framework for evaluating mobility environment with ...

9th South East Asia Technical University Consortium (SEATUC) Symposium. Suranaree University of Technology, Thailand, July 27th - 30th, 2015

FRAMEWORK FOR EVALUATING MOBILITY ENVIRONMENT WITH DERIVED INDEX OF TRAVELER PERCEPTION Shittu AbdulMajeed .O and Muhammad Zaly Shah Department of Urban and Regional Planning, Faculty of Built Environment, University Teknologi Malaysia

ABSTRACT To pursue in depth quantitative assessment of mobility, a reliable mobility trend data base is needed, which most cities of developing nations lack. This challenge is heightened by the seeming lack of wherewithal and political will to build required databases upon which urban mobility analysis can rest. As a result, it becomes improbable to apply advanced, high end, data hungry models of urban mobility evaluation in such situations. Additionally, the difficulty experienced in leveraging outputs of complex mobility appraisal techniques for policy rationalization is also a drawback in these kinds of setting, usually due to inadequate skill and manpower. Therefore, urban mobility issues are approached in a subjective, uncoordinated and piecemeal fashion. Consequently, how urban movement goes on in cities with these attributes can hardly be described, on the one hand, on the other, the mobility support qualities such cities offer also remain unknown. To ameliorate this problem, it is conjectured that, a systematic evaluation of travel perception influencing factors may provide hints about the degree to which

urban areas hinder or enhance mobility, by indexing mobility complexities in a spatial entity, from perceived effects of individual’s travel perception. More so, the progressive urbanization cities of developing nations are experiencing, in the face of inadequate or total lack of holistic planning, underscores the need to explore other possible alternatives of appraising mobility environments. The method involved a meta – analysis of techniques of evaluating urban mobility with quantitative models, as a way of deriving lessons and criteria for framework development. The framework suggested is expected to enable local authorities develop a cost effective and parsimonious way of identifying urban mobility challenges, so as to provide a strategic pathway for a city’s mobility environment to be objectively appraised, in order to satisfactorily target interventions at improving both the mobility environment and the quality of life of city inhabitants. Key Words: mobility environments, mobility perception influencers, travel perception, mobility complexities, model complexity.

 


1.0

INTRODUCTION

The close connection between mobility, individual’s independence, well being and quality of life has been elicited by (Spinney et al, 2009). The fact that mobility provides increased opportunities for individuals to undertake fundamental tasks beyond the home environment, such as work, recreation and shopping, as a necessity in satisfying inherent psychological needs necessary for well being, as stated by (Vella – Brodrick et al., 2013), accords mobility a life support attribute. This stance had earlier been reiterated by Patla and Shamway – Cook (2003) that adequate mobility is needed to realistically meet necessary Instrumental Activity of Daily Living (IADL), thus putting mobility or ability to move amongst the fundamental requirements of human existence. In agreement, Cresswell (2010) asserts that mobility is a central fact of modern and post modern life, stating that the defining characteristic of mobility, that is movement - puts the phenomenon on centre stage of human existence. The important role mobility plays in meeting the demands for human survival in urban areas cannot be over emphasized. Because improved levels and inclusive mobility facilitates accessibility to employment, education, health and other urban services necessary for enhanced welfare (Adeniji, 2001; Haider and Badami, 2004; Olufemi and Oluseyi, 2007). Zhu (1998) also tied accessibility levels to city vibrancy. Against this backdrop, it becomes necessary to understand and apply findings around mobility as an observable reality, in urban planning and development. The fact that mobility has been recognized as a “leading issue” is evidence of its importance as a research agenda (Cresswell, 2010). The central task thus, is to encourage mobility assessment techniques and policies that will cater for the mobility needs of urban residents in a targeted, economically efficient and socially inclusive manner, towards improved urban productivity, livability and sustainability. However, as important as understanding mobility is, the question of inadequate or lack of secondary and trend data to effectively run mobility analysis conventionally for some cities of developing nations has been recognized as a major drawback in the effort at understanding urban mobility in such settings. The seeming unwillingness and lack of political will to gather the needed data to adequately run mobility analysis is also a bane. This makes applicability of conventional data hungry mobility models in mobility evaluation for planning purposes in such situations unfeasible. Also, the computational complexity of

