Travel Path Research Articles

Research on a Low-Cost High-Precision Positioning System for Orchard Mowers

To regulate the energy flow in orchard ecosystems and maintain the environment, weeding has become a necessary measure for fruit farmers, and the use of automated mowers can help reduce labor costs and improve the economic efficiency of orchards. However, due to the complexity of the geographic and spatial environment of the orchard, in particular, the loose and undulating road surface, the interference of satellite signals by large trees, etc., which decreases the positioning accuracy and stability of the positioning system of the mower, and the high cost of the sensor also affect the popularization of intelligent mowers for these applications. To address the above problems, this paper constructs a positioning system through a low-cost global navigation satellite system (GNSS), inertial measurement unit (IMU), and odometry, and utilizes the Kalman filter algorithm based on the error state for a combined GNSS/IMU positioning so that the inertial navigation system can maintain a more accurate positioning when the GNSS signals are poor. Considering the side-slip and error accumulation problems of the odometry of the traction mower, the combined GNSS/IMU positioning information is used to optimize the odometry model and improve the navigation and positioning accuracy. To reduce the measurement error of the IMU and the problem of error accumulation, this paper utilizes the nonholonomic constraint (NHC) of a lawn mower to suppress the dispersion of IMU measurement errors and constructs periodic and nonperiodic zero-velocity updating (ZUPT) strategies in combination with the travel paths of lawn mower navigation operations in the region to update the IMU data to improve the positioning accuracy and stability of the positioning system. The experiments show that the average error of the constructed positioning system is controlled within 0.15 m, the maximum error is maintained at approximately 0.3 m, and the positioning system constructed by using low-cost sensors can achieve a positioning accuracy similar to that of the differential global navigation satellite system (DGNSS), which is beneficial for the promotion and application of intelligent mowers in orchards.

A movement-aware measure for trajectory similarity and its application for ride-sharing path extraction in a road network

Recognizing common travel paths of crowds in a road network is valuable for understanding human mobility patterns and developing intelligent ride-sharing services. To achieve this, it is critical to measure the similarity of their trajectories. Although many measures have been proposed in the past decades, they often ignore movement consistency, exhibit one or more deficiencies in the face of noise and misaligned trajectories, or require extra parameters to tune predictions. In this paper, we propose an improved similarity measure called the directed segment path distance (DSPD), which considers the spatial proximity and movement consistency of trajectories. By integrating the spatial proximity distance and moving direction similarity between trajectories, the DSPD is a competitive parameter-free similarity measure that can effectively distinguish trajectories with different movement characteristics. To verify the effectiveness of the DSPD, we conducted a quantitative comparative study between the DSPD measure and 11 state-of-the-art trajectory similarity measures on six simulated trajectory datasets and applied the DSPD to two typical application scenarios: trajectory clustering for road network generation and retrieving common trajectories for ride-sharing path planning. The results demonstrate the effectiveness, robustness, and superiority of the DSPD and its great potential in trajectory search, clustering, and classification.

The influence of progeny-Parents family travel on the well-being of the elderly in filial piety culture.

Grounded in the cultural context of Chinese filial piety, this study employs structural equation model to analyze survey data from elderly participants. It explores the effect and path of progeny-parents family travel on the elderly's sense of well-being and examines the mediating roles of generational interaction, optimistic emotion, and psychological resilience. The findings indicate that progeny-parents family travel positively influences the well-being of the elderly, with generational interaction, optimistic emotion, and psychological resilience serving as intermediary roles. Theoretically, this study enriches the localized perspective of family travel's psychological and behavioral impact on the elderly. It elucidates the spillover effects of family travel within the framework of filial piety culture, delineates the mechanisms by which family travel enhances elderly well-being, and offers theoretical insights for businesses to develop customized family travel products and services.

Non-additive network pricing with non-cooperative mobility service providers

This study addresses a mobility network pricing problem in a competitive environment. We consider a multimodal transportation network where the links are operated by multiple profit-maximizing, mobility service providers (MSPs). We take the perspective of a network regulator that aims to increase ridership in a target mobility network by providing non-additive, path-based subsidies to travelers. We model paths’ attractiveness using generalized cost functions that combine path travel time and path cost, and we use linear elastic travel demand functions to capture the proportion of demand served by a path. MSPs are non-cooperative and adjust link fares according to the subsidy policy implemented by the regulator. The goal of the network regulator is to solve a budget-constrained mobility network pricing problem under MSP competition. This game-theoretical framework is modeled as a single-leader multi-follower game (SLMFG) wherein the leader player represents the network regulator and multiple follower players represent the MSPs. We conduct a theoretical analysis of this SLMFG by identifying necessary and sufficient conditions for the existence of solutions to the parameterized generalized Nash equilibrium problem (GNEP) that is played amongst MSPs. We show that this GNEP is jointly convex and we use this property to develop an exact numerical approach to solve the SLMFG based on customized branch-and-bound algorithms. Numerical results reveal the impact of MSP competition in this mobility network pricing problem and shed novel insights into the design of optimal path-based subsidy policies.

