An individual-based spatio-temporal travel demand mining method and its application in improving rebalancing for free-floating bike-sharing system

2021 ◽  
Vol 50 ◽  
pp. 101365
Author(s):  
Yuan Tian ◽  
Xinming Zhang ◽  
Binyu Yang ◽  
Jian Wang ◽  
Shi An
2020 ◽  
Vol 81 ◽  
pp. 101483 ◽  
Author(s):  
Rui Zhu ◽  
Xiaohu Zhang ◽  
Dániel Kondor ◽  
Paolo Santi ◽  
Carlo Ratti

2019 ◽  
Vol 11 (19) ◽  
pp. 5525 ◽  
Author(s):  
Jinjun Tang ◽  
Fan Gao ◽  
Fang Liu ◽  
Wenhui Zhang ◽  
Yong Qi

Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.


Author(s):  
Jeroen van Ameijde ◽  
Zineb Sentissi

Ongoing urbanization, combined with market fundamentalism as the prevailing mode of political management, is leading to the spatial and social segregation of economic classes in cities. The housing market, being driven by economic interests rather than public policy, favors inflexible forms of ownership or tenancy that are increasingly incompatible with the more diverse forms of live-work patterns and family structures occurring in the society. This paper presents a research-by-design project that explores a speculative future scenario of housing, based on current developments in digital technologies and their impact on the mobility and accessibility to services enjoyed by urban residents. It references technology platforms that underpin the 'sharing economy' or 'gig economy', such as 'pay-as-you-go' car and bike sharing programs or internet and smartphone-based services for taxis or temporary accommodation. The study explores how new forms of participation in the housing market could circumvent the current segregation of different communities across the city. It describes a speculative system of distributed residential spaces, accessible to all on a 'pay-for-time-used' basis. By offering freedom of choice across domestic functions of greater range and accessibility than found within existing housing or hotel accommodation, the system would enable opportunistic or nomadic forms of living linked to the dynamic spatio-temporal occurrences of social, cultural or economic opportunities. The research references how new forms of social networking create new challenges and opportunities to participate in communities and explores how new technologies, applied to housing, can help to find a 'sense of belonging' within the technological society.


Author(s):  
Jingjing Li ◽  
Qiang Wang ◽  
Wenqi Zhang ◽  
Donghai Shi ◽  
Zhiwei Qin

Influenced by the era of the sharing economy and mobile payment, Dockless Bike-Sharing System (Dockless BSS) is expanding in many major cities. The mobility of users constantly leads to supply and demand imbalance, which seriously affects the total profit and customer satisfaction. In this paper, we propose the Spatio-Temporal Mixed Integer Program (STMIP) with Flow-graphed Community Discovery (FCD) approach to rebalancing the system. Different from existing studies that ignore the route of trucks and adopt a centralized rebalancing, our approach considers the spatio-temporal information of trucks and discovers station communities for truck-based rebalancing. First, we propose the FCD algorithm to detect station communities. Significantly, rebalancing communities decomposes the centralized system into a distributed multi-communities system. Then, by considering the routing and velocity of trucks, we design the STMIP model with the objective of maximizing total profit, to find a repositioning policy for each station community. We design a simulator built on real-world data from DiDi Chuxing to test the algorithm performance. The extensive experimental results demonstrate that our approach outperforms in terms of service level, profit, and complexity compared with the state-of-the-art approach.


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