=Paper=
{{Paper
|id=Vol-3910/aics2024_p61
|storemode=property
|title=Characterizing Multi-Source Data for Effective Urban Mobility Modelling: The Case of New York City
|pdfUrl=https://ceur-ws.org/Vol-3910/aics2024_p61.pdf
|volume=Vol-3910
|authors=Tonny Rutayisire,Jun Liu,Samuel Moore,Carlos Balsas
|dblpUrl=https://dblp.org/rec/conf/aics/Rutayisire0MB24
}}
==Characterizing Multi-Source Data for Effective Urban Mobility Modelling: The Case of New York City==
https://ceur-ws.org/Vol-3910/aics2024_p61.pdf
Characterizing Multi-Source Data for Effective Urban
Mobility Modelling: The Case of New York City
Tonny Rutayisire1,∗ , Jun Liu1,† , Samuel Moore1,† and Carlos Balsas2,†
1
School of Computing, Ulster University, UK
2
School of Architecture & Built environment, Ulster University, UK
Abstract
To explore a more holistic view of urban human mobility, a few researchers have previously attempted to
build models driven by a fusion of multi-source datasets. Yet, owing to limited understanding of the intrinsic
characteristics and relationships underlying multi-source datasets, most of such interventions naively merge these
datasets, producing sub-optimal prediction models. To address this issue, we propose a three-step methodological
framework to capture urban mobility from an integrative point of view. The novelty of our framework is three-fold:
(i) a systematic characterization of the multi-source data to leverage hidden relationships when integrating the
data; (ii) integrating data with contextual information of the urban environments to improve explainability; and
(iii) conforming the model building to incremental learning paradigm to adapt to changing patterns of mobility
data. Using the New York City (NYC) case study, extensive analyses show salient relationships within datasets
which could form a basis for optimal data fusion and subsquently improving the mobility modelling pipeline in
line with the proposed three-step methodological framework.
Keywords
human mobility, data fusion, multi-view modelling,
1. Introduction
Human mobility patterns in urban settings are indicative of various phenomena such as travel demand,
traffic congestion, resource allocation, CO2 emission, and infectious disease spread. Therefore, under-
standing such patterns is crucial to applications such as transportation management, urban planning,
resource optimization, and epidemiology among others. In recent years, the pervasive use of sensing
technologies has produced an enormous amount of human mobility data that researchers have used
to answer critical questions pertaining to when, why, and how people move from one place to the
other [1]. A wide range of data-driven models and techniques have been proposed to capture urban
human mobility, based on a variety of dataset: taxi trip records [2] [3], call detail records (CDRs) [4] [5],
social media [6], [7]. Despite the large number of studies on urban human mobility, majority of existing
techniques have typically been built on single-source empirical data in isolation from other mobility
flows. Human mobility, especially in urban settings is multi-faceted, mainly due to diverse travel modes
and different spatial/temporal scales. It inevitably introduces a bias, against uninvolved flows, when the
capture of urban-scale mobility is single source data-driven. To explore a more holistic view of urban
mobility dynamics, a few researchers have attempted to build models and techniques driven by a fusion
of multi-source datasets. Zhang et al. [8] proposed coMobile, a multi-view learning framework based
on integration of transit view and cellphone view. The approach outperforms 2 single-view models
WHERE and TRANSIT by 51% and 58% in terms of Mean Average Percetage error (MAPE), respectively.
Jiang et al. [9] also utilized taxi GPS trajectories, smart card transaction data of subway and bus from
Beijing to model human mobility in space. It is reasonable to assume that the fusion of these mobility
datasets addresses the biases to some degree and provides a representative view of a broad spectrum of
the population. Yet, owing to limited understanding of the intrinsic characteristics and relationships of
AICS’24: 32nd Irish Conference on Artificial Intelligence and Cognitive Science, December 09–10, 2024, Dublin, Ireland
∗
Corresponding author.
†
These authors contributed equally.
