Model-based predictive control for bicycling in urban intersections

In this paper, a model predictive control approach for improving the efficiency of bicycling as part of intermodal transportation systems is proposed. Considering a dedicated bicycle lanes infrastructure, the focus in this paper is to optimize the dynamic interaction between bicycles and vehicles at the multimodal urban traffic intersections. In the proposed approach, a dynamic model for the flows, queues, and number of both vehicles and bicycles is explicitly incorporated in the controller. For obtaining a good trade-off between the total time spent by the cyclists and by the drivers, a Pareto analysis is proposed to adjust the objective function of the MPC controller. Simulation results for a two-intersections urban traffic network are presented and the controller is analyzed considering different methods of including in the MPC controller the inflow demands of both vehicles and bicycles.     

Prioritizing bicycle paths in Belo Horizonte City,Brazil: Analysis based on user preferences and willingness considering individual heterogeneity

Using bicycles as a commuting mode has proven to be beneficial to both urban traffic conditions and travelers’ health. In order to efficiently design facilities and policies that will stimulate bicycle use, it is necessary to first understand people’s attitudes towards bicycle use, and the factors that may influence their preferences. Such an understanding will enable reliable predictions of bicycle use willingness level, based on which cycling facility construction can be reasonably prioritized.As people often have different perceptions on exercising, green transportation, and traffic conditions, effects of potentially influencing factors on people’s willingness of using bicycles tend to be highly heterogeneous. This paper uses a random parameter ordered probit model to analyze how travelers’ willingness of using bicycles is influenced by various socio-economic factors in Belo Horizonte City, Brazil, with the consideration of individual heterogeneity. The data was collected through the 2010 bicycle use survey in Belo Horizonte City. Results show that, first, the willingness of using bicycle is favored by middle income class household, and negatively related with commuting time. Second, people who rent apartments tend to be more willing to use bicycles. Third, if a person is currently walking a long time to work, he/she would be most willing to commute with a bicycle in the future. Those currently commuting a relatively short distance by motorcycle and bus follow this group in terms of willingness to commute by bicycle in the future. Car users seem to be difficult to convert to bicycle users. Moreover, the estimation shows clear evidence that significant individual heterogeneity indeed exists, especially for education level, necessitating the consideration of such an effect. With the calibrated model, residents’ willingness of using bicycle commuting is then estimated for the entire Belo Horizonte City using the 2010 Census and the 2012 O/D survey data. The results are cross validated using the bicycle path preference information, also obtained from the 2010 bicycle use survey.     

Prioritizing new bicycle facilities to improve low-stress network connectivity

This paper introduces a new method to prioritize bicycle improvement projects based on accessibility to important destinations, such as grocery stores, banks, and restaurants. Central to the method is a new way to classify “bicycling stress” using marginal rates of substitution which are commonly developed through empirical behavioral research on bicyclist route choice. MRS values are input parameters representing bicycling stress associated with the number of lanes and speed limit of a street. The method was programmed as a geographic information system tool and requires commonly available data. The tool is demonstrated on three improvement scenarios that were recently proposed for Seattle, Washington. The full build-out scenario consists of 771 projects that include various new bike lanes, protected bike lanes, and multi-use trails. The tool produces priority rankings based on a project’s ability to improve low-stress connectivity between homes and important destinations. The analysis identifies specific areas and neighborhoods that can be expected to exhibit better bikeability. Transportation planners can use the tool to help communicate anticipated project impacts to decision-makers and the public.     

Estimating cycleway capacity and bicycle equivalent unit for electric bicycles

With the rapid increase of electric bicycles (E-bikes) in China, the heterogeneous bicycle traffic flow comprising regular bicycles and E-bikes using shared cycleway creates issues in terms of efficiency as well as safety. Capacity and bicycle equivalent units (BEUs) for E-bikes are two most important parameters for the planning, design, operation, and management of bicycle facilities. In this paper, eight traffic flow fundamental diagrams are developed for one-way cycleway capacity estimation, and a novel BEU estimation model is also proposed. Eleven datasets from different shared cycleway sections with different cycleway widths were collected in Hangzhou, China for estimation and evaluation purposes. The results indicate that, with around 70% share of E-bikes, the mean estimated capacity is 2348 bicycle/h/m. The effects on the capacity of the proportions of E-bikes, gender of cyclists, age of cyclists, and cyclists carrying things were also analyzed. The results implied that the estimated capacity is independent of a cyclist’s gender and age, but increases with the proportion of E-bikes. According to this study, the mean BEU for the E-bike is 0.66, and the converted capacities of pure regular bicycles and pure E-bikes are 1800 and 2727 bicycle/h/m, respectively. These findings can be used to propose practical countermeasures to improve the capacity of heterogeneous bicycle traffic flow on shared cycleway.     

