Bicycle Trips Research Articles

Impact of COVID-19 lockdown on the behavior change of cyclists in Lisbon, using multinomial logit regression analysis

The 2019 novel coronavirus outbreak hit most countries and cities globally, dramatically impacting how people live during lockdown periods. Compulsorily, socioeconomic activities and mobility patterns changed while long-lasting structural changes might remain. Focusing on this very particular liminal event, this paper aims to present and analyze the impact of the SARS-CoV-2 virus lockdown on the behavior change of cyclists and previously non-cyclists in Lisbon, Portugal, knowing that no concomitant interventions occurred in the cycling environment during the period analyzed (e.g., pop-up interventions).From a 1-min questionnaire in 5 locations in Lisbon’s existing cycling lanes, we aimed to collect (n = 493) revealed preferences on cycling frequency before and after the lockdown, which we used to calibrate a weighted multinomial logit model to analyze respondents’ probability of increasing, maintaining, or decreasing their cycling frequency. Results suggest that people tended to cycle more often after the lockdown than before. For every five cyclists, two cycled more frequently while two others maintained their cycling frequency. Most cycling trips were recreational or to exercise, and these increased after the lockdown, while trips for work and school decreased, as expected. Moreover, the lower the individuals’ cycling frequency levels before the lockdown, the more they cycled after it.Our study diagnoses the impact of the lockdown on cycling habits, indicating an overall propensity to cycling more by the Lisbon citizens. Hence, authorities need to act and make quick infrastructural changes (e.g., pop-up cycling lanes) and encourage the population to use more bikes (e.g., financial incentives for bike purchases).

Comparing spatial interaction models and flow interpolation techniques for predicting “cold start” bike‐share trip demand

AbstractBike‐sharing systems are expanding rapidly in metropolitan areas all over the world and individual systems are updated frequently over space and time to dynamically meet demand. Usage trends are important for understanding bike demand, but an overlooked issue is that of “cold starts” or the prediction of demand at a new station with no previous usage history. In this article, we explore a methodology for predicting the bike trips from and to a cold start station in the NYC Citi Bike system. Specifically, gravity‐type spatial interaction model and spatial interpolation models, including natural neighbor interpolation and kriging, are employed. The overall results come from experiments of a real‐world bike‐sharing system in NYC and indicate that the regression kriging model outperforms the other models by taking advantage of the robustness and interpretability of gravity‐type spatial interaction regression models and the capability of ordinary kriging to capture spatial dependence.

Is the 15-minute city within reach? Evaluating walking and cycling accessibility to grocery stores in Vancouver

Leaders around the world have embraced the idea of a “15-minute city”. This urban planning concept proposes a city where residents can meet their essential needs within a short walking or cycling trip from their home. Local access to grocery stores is a necessary component for cities to achieve the 15-minute city. This study aims to evaluate local accessibility to grocery stores by walking and cycling in the City of Vancouver. We used a cumulative opportunity measure to count the number of grocery stores available within a 15-minute walk and cycle from people’s homes. To evaluate accessibility from the perspective of younger and older travellers, we considered different travel speeds. Our results show there is good accessibility to grocery stores when cycling, with less than 1% of the city’s population not having a grocery store within a 15-minute cycle. When assuming a walking speed of an older pedestrian, around one-fifth of the population did not have access to a grocery store in their local area. The neighbourhoods that did not have a store within a 15-minute walk had higher proportions of children, older adults, and visible minorities, and lower rates of employment and post-secondary education attainment. In seeking to improve accessibility via walking and cycling, cities should prioritize grocery store locations and investments in pedestrian and cycling infrastructure to underserved neighbourhoods and populations.

Analysis of initial speed pedelec usage for commuting purposes in Flanders

Speed pedelecs, pedal-powered two-wheelers with motor assistance up to 45km/h, are relatively new vehicles for active travel on European roads, with Flanders at the forefront of adoption. Policies by European and national entities have allowed speed pedelecs to be used, yet the policies have been based on assumptions and modelling about speeds reached, rather than measured data. This paper presents analysis of naturalistic speed pedelec behaviour by 98 individuals at 10 companies in Flanders, who logged commuting and leisure rides with smartphone GPS during three-week test periods as part of the 365SNEL project using fifteen speed pedelecs, ranging in motor power from 250W to 800W. The cruising speed, the speed at which the largest distance is covered, and the 95th percentile (P95) speed (as a realistic maximum speed) are proposed as Key Performance Indicators to better evaluate speed pedelec behaviour. Cruising speeds for men were consistently higher than women (mean values: men 38.2km /h, women 33.5km /h). For all participants, the mean commuting P95 speed of 40.1km/h is 5km/h below the expected 45km/h, which points to potential over-regulation of speed pedelecs according to their expected maximum speed. Contrasting logged commuting cycling trips with leisure trips indicates that speed pedelecs can be characterised by their speed metrics, regardless of their travel purpose. Policymakers can therefore facilitate active travel with its commensurate physical and mental health benefits by investing in and designating routes for higher-speed (active) travel, and conversely reserve other routes for slower travel modes.