$ rutayisire-t@ulster.ac.uk (T. Rutayisire); j.liu@ulster.ac.uk (J. Liu); s.moore2@ulster.ac.uk (S. Moore);
c.balsas@ulster.ac.uk (C. Balsas)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
these multi-source datasets, a considerable number of such interventions naively merge these datasets,
which end up introducing hidden drawbacks to the data fusion and subsequently producing sub-optimal
prediction models. For instance, in scenarios where space is a limitation, merging all multi-source
datasets can become an issue, yet correlations within different datasest can be leveraged to use part
of the dataset to infer the rest. Moreover, simplistic merging of datasets could actually worsen the
sampling bias problem where datasets with low levels of representativeness contribute equally or more,
to the data fusion. To this end, we propose a three-step methodological framework to capture urban
mobility from an integrative point of view: (i) In the first step, we leverage intrinsic characteristics
of multi-source data to learn, select, and integrate the most optimal vectors of the data for effective
urban mobility modelling; (ii) we then integrate the modelling process with prior knowledge contexts
of the domain to minimize the amount of data requirement, manage the uncertainties, and achieve
model interpretability; (iii) and in the third step, we conform the modelling process to incremental
learning paradigm where the model learns and updates by integrating new data streams as and when
they become available. For multi-view learning, we argue that the effectiveness of the model can be
enhanced when the above-mentioned aspects are incorporated together in the modeling process.
The rest of the paper is organized as follow: Section 2 summarizes the challenges and our motivation for
this research. In Section 3, we introduce our generic methodological framework for integrative human
mobility modeling. Section 4 introduces our case study area, the mobility datasets, and the approaches
we have used to characterize the multi-source datasets. The results and their discussions are presented
in Section 5, while the conclusion and future work are provided in Section 6.
2. Motivation
Urban mobility is multi-faceted in nature, mainly due to different modes of transport. As such, we
have recently seen a growing shift from single source to multi-source data-based models for human
mobility modelling in urban settings [9] , [10], [11], [12]. In reality, single data sources provide a limited
perspective on human mobility. For instance, taxi trip data may not represent the people who cannot
afford taxi and choose to use other means of transport such as bus, ride-sharing, and subway. On the
other hand, data from bike-sharing apps is not as representative during the winter season, and or across
unfavourable topography. Figure 1 shows the differences in trip counts for Yellow taxi & Green taxi
across NYC taxi zones. Even with the same mode of transport (but owing to different urban contexts),
we observe a huge divergence in the flow of mobility produced by yellow and green taxi, hitting as high
as 380,000 trips in some zones. Hence, whereas fusion of multiple data sources may provide a more
representative understanding of urban mobility patterns, simply adding together a series of features
extracted from multi-source datasets lays a sub-optimal foundation for multi-view mobility modeling.
It can’t simply be assumed that all multi-source datasets contribute equally to the mobility flow aspect
being learned. In [11] , authors highlight that the representativeness of each source largely depends on
the demographics of the service users in relation to the demographics of the local population, and this
dynamic is yet to be fully investigated. An effective approach to multi-view mobility modeling has to
capture the true spatial-temporal context of the datasets, and their respective qoutas for the fusion. We
argue that leveraging intrinsic characteristics of the datasets, coupled with existing domain knowledge
could result in an appropriate weighting mechanism when multi-source mobility datasets are being
fused. Therefore, our motivation stems from the need for a novel framework to effectively integrate and
learn multi-source data dynamically with domain knowledge for optimal multi-view mobility modeling.
3. Proposed Integrative Methodological Framework
To optimize multi-view mobility modeling, we propose an integrative three-step methodological frame-
work, as shown in Figure 2. The framework combines multi-source mobility data for a more representa-
tive picture of travel behaviours, then integrates it with existing domain-knowledge to minimize data
requirement while enhancing explainability, and finally conforms the mobility modeling to incremental
Figure 1: Difference in trip counts by NYC taxi zones
learning, adapting to ever-changing urban mobility conditions. Each component is briefly overviewed
in the following subsequent sections.
3.1. Multi-Source Data Fusion
In the context of multi-view learning, data fusion captures the comprehensive view of urban mobility
patterns, and also diffuses the limitations that come along with relying solely on single-source data.