We can all get along: The alignment of driver and bicyclist roadway design preferences in the San Francisco Bay Area

Two trends in the United States—growth in bicycling and enthusiasm for complete streets—suggest a need to understand how various roadway users view roadway designs meant to accommodate multiple modes. While many studies have examined bicyclists’ roadway design preferences, there has been little investigation into the opinions of non-bicyclists who might bicycle in the future. Additionally, little research has explored the preferences of the motorists who share roads with cyclists—despite the fact that motorists compose the vast majority of roadway users in the United States and similarly developed countries.This paper presents results from an internet survey examining perceived comfort while driving and bicycling on various roadways among 265 non-bicycling drivers, bicycling drivers, and non-driving bicyclists in the San Francisco Bay Area. Analysis of variance tests revealed that both drivers and bicyclists are more comfortable on roadways with separated bicycling facilities than those with shared space. In particular, roadways with barrier-separated bicycle lanes were the most popular among all groups, regardless of bicycling frequency. Striped bicycle lanes, a common treatment in the United States, received mixed reviews: a majority of the sample believed that they benefit cyclists and drivers through predictability and legitimacy on the roadway, but the lanes were rated significantly less comfortable than barrier-separated treatments—particularly among potential bicyclists.These findings corroborate research on bicyclists’ preferences for roadway design and contribute a new understanding of motorists’ preferences. They also support the U.S. Federal Highway Administration’s efforts to encourage greater accommodation of bicyclists on urban streets.     

Characterizing the speed and paths of shared bicycle use in Lyon

Data gathered relating to the Lyon’s shared bicycling system, Vélo’v, is used to analyze 11.6 millions bicycle trips in the city. The data show that bicycles now compete with the car in terms of speed in downtown Lyon. It also provides information on cycle flows that can be of use in the planning of dedicated bicycle lanes and other facilities.     

Factors associated with bicycle ownership and use: a study of six small U.S. cities

As a means of transportation and as a form of physical activity, bicycling generates benefits to the bicyclist as well as to the community as a whole. Bicycling now accounts for less than 1 percent of all trips for all purposes in the U.S., but evidence from other western countries suggests that under the right conditions, bicycling levels can be significantly higher. Indeed, the experiences of some U.S. cities suggest that it is possible to create conditions conducive to higher levels of bicycling even in the U.S. However, the extent to which bicycle investments have contributed to bicycling levels in these communities has not been rigorously assessed. The purpose of this study is to provide a better understanding of the determinants of bicycle ownership and use as a basis for identifying ways to promote bicycling. A cross-sectional study of six cities was designed to test the importance of bicycle infrastructure and other physical environment factors relative to individual factors and social environment factors, using a nested logit model to examine ownership and use decisions jointly. The results show strong effects of individual attitudes and physical and social environment factors on bicycle ownership and use.     

Roadway and traffic characteristics for bicycling

The promotion of bicycle transportation includes the provision of suitable infrastructure for cyclists. In order to determine if a road is suitable for bicycling or not, and what improvements need to be made to increase the level of service for bicycles on specific situations, it is important to know how cyclists perceive the characteristics that define the roadway environment. The present paper describes research developed to define which roadway and traffic characteristics are prioritized by users and potential users in the evaluation of quality of roads for bicycling in urban areas of Brazilian medium-sized cities. A focus group discussion identified 14 attributes representing characteristics that describe the quality of roads for bicycling in Brazilian cities. In addition, an attitude survey was applied with individuals to assess their perception on the attributes, along with the importance given to each one of them. The results were analyzed through the Method of Successive Intervals Analysis, which allows the transformation of categorical data into an interval scale. The analysis suggests that both the roadway and traffic characteristics related to segments and those related to intersections are important to the survey respondents. The five most important attributes, in their opinion, are: (1) lane width; (2) motor vehicle speed; (3) visibility at intersections; (4) presence of intersections; and (5) street trees (shading). Therefore, the research suggests that to promote bicycle use in Brazilian medium-sized cities, these attributes must be prioritized.     