Bicycle trips in Endomondo, Google Maps, and MapQuest: A comparison between South Florida and North Holland

ABSTRACT Comparison of routes recommended by online trip planners, such as Google Maps and MapQuest, with routes traveled by cyclists allows estimation of whether the routing criteria used in such trip planners are the same as those used by cyclists. This study uses GPS tracking data from the Endomondo fitness tracker app from trips in Miami-Dade (Florida) and North Holland and compares their characteristics to cycling routes suggested by Google Maps, MapQuest and the shortest path. Results highlight which trip attributes differ between Endomondo and its alternative trips, and how consistent those differences are across the two study areas. For example, Endomondo cyclists in both regions go through fewer traffic signals, and use more cycleways, footways, or bike lanes than suggested routes from Google and MapQuest. A multinomial logit model on observed Endomondo trips provides insight into the routing preferences of Endomondo commute and sport cyclists in both study regions.

A randomised controlled trial to enhance travel-related physical activity: A pilot study in patients with coronary heart disease

IntroductionPatients suffering from coronary heart disease (CHD) are frequently less active. Physical Activity (PA) could be increased through changes in routine travel behaviour. Achieving a certain PA amount is essential in the secondary prevention of CHD patients. ObjectiveThis study combines objective monitoring together with an intervention using advanced Information, Communication and Technology means in a framework to improve travel-related PA. This paper aims to describe an integrated research framework and tests the effectiveness of a theory-based (Trans-theoretical Model, TTM) intervention. MethodsA pilot Randomised Controlled Trial (70:30) was conducted in Belgium. Participating patients attended a preparatory work session intended to get their demographic information, assess their Stage of Change, and learn using a smartphone app (developed to monitor travel behaviour). After the work session, patients in the experimental group were monitored for three weeks and then received a customized feedback report. After feedback, the patients were monitored for another three weeks; while patients in the control arm were continuously monitored for 6 weeks without an intermediate feedback report. At the end of the study period, both groups received their feedback report. ResultsThe data of 25 patients were used in the trial, of which 18 were in the experimental group. The outcome measure used was the Active Travel Score (ATS), i.e. the PA achieved by walking and cycling trips. A significant (p<0.05) positive increase in ATS (X‾=14.2,95%,CIs=2.1−26.4) was observed after the intervention. Multiple pairwise comparisons confirmed that the effect of the intervention lasted for a week, indicating an important short-term effect. ConclusionObjective monitoring and TTM-based interventions can be useful to provide a low-cost solution to achieve an increase in PA in secondary prevention of cardiac patients. Although these results are promising, RCTs with larger sample sizes are required to confirm the current findings.

Evaluation of methods to distinguish trips from activities in walking and cycling GPS data

An abundance of GPS data on walking and cycling requires substantial data processing – a primary component of which is trip identification to distinguish between data recorded during travel versus activities. The objective of this paper is to systematically evaluate trip identification algorithms in the literature and provide recommendations to improve performance for walking and cycling trips. Fourteen algorithms are applied to 1685 GPS trajectories from Vancouver, Canada, and evaluated on the bases of their agreement, distinction of trip and activity data characteristics, processing time, and accuracy (based on a labeled subset). Error sources are identified in relationship to trajectory, network, and weather factors. Results indicate poor concordance and widely varying performance, with no algorithm best across all measures. Four high-performing algorithms are identified with at least 90% record-level accuracy; other considerations include accuracy of the inferred number and duration of trips, precision of identified trip end points, and computational resource requirements. Density is a key variable for trip identification in the best-performing algorithms. Proximity to tree canopy, buildings, bridges, and tunnels affects the accuracy of some algorithms more than others. Most algorithms err almost entirely with false trips or false activities, which is a bias of concern for analysis. The importance of trip identification decisions should motivate more thorough reporting to enhance reproducibility and reliability.

