Home-Work Zone Identification from Taxi GPS Data: A Case Study of Shanghai, China

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Huang Yan ◽  
Yue Li ◽  
Xujian Guo ◽  
Changpeng Guo
Keyword(s):  
Gps Data ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 230
Author(s):  
Onel Pérez-Fernández ◽  
Juan Carlos García-Palomares

Moped-style scooters are one of the most popular systems of micro-mobility. They are undoubtedly good for the city, as they promote forms of environmentally-friendly mobility, in which flexibility helps prevent traffic build-up in the urban centers where they operate. However, their increasing numbers are also generating conflicts as a result of the bad behavior of users, their unwarranted use in public spaces, and above all their parking. This paper proposes a methodology for finding parking spaces for shared motorcycle services using Geographic information system (GIS) location-allocation models and Global Positioning System (GPS) data. We used the center of Madrid and data from the company Muving (one of the city’s main operators) for our case study. As well as finding the location of parking spaces for motorbikes, our analysis examines how the varying distribution of demand over the course of the day affects the demand allocated to parking spaces. The results demonstrate how reserving a relatively small number of parking spaces for scooters makes it possible to capture over 70% of journeys in the catchment area. The daily variations in the distribution of demand slightly reduce the efficiency of the network of parking spaces in the morning and increase it at night, when demand is strongly focused on the most central areas.


2017 ◽  
Vol 9 (6) ◽  
pp. 949 ◽  
Author(s):  
Ying Hui ◽  
Mengtao Ding ◽  
Kun Zheng ◽  
Dong Lou
Keyword(s):  
Gps Data ◽  

Author(s):  
Christoffel Venter ◽  
Nyasha Minora ◽  
Kory Shukrani ◽  
Jacques du Toit

This chapter describes the use of GPS in a multi-method approach to explore environmental factors affecting walking patterns in South Africa. Quantitative measures of walking activity are derived from multiday GPS tracks of a sample of people in three case study areas in Pretoria, South Africa. The data suggests that a significant amount of walking takes place across a range of neighbourhood types. The authors then describe a methodology that marries the strengths of GPS data—notably its detail and its suitability for visualisation—with the benefits of more open-ended qualitative research methods to obtain richer insights into the motivations behind the observed behaviours, and the extent to which these are related to built environment factors. A key finding is that personal security and fear of crime is a critically important factor driving both the perceptions and behaviour of pedestrians, especially women. Specific adaptive behaviours are observed that warrant further research.


2020 ◽  
Vol 12 (16) ◽  
pp. 6368 ◽  
Author(s):  
Carlos Oliveira Cruz ◽  
Joaquim Miranda Sarmento

Urban mobility is experiencing a profound change. Mobility patterns are becoming more complex, and typical home–work–home travel is no longer the rule, as journeys tend to connect multiple points in a rather inconstant pattern. This has changed the approach to transport planning. Existing transportation planning and operation approaches have been focussed on the ability to identify and forecast typical home–work/school–home travel and subsequently plan the transport system accordingly. The traditional approach has been: Forecast - > plan - > deliver. New mobility patterns and mobility solutions are characterised by greater flexibility, taking advantage of the “sharing concept” and simultaneously providing solutions that have lower greenhouse gas (GHG) emissions. These dynamics and an evolving environment raise several new challenges at different levels, fostering the development of Mobility-as-a-Service (MaaS). This system transforms the physical transportation system into a commodity and takes advantage of the internet of things (IoT). However, the onset of MaaS solutions is anything but linear. Several business models have emerged, with different partners originating from different industries (e.g., technological, transport operators, infrastructure managers, etc.) developing their own solutions, often in competition with others. It is not unusual to find different MaaS solutions in the same city, which integrate different solutions. This paper intends to provide an analysis on the main challenges affecting mobility in general, and MaaS in particular, as well as the main business models used for delivering MaaS solutions. The paper uses a case study in Lisbon to illustrate some of the challenges.


Author(s):  
Y. Sung Bum ◽  
J. Sungha ◽  
L. Hyoung Joon ◽  
P. Sang Yoon ◽  
H. Joon

2000 ◽  
Vol 52 (11) ◽  
pp. 1043-1047 ◽  
Author(s):  
Matt King ◽  
Richard Coleman ◽  
Peter Morgan
Keyword(s):  
Gps Data ◽  

Sign in / Sign up

Export Citation Format

Share Document