Bicycle Comfort Level Rating (BCLR) model for urban street segments in mid-sized cities of India
Introduction
The health benefits of bicycling are manifold, both physiologically and psychologically. However, the lack of cycling infrastructure and healthy environment has led to the discouragement for bicycle use, particularly in developing countries. Various studies have attempted to relate bicyclists’ perceived level of satisfaction with roadway geometric and traffic conditions only. However, the quality of surrounding environment has also a great impact on bicycling comfort, which is yet to be investigated. As bicyclists are unprotected road users, they are hugely affected by the air and noise pollution generated by road transport. Hence, in addition to roadway factors, this study aims to investigate the impact of these environmental healthiness factors on bicycling comfort. The outcomes of such an attempt would help to make efficient decisions for the improvement of bicycling infrastructure as well as human health. In developing countries, bicycle infrastructures like separate bicycle lanes, shared-use paths and wide shoulders, etc. are rarely established. As a result, bicyclists are inevitably using the lanes available on the main carriageway and encountering complicated interferences with various types of motor-vehicles (widely varying in their sizes and operational characteristics). An in-depth understanding of the factors affecting bicycling comfort under such conditions is highly important for improvising the quality of existing facilities and establishing new better-class facilities. Previous studies have neglected the health concerns of bicyclists and investigated the infrastructure characteristics only. In addition, these studies are mostly carried out in developed countries. Thus, the applicability of these studies is limited to homogeneous traffic conditions only. However, the mainstream traffic in developing countries combines the performances of slow and fast-moving vehicles with a weak lane discipline, called heterogeneous traffic. To this end, an innovative approach for the investigation and assessment of perceived comfort levels of bicyclists under the latter context is presented in this paper.
For investigation purposes, a large quantity of data is collected from sixty road segments of three Indian mid-sized cities (population size within 0.5–1.0 million). Various types of information are collected from these segments including their geometric, traffic and built-environmental details, and the socio-demographic and travel-related characteristics of bicycle users. On-site bicyclists have rated the considered road segments on a Likert scale of 1–6 (excellent–worst) based on their perceived comfort levels and environmental healthiness. The resulting ratings are termed as perceived Bicycle Comfort Level Ratings (BCLRs). The attributes having significant influences on the perceived BCLRs are identified, and a stepwise regression-based BCLR model is developed for the assessment of road and environmental quality. Further, the overall BCLRs obtained for individual segments are classified into six classes ranging from ‘A’ = excellent to ‘F’ = worst by utilizing a highly efficient technique namely, Genetic Programming (GP) clustering. The study outcomes would help transportation engineering professionals to assess the quality of roadways and surrounding environment in mid-sized cities of India (and other developing nations). Subsequently, judicious decisions could be made for the improvement of the bicycling environment and public health. Various policies are also suggested in this study for the improvement of public health through increased bicycling.
Section snippets
Review of literature
Several models are developed in the recent past for the prediction of bicyclist’ perceived service quality. Although these models are primarily developed for homogeneous traffic conditions and not transferable to the present context, the review of those studies provided a strong base for carrying out the present work. Crucial findings of these studies are summarized in the succeeding sub-section, followed by a brief introduction of the GP cluster technique.
Research methodology
The methodology of this study mainly follows three steps, namely, (1) the identification of variables having significant (p < 0.001) influences on the bicycling comfort, (2) development of BCLR model using step-wise regression analysis, and (3) classification of BCLRs into bicycle comfort levels (A–F) using the GP cluster tool. In this study, the spearman's correlation analysis has been opted for identifying the sets of significant variables as it is well-capable of dealing with both continuous
Site selection and data collection
The basic site selection principle was that the variabilities and complexities existing in the urban bicycling environments of medium-sized cities should be reflected in the database. Hence, the road segments were identified from diversified locations (city centres, suburbs and outskirts of cities) with varying roadside developments, geometric details and operational characteristics. Total sixty segments were selected, which are located in three Indian mid-sized cities namely, Bhubaneswar,
Analysis, results and discussion
This section gives a detailed discussion of the data analysis, model development, and interpretation of results.
Conclusion
Dealing with the investigation of bicycling comfort under heterogeneous traffic conditions in mid-sized cities, several crucial conclusions are documented in this study. The primary data analysis has concluded that the parameters describing environmental healthiness such as air and noise pollution (generated by road transport) have a significant influence on bicycling comfort. Thus, the improvement in such factors would largely improve the quality of bicycling as well as human health. Apart
Financial disclosure
The Authors did not receive any specific funding for this work.
CRediT authorship contribution statement
Sambit Kumar Beura: Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Writing - original draft, Writing - review & editing. Haritha Chellapilla: Data curation, Formal analysis, Software, Writing - original draft. Mahabir Panda: Supervision. Prasanta Kumar Bhuyan: Conceptualization, Supervision.
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