap between free-entry equilibrium and socially optimal values. Coordinating carrier operations minimizes empty vehicle flows, moving the system equilibrium closer to the socially optimal outcome. Subsequently, the model is extended to analyze the equilibrium across multiple geographically separated markets interconnected through a system integrating different trans port modes, such as roadways and railways. This extended model captures the strategic decisions of shippers and carriers, including route selection, mode choice, and rate nego tiations, providing a comprehensive view of equilibrium in the transport market. It examines how empty vehicle movements influence freight flows, transport costs, and com petitive dynamics among operators, a crucial aspect for understanding the implications of capacity and resource allocation decisions in multimodal environments. The model can be validated experimentally by estimating demand parameters through surveys and cali brating vehicle and freight flows using data from weigh stations. This approach accounts for factors such as weight, volume, commodity type, and specific transport requirements, enabling the simulation of the impacts of regulations or infrastructure changes to support decision-makers in mitigating risks and seizing economic opportunities.Finally, the model’s application requires precise calibration of projected flows to repli cate observed patterns, involving the estimation of loaded and empty vehicle flows across various locations. Given the lack of systematic traffic measurements, an approach was de veloped using data from Weigh-in-Motion (WIM) stations and supervised learning techni ques to estimate Annual Average Daily Traffic (AADT) and vehicle weights. This method enhances efficiency, delivering accurate predictions with fewer data inputs, thereby expan ding its applicability to various strategic locations.
Description
Tesis (Doctor en Ciencias de la Ingeniería)--Pontificia Universidad Católica de Chile, 2024.
Keywords
Carga de retorno, Vhículos vacíos, Externalidades, Equilibrio de Bertrand y Cournot, Aprendizaje de máquinas, Oligopolio logit, Backhaul, Empty vehicles, Externalities, Bertrand and Cournot equilibrium, Machine learning, Logit oligopoly