Integrating network science and public transport accessibility analysis for comparative assessment

Abstract

Network science offers powerful concepts and methods for studying complex systems, such as public transport networks. However, many existing studies on complex network analysis of public transport networks were primarily motivated to test network science concepts using real-life networks. Consequently, important properties related to public transport services in these studies have been overlooked. This has led to claims made by transport researchers that these works do not necessarily improve the understanding of system properties and performance. To bridge this gap, this study integrates network science and public transport accessibility analysis for comparative assessment across multiple public transport networks. The primary contribution of this study pertains to developing a method based on network science for computing public transport accessibility measured as the average travel impedance. The travel impedance metric is defined based on the generalized travel cost between stop pairs, containing initial and transfer waiting times, in-vehicle travel times and time-equivalent transfer penalty costs. To perform efficient computation, we propose a new type of weighted graph representation of public transport networks, which explicitly incorporates travel costs that are derived from general transit feed specification (GTFS) data. Besides the methodological contribution, the secondary one of this study consists in performing a comparative assessment of worldwide tram networks' accessibility. The analysis shows insights into how different travel components (e.g., in-vehicle travel times and waiting and transfer times) specifically account for the variance in accessibility across different networks. Additional references for improving the designing and planning of public transport networks can be obtained from such comparative assessments.

The Reliability Model for Bike‐Sharing Dispatch Based on Hotspot Detection and Hypothesis Test: A Case Study in Beijing

“…By utilizing the tool of cutting, OD data of shared bikes in the research area are obtained. To sum up, we only need to obtain the amount of OD within each dispatch area without the need for assistance from other multisource spatial data (like spatial accessibility, social networking data, simulation data, and so on [4][5][6][7][8][9][10][11][12][13][14][15][16][17]), which can be easily achieved through GIS software. More importantly, the method in this article does not need to predict the travel demand like the existing research (such as literature [4][5][6]8]), which reduces the complexity and time consumption in practical application.…”

“…In recent years, with the development of data acquisition and analysis technology, more and more algorithms have been employed for experiments with site selection or bike-sharing dispatch by GPS and mobile cellular: clustering progress [7][8][9], incentive mechanism [10][11][12], mixed-integer linear programming [13], and heuristic algorithm [14,15]. Furthermore, these models can be assessed by comfort level [16], the total cost of management [11], walking time [3,17], and accessibility [18,19], which have been well published and applied. Before studying bike-sharing dispatch, a very important step is to predict the demand and analyze the travel behavior.…”

Structural Analysis in Transit System Using Network Theory Case of Guadalajara, Mexico

“…To measure these physical and structural features, transport planners have resorted to network sciences and the measure of network properties derived from this field. Network proprieties are rarely 2 of 17 taken into consideration in transport planning, despite the remarkable information that can be generate related to the performance of a transport network [3].…”

“…To measure these physical and structural features, transport planners have resorted to network sciences and the measure of network properties derived from this field. Network proprieties are rarely taken into consideration in transport planning, despite the remarkable information that can be generate related to the performance of a transport network [3] Network science is an academic field that uses discrete mathematics in order to understand the structural proprieties of a network. To do this, there is a necessity to have a network representation of the phenomena of study.…”

Unraveling the Hierarchy of Public Transport Networks

“…Complex network science emerged as an effective and quantitative method to study PTN and is able to capture the topological properties of PTN structures [12], [13]. Several studies have applied network science indicators to analyze PTN, including estimation of passenger flow [14], identification of hubs [15] and accessibility analysis [16]. Besides, complex networks theory can also potentially offer an approach for unraveling network hierarchy since it is witnessed in several domains such as metabolic networks [17], social networks [18] and internet networks [19].…”