2020 Scienceline Publication

Journal of Civil Engineering and Urbanism

Volume 10, Issue 4: 35-41; July 25, 2020

ISSN-2252-0430

DOI: https://dx.doi.org/10.29252/scil.2020.jceu6

Spatio-Temporal Understanding and Representation of Transformative

Urban Mobility and Trip Patterns, A Review

Arian Behradfar¹^and Soheil Mohammadi²Error! Bookmark not defined.

¹Department of Geomatics and Spatial Information Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran

²Department of Naval, Electrical, Electronic and Telecommunications Engineering, DITEN, University of Genoa, Genoa 16126, Italy

^Corresponding author’s Email: arian.behradfar92@gmail.com

ABSTRACT

The rapid development in transport system monitoring provide planners and researchers with new opportunities to

understand the trends of mobility patterns in urban areas, known as transformation of urban mobility. It brings

businesses and cities together to implement system-level integrated initiatives to conducting urban mobility and

transport system toward a more efficient future. As a result, identification of the trip patterns and spatio-temporal

dependencies in urban areas requires a comprehensive understanding of high-dimensional human mobility

dynamics. These emerging trends need a framework to identify urban mobility patterns from a spatio-temporal

perspective that includes various visualized representation of mobility patterns and travel behaviour. The main

purpose of this study is to investigate different data sources and methods used in the literature to obtain the

proposed patterns in urban areas. The spatio-temporal models evaluated in this review can be used in a wide range

of mobility studies suggesting trip patterns and related variables are significantly affected by spatial and non-spatial

impacts.

Keywords: Transformation of urban mobility, Mobility flows, Trip patterns, spatio-temporal dependencies, spatial

analysis

INTRODUCTION

creation and deployment of sustainability. As a result, the

identification of main individual aspects that provide vital

information on the progress and effectiveness of mobility

transformation is completely essential in transportation

engineering. These individual aspects have to comprise

various characteristics of interaction trends and precisely

express mobility specifics in urban areas so that

simultaneously take into account the three dimensions of

sustainability: social, economic and environmental issues

(Gheitarani et al., 2020: Bibri, 2019 ).

Mobility can be considered as an important socio-

economic resource and articulator in society, since it is

directly related to the movement of people between

different spatio-temporal hierarchies. The rapid growth in

urban areas, private vehicle fleet and the lack of proper

planning of transportation systems have led to increasing

deterioration of mobility, emerging different social

challenges and environmental problems (Costa et al.,

2017: Khanian et al., 2018 ).

One of the best sources for monitoring the

transformation of urban mobility is obtaining daily

mobility patterns of an urban area. These regular mobility

flows are reflected in different aspects of people’s

movement trajectory and transport system structure. Urban

trips distribution, travel behavior, spatial interaction,

activity identification, temporal dependencies, and trip

pattern segments. This information can lead to

understanding the current structure of urban mobility and

existing trends and challenges in large-scale from a spatio-

temporal perspective (Sun et al., 2016 ).

Urban areas will require mobility solutions and

changes that are sustainable, affordable, inclusive and

integrated with people-centric infrastructure and services.

These solutions rest on the transformation of urban

mobility that includes a holistic and systemic approach

that acts in the intersection between mobility

infrastructure, social benefits, economic efficiency, and

environmental impact. The transformation of urban

mobility delivers a cohesive set of initiatives that act at the

system level. Each co-designed action aims to remove

barriers and mobilize stakeholders to contribute to

transforming mobility (Hegyi et al., 2019 ).

As we discussed, the main purpose of this study is to

understand the trip patterns in urban areas based on travel

behaviour analysis. Deriving the major characteristics of

The future of mobility represents opportunities for

cities and planners to collaborate and accelerate the

To cite this paper: Behradfar A and Mohammadi S. (2020). Spatio-Temporal Understanding and Representation of Transformative Urban Mobility and Trip Patterns, A Review. J.

Civil Eng. Urban., 10 (4): 35-41. DOI: https://dx.doi.org/10.29252/scil.2020.jceu6

Behradfar and Mohammadi, 2020

mobility system and the necessitate trends in different

to return to a few highly frequented locations (Gonzalez et

al., 2008 ). The diversity of spatio-temporal regularity was

found to be constrained, providing an encouraging

foundation for studying and analysing mobility (Song et

al., 2010 ).

parts of the mobility strategies makes it possible to analyse

different aspects of the transformation of urban mobility.

