Route Recommendation Research Articles

Opinion Mining for Travel Route Recommendation using Social Media Networks (Twitter)

Most of the organizations use text analytics to uncover purposeful information from an unstructured text as a result of considering the linguistic communication process techniques area unit extremely difficult. They typically cause several issues because of the inconsistency in syntax and linguistics. Sentiment analysis based on the opinion of the users. On twitter, many people post about their experience on the traffic routes. This project discusses the prediction of text mining analysis. On that post collecting from the data set and we find out which path is the best path for the travellers and waiting for commuters. In this project we discuss the traffic mining tweets using the keywords predicting the positive and negative comment on the Twitter. Experimentation involves discussion and comparison of ensemble classifiers over tagged tweets. Finally, it will be finding the best accuracy.

A personalized POI route recommendation system based on heterogeneous tourism data and sequential pattern mining

Planning a personalized POI route before touring a new city is an important travel preparation activity; however, it is a challenging and time-consuming task for tourists. Although some previous works focus on suggesting POI visit list or sequences, they fail to suggest personalized POI routes due to ignoring multifaceted tourism contexts. Also, they often suffer from tourist cold start or data sparsity problem because of the lack of tourism related data. To address the above weaknesses, we first propose a novel method to integrate heterogeneous tourism data collected from websites to construct a POI knowledgebase and massive structured POI visit sequences. Next, a POI-Visit sequential pattern mining algorithm is proposed to generate various fine-grained candidate POI routes from POI visit sequences while considering various tourism contexts. At the POI route recommendation stage, our system retrieve and rank a list of candidate routes according to the querying tourist’s tourism contexts, including the intended travel duration, the companion type in trip, the visit season and the preferring POI tourism types, etc. In our validation experiments, we select Guilin city in China as an example to construct a real POI knowledgebase which consists of 132 POIs and 8778 POI traffic time, and construct 5694 structured POI visit sequences based on 10,109 downloaded original travelogues. The experimental results demonstrate the advantages of our system in recommending fine-grained and high personalized POI routes for specific tourists.

Urban Traffic Coulomb’s Law: A New Approach for Taxi Route Recommendation

Recently, an increased amount of effort has been focused on optimizing the selection of routes for taxis, as part of the development of smart urban environments, and the increase of the accumulated trajectory data sets. One challenging issue is to match and recommend appropriate cruising routes to taxis, as most taxis cruise on streets aimlessly looking for passengers. Drivers encounter lots of difficulty in optimizing their cruise routes and hence increasing their incomes, and such inability not only decreases their profit but also increases the traffic load in urban cities. In this paper, the concept of urban traffic Coulomb’s law is coined to model the relationship between taxis and passengers in urban cities, based on which a route recommendation scheme is proposed. Taxis and passengers are viewed as positive and negative charges. It first collects useful information such as the density of passengers and taxis from trajectories, then calculates the traffic forces for cruising taxis, based on which taxis are routed to optimal road segments to pick up desired passengers. Different from existing route recommendation methods, the relationship among taxis and passengers are fully taken into account in the proposed algorithm, e.g., the attractiveness between taxis and passengers, and the competition among taxis. Moreover, real-time dynamics and geodesic distances in road networks are also considered to make more accurate and effective route recommendations. Extensive experiments are conducted on the road network using the trajectories generated by approximately 5,000 taxis to verify the effectiveness, and evaluations demonstrate that the proposed method outperforms existing methods and can increase the drivers’ income more than 8%.

VR system for spatio-temporal visualization of tweet data and support of map exploration

Social media analysis is helpful to understand the behavior of people. Human behavior in social media is related to time and location, which is often difficult to find the characteristics appropriately and quickly. We chose to apply virtual reality (VR) technologies to present the spatio-temporal social media data. This makes us easier to develop interactive and intuitive user interfaces and explore the data as we want. This paper proposes a VR system featuring two visualization techniques. One of the techniques is a three-dimensional temporal visualization of tweets of microblogs with location information. It consists of the two-dimensional map and a time axis. In particular, we aggregate the number of tweets of each coordinate and time step and depict them as piled cubes. We highlight only specific cubes so that users can understand the overall tendency of datasets. The other technique provides a route recommendation based on tweets of microblogs. Our technique supports users to explore attractive events and places by selecting effective tweets and suggesting routes. We also developed user interfaces for operating these objects in a VR space which indicate details of tweets.