UAV-enabled software defined data collection from an adaptive WSN

AbstractUnmanned aerial vehicle (UAV)-based data gathering from wireless sensor networks is one of the recent research topics that has currently attracted research interest. One of the challenges for the UAV-aided WSN data collection efforts is to design an energy-efficient UAV/drone communication with arbitrarily dispersed ground sensors by improving the ground network structure. This paper aims to develop a technique titled UAV Fuzzy Travel Path' that supports UAV smooth path design and enables ground network topology shifting. A comprehensive UAV-based data collection model is proposed to enable dynamic orchestration/re-orchestration of wireless ground sensors to jointly improve network performance and UAV path fluidity. This provides a more flexible ground network framework that can be restructured based on network demands and UAV optimal paths, effectively allowing for a software-defined network concept. The main contribution of this work is the implementation of the software-defined wireless sensor network on the ground network that adaptably supports the movement of the UAV and enhances the communication network’s energy efficiency with a proposed latency analytical analysis via network orchestration/re-orchestration phases. The main significance of this research is in offering a flexible span for UAV path design than being fixed in one strict route for data gathering purposes. Four various simulation tools are employed for modelling and performance evaluation, namely MATLAB, CupCarbon, Contiki-Cooja and Mission Planner. The proposed software-defined ground network system demonstrates encouraging results in terms of network performance metrics including energy consumption of UAV versus ground sensor nodes energy usage, packet delivery rate, and the communication time of the ground orchestrated or/and re-orchestrated network.

Architecture dependent transport behavior of iron (0) entrapped biodegradable polymeric particles for groundwater remediation

Polylactic acid based spherical particles with three architectural variations (Isotropic (P1), Semi porous (P2), and Janus (P3)) were employed to encapsulate zero valent iron nanoparticles (ZVINPs), and their performance was extensively evaluated in our previous studies. However, little was known about their transportability through saturated porous media of varying grain size kept under varying ionic strength. In this particular study, we aimed to investigate the architectural effect of polymeric particles (P1–P3) on their mobility through the sand column of varying grain size in presence of mono, di, and tri-valent ions of varying concentrations (25–200 mM (millimoles)). As per column breakthrough experiments (BTCs) using various types of sands, amphiphilic Janus type (P3) particles exhibited the maximum transportability among all the tested particles, irrespective of the nature of the sand. Owing to the narrower travel path, sands with lower porosity (31%) delayed the plateau by shifting it to a higher pore volume with a minimum retention of iron (C/Co: 0.94 for P3) in the column. The impact of mono (Na+, K+), di (Ca2+, Mg2+), and trivalent (Al3+) ions on their transportability was progressively increased from P3 to P1, especially at higher ionic concentrations (200 mM), with P3 being the most mobile particles (C/Co:0.54 for Al3+). Among all the ions, Al3+ exhibited maximum hindrance to their mobility through the sand column. This could be due to their strong charge screening effect coupled with cation bridging complex formation with moving particles. Experimental results obtained from BTCs were found to be well-fitted with a theoretical model based on advection-dispersion equation, showing minimum retention for P3 particles. Overall, it can be inferred that encapsulation of ZVINPs inside Janus particles (P3) with a right balance of amphiphilicity and highly negative surface charge would be required to achieve considerable transportability through sand aquifers to target contaminants in polluted groundwater existing under harsh conditions (high ionic concentrations).