The emergence of such trends with high-order interactions

among space, time and the mentioned individual attributes

indeed brings us new opportunities to integrate more

knowledge into urban planning and decision-making

system in urban areas.

In this regard and as a literature review, different

location-based data sources and proposed spatial analysis

have been taken into account in the identification of urban

mobility and trip patterns have been considered and

discussed. Furthermore, the most frequent models and

methods in the literature will be introduced and various

spatial and non-spatial aspects have been taken into

account

Another emerging area of urban mobility studies

relies on network theory and statistical tools. These studies

that termed as computational science focus on analysing

social interactions and temporal attributes of mobility

patterns (Hamedmoghadam et al., 2019 ). In a series of

papers, GIS-based accessibility modelling and network

analysis have been used to trip generation assessment and

patterns of trip occurrence in accessibility infrastructure

(Castanho et al., 2020 ), human mobility patterns with the

support of location-based services (Ebrahimpour et al.,

2020: Habibi et al, 2020 ), similarity and dissimilarity of

the dynamic mobility patterns on the basis on spatio-

temporal characteristics (Yuan et al., 2012 ).

LITERATURE REVIEW

Location tracking and location-based service patterns

are widely being used in a growing number of applications

and different levels (Toch et al., 2019: Kahvand et al,

2015 ). To contribute to the transformation of urban

mobility, studies have focus on geographical area

subdivision-based mobility patterns with distinct

characteristics. Different clustering algorithms and feature

extraction methods have been used to detect spatial

operation patterns in urban mobility (Kang et al., 2016 ).

Mobility behaviour analysis, trip distribution, activity

identification, understanding spatio-temporal interactions

and social characteristics are the most common ways that

represents reliable sources to analyze mobility patterns in

urban areas (Ali et al., 2016 ; Sun et al., 2016 ).

Various data mining methods have been developed

to uncover transit behaviour patterns on the basis of

heterogeneous geospatial datasets, including public

transport-oriented and passenger-oriented approaches for

mobility analysis (El Mahrsi et al., 2017 ), spatial affinity

propagation with spatial-behavioural features in trip

segments (Kieu et al., 2018 ), trip chaining methods for

pattern estimation, validation and diversification (Li et al.,

2018 ), and choice modelling for activity identification and

analysis (Wang et al., 2017 ).

The first efforts to learn human mobility patterns were

associated with classic transport sciences. Since the

nineteenth century and as time-use or time-budget studies,

various measures have been taken to identify different

activities during the day (Szalai, 1966 ). Transportation

forecasting was one of the first research fields that made

use of mobility analysis, and mobility patterns have been

studied since the late 1950s. The first analytical models

implemented pre-calculated probability distributions of

possible urban trip patterns mainly based on land use and

socio-demographic characteristics (Douglass et al., 1957 ).

Starting in the 2000s, this approach was gradually

enriched by activity-based models, e.g., computerized

models that use agent-based simulations, where the agents

are modelled and driven by specific human activity such

as work, leisure, and shopping (Kitamura et al., 2000 ).

Over recent years and with the fast development of

information and communication technologies (ICT) and

intelligent transportation system (ITS), large quantities of

digital traces and geo-referenced social media data plus

transit smart cards and time table data that register

individual activity and trip behaviour at both spatial and

temporal scales have become available (Batty et al., 2012 ).

These datasets enable planners and researchers to model

and analyse spatio-temporal interactions in urban mobility

based on variety of proxies on human activities (Han et al.,

2016; Sun et al., 201 4).

Over the past few years, many studies have been

conducted to explore urban trip patterns using various

modelling and analytic approaches based on massive

human mobility data, such as optimization-based routing

equilibrium models for congestion alleviation (Çolak et

al., 2016 ), clustering-based correlated analyses of mobility

similarities and relationships, low-level mobility pattern

discovery and multi-scale exploration of social

Large-scale studies showed human trajectories have

a high degree of temporal and spatial regularity, each

individual being characterized by a time-independent

characteristic travel distance and a significant probability

J. Civil Eng. Urban., 10 (4): 35-41, 2020

fragmentation (Schneider et al., 2013 ). With the

activity identification along with priorities, types and

scheduling. In fact, travel behavior is a part of complex

hierarchical structures that include activity trends, mobility

flows, spatial interactions, and finally, activity

identification to derive mobility patterns in urban areas.