high end models of mobility analysis brings about some loathing on the part of policy makers of cities fitting this description. In technical terms, a common trait among contemporary mobility models, whether employed in the evaluation of mobility patterns based on studying trajectories, dynamic proximity networks or flow on networks is that, they cannot adequately capture constraints of human mobility, a critical determinant of mobility capabilities of individuals (Isaacman et al, 2012; Azevedo et al, 2009; Calabrese et al, 2010; Hong , 2010; Wang, 2011). For these reasons mobility evaluation in data deficient cities remain largely unexplored, where some data are available, they are most often than not, dirty. The dire shortage of expertise to handle advance modeling techniques for policy development and planning at the local levels is also a source of worry. Despite all of these debilitating factors, mobility planning still proceeds, without concrete bases in cities such as described, because authorities of data deficient cities continue to make investment that target urban mobility improvements, even though disjointed and intermittent. This creates a need to fashion out alternative ways of assessing mobility needs and requirements of such cities. Hence, a parsimonious traveler percept based index option of assessing urban mobility environments, as a sub set of mobility analysis is suggested. Based upon indicators that can help gauge mobility ease from the context of what is deducible from the interaction between the moving subject (humans) and the containment within which mobility takes place (the environment). Tapping into the culminating stated perception of travel or movement by the traveler, from which engendered inhibitors and enhancers of mobility can be deciphered, may be a useful aid in urban mobility planning in data deficient settings as obtainable in some cities of the developing world. This paper puts forward an alternative framework for appraising mobility environments, where the application of contemporary mobility modeling techniques may not be appropriate. 2.0 METHODICAL AND CONSIDERATIONS

PROCEDURAL

Mobility as an occurrence has been connected to accessibilty and social equity (Colleoni, 2013). Its evaluation is thought to provide useful clues in dealing with socio – economic, transportation, and human physical activity problems, thereby pointing to prevalent and associated factors underlying related occurrences (Florindo, et al. 2009). Bearing this in

 


mind, four important considerations are examined towards grounding the framework; these are approaches to evaluating mobility, the phenomenon “perception”, the prospect of deriving a perception based index, and the likelihood of deriving a useful tool for appraising a city’s mobility environment, out of them. 2.1 Methods of Evaluating Mobility One of the main purpose of studying human or urban mobility is to investigate the population flow between different places (Hunter et al, 2011; Holme et al, 2003), at the microscopic and macroscopic level. Three major approaches have been employed over the years; the pragmatic, the mathematical and the interactive. 2.1.1 The Pragmatic Approach The pragmatic approach attempts to develop an inclusive description of mobility behaviour via a mixture of qualitative and quantitative studies which are in turn presented in a non – quantitative form supported by simple tabulations and correlations. Specific aspects are then modeled by means of regular econometric techniques. For example, the non – quantitative description may be used to specify the circumstances under which movement take place. An example of this type of approach is Musselwhite and Hadad’s (2010) classification of mobility need, see Fig. 1, the study classified mobility need into three groups, namely;   

Utilitarian or primary needs for mobility; Affective or secondary needs for mobility; and Aesthetics or tertiary needs for mobility

Figure 1:

Three levels of Mobility Needs Least Awareness

Tertiary travel needs (Aesthetic needs) the need to travel for its own sake and view life and nature Secondary travel needs (Affective needs) the need for independence, control, status and roles Primary travel needs (Utilitarian needs) make appointments, access shops and services, work in a safe, convenient and comfortable environment