Wide-Angle Repeat Pass Bistatic SAR Interferometry: A Geometrical Introduction

Positioning a Synthetic Aperture Radar (SAR) receiver at a significant distance from an illuminator in a Low Earth Orbit (LEO) enables wide-angle repeat-pass bistatic interferometry. It’s crucial to define the positioning limits of the receiver with respect to the illuminator to ensure reasonable coherences, altitudes of ambiguity, and a suitable common wavenumber support. I extended the standard monostatic formula, which yields the change in the interferometric travel path due to the vertical baseline. In the wide-angle bistatic case, the range-azimuth coordinate system is no longer orthogonal and we must consider the full 2D vector nature of the LOS orthogonal components. The two vertical baselines and the horizontal baseline of the receiver are needed to parameterize the system. Their impact on the interferometric coherence is quantified and bistatic limits are seen to be more flexible than those in the monostatic case due to the combination of independent baselines. I estimated the coherence, and then extended the analysis to the wavenumber domain, to quantify the common spectral support due to the wavenumber shifts due to the three baselines. The analysis carried out geometrically allows for combining together the effects of the transmitter’s and receiver’s vertical baselines and thus representing the residual spectral fraction with two instead of three parameters. Finally, I examined the achievable spatial resolution of the position of the interferer obtainable from the delay of the arrival of the interference between the illuminator (supposed to be silent) and the receiver.

First train timetabling and passenger transfer routing problems in urban rail transit networks

This paper incorporates the shortest passenger travel paths into the first train timetabling problem in urban rail transit networks. We first propose a path-based first train timetabling problem, which is formulated as a mixed-integer nonlinear programming model to reduce the travel time for passengers. To solve this model, we present a tailored branch-and-bound combined with the timetable-based Dijkstra algorithm. To further enhance the efficiency of the exact algorithm, we design a rolling time heuristic algorithm as a substitute for the branch-and-bound algorithm. Then, we proved that the rolling time heuristic combined with the timetable-based Dijkstra algorithm achieves 97.86% and 99.39% reductions in CPU time while sacrificing only 0.007% and 0.057% of optimality gap by two numerical experiments. Finally, the results based on two test networks with different structures demonstrate that the path-based first train timetabling model effectively improves passenger travel efficiency. Specifically, the travel times are reduced by 744 mins and 761 mins, while the transfer waiting times are reduced by 37.87% and 58.91% compared to the existing timetables. Moreover, the results also demonstrate that loop line networks will increase the difficulty of timetabling and lead to many passengers taking detours. To further enhance the efficiency of commuting for passengers who take the first train, URT operators may consider relaxing the limitations on the departure time of the depot stations.

CITY TRAFFIC SIMULATION ON MICROSERVICES

Population growth and urbanization in Kazakhstan have led to significant traffic management problems in large urban areas. Initiatives such as intelligent road systems, real-time traffic information, and traffic management have been implemented to address these issues. However, congestion and delays remain as major problems. Effective traffic modeling is necessary to understand traffic patterns, identify bottlenecks, and implement effective solutions for reducing congestion. Microservice architecture has recently become a trend owing to its advantages, and the authors of this paper propose the use of microservices for urban traffic modeling to solve these problems. In this study, any moving object between two points: a car, a bicyclist, or a pedestrian – was presented as an actor. Each actor is represented as a microservice. They communicate their movements through city microservice. After sending their movements, a red line is displayed on the map, indicating their travel paths. This solution helps to simulate movement more realistically because each microservice is independent and can move randomly.

How Do Escape Distance Behavior of Broiler Chickens Change in Response to a Mobile Robot Moving at Two Different Speeds?

In poultry farming, robots are considered by birds as intruder elements to their environment, because animals escape due to their movement. Their escape is measured using the escape distance (ED) technique. This study analyzes the behavior of animals in relation to their ED through the use of a robot with two speeds: 12 rpm and 26 rpm. The objective is to understand whether the speeds cause variations in ED and their implications for animal stress. A broiler breeding cycle was analyzed (six weeks) through the introduction of the robot weekly. ED analyses were carried out on static images generated from footage of the robot running. The results indicate higher escape distance rates (p < 0.05) peaking midway through the production cycle, notably in the third week. Conversely, the final weeks saw the lowest ED, with the most significant reduction occurring in the last week. This pattern indicates a gradual escalation of ED up to the fourth week, followed by a subsequent decline. Despite RPM12 having shown low ED results, it did not show enough ED to move the animals away from their path of travel, causing bumps and collisions. RPM26 showed higher ED in all breeding phases, but showed ED with no bumps and collisions.

