Measuring low-stress connectivity in terms of bike-accessible jobs and potential bike-to-work trips: A case study evaluating alternative bike route alignments in northern Delaware

Peter G Furth

Northeastern University

Theja VVK Putta

Northeastern University

Paul Moser

Delaware Department of Transportation

DOI: https://doi.org/10.5198/jtlu.2018.1159

Keywords: Bicycling Network, Bicycle Demand


Abstract

When road segments with high traffic stress are excluded, the remaining network of low-stress roads and trails can be fragmented, lacking connections between many origin-destination pairs or requiring onerous detour. Low-stress connectivity is a measure of the degree to which origins (for this study, homes) and destinations (jobs) can be connected using only low-stress links and without excessive detour. Revision 2.0 to Level of Traffic Stress criteria is introduced and applied to the road and trail network of northern Delaware. A propensity model is proposed to reflect people’s declining willingness to ride a bike with greater trip length and detour, accounting for the impact to health and other benefits of cycling. New connectivity measures are introduced that can be interpreted as the number of bike-accessible jobs and the potential number of bike-to-work trips, powerful measures for evaluating alternatives. These connectivity measures are applied in a case study evaluating alternative alignments for a bike route between Wilmington and Newark, Delaware’s two largest cities, separated by a distance of about 20 km through a largely suburban landscape. The case study explores the benefits of enhancing alternatives with branches that help connect to population and employment centers. We also find that the connectivity gain from constructing multiple alignments is greater than the sum of connectivity gains from individual alignments, indicating that complementarity between the alternatives, which are spaced roughly 5 km apart, overshadows any competition between them.

Author Biographies

Peter G Furth, Northeastern University

Professor of Civil Environmental Engineering

Theja VVK Putta, Northeastern University

PhD candidate in Civil Engineering

References

Broach, J., Dill, J., Gliebe, J. (2012). Where do cyclists ride? A route choice model developed with revealed preference GPS data. Transportation Research A, 46, 1730–1740.

CROW. (2017). Design Manual for Bicycle Traffic. Ede, NL: CROW.

Dill, J., McNeil, N. (2013). Four types of cyclists? Examination of typology for better understanding of bicycling behavior and potential. Transportation Research Record: Journal of the Transportation Research Board, 2387, 129–138.

Furth, P. G. (2008). On-road bicycle facilities for children and other ‘easy riders’: Stress mechanisms and design criteria (paper 08-1074). Proceedings from the Transportation Research Board 87th Annual Meeting, Washington, DC.

Furth, P. G., Mekuria, M. C., &, Nixon, H. (2016). Network connectivity for low-stress bicycling. Transportation Research Record, 2587, 41–49.

Furth, P. G. (2017). Level of traffic stress criteria for road segments, version 2.0. Retrieved from http://www.northeastern.edu/peter.furth/criteria-for-level-of-traffic-stress/

Geller, R. (2006). Four types of cyclists. Portland, OR: Portland Bureau of Transportation

Harkey, D., Reinfurt, D., & Knuiman, M. (1998). Development of the bicycle compatibility index. Transportation Research Record: Journal of the Transportation Research Board, 1636, 13–20.

Iacono, M., Krizek, K. J., & El-Geneidy, A. (2010). Measuring non-motorized accessibility: Issues, alternatives, and execution. Journal of Transport Geography, 18(1), 133–140.

Landis, B., Vattikuti, V., & Brannick, M. (1997). Real-time human perceptions: Toward a bicycle level of service. Transportation Research Record, 1578, 119–126.

Lovelace, R.,Goodman, A., Aldred, R., Berkoff, N., Abbas, A., & Woodcock, J. (2017). The Propensity to Cycle Tool: An open source online system for sustainable transport planning. The Journal of Transport and Land Use, 10(1), 505–528.

Mekuria, M. C., Furth, P. G., & Nixon, H. (2012). Low-stress bicycling and network connectivity. San Jose, CA: Mineta Transportation Institute, San Jose. Retrieved from http://transweb.sjsu.edu/PDFs/research/1005-low-stress-bicycling-network-connectivity.pdf

Whitman, Requardt, and Associates (2014). Wilmington-Newark trail study. Dover, DE: Delaware Department of Transportation.

Winters, M., Davidson, G., Kao, D., & Teschke, K. (2011). Motivators and deterrents of bicycling: Comparing influences on decisions to ride, Transportation, 38, 153–168.