Impact of COVID-19 travel-restriction policies on road traffic accident patterns with emphasis on cyclists: A case study of New York City

Since the COVID-19 outbreak, travel-restriction policies widely adopted by cities across the world played a profound role in reshaping urban travel patterns. At the same time, there has been an increase in both cycling trips and traffic accidents involving cyclists. This paper aims to provide new insights and policy guidance regarding the effect of COVID-19 related travel-restriction policies on the road traffic accident patterns, with an emphasis on cyclists’ safety. Specifically, by analysing the accidents data in the New York City and estimating three fixed effects logit models on the occurrence of different types of accidents in a given zip code area and time interval, we derived the following findings. First, while the overall number of road traffic accidents plummeted in the NYC after the stay-at-home policy was implemented, the average severity increased. The average number of cyclists killed or injured per accidents more than tripled relative to levels in similar times in previous years. Second, the declaration of the New York State stay-at-home order was significantly associated with a higher risk of accidents resulting in casualties. The number of Citi Bike trips in the area at the time overwhelmingly predicted severe risk for cyclists. Last, we applied the models to detect hot zones for cyclists’ severe accidents. We found that these hot zones tend to be spatially and temporally concentrated, making it possible to devise targeted safety measures. This paper contributes to the understanding of the impact of COVID-19 travel-restriction policies on accidents involving cyclists, reveals higher risks for cyclists as an unintended consequence of travel-restriction policies, and provides an analytical tool for road safety impact evaluation should future travel restrictions be considered.

Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning.

An important question in planning and designing bike-sharing services is to support the user’s travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This study focuses on the short-term forecasting, 15 min ahead, of the shared bikes demand in Montreal using a deep learning approach. Having a set of bike trips, the study first identifies 6 communities in the bike-sharing network using the Louvain algorithm. Then, four groups of LSTM-based architectures are adopted to predict pickup demand in each community. A univariate ARIMA model is also used to compare results as a benchmark. The historical trip data from 2017 to 2021 are used in addition to the extra inputs of demand related engineered features, weather conditions, and temporal variables. The selected timespan allows predicting bike demand during the COVID-19 pandemic. Results show that the deep learning models significantly outperform the ARIMA one. The hybrid CNN-LSTM achieves the highest prediction accuracy. Furthermore, adding the extra variables improves the model performance regardless of its architecture. Thus, using the hybrid structure enriched with additional input features provides a better insight into the bike demand patterns, in support of bike-sharing operational management.

Arguments for cycling as a mechanism for sustainable modal shifts in Bogotá

As a clean, accessible and healthy transport alternative, the benefits of cycling have been well-documented and suggest positive health, environmental and affordability outcomes. Despite a favourable rhetoric for cycling, in many cities in Latin America, its uptake has gained more traction among younger and carless populations than frequent users of private motorised vehicles. Recognising this reality, our article poses arguments for cycling beyond its classic benefits via direct comparisons of its performance against that of private cars. Our paper compares cycling and car-based trips, presenting evidence in cycling for policy and decision-making targeting a demand segment that has historically been more resistant to modal shifts to sustainable mobility. We contrast performance of both modes in relation to coverage and accessibility, testing different modal shift scenarios in the context of Bogotá, Colombia's capital city. As a city that has been recognised in the international literature for both its successes in urban transport policy and its persisting mobility and access inequalities, Bogotá's contrasts are an ideal setting for this research. We build on a geo-coded household travel survey for 2015 and API-sourced datasets to develop spatial coverage and potential accessibility metrics for cycling and car-based trips. Findings suggest Bogotá's large potential for further increases in cycling, particularly from car-users, which can lead to overall societal gains in terms of sustainable accessibility and present a fertile ground for bike-sharing systems. The paper builds on spatially and socially distributed findings to identify areas with the highest potential for bike-sharing in Bogotá.

From System-Wide to Road-Specific Exploration of Bike Trips for Changes in Bike Sharing System Usage after Construction of Bike Lanes

From System-Wide to Road-Specific Exploration of Bike Trips for Changes in Bike Sharing System Usage after Construction of Bike Lanes

Cyclist’s waiting time estimation at intersections, a case study with GPS traces from Bologna