A Framework for an Intelligent and Personalized Fire Evacuation Management System.

Many research studies have focused on fire evacuation planning. However, because of the uncertainties in fire development, there is no perfect solution. This research proposes a fire evacuation management framework which takes advantage of an information-rich building information modeling (BIM) model and a Bluetooth low energy (BLE)-based indoor real-time location system (RTLS) to dynamically push personalized evacuation route recommendations and turn-by-turn guidance to the smartphone of a building occupant. The risk score (RS) for each possible route is evaluated as a weighted summation of risk level index values of all risk factors for all segments along the route, and the route with the lowest RS is recommended to the evacuee. The system will automatically re-evaluate all routes every 2 s based on the most updated information, and the evacuee will be notified if a new and safer route becomes available. A case study with two testing scenarios was conducted for a commercial office building in Tianjin, China, in order to verify this framework.

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Tourism recommendation systems play a vital role in providing useful travel information to tourists. However, existing systems rarely aim at recommending tangible itineraries for tourists within a specific POI due to their lack of onsite travel behavioral data and related route mining algorithms. To this end, a novel travel route recommendation system is proposed, which collects tourist onsite travel behavior data automatically regarding a specific POI based on smart phone and IoT technology. Then, the proposed system preprocesses the behavior data to transform raw behavior sequences into Tourist-Behavior pattern sequences. Subsequently, the system discovers frequent travel routes from the generated pattern sequences by using an original route mining algorithm, named Tourist-Behavior PrefixSpan. Finally, a route-recommending method is designed to search and rank tangible travel routes according to the querying tourist’s profile and constraint. The experimental results demonstrate that the proposed system is efficient and effective in recommending POI-oriented tangible travel routes considering tourists’ route constraints and personal profile while ensuring that the suggested routes have considerable route values.

Spatial keyword search: a survey

Spatial keyword search has been playing an indispensable role in personalized route recommendation and geo-textual information retrieval. In this light, we conduct a survey on existing studies of spatial keyword search. We categorize existing works of spatial keyword search based on the types of their input data, output results, and methodologies. For each category, we summarize their common features in terms of input data, output result, indexing scheme, and search algorithms. In addition, we provide detailed description regarding each study of spatial keyword search. This survey summarizes the findings of existing spatial keyword search studies, thus uncovering new insights that may guide software engineers as well as further research.

Road Network Construction with Complex Intersections Based on Sparsely Sampled Private Car Trajectory Data

A road network is a critical aspect of both urban planning and route recommendation. This article proposes an efficient approach to build a fine-grained road network based on sparsely sampled private car trajectory data under complex urban environment. In order to resolve difficulties introduced by low sampling rate trajectory data, we concentrate sample points around intersections by utilizing the turning characteristics from the large-scale trajectory data to ensure the accuracy of the detection of intersections and road segments. In front of complex road networks including many complex intersections, such as the overpasses and underpasses, we first layer intersections into major and minor one, and then propose a simplified representation of intersections and corresponding computable model based on the features of roads, which can significantly improve the accuracy of detected road networks, especially for the complex intersections. In order to construct fine-grained road networks, we distinguish various types of intersections using direction information and detected turning limit. To the best of our knowledge, our road network building method is the first time to give fine-grained road networks based on low-sampling rate private car trajectory data, especially able to infer the location of complex intersections and its connections to other intersections. Last but not the least, we propose an effective parameter selection process for the Density-Based Spatial Clustering of Applications with Noise based clustering algorithm, which is used to implement the reliable intersection detection. Extensive evaluations are conducted based on a real-world trajectory dataset from 1,345 private cars in Futian district, Shenzhen city of China. The results demonstrate the effectiveness of the proposed method. The constructed road network matches close to the one from a public editing map OpenStreetMap, especially the location of the road intersections and road segments, which achieves 92.2% intersections within 20m and 91.6% road segments within 8m.

Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data

With the swift growth in tourism all around the world, it has become vital to introduce advancements and improvements to the services provided to the tourists, in order to ensure their ease of travel and satisfaction. Optimal travel route identification and recommendation is one of these amenities, which requires our attention as a basic and much-needed facility to improve the experience of travelers. In this work, we propose an optimal route recommendation mechanism for the prediction of the next tourist attraction and optimal route recommendation to the predicted tourist attraction. The algorithms used in the proposed methodology are neural networks for prediction and particle swarm optimization for finding the optimal route. We design an objective function for the route optimization based on the five route parameters of distance, road congestion, weather conditions, route popularity, and user preference. The data used is the tourism data of Jeju Island from December 2016 to December 2017. The performance analysis in the prediction mechanism is performed based on the accuracy of test data results with varying route sizes, while for route optimization, the obtained results are compared with the non-optimized technique. Also, comparisons analysis is performed by comparing the performance of the applied particle swarm optimization algorithm with an identical system-level implementation of the genetic algorithm, which is one of most widely used optimization algorithms. An extended comparative analysis with some related recommendation system studies is also performed based on key optimization factors in route optimization.

A generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis

SummaryThe mining of human mobility can be exploited to support the design of traffic planning, route recommendations, urban planning, emergency management, and land use. Currently, various methods such as the machine learning algorithms, statistical methods, and semantic analysis are widely applied to identify and extract human mobility patterns. In this paper, we propose a simple and generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis. The essential ideas behind the proposed paradigm mainly include (1) creating weighted complex networks of GPS trajectories and (2) extracting the human mobility patterns by analyzing the structures and metrics of the created complex networks of GPS trajectories. To evaluate the performance of the proposed paradigm, we design five groups of experiments and identify the mobility patterns of the selected five persons based on the selected five network analysis metrics. Experimental results indicate that (1) the proposed paradigm is effective and (2) the quite interesting potential information about the human mobility can be mined easily. The proposed paradigm is simple and generic, which can be employed to rapidly identify the human mobility patterns based on the GPS trajectory data.

Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

Profitable Taxi Travel Route Recommendation Based on Big Taxi Trajectory Data

With the advent of GPS tracking technology, how to make use of taxi trajectories to efficiently and effectively reduce taxis cruising distance is an active and challenging research topic. In this paper, we propose a profitable taxi route recommendation method called adaptive shortest expected cruising route (ASER). In ASER, a probabilistic network model is developed to predict pick-up probability and capacity of each location by using Kalman filtering method. To recommend profitable driving routes to taxi drivers, ASER takes the load balance between passengers and taxis into consideration and the shortest expected cruising distance is introduced to formulate potential cruising distance of taxis. Moreover, MapReduce and a novel data structure kdS-tree are applied to improve recommendation efficiency. ASER is evaluated on two real trajectory datasets from San Francisco, CA, USA, and Wuhan, China. The experimental results validate that ASER significantly outperforms the existing methods by reducing the taxi cruising distance 11% and 39%.

Proposal on Edge Vector-based Formulation of Sightseeing Route Recommendation Problem and Its Solution with Simulated Annealing

Proposal on Edge Vector-based Formulation of Sightseeing Route Recommendation Problem and Its Solution with Simulated Annealing

Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites

Although abundant research work has been published in the area of path recommendation and its applications on travel and routing topics, scarce work has been reported on context-aware route recommendation systems aimed to stimulate optimal cultural heritage experiences. This paper tries to address this issue, by proposing a personalized and content adaptive cultural heritage path recommendation system, where location is modeled using mean-shift clustering trained with actual user movement patters. Additionally, topic modeling is incorporated to formalize the implicit cultural heritage content, while first order Markov models address the movement as a temporal transition aspect of the problem. The overall architecture is applied on data collected from actual visits to the archaeological sites of Gournia and Catalhoyuk and extensive analysis on visitor movement patterns follows, especially in comparison to the curated paths in the aforementioned sites. Finally, the offline evaluation results of the proposed recommendation scheme are encouraging, validating its efficiency and setting a positive paradigm for cultural heritage route recommendations.

