Impact of bicycle highways on commuter mode choice: A scenario analysis

2018 ◽  
Vol 47 (4) ◽  
pp. 662-677 ◽  
Author(s):  
Hema S Rayaprolu ◽  
Carlos Llorca ◽  
Rolf Moeckel

The Dutch concept of ‘bicycle highways’ is increasingly being adopted by urban planners owing to rising environmental and health consciousness, and the growing popularity of electric bicycles. Bicycle highways differ from other types of cycling infrastructure in that they avoid intersections with motorised traffic, and are wide enough to allow for safe overtaking, thereby increasing cycling speeds. While many studies investigate the feasibility of constructing bicycle highways, few explore their effect on users’ travel preferences. In this context, our study aims to assess the potential impact of bicycle highways on commuter mode choice. We built a discrete choice model based on individual commute data from a national household travel survey, Mobilität in Deutschland 2008. The model was estimated in a logit modelling framework using Biogeme. We estimated multinomial logit and nested logit models and found nested logit to be more appropriate. The model estimates were then applied to forecast mode shares in scenarios with the pilot bicycle highway proposed in the Munich region. The variation in mode shares across scenarios with increasing average cycling speeds was analysed in areas with varying proximity to the infrastructure. The results suggest that bicycle highways reduce motorised travel and increase cycling. The effect is stronger as proximity to the corridor increases. The analysis helps to quantify the potential impact of bicycle highways on commuter mode choice even without considering further benefits beyond travel time reductions, such as increased safety, convenience, comfort, and reduced risks due to fewer interactions with motorised traffic.

2017 ◽  
Vol 38 (2) ◽  
pp. 152-166 ◽  
Author(s):  
Dohyung Kim ◽  
Jiyoung Park ◽  
Andy Hong

This study examines how built environment factors at trip destinations influence nonmotorized travel behavior in the City of Long Beach, California. Using 2008–2009 National Household Travel Survey with California Add-Ons, we found that nonmotorized users tend to choose more clustered destinations than motorized users, and that density, diversity, and design at destinations significantly affect mode choice decisions. Transportation networks and nonmotorized facilities at trip destinations are especially important factors for nonmotorized mode choice. Future policy and research need to consider built environment factors at trip destinations to effectively accommodate nonmotorized travel within a city.


2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.


2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.


2020 ◽  
Vol 37 (02) ◽  
pp. 2050008
Author(s):  
Farhad Etebari

Recent developments of information technology have increased market’s competitive pressure and products’ prices turned to be paramount factor for customers’ choices. These challenges influence traditional revenue management models and force them to shift from quantity-based to price-based techniques and incorporate individuals’ decisions within optimization models during pricing process. Multinomial logit model is the simplest and most popular discrete choice model, which suffers from an independence of irrelevant alternatives limitation. Empirical results demonstrate inadequacy of this model for capturing choice probability in the itinerary share models. The nested logit model, which appeared a few years after the multinomial logit, incorporates more realistic substitution pattern by relaxing this limitation. In this paper, a model of game theory is developed for two firms which customers choose according to the nested logit model. It is assumed that the real-time inventory levels of all firms are public information and the existence of Nash equilibrium is demonstrated. The firms adapt their prices by market conditions in this competition. The numerical experiments indicate decreasing firm’s price level simultaneously with increasing correlation among alternatives’ utilities error terms in the nests.


Urban Science ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 79 ◽  
Author(s):  
Matthew Conway ◽  
Deborah Salon ◽  
David King

The advent of ridehailing services such as Uber and Lyft has expanded for-hire vehicle travel. We use data from the 2017 National Household Travel Survey (NHTS) to investigate the extent of this expansion in the United States. We report changes in the for-hire vehicle market since ridehailing services became available and statistically estimate the determinants of ridehailing use. From 2009–2017, the for-hire vehicle market share doubled. While for-hire vehicles still only account for 0.5% of all trips, the percent of all Americans who use ridehailing in any given month is nearly 10%. Within the for-hire vehicle market, this trend of growth has not been uniformly distributed across demographic groups or geographies; it has been greater in mid-sized and large cities, and among younger individuals and wealthier households. This suggests that understanding the equity implications of ridehailing is an important avenue for research. Multivariate analysis provides evidence that both transit and nonmotorized transport use are correlated with ridehailing use, that ridehailing has a negative relationship with vehicle ownership, and that residents of denser areas have higher ridehailing use. Given the rapid growth of ridehailing, it has become important for cities to include for-hire vehicles in their planning going forward. These NHTS data provide a starting point, but more detailed and frequent data collection is needed to fully understand this many-faceted, rapidly-changing market.


Sign in / Sign up

Export Citation Format

Share Document