However, most existing works make general assumption of the multi-source data when it is being
fused. For example in [13] , Zhang et al. (2014) used the transport data to simply complement modeling
primarily based on cellphone data, and later in [8], Zhang et al. (2017) adjusted and treated the two kinds
of data sources equally, as two independent views. This is bound to integrate the data in proportions
that don’t reflect the true representation of each view. Increasingly, authors in this field [9] [11] [14],
acknowledge that data fusion requires a full understanding of each dataset and their efficient utilization,
which is currently still limited. In our proposed framework, the characteristic context of multi-source
data is leveraged to workout an effective weighting scheme upon which data fusion is done. This will
ensure spatial-temporal patterns are realistically and dynamically captured.
3.2. Data – Knowledge Integration
It is widely known that data-driven models are only as good as the quantity and quality of data they are
trained on. Given issues relating to incompleteness, inconsistencies, sparsity and uncertainity, it is often
difficult to acquire large amounts of quality mobility data. However, integrating empirical data with
domain-specific knowledge often validates the data, enhancing model reliability and interpretability,
which is still an open challenge in human mobility research. To the best of our knowledge, very few
mobility modeling approaches exist in literature today, which incorporate domain-specific knowledge.
In MobTCast [15], the influence of semantic, social, and geographical contexts is incomporated with
historical data to tackle the sparsity problem, while predicting POIs. Likewise in [16], a framework
was proposed to leverage a knowledge graph to accommodate the influence of users, locations and
semantics, in next location recommendation. Meanwhile in [17], authors proposed an MaaS framework
Figure 2: Integrative methodological framework for multi-view mobility modeling
to combine multi-source data with an understanding of travelers’ contexts to present them personalized
and explainable services based on their preferences. Challenges withstanding, their framework aims
to achieve the possibility of providing the right information to the right user with understandable
explanations. The common principal about these methods is the integration of empirical data with one
or more kinds of contextual understanding of the urban environment. Whereas existing efforts have
focused on the influence of somewhat stable-static contexts, our integrative framework goes a step
further to incorporate situational contexts which is rather a dynamically changing context.
3.3. Incremental Learning
Urban mobility is highly dynamic, with context and patterns that typically change from time to time.
Conventional data-driven models, though they have shown considerable performance, they often
struggle to keep at per with evolving urban environments, where mobility patterns keep on changing
due to changing policy, transportation systems, road networks, weather conditions, and events. As
an emerging approach, Incremental Learning (IL) which enhances adaptability of models by learning
continuously from new incoming data streams, is poised to address these challenges suffered by static
models. In our framework, the knowledge-enhanced mobility model will be tuned to continuously
update on only new incoming data, as and when it becomes available, while retaining previously learned
knowledge.
4. Case Study: Urban Mobility in New York City
In the previous section, we give an overview of the proposed integrative three-step methodological
framework. Note however that in the present work, the focus is to preliminarily use the case of New
York City (NYC) to analyze the underlying characteristics of multi-source data, setting up an optimal
foundation for effective data fusion and subsquent multi-view mobility modeling in the line of the
proposed framework.
4.1. Study Area
In this work, we characterize and compare mobility flows extracted from NYC’s taxi and Citi bike trip
records. NYC is the largest city in the United States, known for its fast pace, dynamism and cultural
diversity. It continues to be a global hub for finance, culture, commerce, and innovation. It covers a
total area of 784 km2 with an approximate population of 8.5 million people as of 2023. The city is
composed of five boroughs: Manhattan, Brooklyn, Queens, Bronx, and Staten Island, each with its own
unique character, and it is demarcated into 263 zones, as shown in Figure 3. The city is covered with an
extensive and a well-integrated transport system that is comprised of public transit, commuter rails,
taxis, and bicycles. A number of researchers have utilized NYC as a case study to investigate different
aspects of urban mobility. For example, in [2] F. Miao et al. designed and evaluated the performance of
the data-driven vehicle balancing framework on four years’ long taxi trip data from NYC, in [18, 19]
authors narrowed in on mobility data from NYC in 2019 and 2020 to analyze the impact of COVID-19
on people’s mobility, whereas in [20] Dong et al. utilized anonymous mobile phone location and crash
report data in NYC to study the association of human mobility and road crashes. We also chose NYC as
a case study for this work for three main reasons; (1) open data policy/portal of NYC, (2) shape files for
NYC demarcations, and (3) NYC mobility survey report data.
