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国际学术期刊
Assessing public transport systems connectivity based on Google Transit data
发布时间:2013-12-69:45:33来源:作者:Yuval Hadas   

Yuval Hadas
 The Department of Management, Bar-Ilan University, Ramat Gan 5290002, Israel
 
 
 Highlights


•Google-transit data sources enable an easy analysis of public transport systems.
•The assessing is based solely on Google-transit data and transport networks.
•Five measures for assessment and management of public transport are introduced.
•Differences within and between cities is facilitated by this approach.
•The proposed methodology is demonstrated by analyzing three public transport systems.


Keywords
Connectivity; Network analysis; Public transport; Spatial analysis


Abstract
A PT system consists of various physical features such as roads, railways, routes, and stops which are represented by a complex network of spatial and temporal data. Since these networks are usually very large and include millions of entities, it is difficult to assess PT systems. Assessment in this context is defined as the ability to extract and analyze data in an automated and recurring process so as to enhance decision making and to make it possible to compare between PT networks over time. The unified methodology that this work presents for extracting, storing and analyzing PT data enables relatively easy spatial analysis with GIS techniques based solely on: (a) Google Transit feeds and (b) Transportation networks. In order to implement this new methodology for analyzing a PT system, five connectivity indicators are introduced: (a) transportation network coverage level; (b) average speed; (c) intersection coverage level; (d) stop transfer potential; and (e) route overlap. This work demonstrates the proposed methodology by analyzing PT systems in Auckland (New Zealand), Vancouver (Canada), and Portland (Oregon, USA).



Article Outline
1. Introduction
2. Data acquisition and construction
2.1. Data sources
2.1.1. Google Transit data
2.1.2. Transport network data

2.2. Public transport network construction
2.2.1. Public transport network creation

2.3. Data quality check

3. Connectivity indicators
3.1. Transport network coverage and accessibility level indicators
3.1.1. Transport network coverage level indicator
3.1.2. Transport network speed indicator

3.2. Intersection coverage level indicator
3.3. Stop-transfer potential indicator
3.4. Route overlap indicator

4. Case study
4.1. Transport network coverage level analysis
4.2. Average speed analysis
4.3. Intersection coverage analysis
4.4. Stop-transfer potential
4.5. Route overlap

5. Conclusions
References


Figures
   

Fig. 1.

General modeling framework.


Fig. 2.

Stop connectors to the transport network centerline.


Fig. 3.

Public transport connectivity indicators.


Fig. 4.

Network coverage in Auckland CBD (a) 08:00–10:00 and (b) 20:00–22:00.


Fig. 5.

Transfers when routes do not overlap (a) and overlap (b).


Fig. 6.

Auckland’s PT network.


Fig. 7.

Portland’s PT network.


Fig. 8.

Vancouver’s PT network.


Fig. 9.

Transport network coverage level statistics.


Fig. 10.

Vancouver transport network coverage.



Tables

Table 1. The general transit feed specification data tables (Google Transit 2010).

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