scholarly journals Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon

2021 ◽  
pp. 169580
Author(s):  
Vitória Albuquerque ◽  
Francisco Andrade ◽  
João Ferreira ◽  
Miguel Dias ◽  
Fernando Bacao
Keyword(s):  
Data Driven ◽  
Mobility Patterns ◽  
Bike Sharing ◽  
The City

scholarly journals Impacts of Smart Configuration in Pedelec-Sharing: Evidence from a Panel Survey in Madrid

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
András Munkácsy ◽  
Andrés Monzón
Keyword(s):  
Advanced Technology ◽  
Panel Survey ◽  
Mobility Patterns ◽  
Actual Use ◽  
Bike Sharing ◽  
Service Configuration ◽  
Smart Service ◽  
Barriers And Motivators ◽  
The City

Despite the recently increasing research interest, this is one of the first studies employing a panel sample of users and nonusers to understand the bike-sharing phenomenon (N=205). On the basis of a novel surveying technique, a case study on the clients of the state-of-the-art bike-sharing scheme of Madrid (Spain) is presented. BiciMAD is a system of the latest generation, namely, multimodal demand responsive bike-sharing: a fleet of electric pedal-assisted bicycles (pedelecs) with an advanced technology and unique smart service configuration to tackle challenges that may hinder the promotion of cycling and bike-sharing in the city. A statistical test has verified that there is a moderate association between previous intention and actual use of bike-sharing (Cramer’s V = 0.25) and both barriers and motivators of further use have been identified. Indicators on mobility patterns show that although drawing primarily from other sustainable modes of transport, bike-sharing has increased mobility (total number and distance of trips) and especially active travel but decreased the perceived travel time.

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

Gendered Experiences of Mobility

Transfers ◽  
2013 ◽  
Vol 3 (3) ◽  
pp. 79-98 ◽  
Author(s):  
Shahnaz Huq-Hussain ◽  
Umme Habiba
Keyword(s):  
Middle Class ◽  
Travel Behavior ◽  
Weather Conditions ◽  
Capital City ◽  
Mobility Patterns ◽  
Security Issues ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
The City ◽  
Detailed Picture

This article examines the travel behavior of middle-class women in Dhaka, the capital city of Bangladesh and one of the world's largest and most densely populated cities. In particular, we focus on women's use of non-motorized rickshaws to understand the constraints on mobility for women in Dhaka. Primary research, in the form of an empirical study that surveyed women in six neighborhoods of Dhaka, underpins our findings. Our quantitative and qualitative data presents a detailed picture of women's mobility through the city. We argue that although over 75 percent of women surveyed chose the rickshaw as their main vehicle for travel, they did so within a complex framework of limited transport options. Women's mobility patterns have been further complicated by government action to decrease congestion by banning rickshaws from major roads in the city. Our article highlights the constraints on mobility that middle-class women in Dhaka face including inadequate services, poorly maintained roads, adverse weather conditions, safety and security issues, and the difficulty of confronting traditional views of women in public arenas.

scholarly journals Universal predictability of mobility patterns in cities

2014 ◽  
Vol 11 (100) ◽  
pp. 20140834 ◽  
Author(s):  
Xiao-Yong Yan ◽  
Chen Zhao ◽  
Ying Fan ◽  
Zengru Di ◽  
Wen-Xu Wang
Keyword(s):  
Spatial Scales ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Universal Prediction ◽  
Potential Applications ◽  
Mobility Behaviour ◽  
History Of ◽  
Different Characteristics ◽  
The City

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.

Understanding Skout users’ mobility patterns on a global scale: a data-driven study

2018 ◽  
Vol 22 (6) ◽  
pp. 2655-2673 ◽  
Author(s):  
Rong Xie ◽  
Yang Chen ◽  
Shihan Lin ◽  
Tianyong Zhang ◽  
Yu Xiao ◽  
...  
Keyword(s):  
Global Scale ◽  
Data Driven ◽  
Mobility Patterns

Does Automated Enforcement Presence Impact Collisions and Crime?