At the utilitarian level, mobility needs are seen as derived demand for travel and as requirements in accessibility. Here, travel is tied to serving a function of accessing other human necessities, which means mobility is viewed as a means to an end. Similarly, affective need based mobility connotes movement carried out to affirm independence, control, and status in other to perform defined roles or participate in the society. The third type describes aesthetic needs for mobility. This explains travel done for its own sake, as a way of viewing life and enjoying nature, or for such purposes as recreation. The underscoring theme of this approach is its description of mobility as a necessity for human survival and independence. 2.1.2 The Mathematical Approach The mathematical approach starts from the same descriptive base, but seeks to translate it into rigorous analytical models. These kinds of models are categorized according to their design and targets. The first class of models account for the properties characterizing the regular reappearance of users at a set of preferred locations, the SLAW group of models exemplifies this approach (Lee et al, 2009). The second group focuses on reproducing realistic temporal patterns of human mobility, explicitly including repetitive daily activities in human schedules (Zheng, 2010). The third class, are those aiming at incorporating sociality into models, thus considering human relations as the main driver of individual movements. For example, the SPoT model developed by Karamshuk et al, (2013), which sorts to link together all the three dimensions of human mobility using a flexible and controllable framework, which can be instantiated to the desired mobility scenario and which is naturally suited for mathematical analysis. Asgari et al. (2013) further classified mathematical models of mobility evaluation along three baselines (Fig. 2), namely dynamic proximity networks, trajectory based and flow on networks studies. Mobility researchers have traditionally relied on expensive data collection methods, such as surveys and direct observations, to get a glimpse into the way people are moving. This requires that, a realistic model confront the individual (or household) with own travel environment and invite him to respond to changes, representing the policy or planning issues under review, in a distinct way. The fact that this is difficult to achieve, spurned the third approach to mobility evaluation.

Most Awareness

Source: Musselwhite and Hadad, 2010

 


Fig 2: Three main baselines of mathematical mobility studies

DYNAMIC PROXIMITY NETWORKS, involving contact time, centrality, diffusion and inter - contact time

TRAJECTORY - BASED STUDIES, involving radius of gyration, spatial networks and jump length

HUMAN MOBILITY STUDIES

FLOW ON NETWORKS, involving, spatial networks, velocity, density, queue,delay and flow centrality

Source: Asgari et al, 2013 2.1.3 The Interactive Approach The interactive approach questions the very basis of current econometric techniques. Stating that, if one cannot capture the essentials of an individual’s decision making process in a finite set of mathematical equations, then why not use the individual (or household group) as a surrogate for this part of the model. These kinds of models are not all that common, although, all the priority evaluator and various interactive graphic techniques include the individual in their operational model. None of the techniques has yet offered a wholly satisfactory way of modeling mobility behaviour (Hong, 2010; Clauset et al, 2007). They deal in abstractions too far removed from the framework within which most individuals arrive at decisions. Therefore, the interactive approach considers it more useful to study social behaviours or group activity rather than individual trajectories, contact time and flow on networks (Clauset et al, 2007). From the foregoing, it can be gleaned that a traveler centered approach to mobility evaluation is practicable, without necessarily carrying the complexities and huge data requirements of contemporary mobility models along, meaning, that a parsimonious technique that can suite the skill level and economic capabilities of underprivileged cities can be crafted. 2.2 Perception as a Phenomenon The second important consideration as mentioned earlier, is the phenomenon “perception”. The notion that perception affords the perceiver the opportunity to gather, process, and organize information about the environment in order to make sense out of it (Kovalik and King, 1998), is the main hinge of the procedure.

Perception is important because it results from sensory experience of the world around us, shaped by environmental stimuli and actions in response to these stimuli. Through the perceptual process we gain information about properties and elements of the environment that are critical to our survival (Cherry, 2010). More so, Diener et al., (1985) described perception as an important issue in research on subjective matters. In another submission, reiterating the significance of personal insights, Doi et al., (2008) related that conventional ease of access measures fail to include people’s values or behavioural criteria and ability, which reduce the value for planners to evaluate practical issues, thus, leading to a conclusion that, objective conditions are not necessarily the best predictors of subjective satisfaction. This is so because researcher chosen factors cannot totally represent people’s own judgment, owing to differences in individual capabilities in aspects regarding things like mobility. Consequently, urban mobility planners cannot rely only on the result of objective measurement as a way of understanding space for planning. Therefore, perception based assessments can help contribute valuable information towards improving mobility conditions by summing across individuals’ perception within specific domains (Shafer et al., 2000). The edge here is the capability of perception based constructs to directly show an individual’s predisposition towards certain factors and installations around the city, given effect on personal mobility, thereby providing an opportunity to sum across samples.

