202 research outputs found
BuSCOPE: Fusing individual & aggregated mobility behavior for āLiveā smart city services
While analysis of urban commuting data has a long and demonstrated history of
providing useful insights into human mobility behavior, such analysis has been
performed largely in offline fashion and to aid medium-to-long term urban
planning. In this work, we demonstrate the power of applying predictive
analytics on real-time mobility data, specifically the smart-card generated
trip data of millions of public bus commuters in Singapore, to create two novel
and "live" smart city services. The key analytical novelty in our work lies in
combining two aspects of urban mobility: (a) conformity: which reflects the
predictability in the aggregated flow of commuters along bus routes, and (b)
regularity: which captures the repeated trip patterns of each individual
commuter. We demonstrate that the fusion of these two measures of behavior can
be performed at city-scale using our BuScope platform, and can be used to
create two innovative smart city applications. The Last-Mile Demand Generator
provides O(mins) lookahead into the number of disembarking passengers at
neighborhood bus stops; it achieves over 85% accuracy in predicting such
disembarkations by an ingenious combination of individual-level regularity with
aggregate-level conformity. By moving driverless vehicles proactively to match
this predicted demand, we can reduce wait times for disembarking passengers by
over 75%. Independently, the Neighborhood Event Detector uses outlier measures
of currently operating buses to detect and spatiotemporally localize dynamic
urban events, as much as 1.5 hours in advance, with a localization error of 450
meters.Comment: ACM MobiSys 201
Spatiotemporal Analysis of Competition Between Subways and Taxis Based on Multi-Source Data
Excessive competition between taxis and subways has eroded the advantages of public transit systems such as worsening road traffic congestion and environment. This study aims to improve the appeal of subways by a comprehensive understating of competition between taxis and subways. We investigate competitive relationship between these two transportation modes by using empirical multi-source data. First, non-negative matrix factorization (NMF) algorithm is used to discover the spatiotemporal travel patterns of subway-competing taxi users (SCTUs). Second, we propose a new index to quantify the competitiveness of subways based on the actual mode choices results. Then, we reveal the spatiotemporal heterogeneity of competitiveness from perspective of subway network. Taking Beijing, China, for a case study, we extract a week's worth of GPS records on taxi trajectory and smartcard data of subways. Subway-competing taxi trips (SCTTs) account for the largest proportion of the total taxi trips. As a result, three basic patterns are found in SCTTs. Subway station pairs with high and less competition are divided according to competitiveness index. Among low competition station pairs, three spatial structures are observed, including low-competition collinearity corridors, radial communities, and links between paralleled subway lines. Combining the distribution results of travel pattern and competitiveness degree, short-term and long-term planning suggestions are recommended respectively for station pairs with high demand but low competitiveness and those with low demand and low competitiveness. These findings provide useful insights into promoting more effective and sensitive policies to balance the competition and attract more taxi passengers to the subway system
Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps
Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes
Urban Accessibility Measurement and Visualization ā A Big Data Approach
Accessibility measurement has always been an important question in different areas including transportation, urban planning, politics, and sociology. However, how to measure transportation accessibility in different areas have been limited to data availability and technology. Recently, with increasing availability in public transportation data, we found a gap between current methods and large volume of data now available. This thesis developed a new method to measure multi-mode transportation data, including taxi, bus, and subway. Based on this measurement, we can visualize and understand the spatiotemporal patterns of accessibility in New York City (NYC). With historical travel records and public transit schedule, Relative Index (RI) is developed in this thesis to measure and compare the differences in the accessibility in NYC. RI distribution patterns during different time periods were also compared and analyzed for more information about transportation in NYC. By the end of this thesis, a practical application that measured accessibility for nine major hospitals in NYC was provided. Results in this thesis showed that subways have more impacts about accessibility than bus. Also, service frequency during different time of a day has affect accessibility
Understanding Mobility and Transport Modal Disparities Using Emerging Data Sources: Modelling Potentials and Limitations