2019 ◽  
Vol 2673 (10) ◽  
pp. 522-531
Author(s):  
Shewkar Ibrahim ◽  
Tarek Sayed
Keyword(s):  
Traffic Safety ◽  
Multiple Objectives ◽  
Data Driven ◽  
Limited Resources ◽  
Traffic Enforcement ◽  
Lognormal Model ◽  
Deployment Strategy ◽  
New Approaches ◽  
Efficiency And Effectiveness ◽  
The City

Enforcement agencies generally operate under a strict budget and with limited resources. For this reason, they are continually searching for new approaches to maximize the efficiency and effectiveness of their deployment. The Data-Driven Approaches to Crime and Traffic Safety approach attempts to identify opportunities where increased visibility of traffic enforcement can lead to a reduction in collision frequencies as well as criminal incidents. Previous research developed functions to model collisions and crime separately, despite evidence suggesting that the two events could be correlated. Additionally, there is little knowledge of the implications of automated enforcement programs on crime. This study developed a Multivariate Poisson-Lognormal model for the city of Edmonton to quantify the correlation between collisions and crime and to determine whether automated enforcement programs can also reduce crime within a neighborhood. The results of this study found a high correlation between collisions and crime of 0.72 which indicates that collision hotspots were also likely to be crime hotspots. The results of this paper also showed that increased enforcement presence resulted in reductions not only in collisions but also in crime. If a single deployment can achieve multiple objectives (e.g., reducing crime and collisions), then optimizing an agency’s deployment strategy would decrease the demand on their resources and allow them to achieve more with less.

Data-Driven Utilization-Aware Trip Advisor for Bike-Sharing Systems

2017 ◽  
Author(s):  
Ji Hu ◽  
Zidong Yang ◽  
Yuanchao Shu ◽  
Peng Cheng ◽  
Jiming Chen
Keyword(s):  
Data Driven ◽  
Bike Sharing

scholarly journals Exploring Travelers’ Characteristics Affecting their Intention to Shift to Bike-Sharing Systems due to a Sophisticated Mobile App

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 264 ◽  
Author(s):  
Andreas Nikiforiadis ◽  
Katerina Chrysostomou ◽  
Georgia Aifadopoulou
Keyword(s):  
Predictive Models ◽  
Classification Tree ◽  
Descriptive Analysis ◽  
Mobile App ◽  
Usage Frequency ◽  
Binary Logit ◽  
Different Types ◽  
Micro Mobility ◽  
Bike Sharing ◽  
The City

Many cities have already installed bike-sharing systems for several years now, but especially in recent years with the rise of micro-mobility, many efforts are being made worldwide to improve the operation of these systems. Technology has an essential role to play in the success of micro-mobility schemes, including bike-sharing systems. In this paper, it is examined if a state-of-the-art mobile application (app) can contribute to increasing the usage levels of such a system. It is also seeking to identify groups of travelers, who are more likely to be affected by the sophisticated app. With this aim, a questionnaire survey was designed and addressed to the users of the bike-sharing system of the city of Thessaloniki, Greece, as well as to other residents of the city. Through a descriptive analysis, the most useful services that an app can provide are identified. Most importantly, two different types of predictive models (i.e., classification tree and binary logit model) were applied in order to identify groups of users who are more likely to shift to or to use the bike-sharing system due to the sophisticated app. The results of the two predictive models confirm that people of younger ages and those who are not currently users of the system are those most likely to be attracted to the system due to such an app. Other factors, such as car usage frequency, education, and income also appeared to have slight impact on travelers’ intention to use the system more often due to the app.

scholarly journals Characterization of Public Transit Mobility Patterns of Different Economic Classes

2020 ◽  
Vol 12 (22) ◽  
pp. 9603
Author(s):  
Priscila Santin ◽  
Fernanda R. Gubert ◽  
Mauro Fonseca ◽  
Anelise Munaretto ◽  
Thiago Henrique Silva
Keyword(s):  
Public Transit ◽  
Urban Spaces ◽  
Mobility Patterns ◽  
Household Travel Survey ◽  
Usage Patterns ◽  
Data Source ◽  
The Common ◽  
Household Travel ◽  
The City

This paper analyzes public transit mobility of different economic classes of Curitiba, Brazil, exploring an official smart card dataset provided by the city. With the population divided into subsets corresponding to economic strata, we characterized vital spatial-temporal transit usage patterns, such as departure times and destinations reached by different economic classes. We also constructed a network representing the common origin and destination of public transit users, enabling discovering distinct patterns. Among the results, we observe that with the increase in wealth, the morning activity is postponed (on average for 2 h), and the spatial distribution of the trips becomes more localized compared with lower classes. We also show that our model captures fairly well realistic mobility patterns exploring a cheaper and larger-scale data source by comparing our results with a household travel survey from Curitiba. Understand how people in different economic classes appropriate urban spaces help to provide subsidies for, e.g., more sustainable economic development propositions.

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