自行车在山地城市应用的可行性探究

随着中国城镇化、机动化水平的提高,"以车为本"的交通发展导向已经暴露出了诸多的弊端。为了有效缓解山地城市交通的拥堵问题以及环境污染问题,本文分析了自行车在山地城市发展的重要意义,并且从环境、交通、社会以及经济四个方面详细阐述了自行车交通在山地城市运用的可行性以及注意事项,从而促进山地城市自行车系统的良好发展,倡导山城市民拥有绿色低碳出行的理念。     

Factors associated with proportions and miles of bicycling for transportation and recreation in six small US cities

The majority of bicycling in the US is for recreation rather than transportation purposes but few studies have examined the question of bicycling purpose. We use data from an online survey conducted in 2006 in six small cities in the western US to examine factors affecting bicycling for transportation compared to bicycling for recreation. The results indicate that individual, social-environment, and physical-environment factors have important influences on the balance between transportation and recreational bicycling and on miles of bicycling for each purpose. Bicycling comfort and an aversion to driving are associated with more transportation bicycling. A culture of utilitarian bicycling and short distances to destinations are also key factors for transportation bicycling. Bicycle infrastructure appears to play an indirect role through its effect on perceived bicycling safety and through the self-selection effect, by attracting bicycling-inclined people to bicycling-supportive communities.     

Random parameter models for estimating statewide daily bicycle counts using crowdsourced data

Persistent lack of non-motorized traffic counts can affect the evidence-based decisions of transportation planning and safety-concerned agencies in making reliable investments in bikeway and other non-motorized facilities. Researchers have used various approaches to estimate bicycles counts, such as scaling, direct-demand modeling, time series, and others. In recent years, an increasing number of studies have tried to use crowdsourced data for estimating the bicycle counts. Crowdsourced data only represents a small percentage of cyclists. This percentage, on the other hand, can change based on the location, facility type, meteorological, and other factors. Moreover, the autocorrelation observed in bicycle counts may be different from the autocorrelation structure observed among crowdsourced platform users, such as Strava. Strava users are more consistent; hence, the time series count data may be stationary, while bicycle demand may vary based on seasonal factors. In addition to seasonal variation, several time-invariant contributing factors (e.g., facility type, roadway characteristics, household income) affect bicycle demand, which needs to be accounted for when developing direct demand models. In this paper, we use a mixed-effects model with autocorrelated errors to predict daily bicycle counts from crowdsourced data across the state of Texas. Additionally, we supplement crowdsourced data with other spatial and temporal factors such as roadway facility, household income, population demographics, population density and weather conditions to predict bicycle counts. The results show that using a robust methodology, we can predict bicycle demand with a 29% margin of error, which is significantly lower than merely scaling the crowdsourced data (41%).     

Bicyclist commuters’ choice of on-street versus off-street route segments

When using limited funds on bicycle facilities, it would be helpful to know the extent to which a new facility will be used. If a bicycle lane is added to a street, how many bicyclists will no longer use the adjacent sidewalk? If a separate bicycle path is constructed, how many bicyclists will move from the street or sidewalk? This study seeks to identify factors that explain a bicyclist’s choice between available facility choices—off-street (sidewalk and bicycle path) or on-street (bicycle lane and roadway). This paper investigates these issues through a survey of bicyclists headed to Purdue University in West Lafayette, IN, USA. The first data collected to address these questions were “site-based”. Bicyclists were interviewed on campus at the end of their trips and asked which part of the cross-sections along their routes they had used—on-street or off-street. The characteristics of a particular cross-section of street right-of-way were then compared against the characteristics of each bicyclist and his/her observed choice of street, sidewalk, lane, or path. Later, “route-based” serial data were also added. The study developed a mixed logit model to analyze the bicyclists’ facility preferences and capture the unobserved heterogeneity across the population. Effective sidewalk width, traffic signals, segment length, road functional class, street pavement condition, and one-way street configuration were found to be statistically significant. A bicycle path is found to be more attractive than a bicycle lane. Predictions from the model can indicate where investments in particular bicycle facilities would have the most desirable response from bicyclists.     

