A GIS-Based Evaluation of the Effectiveness and Spatial Coverage of Public Transport Networks in Tourist Destinations

Domènech, Antoni; Gutiérrez, Aaron

doi:10.3390/ijgi6030083

Cited by 24 publications

(20 citation statements)

References 53 publications

(69 reference statements)

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“…[12], [15], [16], thus improving the accuracy of the extraction. Third, due to the limited data collected, weather, events, and holiday impacts [50] have not yet been incorporated into the methodology. The inclusion of these influence factors helps to make the methodology more general.…”

Section: Discussionmentioning

confidence: 99%

“…The area is gridded and the color of different grid is dyed according to the population, and the bus route will be allocated according to the change of grid color to serve the most demanding area. Domènech and Gutiérrez [50] used GIS to present the proportion of population near the station and the connectivity of different areas when evaluating the coverage and utility of PT systems in tourist areas, so as to determine the bus plan in different seasons. Lee and Miller [51] used GIS to outline the spatio-temporal accessibility maps of different bus network combinations when evaluating the urban redesigned PT system and the new rapid transit system, CMAX.…”

Section: Mining Methodology Of Traffic Demandmentioning

confidence: 99%

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Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System

Cheng

Huang

et al. 2019

IEEE Access

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In recent years, to fix the shortcomings of traditional bus service and meet the diversified needs of passengers, a new type of transit system, the customized bus (CB), has been proposed. However, how to define and mine the CB's demand is still less being addressed. Since the data of bus smart cards can provide more travel information, it makes the mining of potential CB's demand spots more possible, which can be helpful in CB service design. In order to mine the demand spots more scientifically, this paper, for the first time, quantitatively defines the CB demand characteristics and criteria of selecting potential area, and develops a demand hotspots extraction methodology for CB. The methodology solves two issues primarily. One is how to organize massive smart card data and obtain the space-time pattern and mobility of passenger efficiently; the other is how to mix the CB demand characteristics into the method. This demand spots extraction method can generate multi-style maps, including the heat and origin-destination maps, for spatial cluster of CB's demand spots in rational areas in terms of the CB demand characteristics based on geographic information system. By using the bus smart card data in Beijing, China, this paper carries out a case study to validate the method. The empirical data mining analysis shows that our proposed method can define demand spots ideally. Our work can provide a valuable reference for decision makers to design CB system. INDEX TERMS Bus smart card data, customized bus, potential demand area, geographic information system, spatial clustering analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Mining Methodology Of Traffic Demandmentioning

confidence: 99%

Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System

Cheng

Huang

et al. 2019

IEEE Access

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“…It is located peripherally at 14 and 17 km, respectively, from the cities of Tarragona and Reus and approximately 20 km from the chief tourist destinations on the coast. Its major deficiency is poor accessibility due to weak connectivity with the regional public transportation network [ 44 , 45 ]. Such differences in station location and their uneven accessibility from surrounding areas provide another interesting aspect for comparative analysis.…”

Section: Methodsmentioning

confidence: 99%

High speed rail and coastal tourism: Identifying passenger profiles and travel behaviour

Gutiérrez

Ortuño

2017

PLoS ONE

Self Cite

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In this paper, we characterise tourists most likely to visit a coastal destination by high-speed rail (HSR). Our data came from a survey conducted among HSR passengers during 2014’s high season (July and August) at Spain’s Camp de Tarragona and Alicante Stations, each of which is near a mass tourism destination on the Mediterranean coast: the Costa Daurada and the Costa Blanca, respectively. We used responses to the survey, which presented binary discrete-choice situations, to construct a database necessary for a logistic regression model that allowed us to examine how passenger profile, trip characteristics, and stay conditions influenced the use of HSR services on visits to each coastal destination. Results highlighted significant differences in the profiles of tourists who arrived at each destination by HSR and, in turn, that no specific tourist profile is associated with HSR, even for two stations that serve sunny beach destinations. Among its implications, to analyse travellers that HSR can attract, it is vital to consider the specific characteristics of each destination and its current market.

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“…• User's range of age: 6 intervals [0,16], [17,18], [19][20][21][22][23][24][25][26], [27][28][29][30][31][32][33][34][35][36][37][38][39][40]…”

Section: Datasetmentioning

confidence: 99%

“…A public transport system relies on a multiplex network in which different transport modes complement one another. An optimally designed public transport network should provide full coverage, which ideally would connect any pair of points in the space [35]. BSSs provide a public transport network with virtually full coverage, as users can travel the city in a single transport mode.…”

Section: Underlying Bss Public Transport Networkmentioning

confidence: 99%

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

Wilby

Díaz

Pozo

et al. 2020

Sensors

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Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD’s use fall in the category of transport, which demonstrates our first statement.

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A GIS-Based Evaluation of the Effectiveness and Spatial Coverage of Public Transport Networks in Tourist Destinations

Cited by 24 publications

References 53 publications

Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System

Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System

High speed rail and coastal tourism: Identifying passenger profiles and travel behaviour

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

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