Hindawi Journal of Advanced Transportation Volume 2018, Article ID 9247102, 13 pages https://doi.org/10.1155/2018/9247102 Research Article Impact of a New Metro Line: Analysis of Metro Passenger Flow and Travel Time Based on Smart Card Data Xiao Fu 1 and Yu Gu 2 1 School of Transportation, Southeast University, Nanjing 210096, China 2Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Centre of Modern Urban Trafc Technologies, School of Transportation, Southeast University, Nanjing 210096, China Correspondence should be addressed to Xiao Fu; [email protected] Received 27 May 2018; Accepted 6 August 2018; Published 19 August 2018 Academic Editor: David Z. W. Wang Copyright © 2018 Xiao Fu and Yu Gu. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Over the past few decades, massive volumes of smart card data from metro systems have been used to investigate passengers’ mobility patterns and assess the performance of metro network. With the rapid development of urban rail transit in densely populated areas, new metro lines are constantly designed and operated in recent years. Te appearance of new metro lines may signifcantly afect passenger fow and travel time in the metro network. In this study, smart card data of metro system from Nanjing, China, are used to study the changes of metro passenger fow and travel time due to the operation of a new metro line (i.e., Line 4, opened on 18 January 2017). Te impact of the new metro line on passenger fow distribution and travel time in the metro network is frst analysed. As commuting is one of the major purposes of metro trips, the impact of the new metro line on commuters’ trips is then explicitly investigated. Te results show that the new metro line infuences passenger fow, travel time, and travel time reliability in the metro network and has diferent impacts on diferent categories of commuters. 1. Introduction time reliability may also change as a result of the expansion of metro network. As commuters are the majority of metro users Multimodal transit networks provide convenient and efcient in peak hours and are expected to beneft from the opening of ways for individuals’ daily travel in congested urban areas. new lines, the impact of new metro line on commuters’ trips Metro system, as a major component of multimodal transit should be explicitly studied. In the literature, little attention system, has been signifcantly developed in recent years in has been given to the changes of commuters’ spatial/temporal many cities with large populations. Taking Nanjing, one of the distributions and travel times afer the opening of new metro most densely populated cities in China, for instance, 6 new lines. metro lines have been constructed and operated in Nanjing In this study, a comprehensive analysis of metro pas- in the past decade and 11 new metro lines are planned to be senger fow and travel time is presented on the basis of developed in the next fve years. smart card data from Nanjing, China. Te impact of new To study the passenger fow characteristics and evaluate metro line on metro passenger fow and travel time is the performance of urban transit network, a substantial body frst analysed on the level of whole metro network. Spa- of literature has been developed on analysing the distribution tial and temporal distributions of metro passenger fow in of passenger fow and travel time based on smart card data. peak and nonpeak hours are investigated before and afer However, limited efort has been devoted to investigating the the opening of the new line. Te changes of passenger impact of new metro line on metro network and passengers. travel time and travel time reliability are also studied. To Te development of new metro lines changes the topology of investigate the impact of the new metro line on com- metro network and infuences the accessibility of city area. muters’ trips, this study identifes and classifes the pas- Te operation of new metro lines may afect passenger fows sengers commuting by metro and investigates the impact of existing metro stations. Passengers’ travel times and travel of the new metro line on fow distribution, travel time, 2 Journal of Advanced Transportation and travel time reliability of diferent categories of metro travel time reliability metrics include the schedule-based commuters. metrics such as excess journey time and reliability bufer Te outline of this paper is as follows. Section 2 presents a time [28, 29], as well as statistical metrics such as standard review of studies using smart card data and studies discussing deviation (SD) and coefcient of variation (CV) of travel time the impact of new rail transit lines. Data used in this study [4, 30–33]. are described in Section 3. Impacts of new metro line on passenger fow and passenger travel time are investigated 2.2. Impact of New Rail Transit Lines. Construction of a new in Sections 4 and 5. Conclusions and recommendations for rail transit line may result in changes of individuals’ travel future research are given in the last section. choice behaviour. Te change in mode choice attracts much attention in the literature. Senior [34] analysed the modal 2. Literature Review shif from bus and private vehicles to the light rail transit based on household survey and census data. Cao and Schoner 2.1. Applications of Smart Card Data. Smart card data, [35] explored the impact of new light rail transit line on which are passively collected by automated fare collection transit use. Harvey et al. [36] investigated people’s willingness systems with the use of smart cards for transit services, are to travel by the newly built high speed rail line. Gadzinski´ important data source for transport researchers. Because of and Radzimski [37] combined household survey data with the large quantity and high precision of smart card data, property sales transactions data to investigate the efect of a many researchers believe that such innovative data source new tram on households’ mode choice behaviour. In addition can complement or even replace the traditional collected to studying the mode choice change, Saidi, Wirasinghe, and transport data such as travel survey data [1, 2]. Existing Kattan [38] assessed the changes in passengers’ cost afer transport studies based on smart card data mainly focus on introducing a new rail line based on job distribution data three areas: analysis of passenger fow patterns, investigation and origin-destination data. Hong, Boarnet, and Houston of travel behaviours, and assessment of transit network [39] adopted sociodemographic data, travel survey data, and performance. activity data to investigate the impact of a new light rail transit Many studies are devoted to investigating the charac- line on individuals’ travel behaviour. teristics of metro passenger fow patterns based on smart Besides the studies focusing on the change of travel card data. Smart card data record the entrance/exit stations behaviour, a few studies reveal the impact of new metro lines and entrance/exit times of metro passengers, so the data on passenger fow. Li [40] analysed changes in passenger fow are widely used to investigate and visualize the spatial and pattern caused by diferent types of new metro lines. Based on temporal distributions of metro passenger fow [3–5]. Based smart card data, Liu, Yao, and Li [41] analysed the relationship on the historical entrance and exit passenger fow records, the between metro network expansion, land use of station neigh- future passenger fow can also be predicted with smart card bourhood, and the growth of station passenger fow. data [6, 7]. However, existing studies mainly focus on the change Smart card data can also be used to analyse travel of passenger fow distribution on the network level, while behaviours and extract travel patterns of transit users. Travel little attention has been paid to the impact of new lines on behaviours in transit networks can be investigated such as diferent individuals. Transportation analyses which are not transit ridership and origin-destination (OD) choices [8, 9], conducted from individuals’ perspective may be biased as train and route choices [10, 11], and transfer behaviours [12]. they fail to depict individuals’ real travel experience [42]. Based on the investigation of travel behaviours, many studies Some studies have been made to investigate the performance are conducted to extract individuals’ spatial/temporal travel of urban transportation network from the perspective of patterns [13–15] and mobility patterns [16–18]. Te dynamics individual travellers using diferent kinds of data sources [33, of travel behaviours and the variation of travel pattern were 43]. In densely populated urban areas, as a large proportion of also studied [19–22]. Some other studies focused on the passengers’ daily travels are made by metro, the opening of a regularity of individual travel pattern, using smart card data new metro line may signifcantly infuence some individuals’ to classify transit passengers [23, 24]. daily travel patterns while other individuals may not be In addition, smart card data can also be applied to inves- afected. For giving a comprehensive analysis of the impact of tigate transit network performance such as the passenger new metro line on metro passenger fow and travel time, the travel time and the service reliability of transit systems. For analysis in this study is frst conducted on the network level instance, some studies investigated the spatial and temporal to present an overview of the metro system and investigate distribution of trip travel times [25], travel time diferences the changes of passenger fow and travel time afer the new between diferent transit modes [12], and splitting and esti- line opening.