An approach to generate demand for freight deliveries in city areas with traffic restrictions

Abstract: Accurate modeling of travel demand is paramount for effective transportation planning and policy-making. The traditional four-step travel demand model, while a well-established framework, often struggles to capture the nuances of urban cargo delivery operations, particularly in compact, vehicle-restricted zones. Our study introduces a modified four-step approach that incorporates a probability-based trip origin assignment to enhance the realism and precision of demand simulation in such environments.
By calculating the probability of assigning a trip origin based on its proximity to the nearest entry point, the proposed model offers a more nuanced representation of trip distribution compared to conventional gravity-based models. This is particularly advantageous in urban areas with restricted traffic zones, where the distribution of delivery origins is significantly influenced by accessibility constraints.
A case study conducted in the historic district of San Sebastian (Spain), a well-defined urban area with limited vehicle access, demonstrates the efficacy of the proposed approach. The obtained simulation results highlight substantial discrepancies between the proposed method and the traditional gravity-based model, underscoring the importance of considering spatial constraints and accessibility factors.
The proposed travel demand model offers the following key advantages:
• by accounting for the influence of accessibility constraints, the model generates more realistic trip origin assignments, leading to improved predictions of traffic flows and congestion,
• the probability-based approach provides a more accurate representation of real-world behavior, particularly in urban areas with complex traffic patterns and diverse delivery scenarios,
• the developed model can be adapted to various urban contexts by adjusting the probability calculation based on specific local conditions, such as road network characteristics, traffic regulations, and delivery time windows.
Our research provides a foundational framework for further exploration of probability-based approaches in modeling urban cargo delivery demand. Future studies could expand the scope of the model by incorporating additional factors such as vehicle capacity constraints, delivery time windows, and alternative modes of transport. Additionally, integrating the proposed demand generation approach with existing traffic simulation tools could lead to more comprehensive and realistic assessments of urban logistics operations.