2.3

Applicability of Perception Constructs in Urban Analysis

Based

Many perception based urban studies have been carried out to determine a number of issues. To get insight into the determinants of cycling to school, Aarts, et al. (2013) used school pupils parent’s perception of cycling routes in socio – ecological models (individual, social and physical environmental factors) to identify correlates at multiple levels. Cole et al. (2010) also used perception of traffic to demonstrate that demographic and environmental factors, such as traffic and busy roads can determine whether an individual’s perception of an environment within which mobility takes place is negative or positive. Likewise, a study on a walking intervention programme, towards creating safe walking routes to school in a rural area of California, USA, succeeded in increasing walking rates to school, by using perception based criteria to identify needed interventions (Alton, et al. 2007). Another study showed how transit is perceived to be for the elderly,

 


the poor and the socially or physically disadvantaged, especially where car ownership levels are very high. Therefore, “perception” as a phenomenon lends itself well to urban analysis, a sub set of which is mobility evaluation. Also, the fact that each individual perceiver contributes in a unique manner to rating the mobility environment as either an enhancer or inhibitor of mobility is an important element of strength the tool imbibes. The process itself embeds democratic traits, and fosters user participation, consequently reducing the index of top – bottom influence common in developing nations.

indicators, including individual’s self – report about conditions, subjective perception and other relevant social – economic indicators, such as employment rate have been used to gain insight into user sensitivity matters, mainly because a rational methodology to execute such a procedure can be attained. The normal process of an indicator programme is the same as building an information pyramid (Mingshun, 2006).

2.4

An index provides an empirical and numerical basis for evaluating performance, calculating the impact of activities, categorizing, or connecting past and present activities to attain future goals (Giorgos and Gillian, 2005). An index jointly summarizes a system or indicates its status. To arrive at a descriptive index of the mobility support qualities of a city’s environment, a thirteen (13) step procedure is proposed. The progressions are as listed below;

Prospect of Deriving a Perception Based Index Construct There are some major considerations in using indicators or index constructs in the measurement of phenomenon. Mingshun (2006) mentioned policy relevance, scientific soundness and measurability among some of the major attributes that should be considered. Off these three concepts, measurability is the most pertinent in this effort. In terms of measurability, indicators or index derivations should be based on data that are: i. ii. iii. iv. v.

Readily available at reasonable cost versus benefit ratio; Easily understood and applicable by potential users; Acceptable to stakeholders; Adequately documented , of good quality; and Updated at regular intervals.

To resolve the issue of measurability, two known methods of aggregating indicators into indices are applicable. These are the weighting and the non – weighting index aggregation techniques. Weighting indicators have been commonly used in the aggregation of environmental indicators, the preeminent example of weighting aggregated environmental indicators is provided in the works of Adriaanse (1993) and Hammond et al., (1995). However, it is the introduction of normalization technique which scales all numeric variables between the range [0, 1] as explained by (Saitta, 2007), without units and disregarding physical dimensions, thereby eliminating bias and worries stemming from differing dimensional attributes of factors influencing perception, that enables direct comparison of values across board. This underscores the possibility for reliable measurement and indexing. It is therefore, evident that an indicator or index based construct to appraise a city’s mobility environment qualities is attainable, because both objective and subjective

3.0 Framework for Technique of Environment

i.

ii.

iii.

iv.

v.

vi. vii.

viii.

Percept Based Index Appraising Mobility

Harvest a list of potential mobility influencing factors from related literature, so as to create a compendium for further assessment. Enable expert contextual relevance assessment of harvested mobility influencing factors, in order to identify factors that are truly representative of situation in the environment under examination. Extract contextually relevant mobility influencing factors as per expert/practitioners recommendations; Categorize contextually relevant mobility influencing factors into thematic areas based on attribute similarities. For instance, factors with attributes describing road network characteristics are listed under such a thematic area. Characterization is required to justify group by group comparison of results, since factors work in unison to shape the perception of individual, as attested to by Sokolowska (2014); Conduct a city wide mobility influencing factors’ perception survey, based on template derived from thematic characterization; Rank factors based on general perception survey feedback using weighted mean; Assign accentuated factor Rank Order of Importance Points (ROIP), to define magnitudes of rank positions; Derive (ROIP) factor Index Equivalents (IE), by normalizing the sum of accentuated factor

 


ix.

x.

xi.

xii.

xiii.

