The promotion of active transport (a type of sustainable transportation) such as walking is a form of response against environmental pollution engendering from transport sector. Pedestrian level of service (PLOS) is a measurement tool to evaluate the degree of pedestrian accommodation on roadway to provide a comfortable and safe walking environment. The roadway characteristics-based model to measure PLOS has been widely applied since this approach is conceived as being transferable to different contexts. We present a comprehensive framework to measure the influence of pedestrian facilities on perceived PLOS qualitatively and quantitatively. We modeled triangular relationships among pedestrian facilities, perceived roadway conditions (accessibility, safety, comfort, and attractiveness), and perceived PLOS to identify pedestrian facilities, related to footpath, carriageway, and transit, influencing perceived PLOS. We developed these models for a case study of Chittagong Metropolitan Area in Bangladesh. Poor condition of pedestrian facilities in the region resulted in PLOS B as the highest tier of perceived PLOS. Findings of this study showed that accessibility and attractiveness influenced the perceived PLOS for footpath, carriageway, and transit, whereas safety is an important roadway condition for carriageway and transit facilities. We further measured the influence of 22 selected parameters of pedestrian facilities on roadway conditions and perceived PLOS. We concluded that achieving a better perceived PLOS is dependent on the availability, maintenance, and planning of different pedestrian facilities, as improper placement and poor condition of such facilities increased the probability that a lower level PLOS will be perceived.
Active transport Walking Pedestrian facilities Pedestrian level of service Roadway condition Chittagong
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We are pleased to express our gratitude to the Department of Urban and Regional Planning (DURP) of Chittagong University of Engineering and Technology (CUET) for providing logistic support to carry out this study. We also thank the anonymous reviewers for their careful reading of our manuscript and insightful comments and suggestions.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
Black WR (2010) Sustainable transportation: problems and solutions. Guilford, New YorkGoogle Scholar
Winters M, Friesen MC, Koehoorn M, Teschke K (2007) Utilitarian bicycling: a multilevel analysis of climate and personal influences. Am J Prev Med 32(1):52–58CrossRefGoogle Scholar
Clark SD (2009) The determinants of car ownership in England and Wales from anonymous 2001 census data. Transp Res Part C Emerg Technol 17(5):526–540CrossRefGoogle Scholar
Kenworthy JR, Laube FB (1999) Patterns of automobile dependence in cities: an international overview of key physical and economic dimensions with some implications for urban policy. Transp Res Part A Policy Pract 33(7):691–723CrossRefGoogle Scholar
Wee BV (2007) Environmental effects of urban traffic. Threats from car traffic to the quality of urban life: problems, causes and solutions. Emerald Group, Bingley, pp 9–32CrossRefGoogle Scholar
Baltes M (1996) Factors influencing nondiscretionary work trips by bicycle determined from 1990 US census metropolitan statistical area data. Transp Res Rec J Transp Res Board 1538:96–101CrossRefGoogle Scholar
Gouda AA, Masoumi HE (2017) Sustainable transportation according to certification systems: a viability analysis based on neighborhood size and context relevance. Environ Impact Assess Rev 63:147–159CrossRefGoogle Scholar
Oswald Beiler MR (2016) Sustainable mobility for the future: development and implementation of a sustainable transportation planning course. J Profess Issues Eng Educ Pract 143(1):05016007CrossRefGoogle Scholar
Pucher J, Buehler R, Bassett DR, Dannenberg AL (2010) Walking and cycling to health: a comparative analysis of city, state, and international data. Am J Public Health 100(10):1986–1992CrossRefGoogle Scholar
De Geus B, De Bourdeaudhuij I, Jannes C, Meeusen R (2008) Psychosocial and environmental factors associated with cycling for transport among a working population. Health Educ Res 23(4):697–708CrossRefGoogle Scholar
Hart J, Parkhurst G (2011) Driven to excess: impacts of motor vehicles on the quality of life of residents of three streets in Bristol UK. World Transp Policy Pract 17(2):12–30Google Scholar
Jones TF, Eaton CB (1994) Cost-benefit analysis of walking to prevent coronary heart disease. Arch Fam Med 3(8):703CrossRefGoogle Scholar
Bopp M, Gayah VV, Campbell ME (2015) Examining the link between public transit use and active commuting. Int J Environ Res Public Health 12(4):4256–4274CrossRefGoogle Scholar
Kaczynski AT, Bopp MJ, Wittman P (2010) Association of workplace supports with active commuting. Prev Chron Dis 7(6):A127Google Scholar
Caspersen CJ, Pereira MA, Curran KM (2000) Changes in physical activity patterns in the United States, by sex and cross-sectional age. Med Sci Sports Exerc 32(9):1601–1609CrossRefGoogle Scholar
Cohen JM, Boniface S, Watkins S (2014) Health implications of transport planning, development and operations. J Transp Health 1(1):63–72CrossRefGoogle Scholar
Gordon-Larsen P, Nelson MC, Beam K (2005) Associations among active transportation, physical activity, and weight status in young adults. Obes Res 13(5):868–875CrossRefGoogle Scholar
Davis A (2010) Value for money: an economic assessment of investment in walking and cycling. Department of Health and Government Office of the South-west, LondonGoogle Scholar
Alam M, Mainuddin K, Rahman A, Uzzaman R (2007) Governance screening for urban climate change resilience-building and adaptation strategies in asia: assessment of Chittagong City, Bangladesh. Report of the Bangladesh Centre for Advanced Studies (BCAS). Institute of Development Studies. Report of the Bangladesh Centre for Advanced Studies (BCAS) Institute of Development Studies, University of Sussex 15Google Scholar
Hoque MM, Pervaz S, Paul AK (2016) Safety ratings of complex pedestrian routes in Dhaka metropolitan city. In: ARRB conference, 27th, 2016, Melbourne, Victoria, AustraliaGoogle Scholar
CDA (2009) Preparation of detailed area plan (DAP) for Chittagong Metropolitan Master Plan (CMMP). Chittagong Chittagong Development Authority, ChittagongGoogle Scholar
Zhou H, Hsu P, Chen S (2010) Identifying key factors affecting students’ travel modes using the multi-perspectives diagnosis approach. Traff Transp Stud 2010:545–556CrossRefGoogle Scholar
Israel GD (1992) Determining sample size. PEOD6. Agricultural Education and Communication Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, GainesvilleGoogle Scholar
Washington SP, Karlaftis MG, Mannering F (2010) Statistical and econometric methods for transportation data analysis. Chapman and Hall/CRC, Boca RatonzbMATHGoogle Scholar