International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 08 | Aug 2025
p-ISSN: 2395-0072
www.irjet.net
Optimizing Tourist Landmark Visits in Bangalore Using a Special Case of TSP Jay Jeswani1 1National Public School Indiranagar, Bengaluru, Karnataka 560008
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Abstract - This paper presents a novel approach to
nature of urban environments. The Travelling Salesman Problem (TSP) has been extensively studied in operations research, but its application to tourism requires significant modifications to handle temporal constraints and realworld complexities. The emergence of smart city initiatives and IoT infrastructure provides new opportunities to develop sophisticated optimization models that can adapt to changing conditions.
optimizing tourist landmark visit sequences in Bangalore using a Time-Dependent Travelling Salesman Problem (TDTSP) with Soft Time Windows formulation. The research addresses the challenge of creating efficient tourist itineraries that account for temporal variations in crowd levels, dynamic travel times due to traffic variability, and flexible visit timing preferences. Our model incorporates temporal cost functions based on crowd density patterns, dynamic edge weights reflecting traffic conditions, and soft time window constraints that allow schedule flexibility with penalty costs. The methodology is applied to four major Bangalore landmarks: Brigade Road (KLING), Chinnaswamy Stadium, Bangalore Palace, and Lalbagh Garden. Analysis of temporal patterns reveals significant day-of-week variations, with Sunday showing peak crowding levels (24-55% busy) and Monday consistently showing minimum crowding (12-18% busy) across all landmarks. Travel time analysis demonstrates variability factors of 2.3-2.7x between minimum and maximum durations, emphasizing the need for time-dependent optimization. The proposed algorithm considers these temporal variations to generate optimal tourist routes that adapt to changing conditions. This work contributes to the growing field of smart tourism systems and provides a practical framework for tourism optimization in metropolitan areas.
This research addresses the gap between classical TSP formulations and practical tourism optimization requirements by proposing a Time-Dependent TSP with Soft Time Windows (TD-TSP-STW) model. The approach incorporates temporal cost functions based on crowd density patterns, dynamic edge weights reflecting traffic variability, and flexible time window constraints that balance efficiency with tourist preferences.
1.1 Research Motivation Urban tourism faces several critical challenges that traditional optimization approaches cannot adequately address. Temporal variations in crowd levels significantly impact tourist experience, with popular landmarks experiencing dramatic changes in visitor density throughout the day and week. Dynamic traffic conditions in metropolitan areas like Bangalore create substantial variations in travel times between attractions, making static routing algorithms ineffective. Tourist preferences for flexible scheduling require optimization models that can accommodate soft constraints rather than rigid time windows.
Key Words: Travelling Salesman Problem, Tourism Optimization, Time-Dependent Routing, Soft Time Windows, Smart Tourism, Bangalore
1.INTRODUCTION
The COVID-19 pandemic has further emphasized the importance of crowd-aware tourism planning, making temporal optimization not just a convenience but a necessity for safe and enjoyable tourist experiences. Smart tourism systems that can dynamically adapt to changing conditions represent the future of urban tourism management.
The tourism industry has experienced unprecedented growth in recent years, with urban destinations like Bangalore attracting millions of visitors annually. As the Silicon Valley of India, Bangalore combines rich historical heritage with modern technological infrastructure, creating unique opportunities for smart tourism applications. However, tourist route planning remains a complex optimization challenge, particularly when considering temporal variations in crowd levels, traffic conditions, and visitor preferences.
1.2 Literature Review The application of TSP variants to tourism optimization has gained significant attention in recent years. Gavalas et al. [6] provided a comprehensive survey of algorithmic approaches for tourist trip design problems, highlighting
Traditional tourist route planning approaches often rely on static models that fail to account for the dynamic
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