Rank Order of Importance Points (ROIP) sequentially; Develop index interpretation table with mathematically attainable IE maximum and minimum accruable values; Assignment of factor Index Equivalents (IE) according to perception defined rank positions for each city sub unit, Derive Average Category Index Equivalent (ACI) for thematic areas, as previously grouped; Compute city sub unit percept based estimate of mobility enhancement/hindrance level, by adding ACI values for all thematic categories; Lastly, estimate City Mobility Environment support quality Index by finding the average value for all city sub units. Then compare resultant value to the index interpretation table, as described in ix above.

Fig 3: Schema of Urban Mobility Environment Appraisal Framework Inventory of Factors Influencing Mobility from Meta Analysis of Existing Literature

Contextual Relevance Rating by Professional Transport Planners

Selection and Categorization of Contextually Relevant Factors

Weighted Mean Ranking of Factors’ Perception Ratings

Traveler Perception Rating of Mobility influencing Factors

Determination of Rank–Position Index Equivalents of Factors’ Perception Ratings

Factor Index Equivalent Assignment

Urban Sub Unit Mobility Environment Support Quality Level Index Determination

Category Average Index Equivalent Derivation

City Mobility Environment Quality Support Level Index Aggregation

Two types of surveys are needed to acquire the minimal required data and information. The first one target urban mobility and transportation practitioners, while the second type is directed at general respondents. The experts are expected to help with factor contextualization and reduction exercise. The general survey on the other hand, is to elicit traveler percepts of contextually relevant mobility influencing factors, and to seek information regarding socio – economic characteristics and mobility needs of respondents’. Sampling should consider the official spatial sub-units of the city, administrative or planning districts or wards as the case may be. This is important for appropriate spatial delineation, questionnaire allotment, and task planning. The ultimate aim of the approach is to help identify constraints of mobility, so

as to ease movement and foster adequate accessibility to component areas of a spatial entity, in a manner that will accommodate motorized and non – motorized travelers. This will expectedly be achieved by enabling proper issue targeting by decision makers. 4.0

Applicability of Perception Based Mobility Environment Appraisals

The percept based concept is conceived to provide an alternative avenue for urban mobility assessments, where standard, complex and contemporary instruments may not be appropriate. It is targeted at reducing the negative implications of data inadequacy, indeterminate mobility levels and seemingly baseless and piecemeal interventions that characterize mobility appraisals in cities such as earlier described. The tool will be useful in such scenarios because it uses minimal, cost effective and easily gathered data to appraise urban mobility environment support levels in cities without standard data bases for urban mobility assessments. City managers can employ the index tool in identifying where, when and what kind of interventions are needed to improve mobility, as such cities evolve. Index interpretations can also provide urban managers with an understanding of the kind of environment preferred by citizens within the planning district, by helping to establish a local benchmarking system. More so, the tool can help classify and rate environmental challenges to mobility as perceived by cross sections of the population. Intervention prioritization and budgeting matters is expected to become even more straightforward and objective. Ultimately, the tool will enable comparative analysis of mobility environment qualities within and between spatial units of interest, such as neighbourhoods, districts and cities. The framework can be used to identify lagging districts of cities or compare one city to another. The overall requirements of attaining stated mobility targets for different situations can easily be identified, pursued and monitored. The relative simplicity of the tool makes leveraging outputs or results for policy rationalization simple. The tool is likely to be more acceptable to policy makers who seem to loath highly technical approaches that usually require specialized knowledge to interpret. It is necessary to try options such as this in gauging urban mobility environment support qualities, not only for its simplicity, but also because it captures aspects of human mobility complexities, alongside being useful where data problems are evident. Parsimonious techniques such as this are therefore essential, if quality of life issues, urban mobility problems and their secondary, tertiary and quaternary offshoots are not to persist, in already disadvantaged settings.

 


Adeniji, K. 2000. Transport challenges in Nigeria in the Next Two Decades. Monograph Ibadan: Nigerian Institute of Social and Economic Research (NISER), Transport Studies Unit.

