TourSense: A Framework for Tourist Identification and Analytics Using Transport Data

Lu, Yu; Wu, Hong; Liu, Xin; Chen, Penghe

doi:10.1109/tkde.2019.2894131

Cited by 24 publications

(6 citation statements)

References 43 publications

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“…Zhang et al [20] analyzed the passenger's transfer time and travel time, and established a new algorithm, the result showed that the accuracy of the algorithm was accuracy. Lu et al [21] identified the tourists of common diligence and a model that considered their travel preferences was established to learn and predict their next trip. In addition, some scholars also analyze the big data in the field of transportation to release the travel behavior of passengers [22]- [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to the statistics of the travel time distribution between Xizhimen station and Wangfujing station, the travel time distribution ratio in the range [21], [23] is 45%, the travel time distribution ratio for 24minutes is 16%, the travel time distribution ratio in the range [25], [27] is 38%, and the travel time distribution ratio for the remaining is 1%. Therefore, it can be determined that relatively more passengers choose the path of line 2 and then take line 1 as the best choice.…”

Section: ) the Valid Path Requires The Same Number Of Subway Transfersmentioning

confidence: 99%

“…In this valid path, passengers take line 1 at Fuxingmen station and directly arrive at Guomao station, the total travel time of passengers on this path is about 22 minutes. According to the statistics of the travel time distribution between O-D stations, the travel time distribution ratio in the range [21], [25] is 92%. Therefore, in this situation, the fare strategy should be formulated according to the mileage of the shortest path without subway transfer.…”

Section: Fare Strategy Of Urban Rail Transitmentioning

confidence: 99%

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Big Data Analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

et al. 2020

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This paper proposed improved measures for the shortest path fare scheme of urban rail transit. Firstly, this paper simulated Beijing rail transit by using Anylogic simulation technology and shortest path algorithm. Then, in order to find the travel time between any originations and destinations, this research measured the inbound time, waiting time, interval time, section running time, transfer time and outbound time. In addition, this paper used big data analysis technology to obtain the actual travel time distribution between any originations and destinations by processing the basic data of passengers entering and leaving the station. Finally, by comparing the valid path travel time calculated by any originations and destinations with the actual travel time distribution of passengers, the path taken by majority of passengers was pushed back to determine the ticket price based on the mileage of the path taken by the majority of passengers. The results reduced the dependence on government subsidies by rail transit operation and made up for the operation and maintenance costs. INDEX TERMS Shortest path, Anylogic simulation, travel time, time distribution, big data analysis, pricing scheme of urban rail transit.

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Section: Literature Reviewmentioning

confidence: 99%

Section: ) the Valid Path Requires The Same Number Of Subway Transfersmentioning

confidence: 99%

Section: Fare Strategy Of Urban Rail Transitmentioning

confidence: 99%

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Big Data Analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

et al. 2020

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“…Medina [22] (2018) used discrete selection models to extract features from smart card data, applied DBSCAN clustering algorithms to these vectors, and estimated the likelihood of activity as HOME, WORK/STUDY or OTHER. Lu et al [23] (2019) proposed a graph-based iterative propagation learning algorithm to identify visitors from public commuters and then designed a tourism preference analysis model to learn and predict their next trip. The literature [3,[17][18][19][20][21][22][23] primarily analyzed the passenger's travel mode from a spatial and temporal perspective.…”

Section: Travel Pattern Miningmentioning

confidence: 99%

Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data

et al. 2020

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Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system.

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“…This requires identifying markets and targets—for example, identifying the profile of the tourists visiting the destination, their attitudes and preferences for the city, the most visited attractions, the most common trip format, the used transportation systems, etc. [12,13,14]. Ultimately, the objective is to exploit and adapt tourism resources to the identified requirements, to show the privileges of the region, to foster sightseeing, outstanding places, accommodation facilities and commercial activities—in a nutshell, to improve tourism competitiveness by creating and sharing the story about the destination to promote [15,16].…”

Section: Introductionmentioning

confidence: 99%

Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks

Bustamante

Sebastiá

Onaindía

2019

Sensors

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Promoting a tourist destination requires uncovering travel patterns and destination choices, identifying the profile of visitors and analyzing attitudes and preferences of visitors for the city. To this end, tourism-related data are an invaluable asset to understand tourism behaviour, obtain statistical records and support decision-making for business around tourism. In this work, we study the behaviour of tourists visiting top attractions of a city in relation to the tourist influx to restaurants around the attractions. We propose to undertake this analysis by retrieving information posted by visitors in a social network and using an open access map service to locate the tweets in a influence area of the city. Additionally, we present a pattern recognition based technique to differentiate visitors and locals from the collected data from the social network. We apply our study to the city of Valencia in Spain and Berlin in Germany. The results show that, while in Valencia the most frequented restaurants are located near top attractions of the city, in Berlin, it is usually the case that the most visited restaurants are far away from the relevant attractions of the city. The conclusions from this study can be very insightful for destination marketers.

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TourSense: A Framework for Tourist Identification and Analytics Using Transport Data

Cited by 24 publications

References 43 publications

Big Data Analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

Big Data Analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data

Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks

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