Managing a smart bicycle system when demand outstrips supply: the case of the university community in Seville

Of the many public initiatives used to promote the use of bicycles in the urban environment, the one that has achieved the most spectacular results in a short period of time is the public bicycle hire system. The experience of Seville is one of the most successful internationally, where 6.6 % of mechanised trips were being made by bicycle within 30 months. This paper analyses this experience in the university community, which represents one-third of system users. We conclude that the people who are most satisfied with the system are those who use it for leisure and recreation activities, non-residents of the city, more environmentally aware people and those who have no alternative mode of transportation. Their satisfaction is also closely linked to their appreciation of the bicycles’ level of comfort, the ease with which users can hire bicycles and return them and the small amount of paperwork involved required to sign up for the system. However, user appreciation has fallen over time because the system’s rapid success has caused it to become overloaded. This experience therefore provides one main lesson: the system’s success can result in eventual difficulties.     

Roadway determinants of bicyclist exposure to volatile organic compounds and carbon monoxide

Few studies have quantified relationships between bicyclist exposure to air pollution and roadway and traffic variables. As a result, transportation professionals are unable to easily estimate exposure differences among bicycle routes for network planning, design, and analysis. This paper estimates the effects of roadway and travel characteristics on bicyclist exposure concentrations, controlling for meteorology and background conditions. Concentrations of volatile organic compounds (VOC) and carbon monoxide (CO) are modeled using high-resolution data collected on-road. Results indicate that average daily traffic (ADT) provides a parsimonious way to characterize the impact of roadway characteristics on bicyclists’ exposure. VOC and CO exposure increase by approximately 2% per 1000 ADT, robust to different regression model specifications. Exposure on off-street facilities is higher than at a park, but lower than on-street riding – with the exception of a path through an industrial corridor with significantly higher exposure. VOC exposure is 20% higher near intersections. Traffic, roadway, and travel variables have more explanatory power in the VOC models than the CO model. The quantifications in this paper enable calculation of expected exposure differences among travel paths for planning and routing applications. The findings also have policy and design implications to reduce bicyclists’ exposure. Separation between bicyclists and motor vehicle traffic is a necessary but not sufficient condition to reduce exposure concentrations; off-street paths are not always low-exposure facilities.     

Crowdsourced data for bicycling research and practice

ABSTRACT

Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.     

Using a stages of change approach to explore opportunities for increasing bicycle commuting

Given the potential benefits of bicycling to the environment, the economy, and public health, many U.S. cities have set ambitious goals for increasing the bicycle share of commute trips. The Transtheoretical Model of Behavior Change, which seeks to describe how positive and permanent change can be fostered in individuals, may shed light on how cities can most effectively increase bicycle commuting. We use the model’s “stages of change” framework to explore the potential for increased bicycle commuting to the UC Davis campus in Davis, California. Our analysis uses data from the 2012 to 2013 UC Davis Campus Travel Survey, an annual online survey that is randomly administered to students and employees at UC Davis. Based on their responses to questions about current commute mode and contemplation of bicycle commuting, respondents are divided into five stages of change: Pre-contemplation, Contemplation, Preparation, Action, and Maintenance. We construct a Bayesian multilevel ordinal logistic regression model to understand how differences in socio-demographic characteristics, travel attributes, and travel attitudes between individuals explain their membership in different stages of change. In addition, we use this model to explore the potential of various intervention strategies to move individuals through the stages of change toward becoming regular bicycle commuters. Our results indicate that travel attitudes matter more to progression toward regular commute bicycling than travel attributes, tentatively supporting the efficacy of “soft” policies focused on changing travel attitudes.     

Prohibited–permitted right-turn phasing strategy based on capacity analysis of right-turn movements

In traffic-crowded metropolitan areas, such as Shanghai and Beijing in China, right-turn vehicles that operate with a permitted phase at signalized intersections are normally permitted to filter through large numbers of pedestrians and bicycles. To alleviate such conflicts and improve safety, traffic engineers in Shanghai introduced a prohibited–permitted right-turn operation, adding a subphase to the permitted phase in which right-turns are prohibited. Unfortunately, the prohibited subphase would reduce the capacity of right-turn movements when it prohibits right turns even if there are few pedestrians and bicycles crossing the street. This paper aims at quantifying the impact of both non-vehicular flows and the prohibited subphase on the right-turn capacity, and then proposes a strategy to determine appropriate prohibited–permitted right-turn operation that minimizes the capacity reduction caused by the prohibited subphase. To achieve this goal, we improved the pedestrian and bicycle adjustment factor described in the Highway Capacity Manual by taking into account: (1) the variety in space competition between pedestrians and bicycles, and (2) the effect of two conflict zones in each phase on right-turn operation. In addition, we revised the capacity estimation model in the Highway Capacity Manual, and developed a model based on bicycle/pedestrian volume fluctuation to describe the capacity reduction due to both non-vehicular flows and the prohibited subphase. Furthermore, we proposed a timing strategy for the onset and duration of appropriate prohibited subphase. When bicycle and pedestrian volumes are low, the actuated strategy turns to the permitted phase. When these volumes are moderate, the strategy turns to the prohibited–permitted operation. With the volumes increasing, the prohibited subphase onset advances and duration increases. In these two scenarios, the new strategy has higher right-turn capacity than the current pretimed prohibited–permitted operation. Unfortunately, when bicycle and pedestrian volumes are high, the strategy yields similar right-turn capacity. However, the new prohibited subphase has less potential vehicle–bicycle and vehicle–pedestrian conflicts.     