Figure 3: NYC Boroughs & Zones
4.2. Datasets
In this section, the three NYC datasets used in this study, are summarized. The Yellow taxi and Green
taxi datasets are provided by the Taxi & Limousine Commission of NYC, while the bike-sharing dataset
is provided by Citi bike NYC. For all datasets, each record is a distinctive trip extracted, with an origin,
destination, duration, distance and fare among other trip attributes. After pre-processing of the datasets,
a total of 10,047,135 trips were successfully extracted from the Yellow taxi, 1,080,844 trips from the
Green taxi, and 1,000,000 trips from the NYC Citi bikes. To ensure temporal compatibility, the collection
period was the same for all the three datasets, running from April 1, 2017 to April 30, 2017.
4.3. Approach
We characterize multi-source datasets based on three meta-metrics: (1) correlation; and (2) representa-
tiveness. We argue that examining and leveraging these three meta-metrics will form a basis for optimal
data fusion and subsquently improving the multi-view mobility modelling pipeline.
4.3.1. Trip extraction
Firstly, we extract trips – which is the basic unit of mobility for this study. With all the three datasets
having pickup and drop-off attributes, extracting origins and destinations (OD) is simple and straight-
forward. However, to reduce the size and address errors, irrelevant attributes and errant records were
intuitively eliminated right away before any form of feature engineering was done. For example, all
records with trip duration < 5 minutes and/or trip distance > 30 miles, were automatically dropped for
all datasets.
4.3.2. Correlation analysis
We examine mobility (travel demand) to evaluate whether there exist temporal correlation between
Yellow taxi, Green taxi, and Citi bikes trip patterns in NYC. Owing to significant difference in scale among
the datasets , we observe in Figure 4 that both Green taxi and Citi bike data temporally lag the Yellow taxi
data, but there exist similar mobility trends worth further investigation. To provide fair and meaningful
comparison, we normalize the data to capture the hourly variations as a proportion of total mobility
flow for each dataset in Figure 5. We then use three different measures (Cosine-similarity, Pearson
Correlation, and Spearman Rank Correlation), to explore similarities in their temporal distribution.
These measures have been widely used in previous studies to quantify the similarity between two
vectors in a multi-dimensional space, depending on the scale and context of the data. In our context, we
opted for them because they quantify similarity in patterns regardless the difference in scale.
Figure 4: Temporal variation of mobility flow (a) Yellow taxi, (b) Green taxi, and (c) Citi bikes
4.3.3. Representation analysis
Taxi and Bike-sharing mode choices provide NYC residents with certain levels of convenience, timeliness,
and flexibility. However, they co-exist and complement other modes in an integrated transport system.
It is thus crucial to examine if travel demand extracted from the three datasets reflect their actual
respective mobility activity in NYC.
We perform a spatial coverage analysis to ensure that all significant zones are truly represented. For
each dataset, we compute travel demand as the proportion of total outgoing trip counts at the zone
level. We then use a choropleth map to visualize and compare the spatial distribution of these values,
for the three datasets. We then use official statistics from the 2017 citywide mobility survey of NYC, to
benchmark patterns derived from our datasets. Particularly we compare the trip proportions extracted
Figure 5: Normalized hourly trip counts for Yellow taxi, Green taxi, and Citi bikes
Figure 6: Spatial distribution of trip counts for (a) Yellow taxi, (b) Green taxi, and (c) Citi bike in NYC
from the datasets to the relative trip proportions extracted from the 2017 citywide mobility survey of
NYC. And consequently we perform statistical tests on some aspects of the observed trips in comparison
with surveyed trips.
5. Results & Discussions
This section summarizes the results obtained along with detailed discussions.
5.1. Correlation
Figure 5 depicts the temporal distribution of normalized travel demand as extracted from Yellow taxi,
Green taxi, and Cite bike trip records. Considerable relationships can be observed among the three
datasets within the time dimension. For instance, we can see a similar drop in demand from midnight
up until 5 a.m., across all the datasets. We also see a shoot-up in demand at 8 a.m. and another one at 6
p.m. To probe these relationships further, Table 1 summarizes results of three well-known measures of
similarity against pairs of the three datasets.
