Modelling automobile users’ response pattern in defining urban street level of service


This paper presents a qualitative study on automobile users’ response pattern to assess the provided transportation service quality under heterogeneous traffic flow conditions. An Automobile Users’ Satisfaction index (AUSi) is established using data sets of questionnaire survey collected from 34 urban street segments of three midsized Indian cities. About 977 respondents with a suitable cross-section of gender, age, driving experience etc. were participated in travellers’ intercept survey. Rasch Model (RM) was applied to identify a set of quantitative measures to analyse the complex process of measuring perceived service quality and degree of drivers’ satisfaction together. The present study comprehends the multidimensional nature of users’ perception to evaluate AUSi with the help of six-dimensional variables such as roadway geometry, traffic facilities, traffic management, pavement condition, safety and aesthetics. RM offers a particular score to each user and each dimensional attribute along with a shared continuum. This way, the attributes those are more demanding to produce satisfaction as well as the variation in response of different modes of transport are evidently identified. The key findings indicate that the participants reported lower satisfaction level mainly due to the absence of separate bike/bus pull-out lanes, improper parking facilities and interruption by non-motorised vehicles/public transit or roadside commercial activities. Fuzzy C-Means (FCM) clustering was applied to classify AUSi scores into six auto Levels Of Service (LOS) categories (A–F) for each street segment. The model was well validated with a significant matching of predicted Automobile users’ LOS (ALOS) service categories with the users’ perceived Overall Satisfaction (OS) scores for fourteen randomly selected segments. This prediction model is new to mixed traffic flow condition, which uses linguistic information and real-life issues of drivers for the current state of services. Hence, the proposed method would be more credible than conventional models to support the decision makers for long term planning and designing road networks on a priority basis.

Keyword : urban street, level of service, perception survey, rating scale Rasch model, automobile users’ satisfaction index, fuzzy c-mean clustering

How to Cite
Jena, S., Pradhan, D. K., & Bhuyan, P. K. (2019). Modelling automobile users’ response pattern in defining urban street level of service. Transport, 34(3), 287-299.
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May 7, 2019
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Andrich, D. A. 1978. A rating formulation for ordered response categories, Psychometrika 43(4): 561–573.

Bezdek, J. C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press. 256 p.

Bond, T. G.; Fox, C. M. 2003. Applying the Rasch model: fundamental measurement in the human sciences, Journal of Educational Measurement 40(2): 185–187.

Choo, S.; Mokhtarian, P. L. 2008. How do people respond to congestion mitigation policies? A multivariate probit model of the individual consideration of three travel-related strategy bundles, Transportation 35(2): 145–163.

De Battisti, F.; Nicolini, G.; Salini, S. 2010. The Rasch model in customer satisfaction survey data, Quality Technology & Quantitative Management 7(1): 15–34.

De Battisti, F.; Nicolini, G.; Salini, S. 2003. The Rasch Model to Measure Service Quality. UNIMI Economics Working Paper No. 27.2003. 18 p.

Deshpande, R.; Gartner, N. H.; Zarrillo, M. L. 2010. Urban street performance: level of service and quality of progression analysis, Transportation Research Record: Journal of the Transportation Research Board 2173: 57–63.

Dowling, R.; Flannery, A.; Landis, B.; Petritsch, T.; Rouphail, N.; Ryus, P. 2008. Multimodal level of service for urban streets, Transportation Research Record: Journal of the Transportation Research Board 2071: 1–7.

Dunn, J. C. 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics 3(3): 32–57.

Faezi, S. F.; Hamid, H.; Sanij, H. K. 2013. Development of service performance model for exclusive motorcycle lanes, Science International 25(3): 461–468.

Flannery, A.; Rouphail, N.; Reinke, D. 2008. Analysis and modeling of automobile users’ perceptions of quality of service on urban streets, Transportation Research Record: Journal of the Transportation Research Board 2071: 26–34.

Haustein, S. 2012. Mobility behavior of the elderly: an attitude-based segmentation approach for a heterogeneous target group, Transportation 39(6): 1079–1103.

Hummer, J. E.; Rouphail, N.; Hughes, R. G.; Fain, S. J.; Toole, J. L.; Patten, R. S.; Schneider, R. J.; Monahan, J. F.; Do, A. 2005. User perceptions of the quality of service on shared paths, Transportation Research Record: Journal of the Transportation Research Board 1939: 28–36.

Linacre, M. 2012. Differential item functioning, in Winsteps Tutorial 4, 7–10. Available from Internet:

Massof, R. W.; Fletcher; D. C. 2001. Evaluation of the NEI visual unctioning questionnaire as an interval measure of visual ability in low vision, Vision Research 41(3): 397–413.

Mohapatra, S. S.; Bhuyan, P. K.; Rao, K. V. K. 2012. Genetic algorithm fuzzy clustering using GPS data for defining level of service of urban streets, European Transport \ Trasporti Europei 52: 7.

Nicolini, G.; Salini, S. 2006. Customer satisfaction in the airline industry: the case of British airways, Quality and Reliability Engineering International 22(5): 581–589.

Oreja-Rodríguez, J. R.; Yanes-Estévez, V. 2007. Perceived environmental uncertainty in tourism: A new approach using the Rasch model, Tourism Management 28(6): 1450–1463.

Patnaik, A. K.; Bhuyan, P. K. 2016. Application of genetic programming clustering in defining LOS criteria of urban street in Indian context, Travel Behaviour and Society 3: 38–50.

Rasch, G. 1980. Probabilistic Models for Some Intelligence and Attainment Tests. University of Chicago Press. 224 p.

Shao, M.; Sun, L. 2010. United evaluation model of traffic operation level for different types of urban road, Journal of Tongji University (Natural Science) 38(11): 1593–1598. (in Chinese).

TRB. 2010. Highway Capacity Manual. 5th edition. Transportation Research Board (TRB), Washington, DC, US. 1650 p.

US DoT. 2003. Quality of Service and Customer Satisfaction on Arterial Streets. Final Report. US Department of Transportation (US DoT), Washington, DC, US. 103 p. Available from Internet:

Wright, B. D.; Masters, G. N. 1982. Rating Scale Analysis. Mesa Press. 206 p.