Discover the road sequences of bus lines using bus stop information and historical bus locations

Discovering the road sequences traveled by the bus lines is of great importance for public transport management, such as bus journey time prediction and multimodal travel route recommendation, as t...

도로 교통망에 대한 사용자의 선호도 변화를 반영한 경로 추천

도로 교통망에 대한 사용자의 선호도 변화를 반영한 경로 추천

A Tensor-Based Big-Data-Driven Routing Recommendation Approach for Heterogeneous Networks

Telecommunication networks are evolving toward a data-center-based architecture, which includes physical network functions, virtual network functions, as well as various types of management and orchestration systems. The primary purpose of this type of heterogeneous network is to provide efficient and convenient communication services for users. However, the diverse factors of a heterogeneous network such as bandwidth, delay, and communication protocol, bring great challenges for routing recommendations. In addition, the growing volume of big data and the explosive deployment of heterogeneous networks have started a new era of applying big data technologies to implement routing recommendations. In this article, a tensor-based big-data-driven routing recommendation framework, including the edge plane, fog plane, cloud plane, and application plane, is proposed. In this framework, a tensor-based, holistic, hierarchical approach is introduced to generate efficient routing paths using tensor decomposition methods. Also, a tensor matching method including the controlling tensor, seed tensor, and orchestration tensor is employed to realize routing recommendation. Finally, a case study is used to demonstrate the key processing procedures of the proposed framework.

Analyzing the Impact of Anticipatory Vehicle Routing on the Network Performance

Traffic congestion causes lost in time, fuel consumption and frustration of the drivers. Route guidance systems are considered as the most feasible solution to alleviate traffic congestion. However, existing route navigation systems provide route guidance based only on real-time traffic information, without considering the future evolvement of traffic in the network. These strategies, known as reactive routing strategies, react only when congestion has already occurred and do not prevent congestion from happening. Therefore, researchers have been investigating in anticipatory route guidance, to enable the integration of traffic prediction into route recommendation. The anticipatory route guidance is expected to improve the effectiveness of the route guidance systems, however its effects on the overall traffic on the network are unclear. The main aim of this paper is to analyze the impacts of the anticipatory routing strategy on the network performance. To achieve this, the performance of the system during anticipatory routing strategy is compared to current reactive routing strategy. The models of the routing strategies are built and tested in a microscopic simulation environment and the results are compared using suitable network performance indicators, such as average travel time, total delay time and the effects of anticipatory routing on different vehicle types. Evaluation results show that the anticipatory routing strategy can improve the network performance, but only for a limited penetration rate of the vehicles equipped with navigation system based on the predictive traffic information, the average travel time compared to reactive routing is reduced by up to 20 % in the best scenario and the vehicles with the predictive navigation system are the ones to profit the most from the predictive routing strategy.

Roadscape-based Route Recommender System Using Coarse-to-fine Route Search

Roadscape-based Route Recommender System Using Coarse-to-fine Route Search

CAR: The Clean Air Routing Algorithm for Path Navigation With Minimal PM2.5 Exposure on the Move

Transport related pollution is becoming a major issue as it adversely affects human health and one way to lower the personal exposure to air pollutants is to choose a health-optimal route to the destination. Current navigation systems include options for the quickest paths (distance, traffic) and least expensive paths (fuel costs, tolls). In this paper, we come up with the CAR (Clean Air Routing) algorithm and use it to build a health-optimal route recommendation system between the origin and the destination. We combine the open source PM2.5 (Fine Particulate Matter with diameter less than 2.5 micrometers) concentration data for Taiwan, with the road network graph obtained through OpenStreetMaps. In addition, spatio-temporal interpolation of PM2.5 is performed to get PM2.5 concentration for the road network intersections. Our algorithm introduces a weight function that assesses how much PM2.5 the user is exposed to at each intersection of the road network and uses it to navigate through intersections with the lowest PM2.5 exposures. The algorithm can help people reduce their overall PM2.5 exposure by offering a healthier alternative route which may be slightly longer than the shortest path in some cases. We evaluate our algorithm for different travel modes, including driving, cycling and walking. An analysis is done for more than 4,000 real-world travel scenarios. The results show that our approach can lead to an average exposure reduction of 17.1% with an average distance increase of 2.4%.