In general, the total number of daily stops,

particularly between relatively distinct units constructs

urban trips. Therefore, travel behavior can be defined as

the spatial way sequenced segments of the urban trip take

place. There have been considerable efforts extended to

understanding the spatio-temporal characteristics and

complexity of travel behavior using a variety of

abstraction level in modelling (Pritchard et al., 2014 ).

availability of massive human mobility data, machine

learning techniques have been playing a more and more

important role in gaining a deep understanding of human

mobility behaviour (Toch et al., 2019 ), ranging from

movement pattern mining (Chen et al., 2016 ), mobility

prediction (Ouyang et al., 2018 ), and movement mode

classification to lifestyle discovering and prediction (Chen

et al., 2019 ).

DISCUSSION

Travel behaviour analysis toward transformative

urban mobility

Starting from the premise that the mobility is a

fundamental issue, discovering the transformation of urban

mobility impact on different parts of the urban area is

essential. Mobility paradigm shifting in cities happens in a

wide range of other contexts that are host of problems and

challenges including urban accessibility degradation, lack

of social equity, land use conflicts, urban sprawl, and

social exclusion (Bibri, 2019: Naghdi et al, 2016 ). Given

these issues, the transformation of urban mobility

appraisal guidance includes a spectrum of various impacts

through qualitative and quantitative assessments and

comparison analysis (Camagni et al., 2002; Nielsen et al.,

2012: Sarvar et al., 2011 ).

Visualized representation of mobility patterns

Temporal distribution of urban mobility

The high resolution temporal distribution of boarding

times during a 24-hour period includes the number of trips

per minute for any moment. The main advantage of this

diagram is the explicit representation of urban trip peaks in

a day. The diagram in figure 1 indicates that the sharp

peaks in urban mobility occur in starting (8 am) and

ending (7pm) hours of working time in Seol, South Korea

(Ali et al., 2016 ).

In-depth knowledge of spatio-temporal distribution

of urban mobility is a key aspect in characterizing the

behaviour of transit users in urban areas for mobility

transforming identification. This information can provide

valuable insight into urban trips analysis such as when

they begin and end within complete day. Spatial

observations of urban trip distribution can indicate

infrastructure efficiency, transport accessibility and

modality, and traffic congestions in urban areas. Mode

share trends based on daily time, weather condition,

season, and other features allow transit planners to remark

mobility transformation to promote their services (Sun et

al., 2016 ).

Since urban mobility trends are based on the

particular purposes, utilizing integrated attributes like the

trip duration, type and regularity can lead to assigning

activities to individual stakeholders for the activity-based

model. Home, work or educational units can be considered

along with socio-demographic attributes for activity-based

micro-simulations (Gan et al., 2020 ).

The proposed discussion starts with observations

concerning the relationship between the spatial and non-

spatial conceptualization of travel behavior based on

Figure 1. High temporal resolution of trip distribution (Ali

et al., 2016 ).

Accurate distribution of mobility patterns based on

temporal features along with public transport schedule,

private vehicles flows, travel time tables, and qualifying

transfer volume by different modes can lead to a large

scale agent-based simulation model of urban mobility.

Behradfar and Mohammadi, 2020

Advanced statistical approaches for travel behavior

 Spatio-temporal flows of mobility

analysis and activity identification provide a new insight

of mobility patterns to transit planning. Furthermore, it can

be considered as an effective framework to monitor the

transformation of urban mobility and transport

infrastructure performance.

As shown in figure 3, the total mobility flows

entering a distinct region are inflow and the ones leaving a

region for another denote outflow. Both flows track the

mobility patterns between different regions of urban area.

these flows are very applicable in risk assessment, transport

management and particularly, the transformation of urban

mobility studies. They can accurately reflect the spatio-

temporal dependencies of urban mobility such as spatial

correlation, traffic congestion and surrounding conditions.

Furthermore, it is possible to employ the spatio-

temporal residual network analysis methods to model

nearby and distant spatial dependencies of any two regions,

temporal closeness, period and trends, dynamical

aggregated outputs, and assigned weights. In this way, the

effects of different components, internal structures and

external factors on proposed flows can be evaluated (Zhang

et al., 2017 ).

