Weighted complex networks in urban public transportation: Modeling and testing

Wang, Lina; Wang, Kai; Shen, Jianglong

doi:10.1016/j.physa.2019.123498

Cited by 28 publications

(12 citation statements)

References 36 publications

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“…c. Maps and diagrams (graphics). d. These models are a form of simplification of reality to achieve certain goals, such as to represent a condition, and forecasting and understanding [8,9].…”

Section: 2mentioning

confidence: 99%

Transportation modelling using PTV Vissim for the adjacent junction in Sampangan Semarang City

Nugroho

Falah

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The growth of vehicles in the city of Semarang is fast, as calculated based on the BPS Semarang the number of vehicle growth is 11% per year. This causes the road capacity to be periodically increased. As is the case at the adjacent intersections on Dewi Sartika street and Talangsari street initially, to break up delays and queues and increase capacity, efforts were made to add a 7-meter-wide bridge. However, this widening will not solve the problem in the next 5 years so that alternative solutions are needed for the two intersections. This study aims to evaluate the two intersections using the Binamarga method based on the 1993 MKJI, to determine the capacity of the intersection and plan the design of the intersection in the future and then make modelling with PTV Vissim Software to describe the simulation of the actual situation.

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Section: 2mentioning

confidence: 99%

Transportation modelling using PTV Vissim for the adjacent junction in Sampangan Semarang City

Nugroho

Falah

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Past studies have either accounted for congestion using static (Shimamoto et al 2008) or dynamic (Cats and Jenelius 2014) assignment models but at the cost of running times prohibiting a full-scan. Alternatively, they did perform a full-scan by relying on a strictly topological analysis (von Ferber et al 2012;Wang, Wang, and Shen 2020;Krishnakumari and Cats 2020;Zhang et al 2020), used an all-or-nothing assignment (Rodríguez-Núñez and García-Palomares 2014; Pant, Hall, and Blainey 2016;Cats 2016) or a probabilistic route choice (Cats, Koppenol, and Warnier 2017) without congestion and capacity considerations which are particularly relevant when links fail.…”

Section: Questionsmentioning

confidence: 99%

Who is the Weakest Link? A Network Vulnerability Analysis Using a Congested Transport Assignment

Cats

Shelat

2022

Findings

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We propose a user-equilibrium congested transit assignment model for a full-scan network vulnerability analysis by relying on the computations of network science indicators for infrastructure and service graphs. Model output include line-specific passenger loads. Furthermore, we propose a link criticality indicator for measuring the spatial extent of spill-over effects of link closures. In the case of the Amsterdam a disruption on the most critical link in the peak period induces 1,000 passenger-hours loss and a disruption spill overs on average to more than seven other links. Betweenness centrality and passenger loads do not offer good proxies for assessing link criticality.

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“…The POI data for 2018 are sourced from Autonavi (Gaode), which is a popular electronic navigation map in China that provides information on the names, location, and types of various retail stores. Based on previous studies and the classification of POI data [21,23,37], 72 subtypes of retail stores (as illustrated in Table 1) were extracted from the POI dataset. According to the Retail Type Categorization of China (RTCC), they were categorized within six major categories, including shopping malls, supermarkets, convenience stores, specialty stores, electronics stores, and building material stores.…”

Section: Study Area and Data Preparationmentioning

confidence: 99%

“…Several studies have verified that public transport has a substantial impact on retail patterns in city centers when compared to those of out-of-town malls [31][32][33][34]. In the big data era, public transport flow data become available from a smart card system and a number of studies have devised various weighted centrality indices to analyze the complex network of transport flows [35][36][37]. However, to the best of our knowledge, research on the relationships between retail store locations and their centrality in public transport flow network still lacking.…”

Section: Introductionmentioning

confidence: 99%

Weighted Centrality and Retail Store Locations in Beijing, China: A Temporal Perspective from Dynamic Public Transport Flow Networks

et al. 2021

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The spatial relationship between transport networks and retail store locations is an important topic in studies related to commercial activities. Much effort has been made to study physical street networks, but they are seldom empirically discussed with considerations of transport flow networks from a temporal perspective. By using Beijing’s bus and subway smart card data (SCD) and point of interest (POI) data, this study examined the location patterns of various retail stores and their daily dynamic relationships with three weighted centrality indices in the networks of public transport flows: degree, betweenness, and closeness. The results indicate that most types of retail stores are highly correlated with weighted centrality indices. For the network constructed by total public transport flows in the week, supermarkets, convenience stores, electronics stores, and specialty stores had the highest weighted degree value. By contrast, building material stores and shopping malls had the weighted closeness and weighted betweenness values, respectively. From a temporal perspective, most retail types’ largest correlations on weekdays occurred during the after-work period of 19:00 to 21:00. On weekends, shopping malls and electronics stores changed their favorite periods to the daytime, while specialty stores favored the daytime on both weekdays and weekends. In general, the higher store type level of the shopping malls correlates more to weighted closeness or betweenness, and the lower-level store type of convenience stores correlates more to weighted degree. This study provides a temporal analysis that surpasses previous studies on street centrality and can help with urban commercial planning.

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Weighted complex networks in urban public transportation: Modeling and testing

Cited by 28 publications

References 36 publications

Transportation modelling using PTV Vissim for the adjacent junction in Sampangan Semarang City

Transportation modelling using PTV Vissim for the adjacent junction in Sampangan Semarang City

Who is the Weakest Link? A Network Vulnerability Analysis Using a Congested Transport Assignment

Weighted Centrality and Retail Store Locations in Beijing, China: A Temporal Perspective from Dynamic Public Transport Flow Networks

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