Integration of Efficient Techniques Based on Endpoints in Solution Method for Lifelong Multiagent Pickup and Delivery Problem

We investigate the integration of several additional efficient techniques that improve a solution method for the lifelong multiagent pickup-and-delivery (MAPD) problem to reduce the redundancy in the concurrent task execution and space usage of a warehouse map. The lifelong MAPD problem is an extended class of iterative multiagent pathfinding problems where a set of shortest collision-free travel paths of multiple agents is iteratively planned. This problem models a system in automated warehouses with robot-carrier agents that are allocated to pickup-and-delivery tasks generated on demand. In the task allocation to agents, several solution methods for lifelong MAPD problems consider the endpoints of the agents’ travel paths to avoid the deadlock situations among the paths due to the conflict of the endpoints. Since redundancies are found in the problem settings themselves and the concurrency of allocated tasks, several additional techniques have been proposed to reduce them in solution methods. However, there should be opportunities to investigate the integration of additional techniques with improvements for more practical solution methods. As analysis and an improved understanding of the additional solution techniques based on endpoints, we incrementally integrate the techniques and experimentally investigate their contributions to the quality of task allocation and the paths of the agents. Our result reveals significant complementary effects of the additionally integrated techniques and trade-offs among them in several different problem settings.

Understanding the route choice preferences of private and dock-based public bike users using GPS data in Seoul, South Korea

Analyzing route preferences is crucial for comprehending how individuals choose their travel paths, assessing road facilities, and anticipating upcoming traffic conditions. This study aimed to explore the factors influencing route selections made by private and public bike users and compare their decisions during peak and off-peak hours. Global positioning system (GPS) traces of 12,106 private bike users and 74,397 public bike users from a single weekday in Seoul, South Korea, were used. A map-matching process aligned the GPS data with a comprehensive cycling network encompassing bikeways, sidewalks, neighborhood roads, and their street attributes such as urban, park, and river streets. Choice sets composed of the shortest and 10th alternative routes were created to compare various road features with actual routes. By employing a path-size logit model to address overlapping issues among the routes, the results showed that buffered bike lanes separated from car flows positively influenced the route selection of bicyclists. Bike boulevards and shared sidewalk bikeways negatively impacted route decisions made by public bike users, with a more pronounced effect during peak hours. Furthermore, private bike users showed a decreased preference for routes in residential areas during peak hours, while an increased preference during off-peak hours. Moreover, public bike users preferred selecting routes in the presence of shopping-stores or amenities as well as public transit stations, with these effects being more pronounced during peak hours. These findings are expected to contribute to capturing valuable insights into the route preferences of bicyclists for the improvement of bicycle infrastructures.

Dynamic path planning via Dueling Double Deep Q-Network (D3QN) with prioritized experience replay

Path planning is a key requirement for mobile robots employed for different tasks such as rescue or transport missions. Conventional methods such as A* or Dijkstra to tackle path planning problem need a premise map of the robot's environment. Nowadays, dynamic path planning is a popular research topic, which drives mobile robots without prior static requirements. Deep reinforcement learning (DRL), which is another popular research area, is being harnessed to solve dynamic path planning problem by the researchers. In this study, Deep Q-Networks, which is a subdomain of DRL are opted to solve dynamic path planning problem. We first employ well known techniques Double Deep Q-Networks (D2QN) and Dueling Double Deep Q-Networks (D3QN) to train a model which can drive a mobile robot in environments with static and dynamic obstacles within 3 different configurations. Then we propose D3QN with Prioritized Experience Replay (PER) extension in order to further optimize the DRL model. We created a test bed to measure the performance of the DRL models against 99 randomly generated goal locations. According to our experiments, D3QN-PER method performs better than D2QN and D3QN in terms of path length and travel time to the goal without any collisions. Robot Operating System and Gazebo simulation environment is utilized to realize the training and testing environments, thus, the trained DRL models can be deployed to any ROS compatible robot seamlessly.

Ship Global Traveling Path Optimization via a Novel Non-Dominated Sorting Genetic Algorithm

Due to the intensification of economic globalization and the impact of global warming, the development of methods to reduce shipping costs and reduce carbon emissions has become crucial. In this study, a multi-objective optimization algorithm was designed to plan the optimal ship route for safe cross-ocean navigation under complex sea conditions. Based on the traditional non-dominated sorting genetic algorithm, considering ship stability and complex marine environment interference, a non-dominated sorting genetic algorithm model considering energy consumption was designed with the energy consumption and navigation time of the ship as the optimization objectives. The experimental results show that although the proposed method is 101.23 nautical miles more than the large ring route, and the voyage is increased by 10.1 h, the fuel consumption is reduced by 92.24 tons, saving 6.94%. Compared with the traditional genetic algorithm, the voyage distance and time are reduced by 216.93 nautical miles and 7.5 h, and the fuel consumption is reduced by 58.82 tons, which is almost 4.54%. Through experimental verification, the proposed model can obtain punctual routes, avoid areas with bad sea conditions, reduce fuel consumption, and is of great significance for improving the safety and economy of ship routes.