Colleoni, M., 2013. Mobility, Accessibilty and Social Equity: A Comparative and Interdisciplinary Empirical Study in the Metropolitan Areas of Milan, Bologna and Turin. In Henckel, D et al eds. Space – Time Design of the Public City, Urban and Landscape Perspectives. Springer Science

Adriaanse, A. 1993. Policy Performance Indicators. SDU Publishers, the Hague.

Cresswell, T. 2010. Towards a politics of mobility. Environment and Planning D: Society and Space 28: 17–31.

Asgari, F., Gauthier, V., and Becker, M., 2013. A Survey on Human Mobility and its Applications. Available from: complex.luxbulb.org/bibliography?f (author) = 121, Retrieved: 17th November, 2013.

Diener, E., Emmons, R., Larsen, R.,and Griffin, S. 1985. The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71 – 75.

References

Alton, D., Adab, P., and Barret, T. 2007. “Relationship between walking levels and perceptions of the local neighbourhood environment” Archives of Diseases in Childhood. 92(1): 29-33. Aarts, M. J., Mathijssen, J.P., van Oers, J.M., and Schuit, A. 2013. Associations between environmental characteristics and active commuting to school among children: a cross - sectional study. International Journal of Behavioural Medicine, 20:538–555. Azevedo, T.S., Bezerra, R.l., Campos, C.A., and de Moraes, L.F., 2009. An Analysis of Human Mobility using Real Traces. Wireless Comunications and Networking Conference, WCNC 2009. IEEE. (2009), 1-6. Federal University of Rio de Janeiro. Calabrese, F., Di Lorenzo, G. and Ratti, C., 2010. Human Mobility Prediction based on Individual and Collective Geographical Preferences. Intelligent Transportation Systems (ITSC), 13th International IEEE Conference. 312 – 317. Cherry, K. 2010. The Everything Psychology Book: An Introductory Guide to The Science of Human Behavior. 2nd Edition, Adams Media Clauset, A., Eagle, N., 2007. Persistance and Periodicity in a dynamic proximity network. Proceeding of DIMACS Workshop on Computational Methods for dynamic interaction Network. Cole, R., Burke, M., Leslie, E., Donald, M., and Owen, N. 2010. Perceptions of representatives of public, private and community sector institutions of barriers and enablers of physical active transport. Transport Policy, 17, 496 – 504.

Doi, K., Kii, M., and Nakanishi, H. 2008. An Integrated evaluation method of accessibility, quality of life and social interaction. Environment and Planning: Planning and Design, 35, 1098 -1116. Florindo, A., Guimaraes, V., Cesarm C., Barros, m., Alves, M., and Goldbaum, M. 2009. Epidemiology of leisure, transportation, occupational, and household physical activity: Prevalence and associated factors. Journal of Physical Activity and Health, 6, 625 - 632. Giorgos, M, Gillian, A and Kate, B., 2005. Modeling Rurality using Spatial Indicators. Available at: http://iisd.ca/measure/default.htm. Retrieved: 27th November, 2014 Haider, M and Badami, M (2004): Public Transit for the Urban Poor in Pakistan: Balancing Efficiency and Equity. Regional Focus; New Delhi, India. Hammond, A., Adriaanse, A., Rodenburg, E., Bryant, D.,Woodward, R., 1995. Environmental Indicators: A Systematic Approach to Measuring and Reporting on Environmental Policy Performance in the Context of Sustainable Development. World Resources Institute, Washington, DC, 50 pp Holme, P., et al. 2003. Congestion and centrality in traffic flow on complex network. Advances in complex systems, 163 – 176. Hong, S., 2010. Human Movement Patterns, Mobility Models and Their Impacts on Wireless Applications. A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Computer Science Raleigh, North Carolina. Hunter, T., Moldovan, T., Zaharia, M., Merzgui, S., Ma, J., Franklin, J.M., Abbeel, P., and Bayen, A.M.

 


2011. Scaling the mobile millennium system in the cloud. Proceedings of the 2nd ACM Symposium on Cloud Computing – SOCC ’11, 1 - 8.