Waiting time plays an important role in the cyclists’ route choice, most likely because cyclists, after a stop, need to pedal harder to regain their previous speed. Literature review highlights that cyclists generally overestimate waiting time approximately three to five times higher than their actual waiting time. The aim of this paper is to quantify cyclists’ waiting time in function of specific intersection characteristics and person attributes. This aim is achieved in two steps: (1) a recent algorithm that estimates cyclists’ waiting time from GPS traces is validated, using data from a manual survey, (2) a second manual survey has been conducted to test the representativeness of a big data set of 270,000 GPS traces recorded in the city of Bologna, Italy; the same survey also showed how many cyclists pass with the red signal for different maneuvers; and finally (3) the mentioned algorithm is applied to the big data set in order to estimate the waiting time for different intersection types and cyclist attributes. Such estimations have not been addressed in literature due to the difficulty of associating the cyclists’ waiting times with infrastructure elements based using GPS traces. Results show that waiting time represents a not-negligible share of the bike trip (11% of total trip duration). On average, particularly large waiting times have been found (1) at complex intersections by (2) for cyclists younger than 25 years old, (3) for infrequent cyclists and (4) for women. During rush hour, cyclists have recorded waiting times only 6% above the daily average, demonstrating that traffic congestion has a limited effect on waiting times. Furthermore, approximately 14% of all cyclists have crossed the red traffic light, especially when the opposite traffic volume is not high and there is good visibility. The study contributes to provide a novel and validated tool to evaluate waiting times of cyclists from GPS traces, which can support the calibration of cyclists route choice models.

Analysis and O-D Demand Estimation of a Public Bike-Sharing Program in Las Vegas

Bike-share systems are an effective way of mitigating congestion on the road. In addition, bike-share systems have been built in universities to serve for trips to work/commuting as well as the trips on campus. In Las Vegas, a bike-share system was proposed at the University of Nevada, Las Vegas. This study analyzed factors that influence the usage of bike-share program and estimated the origin-destination demand. To achieve these objectives, first, a literature review was conducted on university bike-sharing systems in the U.S. and abroad. Then, a survey with a questionnaire was distributed to UNLV to obtain the users’ preferences to the locations of the proposed bike-share stations and their likelihood and frequency to use the bike-share program. In total, 241 faculty, staff, and students responded to the survey. About 50% of those participating in the survey expressed willingness to use the bike-share system for commuting and 60% said they are willing to use bike share for on-campus travel. Commuting and on-campus travel are two different types of travel, and the factors to determine whether an individual would use the bike-share system are quite different for each. It was estimated that there would be 3450 members for a bike-share program at UNLV, each making bicycle trips with varying frequencies, producing 1966 trips per day.

The First Accounts of Cycling Trips in Italy (late 19th –early 20th Century). Immersion in Reality or Literary Escape?

The First Accounts of Cycling Trips in Italy (late 19th –early 20th Century). Immersion in Reality or Literary Escape?

Purchase intention and actual purchase of cargo cycles: Influencing factors and policy insights

To support the penetration of cargo cycles in commercial transport, this research aims at identifying the significant influencing factors for their purchase, using data from Europe’s largest cargo cycle testing project “Ich entlaste Städte”. This is achieved by developing binary logit models for the intention to purchase (stated at the end of a 3-month vehicle trial), and the actual purchase decision made (queried three months or later, after the end of the trial). Prior to the estimation of the logit models, latent variables have been constructed using explanatory factor analysis. Based on the estimation results, factors that influence the actual purchase decision include catchment area of cargo cycle trips, daily usage during the trial phase, trial phase season, type of cargo cycle tested, mode substituted by cargo cycles during the trial phase and business sector. Furthermore, four other factors, which are latent variables constructed through exploratory factor analysis, are found to have significant influence: perception of operational, soft and cost benefits, as well as importance of deterioration of conditions for conventional vehicles. Based on the influence of these factors, policy measures are suggested under the following categories: (i) Regulation, (ii) Infrastructure, (iii) Finance, (iv) Campaigns and (v) Trial schemes. The data from the project shows the existence of a difference between the intention and the actual purchase decision (around 50% higher intent compared to realized purchase). This implies that there is a need to convert intention to actual decision, when making conclusions based on intention. A comparison, made between the binary logit models of intention and actual purchase decision, brings out the reality that the latter is influenced by hard facts like the deteriorating conditions (e.g., vehicle access restrictions) for conventional vehicles, while the former is influenced by operational concerns towards cargo cycles. This observation suggests the necessity of formulating measures to foster the market penetration of cargo cycles.

Integration of Different Mobility Behaviors and Intermodal Trips in MATSim

MATSim is an open-source simulation framework for mesoscopic traffic simulations that has gained popularity in recent years. In this paper, we present a MATSim model for the city of Vienna, with a particular emphasis on the intermodal routing framework used to create agent trips, and the development of a utility function to specify different agents’ mode preferences. To create agent activity chains, we use mobility diaries from the national transportation survey in Austria and disaggregate the available geospatial information to best fit the reported travel times. The novelty of the intermodal framework is the ability to create trips that do not consist of only one mode of transportation, but to also include bicycle, car, and demand-responsive transport (e.g., cab, car sharing) trips in combination with public transportation. To represent the different mobility behaviors of agents, we divide the population into groups and assign them different utility functions for transportation modes according to their socio-demographic characteristics. After presenting the validation of the model, we discuss ways to improve the model.