The value of time and external benefits in bicycle appraisal

We estimate the value of time savings, different cycling environments and additional benefits in cost-benefit analysis of cycling investments. Cyclists’ value of travel time savings turns out to be high, considerably higher than the value of time savings on alternative modes. Cyclists also value other improvements highly, such as separated bicycle lanes. As to additional benefits of cycling improvements in the form of health and reduced car traffic, our results do not support the notion that these will be a significant part in a cost-benefit analysis. Bicyclists seem to take health largely into account when making their travel choices, implying that it would be double-counting to add total health benefits to the analysis once the consumer surplus has been correctly calculated. As to reductions in car traffic, our results indicate that the cross-elasticity between car and cycle is low, and hence benefits from traffic reductions will be small. However, the valuations of improved cycling speeds and comfort are so high that it seems likely that improvements for cyclists are cost-effective compared to many other types of investments, without having to invoke second-order, indirect effects. In other words, our results suggest that bicycle should be viewed as a competitive mode of travel and not primarily as a means to achieve improved health or reduced car traffic.     

Bicycle lane priority: Promoting bicycle as a green mode even in congested urban area

The main obstacles to boosting the bicycle as a mode of transport are safety concerns due to interactions with motorized traffic. One option is to separate cyclists from motorists through exclusive bicycle priority lanes. This practice is easily implemented in uncongested traffic. Enforcing bicycle lanes on congested roads may degenerate the network, making the idea very hard to sell both to the public and the traffic authorities. Inspired by Braess Paradox, we take an unorthodox approach to seeking latent misutilized capacity in the congested networks to be dedicated to exclusive bicycle lanes. The aim of this study is to tailor an efficient and practical method to large size urban networks. Hence, this paper appeals to policy makers in their quest to scientifically convince stakeholder that bicycle is not a secondary mode, rather, it can be greatly accommodated along with other modes even in the heart of the congested cities. In conjunction with the bicycle lane priority, other policy measures such as shared bicycle scheme, electric-bike, integration of public transport and bicycle are also discussed in this article. As for the mathematical methodology, we articulated it as a discrete bilevel mathematical programing. In order to handle the real networks, we developed a phased methodology based on Branch-and-Bound (as a solution algorithm), structured in a less intensive RAM manner. The methodology was tested on real size network of city of Winnipeg, Canada, for which the total of 30 road segments – equivalent to 2.77 km bicycle lanes – in the CBD were found.     

A cellular automata (CA) model for motorized vehicle flows influenced by bicycles along the roadside

On many urban low‐grade or branch roads, especially in medium or small cities in China, bicyclists and motorists commonly share the non‐barrier road surface. Because bicycles are unpredictable and unstable when moving, motorized vehicles must reduce their speed to safely approach and overtake them. In this study, the gradual deceleration process a motorized vehicle undergoes before it passes a bicycle was analyzed. The motorist was assumed to prefer a comfortable deceleration and to select a higher deceleration rate only when the distance to the bicycle was insufficient to reduce the car's speed to the expected value at a comfortable deceleration rate. Cellular automata (CA) simulations were used to reveal the flow characteristics of motorized vehicles reacting to bicycles traveling along the roadside, and the results show that for the general velocities of motorized vehicles and bicycles traveling on urban branch roads, the road capacity for motorized vehicles is not related to the number of bicycles present. However, the average travel time of motorized vehicles is significantly affected by the presence of bicycles when the number of motorized vehicles on the road is small. In addition, motorized vehicles' average travel time is more influenced by disturbances in the flow of motorized vehicles than by bicycles when the number of motorized vehicles on the road is large. Field observations and surveys were used to validate the traffic behaviors and simulation results. Copyright © 2016 John Wiley & Sons, Ltd.