From the table 1, we can see that travel demand across all pairs of datasets generally has a positive
correlation in the temporal space. Particularly, travel demand patterns from Yellow taxi & Green taxi
exhibit a very strong alignment across time, for all the three measures utilized, whereas the correlation
Table 1
similarity measures for hourly distribution of trips extracted from Yellow taxi, Green taxi, and Citi bike datasets
Cosine-similarity Pearson Correlation Spearman Rank Correlation
yellow - green 0.978 0.919 0.906
green - bike 0.899 0.681 0.657
yellow - bike 0.914 0.759 0.710
between green taxi & cite bikes is relatively weak but still considerably above average.
5.2. Representativeness
Figure 6 depicts the spatial distribution of travel demand extracted from the 3 datasets. The hue
progressions represent the number of outgoing trip counts aggregated at each zone, divided by the
total trip counts for each of the dataset. Yellow taxi & Green taxi are regulated to mainly serve distinct
areas of NYC. Yellow taxi primarily serves the core (lower) Manhatten and the major airports, whereas
the Green taxi are regulated to operate in upper Manhatten and outer boroughs. We can see how the
illustrations in the Figure 6 are compatible with the obvious expectation owing to the above fact. For
example in Figure 6.a, we can observe a very sharp concetration of yellow taxi trips in inner Manhatten,
JFK Airport, and LaGuardia Airport, in contrast with other zones.
Figure 7: Comparison of Trip data and Survey data
To validate this representativeness, we statistically compare our trip data with the 2017 citywide
mobility survey of NYC. Figure 7 illustrates variations in the mode composition as extracted from the
trip data and survey data. We observe that among the three modes, Green taxi exhibits a strong fit,
Figure 8: Comparison of cumulative distributions for trip duration
when trip data is compared against survey data. This is further collaborated by the CDF distribution
of their respective trip durations as shown in Figure 8. We can observe that though Yellow taxi and
Citi bikes both have considerable similar distribution (p-value=>0.05), Green taxi once again exhibit
remarkable values of Kolmogorov-Smirnov (KS) test, which demonstrating a strong representativeness
of the Green taxi records against the actual mode split as by the 2017 city-wide mobility survey of NYC.
6. Conclusions, Limitations & Future Work
In this study, we proposed a three-step methodological framework for modelling urban mobility from
an integration of multi-source data. The preliminary step of our framework involves the systematic
characterization of multi-source data as a foundation for a weight-based data fusion. Using NYC as a
case study, we characterized three mobility datasets based on two meta-metrics: (1) correlation; and (2)
representativeness. Our findings revealed that there exist strong correlation between datasets, which
can be leveraged in data fusion. For instance, we see a strong temporal correlation between Yellow
taxi & Green taxi, in terms of relative travel demand, which could be used to infer missing data in one
dataset using the other. It could also be used in weighted average fusion of the data, where weights are
based on the correlation. Also revealed via our findings is the level of representativeness between the
respective mobility datasets and the actual urban scenarios. For example we see in our case study how
the Green taxi has the most alignment with actual sample data surveyed from the population in the
2017 city wide mobility survey of NYC. This could also be leveraged in the weight scheme to ensure
the most representative and reliable dataset contributes most to the data fusion. It should be noted
that this work does not attempt to examine all the three steps of the proposed framework, but rather
to give its overview, and the characterization of the multi-source data to be used as ingredients in the
subsequent steps of the framework. One of the limitations of this work however, results from the fact
that currently, we only focus on data sources from NYC. If matching multi-source data from other mega
cities like Shenzhen and Beijing was readily available, a comparison of mobility trends from different
urban cities against the two meta-metrics would further validate the characterization of multi-source
data. Another limitation stems from the age of data used (2017), given major mobility shifts that have
occurred with the coming Covid-19 pandemic. While very recent mobility data is available at the NYC
open data portal, for the interest of measuring representativeness of the data, we wanted to avoid a
time difference of data collections between trip records data and the mobility survey data, given that
the city-wide mobility survey data available is of 2017. In our future work, while acknowledging and
working around the above limitations, we plan to leverage the findings in this preliminary work, to
design an effective weighting scheme for the data fusion. We shall then improvise effective ways to
incorporate contextual knowledge in the fused data, and finally employ incremental learning to build
an efficient mobility prediction model in line with the proposed methodological framework for different
application contexts.
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