Acoustic arrival patterns in the Beaufort duct using normal mode and ray trace acoustic prediction models

The stratification of the Beaufort Sea has experienced significant changes over the last few decades resulting in a subsurface duct between 100- and 300-meters depths, known as the Beaufort Duct. This duct allows for long-range acoustic transmissions due to little interaction with the seafloor or sea surface. Acoustic arrival predictions for broadband acoustic sources centered around 250 Hz, such as those deployed in the Beaufort Sea in 2016–2017 show a peak acoustic arrival prior to the final cutoff centered on the sound speed minimum in the duct. This reverse dispersion feature in the acoustic time front can be connected back to the unique ducting features in the sound-speed profile. This relationship is explored using normal mode modeling and geometric optics. Modal speed predictions and ray path lengths and travel times are used to interpret the acoustic arrival patterns, particularly the dispersion feature present in the acoustic time front.

A Reliability-Based Network Equilibrium Model with Electric Vehicles and Gasoline Vehicles

With the popularity of electric vehicles, they have become an indispensable part of traffic flow on the road network. This paper presents a reliability-based network equilibrium model to realise the traffic flow pattern prediction on the road network with electric vehicles and gasoline vehicles, which incorporates travel time reliability, electric vehicles’ driving range and recharge requirement. The mathematical expression of reliable path travel time is derived, and the reliability-based network equilibrium model is formulated as a variational inequality problem. Then a multi-criterion labelling algorithm is proposed to solve the reliable shortest path problem, and a column-generation-based method of the successive average algorithm is proposed to solve the reliability-based network equilibrium model. The applicability and efficiency of the proposed model and algorithm are verified on the Nguyen-Dupuis network and the real road network of Sioux Falls City. The proposed model and algorithm can be extended to other road networks and help traffic managers analyse traffic conditions and make sustainable traffic policies.

REVIEW OF GLOBAL PRACTICE OF USING CELL PHONE DATA TO MEASURE TRAVELLER DATA

JSC “Ukrzaliznytsia” is a national integrated railway company and the largest employer in the country, playing a crucial role in the Ukrainian economy and the labour market. The rail companies faces significant risks, mainly due to prolonged disruptions due to the ongoing armed conflict on Ukrainian territory [1]. In such conditions, the question of the need for a comprehensive solution to the problems of the functioning of passenger railway transport, where the number of travellers serves as a pivotal indicator of the demand for transportation services, offering essential information for planning and evaluations. Advancements in technology have enabled the collection of crowd-sourced traffic data [2]. Crowdsourced smartphone data can collect information about user locations, travel paths, route choices, travel times, and speeds. This analysis explores how the number of passengers in trains is measured, examining technologies and methods that use cell phone data globally to assess their potential application in Ukraine.

Distinctive shear zones demonstrate pervasive laminar cataclastic flow throughout the gigantic Iymek rock avalanche

Intensive fragmentation is a pervasive process during rock avalanche propagation, with a series of typical shearing characteristics being generated, indicating the occurrence of differential shear-induced comminution of clasts. However, much less is known about how shearing evolves within a rock avalanche over long runout. In the Iymek rock avalanche (IRA), pervasive shear zones characterized by multistoried, multiscale, and multistyle shear structures are well developed along the travel path of the IRA, providing an invaluable opportunity to decipher the evolution of shear-induced fragmentation occurred in rock avalanches. In these shear zones, a series of indicative structures, including preserved stratigraphic sequences, jigsaw structures, Riedel shear arrays, sigmoidal shear planes, asymmetrical folds, bookshelf and boudin-like structures, and anastomosing shear networks, are identified. These structures indicate that a laminar-like flow regime characterized by low disturbance but intensive shearing should dominate the emplacement of the IRA, contributing to the formation of these pervasive shear structures and material facies. As indicated by the progressive evolution of these pervasive shear structures, three facies are determined from the transition zone to the accumulation zone, i.e., low-fragmented facies, ductile-dominated facies and brittle-dominated facies. The low-fragmented facies is mainly distributed in the transition zone, similar to a semisolid phase rather than the solid phase of the bedrock in the source area. Conversely, the brittle-dominated facies features a high fragmentation degree and is predominant in the accumulation zone, which mainly consists of a semifluid to fluid phase. This indicates that the rock avalanche mass should evolve from a solid phase to a fully fluid phase during propagation, which involves clast fracturing and comminuting, frictional sliding and rotation. Therefore, we propose that the rock avalanche is a classical cataclastic flow driven by progressive and differential shearing comminution during its laminar-like propagation instead of a granular flow. These results provide a significant basis for interpreting similar structures in other rock avalanche deposits and yield new insights for understanding the emplacement mechanisms of rock avalanches.