Transportation presents a major challenge to curb climate change due in part to its ever-increasing travel demand. Better informed policy-making requires up-to-date empirical mobility data to model viable mitigation options for reducing emissions from the transport sector. On the one hand, the prevalence of digital technologies enables a large-scale collection of human mobility traces, providing big potentials for improving the understanding of mobility patterns and transport modal disparities. On the other hand, the advancement in data science has allowed us to continue pushing the boundary of the potentials and limitations, for new uses of big data in transport.This thesis uses emerging data sources, including Twitter data, traffic data, OpenStreetMap (OSM), and trip data from new transport modes, to enhance the understanding of mobility and transport modal disparities, e.g., how car and public transit support mobility differently. Specifically, this thesis aims to answer two research questions: (1) What are the potentials and limitations of using these emerging data sources for modelling mobility? (2) How can these new data sources be properly modelled for characterising transport modal disparities? Papers I-III model mobility mainly using geotagged social media data, and reveal the potentials and limitations of this data source by validating against established sources (Q1). Papers IV-V combine multiple data sources to characterise transport modal disparities (Q2) which further demonstrate the modelling potentials of the emerging data sources (Q1).Despite a biased population representation and low and irregular sampling of the actual mobility, the geolocations of Twitter data can be used in models to produce good agreements with the other data sources on the fundamental characteristics of individual and population mobility. However, its feasibility for estimating travel demand depends on spatial scale, sparsity, sampling method, and sample size. To extend the use of social media data, this thesis develops two novel approaches to address the sparsity issue: (1) An individual-based mobility model that fills the gaps in the sparse mobility traces for synthetic travel demand; (2) A population-based model that uses Twitter geolocations as attractions instead of trips for estimating the flows of people between regions. This thesis also presents two reproducible data fusion frameworks for characterising transport modal disparities. They demonstrate the power of combining different data sources to gain new insights into the spatiotemporal patterns of travel time disparities between car and public transit, and the competition between ride-sourcing and public transport
Characterizing the temporally stable structure of community evolution in intra-urban origin-destination networks
Intra-urban origin-destination (OD) network communities evolve throughout the
day, indicating changing groups of closely connected regions. Under this
variation, groups of regions with high consistency of community affiliation
characterize the temporally stable structure of the evolution process, aiding
in comprehending urban dynamics. However, how to quantify this consistency and
identify these groups are open questions. In this study, we introduce the
consensus OD network to quantify the consistency of community affiliation among
regions. Furthermore, the temporally stable community decomposition method is
proposed to identify groups of regions with high internal and low external
consistency (named "stable groups"), where each group consists of temporally
stable cores and attaching peripheries. Wuhan taxi data is used to verify our
methods. On the hourly time scale, eleven stable groups containing 82.9% of
regions are identified. This high percentage suggests that dynamic communities
can be well organized via cores. Moreover, stable groups are spatially closed
and more likely to distribute within a single district and separated by water
bodies. Cores exhibit higher POI entropy and more healthcare and shopping
services than peripheries. Our methods and empirical findings contribute to
some practical issues, such as urban area division, polycentric evaluation and
construction, and infectious disease control
The travel pattern difference in dockless micro-mobility: shared e-bikes versus shared bikes
To facilitate the tailoring of dockless bike-sharing and electric bike (e-bike) sharing services and assist in formulating effective regulations, this study aims to unravel the spatio-temporal travel patterns specific to e-bike-sharing and bike-sharing systems, utilising interpretable machine learning methods and a large-scale trip-level dataset in Kunming, China. The results show that shared bikes and e-bikes exhibit overall similarities and subtle differences in many aspects, such as trip attributes and spatial distribution. Additionally, both shared bikes and shared e-bikes have three basic temporal patterns for commuting and recreational purposes. Regarding the differences, e-bike sharing networks are more dispersed and bigger, and bike sharing tends to form densely connected clusters of flow, exhibiting a local concentration of activity. Besides, the commuting activities within e-bike sharing systems exhibit two patterns: direct travel to the destination and integration with public transit. In contrast, shared bikes predominantly rely on public transit transfers for commuting purposes
- ā¦