Spatial distribution of urban mobility

The trip patterns extracted from large-scale datasets

at spatially-aggregated levels, as shown in figure 2, can

efficiently contribute to travel behavior analysis in urban

areas. These visualized representations of the spatial

distribution of urban mobility cover a wide range of

impacts, variables, flows, paradigm shifts, and attributes

related to transportation network in urban areas (Bao et

all., 2018 ). These GIS-based models can be used in

mobility patterns recognition, travel behavior, activity

identification, land use planning, and socio-economic

impacts in urban areas. They also are the core of spatially

integrated approaches for sustainability. The inclusion of

urban trip patterns extracted from such spatial analysis

significantly improve the performance of the transport

system and logistic infrastructure. They can be an

aggregated basis of different quantitative and qualitative

comparison analysis for various purposes (Haselsteiner et

al., 2015 ).

Figure 3. Regional mobility inflow and outflow (a) and

measurements (b) - (Zhang et al., 2017).

Figure 2. Spatial distribution of the coefficient of urban

trip patterns variables (Bao et all., 2018).

J. Civil Eng. Urban., 10 (4): 35-41, 2020

Spatial interaction estimation

CONCLUSION

The emergence of different mobility monitoring

systems brings new opportunities to integrate complex and

higher-order interactions among space, time and attributes.

Analytical frameworks and statistical techniques provide

better understating and representation ways to better

interpret mobility patterns and urban dynamics. In this

regard, by using factorization model to decompose high-

dimensional mobility data into specific patterns, from

which we can extract key information by reasoning about

the semantics of regions and activities in urban areas (Sun

et al., 2016: Zaker Haghighi et al, 2014 ).

Once the urban trips are identified, additional

attributes such as zone properties, coordinates, distances,

and travel mode are considered to generate origin-

destination matrixes (OD) for zone analysis. OD matrix

integrates activity identification data to assign activities to

individual users and trips for the activity-based model, as

different spatial interactions, shown in figure 4. Spatial

interaction models illustrate how different zones are

functionally interdependent. They can also reflect the

human-land relationship has long been a core topic in

urban planning and transport studies, as well as the

transformation of urban mobility.

The present study investigated how the trip patterns and

related spatial characteristics and variables can be

extracted from different data sources to contribute to the

transformation of urban mobility. Trip pattern information

was estimated with the trip generation models developed

based on electronic fare payment system data, transit smart

card data, geo-referenced social media data, large scale

global positioning system (GPS), trip flows based on the

public transport system and scheduled time table, and road

network attributes. Understanding the urban trip flows

mobility patterns and their trends and paradigm shifts is an

immense step toward sustainability in spatial planning.

Geospatial Information System (GIS) plays an undeniable

role in such field. With the advantages of spatio-temporal

analysis, it is possible to evaluate the urban mobility

dynamics based on trip behaviour, distribution of trips

based on various conditions, activity identification, and

departure time table of different public transport modes.

As the transformation of urban mobility has direct effects

on different aspect of society, understanding these changes

can accelerate the creation and deployment of sustainable

mobility and transport system in urban areas.

With the advances in computer science and

technology, the Intelligent Transport Systems (ITS) are

emerging as one of the best solution to tackle the transport

system by providing accurate and real-time data.

Advanced data mining techniques are implemented to

derive the travel behavior patterns and characteristics to

evaluate the unexpected trends in mobility dynamics.

The reviewed models extracted from proposed data

sources by mean of GIS focus on providing a generalized

data-driven framework to better utilize the increasing

amount of individual-based mobility trends, underlying

spatio-temporal structure of urban areas.

The questions regarding mobility patterns and

required action as a response to trends, dynamics and

complexity of urban areas are essential to

spatial/transportation planning. Our review enriches the

information in mobility data by discussing trip patterns in

a multi-dimensional setting, accurate mapping and data

analysis provided by GIS. Comprehensive understanding

of spatio-temporal urban dynamics through collective

transit mobility demonstrates great flexibility in studying

several directions of transformative urban mobility such as

trends in trip behavior, trip purposes, trip chain and

transport modes. Proposed aspects of mobility in urban

areas can be inferred and integrated to the GIS-based

models and analysis to add a new dimension to the

Figure 4. Spatial interactions based on distinct origin-

destination for spatio-temporal patterns (Sun et al., 2016 ).

Behradfar and Mohammadi, 2020

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