Vella – Brodrick, D.A., and Stanley, J. 2013. The Significance of Transport Mobility in Predicting Well Being, Transport Policy, Vol. 29, Pg. 236 - 242

Isaacman, S,. Becker, R., Caceres, R., Martonosi, M., Rowland, J,. Varshavsky, A,. and Willinger, W,. 2012. Human Mobility at Metropolitan Scales. Mobisys’12

Wang, S., 2011. Analysis of Human Mobility Modeling Methods. Seminar presented at the Florida Institute of Technology and Computer Science. USA.

Karamshuk, D., Boldrini, c., Conti, M. and Passarella. 2011. Human mobility models for opportunistic networks, Communication Magazine, IEEE 49 (12) 157 – 165. Kovalik, C. and King, M.E. 1998. Visual Literacy, Available:http://www.educ.kent.edu/community/VLO; Retrieved 27th November, 2014 Lee, K., Hong, S., Kim, S., Rhee, I., and Chong, S. 2009. A new mobility model for human walks, in IEEE/ACM Transactions on 17 (5) 1564 – 1577. Mingshun, Z., 2002. Measuring urban Sustainability in China. Thesis Submitted in Partial fulfillment of the Award of PhD to the Institute for Housing and Urban Development Studies (IHS) and Erasmus University, Rotterdam, the Netherlands. Musselwhite, C., Haddad, H. 2010. Mobility, accessibility and quality of later life. Quality in Ageing and Older Adults, 11(1), 25-37. Olufemi, O.B. and Oluseyi, M.S. (2007): The Urban Poor and Mobility Stress in Nigeria; Environmental Research Journal 1(1 – 4): 1 – 8, Medwell Online. Patla, A.E., and Shumway – Cook, A., 1999. Dimensions of Mobility: Defining the Complexity and Difficulty Associated with Community Mobility. Journal of Aging and Physical Activity. 7. Pp. 7 – 19, Human Kinetics Publishers, Inc. Shafer, C., Lee, B., and Turner, S. 2000. A tatle of three greenway trails: User perceptions related to quality of life. Landscape and Urban planning, 49, 163 – 178. Sokolowska, K. 2014. Determinants and Perceptions of Social Mobility in Poland, 1992 – 2008, Contemporary Economics, Vol. 8, No. 1, pp. 89 – 109. Spinney, J., Scott, D., and Newbold, B. 2009. Transport mobility benefits and quality of life: A time use perspective of elderly Canadians. Transport Policy, (16), 1 -11.

Zheng, Q., Hong, X., Liu, J. and Cordes, D. 2010. Agenda driven mobility modeling, International Journal of Ad Hoc and Ubiquitous Computing 5 (1). Zhu, Y. 1998. ‘Formal’ and ‘informal urbanization’ in China: Trends in Fujian Province, Third World Planning Review 20(3):267-284. Shittu AbdulMajeed is a PhD candidate of the transport research group, department of urban and regional planning, University Teknologi Malaysia (UTM). Shittu’s research interest areas include urban transportation, mobility and urban design. Shittu AbdulMajeed has published articles in Open Journal of Social Sciences, IOSR Journals, Construction Focus, Maiduguri Journal of Arts & Social Sciences, and Journal of the Centre for Settlements, Housing & Urban Development. He is a lecturer with the department of Urban & Regional Planning, Ahmadu Bello University, Zaria – Nigeria. Muhammad Zaly Shah obtained his B.Sc. in industrial engineering from Bradley University, Peoria, Illinois, USA in 1992 and M.Sc. and Ph.D. in transportation planning from Universiti Teknologi Malaysia (UTM), Skudai Johor, Malaysia in 1998 and 2002, respectively. He is an Associate Professor in the field of Transportation Planning at the Faculty of Built Environment, Universiti Teknologi Malaysia. He has published extensively and his articles have appeared in Safety Science, Transport Review, Land Use Policy and Traffic Injury Prevention. His current research interests include pedestrian modelling and traffic safety. Dr. Muhammad Zaly Shah is a chartered member of the Chartered Institute of Logistics and Transport (CILT). Dr. Muhammad Zaly Shah is the recipient of Outstanding Teaching Award of UTM in 2007 and Outstanding Service Award in 2015.

 