Gender Differences in Environmental Correlates of Cycling Activity among Older Urban Adults

Cycling is a form of active transport that can improve the level of health among the elderly population. However, little is known about the environmental correlates of bicycle use among older adults. This study investigated the relationship between the built and social environment and the gender differences in cycling frequency among older urban adults in China. The data were derived from a household travel survey in 2012 and covered thirty-three urban neighborhoods in Zhongshan. The results suggest that denser intersections are negatively related to cycling trips among both older men and women. Reverse associations for either gender, however, are observed between the average income in a neighborhood and cycling frequency. For older women, living far from a bus stop is positively correlated to an increase in daily cycling trips. For older men, social environment, including the proportions of employed or elderly people in a neighborhood, is significantly associated with cycling activity. The findings facilitate the understanding of the gender gap in cycling activity among older urban adults, and help towards designing effective planning strategies as health interventions.

COVID-19 as a window of opportunity for cycling: Evidence from the first wave

Across the world, the COVID-19 pandemic has forced people to reconsider their habits in terms of how they work, how they interact with each other, and of their mobility. During lockdowns, mobility was in general significantly reduced. Means of collective transportation were used much less, and people preferred means of individual transport. Evidence from some cities suggests that people turned to cycling as a resilient and reliable option with a small risk of contagiousness. This spike in demand led some governments to respond by opening additional bike lanes, reducing the fees of bike-sharing systems, banning cars on selected streets, or giving monetary incentives for the purchase of new bikes. We analyze the bike traffic in Basel and Zurich, two major Swiss cities. Throughout the pandemic, no specific measure to promote cycling was implemented in any of the two cities; we can thus see latent demand patterns exposed when conditions change. As cycling depends on the season and weather, we incorporate these data and correct the traffic counts hereby. We can identify a distinct change in cycling traffic over the course of the day. During the lockdown period, relatively more traffic is observed in the afternoon, possibly associated with leisure activities. Furthermore, there is a short-term drop in the corrected cycling traffic and a fast recovery, demonstrating cycling as a resilient transport mode. Soon bike traffic reached pre-lockdown levels, but no significant increase could be identified, possibly attributed to the absence of explicit policy measures. We furthermore survey a panel of bike policy experts to identify policy actions that could be taken in Basel and Zurich to increase bike usage. The COVID-19 pandemic disrupts life as we know it, leading people to reconsider their travel choices. Given authorities' desire to increase bike usage, it represents a window of opportunity to test new policy measures, increase bike trips of active cyclists, and attract new cyclists. As long as this window is open, people are susceptible to policy measures to reconsidering past choices. However, if no policy measures are conducted during the pandemic, as in the case study, it is likely that bike usage is not increased in the long run. Authorities are well-advised to take this opportunity to strengthen cycling and to lead to a more resilient, accessible, safe, and sustainable urban transport system.

Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults.

To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults. Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit's SmartTrack, and Garmin's Move IQ. Trip- and day-level agreement were tested. The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults. The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS' pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.

Premature Mortality of 2050 High Bike Use Scenarios in 17 Countries.

Background:Biking plays a significant role in urban mobility and has been suggested as a tool to promote public health. A recent study has proposed 2050 global biking scenarios based on large shifts from motorized vehicles to bikes. No previous studies have estimated the health impacts of global cycling scenarios, either future car-bike shift substitutions.Objectives:We aimed to quantify changes in premature mortality of 2050 global biking scenarios in urban populations from 17 countries.Methods:Through a quantitative Health Impact Assessment, the mortality risks and benefits of replacing car trips by bike (mechanica bike and electric bike) in urban populations from 17 countries were estimated. Multiple bike scenarios were created based on current transport trends or large shifts from car trips to bike trips. We quantified the estimated change in the number of premature deaths (reduced or increased) concerning road traffic fatalities, air pollution, and physical activity. This study focuses on urban populations between 20 and 64 y old.Results:We found that, among the urban populations (20–64 y old) of 17 countries, 205,424 annual premature deaths could be prevented if high bike-use scenarios are achieved by 2050 (assuming that 100% of bike trips replace car trips). If only 8% of bike trips replace car trips in a more conservative scenario, 18,589 annual premature deaths could be prevented by 2050 in the same population. In all the countries and scenarios, the mortality benefits related to bike use (rather than car use) outweighed the mortality risks.Discussion:We found that global biking policies may provide important mortality benefits in 2050. Current and future bike- vs. car-trip policies should be considered key public health interventions for a healthy urban design. https://doi.org/10.1289/EHP9073