Magnetic fabric of Ocean Plate Stratigraphy mélanges: a tool for unravelling protracted histories of oceanic plates from seafloor spreading to tectonic emplacement into accretionary wedges

Multiple magnetic fabrics, referred to as F1–F5, were revealed through the anisotropy of magnetic susceptibility in an Ocean Plate Stratigraphy (OPS) mélange of the Neoproterozoic–Cambrian Blovice accretionary complex, Bohemian Massif. The fabrics post-date the formation of the mélange and the rotation of basalt blocks within the matrix and are interpreted in terms of a complex structural history of the mélange. Excluding local fabrics, the F1 fabric formed earlier along the mélange belt, recording shortening of the accretionary wedge front, whereas the higher grade F4 fabric pervasively overprinted both blocks and matrix in the SW part of the belt, recording shearing and vertical shortening at deeper structural levels closer to a megathrust surface. The preservation of angular relationships between the F1 and F4 fabrics across different parts of the mélange suggests that the blocks were only strained and not rotated during deformation, exemplifying the notion that the OPS mélanges may be a product of deformation at very shallow levels. The F1–F5 fabrics may be viewed as snapshots in a protracted evolution of OPS, where the earlier fabrics in basalt blocks may record the travel path of an oceanic plate from mid-ocean ridge towards the trench, before being overprinted in the accretionary wedge. Thematic collection: This article is part of the Ophiolites, melanges and blueschists collection available at: https://www.lyellcollection.org/topic/collections/ophiolites-melanges-and-blueschists Supplementary material: Thin-section photomicrographs, anisotropy of magnetic susceptibility data and summary diagrams for magnetic fabric types are available at https://doi.org/10.6084/m9.figshare.c.7008173

Exploring Tourist Flow Patterns through Geotagged Social Media Data: A Case Study from Sri Lanka

Tourism studies often rely on conventional methods such as interviews and site-specific surveys to collect data on tourist behaviour, mobility patterns, and preferences. However, these methods can be expensive, time-consuming, and limited in scope. Additionally, accurately tracing tourist travel paths can be challenging due to recall bias. Recently, user generated content on social media platforms have emerged as an alternative data source in tourism studies. In this study, we used geotagged posts on Flickr to understand the dominant paths taken by inbound tourists in Sri Lanka. The methodology consisted of three steps. First, geotagged photographs were collected from the Flickr API spanning a ten-year period, from 2012 to 2022. These photographs included metadata such as user ID, the timestamp of photo capture, and geo coordinates indicating where the photo was taken. Second, a density-based clustering algorithm was utilized to identify tourist hotspots. Finally, the Markov Chain model was employed to calculate transition probabilities among different attractions, revealing dominant travel routes within the country. Study’s findings indicate that Cultural heritage attractions were the most popular, comprising 55% of all attractions identified by the algorithm, and are particularly popular in districts such as Anuradhapura, Polonnaruwa, Matale, Kandy, and Galle. Nature-based attractions, constituting 37% of the total, were mainly located in Nuwara Eliya and Badulla Districts, as well as Yala National Park in Hambantota District. For coastal tourism, Galle and Matara Districts were the top preferences. The analysis confirms dominant travel patterns between North Central, Central, and Southwest coast regions. Notably, Galle, Kandy, Matale, Matara, Nuwara Eliya, and Colombo attract tourists from a more diverse array of regions compared to other districts in the country, showcasing their significance as tourist hubs. The study findings highlight opportunities in Jaffna for cultural-heritage tourism, the Eastern coast for coastal tourism, and the central highlands for tea tourism. It also emphasizes the need to develop the less-visited natural sites to ease pressure on popular National Parks. This research's significance lies in its contribution to informed decision-making and the sustainable management of tourist destinations. By understanding tourist mobility patterns and identifying popular attractions through social media data, policymakers can effectively manipulate visitor flows and mitigate excessive tourist pressure, while preserving the authenticity and allure of these destinations.&#x0D; &#x0D; Keywords: Sustainable tourism, Geotagged photographs, Social media, Tourist mobility, Flickr