Defining Bicycle Levels of Service Criteria Using Levenberg–Marquardt and Self-organizing Map Algorithms
This article proposes a bicycle level of service (BLOS) model for the assessment of urban roadway segments in mid-sized cities carrying heterogeneous traffic. The bicycling environments persisting on as many as 74 segments of four Indian cities are thoroughly analyzed. On-street bicyclists with varied demographics have rated these segments using a Likert scale ranging from ‘1’ (excellent) to ‘6’ (worst). The influences of various road attributes (geometric, traffic, and built-environmental) and bicyclists’ characteristics (socio-demographic and travel characteristics) on the perceived ratings are assessed using Spearman’s correlation analysis. Subsequently, eight significant variables are identified and used to develop a Levenberg–Marquardt neural network-based BLOS model. The most efficient but less complex model consisted of one hidden layer, three hidden neurons, and hyperbolic tangent activation function. This model produced very high values of correlation coefficient between the actual and predicted perceived ratings (i.e., 0.93 and 0.92 in the training and testing phases, respectively). The applications of Garson’s algorithm and connection-weight approaches explored that the effective width of outermost lane has the highest influence on urban street BLOS. The BLOS criteria are classified into six categories A–F (representing excellent–worst) using the self-organizing map in artificial neural network cluster technique. It was observed that most of the studied segments are offering average to worst kind of services at their present-day conditions. Thus, the influencing variables should be largely prioritized in the planning process to achieve better service levels efficiently.
KeywordsBicycle level of service Urban road segment Heterogeneous traffic Artificial neural network Levenberg–Marquardt algorithm Self-organizing map Clustering
The authors acknowledge the opportunity provided by the 4th Conference of the Transportation Research Group of India (4th CTRG) held at IIT Bombay, Mumbai, India between 17th December, 2017 and 20th December, 2017 to present the work that forms the basis of this manuscript.
- 4.Chellapilla H, Beura SK, Bhuyan PK (2016) Modeling bicycle activity on multi-lane urban road segments in Indian context and prioritizing bicycle lane to enhance the operational efficiency. In: Proceedings of the 12th transportation planning and implementation methodologies for developing countries (TPMDC) conference, IIT Bombay, Mumbai, IndiaGoogle Scholar
- 6.Davis J (1987) Bicycle safety evaluation. Auburn University, City of Chattanooga and Chattanooga-Hamilton County Regional Planning Commission, ChattanoogaGoogle Scholar
- 7.Davis J (1995) Bicycle test route evaluation for urban road conditions. In: Transportation congress: civil engineers—key to the world infrastructure 1 and 2, American Society of Civil Engineers (ASCE), San Diego, CA:1063–1076Google Scholar
- 9.Epperson B (1994) Evaluating suitability of roadways for bicycle use: toward a cycling level-of-service standard. Transp Res Rec 1438:9–16Google Scholar
- 10.FDOT (Florida Department of Transportation) (2009) Quality/level of service handbook. Florida Department of Transportation, TallahasseeGoogle Scholar
- 13.Highway Capacity Manual (2010) Transportation Research Board, Washington, D.C., p 1650Google Scholar
- 14.IRC (Indian Road Congress) (1990) Guidelines for capacity of urban roads in plain areas. In: IRC: 24 106. Indian Road Congress, New DelhiGoogle Scholar
- 16.Landis BW (1994) Bicycle interaction hazard score: a theoretical model. Transp Res Rec 1438:3–8Google Scholar
- 19.Mozer D (1994) Calculating multi-mode levels-of-service. International Bicycle Fund, SeattleGoogle Scholar
- 22.Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error propagation. Parallel distributed processing 1. MIT Press, Cambridge, pp 318–362Google Scholar
- 23.Sorton A, Walsh T (1994) Bicycle stress level as a tool to evaluate urban and suburban bicycle compatibility. Transp Res Rec 1438:17–24Google Scholar