This study aims to enhance people's mobility in the context of mobility restrictions in the Palestinian territories, West Bank. It aims to develop a comprehensive route planning model that prioritises safety and optimises travel time while also considering sustainability issues. Unlike previous research, which has often focused solely on traffic crashes and physical road considerations in safety route planning, this study addresses the gap by developing a comprehensive model that integrates new risk criteria including mobility restrictions and violent events. The methodology involves historical and real-time data collection and processing, machine learning-based travel time prediction, and route optimisation using Dijkstra's algorithm. The results highlight the significant impact of violent incidents on comprehensive risk scores, offering insights for proactive, sustainable measures. The waiting time prediction model performs strongly, with (R-squared) R2 values ranging from 80% to 92%. The developed route planning model provides three categorised routes under mobility restrictions-safest, fastest, and shortest-offering travellers sustainable and tailored options.

Route Planning under Mobility Restrictions in the Palestinian Territories

Giglio, Carlo
2024-01-01

Abstract

This study aims to enhance people's mobility in the context of mobility restrictions in the Palestinian territories, West Bank. It aims to develop a comprehensive route planning model that prioritises safety and optimises travel time while also considering sustainability issues. Unlike previous research, which has often focused solely on traffic crashes and physical road considerations in safety route planning, this study addresses the gap by developing a comprehensive model that integrates new risk criteria including mobility restrictions and violent events. The methodology involves historical and real-time data collection and processing, machine learning-based travel time prediction, and route optimisation using Dijkstra's algorithm. The results highlight the significant impact of violent incidents on comprehensive risk scores, offering insights for proactive, sustainable measures. The waiting time prediction model performs strongly, with (R-squared) R2 values ranging from 80% to 92%. The developed route planning model provides three categorised routes under mobility restrictions-safest, fastest, and shortest-offering travellers sustainable and tailored options.
2024
safety
risk
travel time
waiting time
random forest regression
Dijkstra's algorithm
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/363837
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