생체의 모델링과 Simulation

Rapid urbanization creates new challenges and issues, and the smart city concept offers opportunities to rise to these challenges, solve urban problems and provide citizens with a better living environment. This paper presents an exhaustive literature survey of smart cities. First, it introduces the origin and main issues facing the smart city concept, and then presents the fundamentals of a smart city by analyzing its definition and application domains. Second, a data-centric view of smart city architectures and key enabling technologies is provided. Finally, a survey of recent smart city research is presented. This paper provides a reference to researchers who intend to contribute to smart city research and implementation.

A literature survey on smart cities

As increasing volumes of urban data are captured and become available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based decision making and policies. In this paper, we focus on a particularly important urban data set: taxi trips. Taxis are valuable sensors and information associated with taxi trips can provide unprecedented insight into many different aspects of city life, from economic activity and human behavior to mobility patterns. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses (e.g., compare different regions over time). This problem is compounded due to the size of the data-there are on average 500,000 taxi trips each day in NYC. We propose a new model that allows users to visually query taxi trips. Besides standard analytics queries, the model supports origin-destination queries that enable the study of mobility across the city. We show that this model is able to express a wide range of spatio-temporal queries, and it is also flexible in that not only can queries be composed but also different aggregations and visual representations can be applied, allowing users to explore and compare results. We have built a scalable system that implements this model which supports interactive response times; makes use of an adaptive level-of-detail rendering strategy to generate clutter-free visualization for large results; and shows hidden details to the users in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and economists that show how our model and system enable domain experts to perform tasks that were previously unattainable for them.

Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips

The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect The obtained gravity model also closely reproduces the exponential trip displacement distribution The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy We also construct a spatial network where the edge weights denote the interaction strengths The communities detected from the network are spatially cohesive and roughly consistent with province boundaries We attribute this pattern to different distance decay parameters between intra-province and inter-province trips

Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data

Revealing travel patterns and city structure with taxi trip data

As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users’ locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis’ traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities.

The scaling of human mobility by taxis is exponential

People often have the demand to decide where to wait for a taxi in order to save their time. In this paper, to address this problem, we employ the non-homogeneous Poisson process (NHPP) to model the behavior of vacant taxis. According to the statistics of the parking time of vacant taxis on the roads and the number of the vacant taxis leaving the roads in history, we can estimate the waiting time at different times on road segments. We also propose an approach to make recommendations for potential passengers on where to wait for a taxi based on our estimated waiting time. Then we evaluate our approach through the experiments on simulated passengers and actual trajectories of 12,000 taxis in Beijing. The results show that our estimation is relatively accurate and could be regarded as a reliable upper bound of the waiting time in probability. And our recommendation is a tradeoff between the waiting time and walking distance, which would bring practical assistance to potential passengers. In addition, we develop a mobile application TaxiWaiter on Android OS to help the users wait for taxis based on our approach and historical data.

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Xiao Liang

Papers

The scaling of human mobility by taxis is exponential

Where to wait for a taxi