Bicycle Flow Research Articles

Expanding the Scope of the Bicycle Level-of-Service Concept: A Review of the Literature

Research into the bicycle level-of-service (BLOS) has been extensively conducted over the last three decades. This research has mostly focused on user perceptions of comfort to provide guidance for decision-makers and planners. Segments and nodes were studied first, followed by a network evaluation. Besides these investigations, several variables have also been utilized to depict the users’ perspectives within the BLOS field, along with other cycling research domains that simultaneously scrutinized the users’ preferences. This review investigates the variables and indices employed in the BLOS area in relation to the field of bicycle flow and comfort research. Despite general agreement among existing BLOS variables and the adopted indices, several important research gaps remain to be filled. First, BLOS indices are often categorized based on transport components, while scarce attention has been paid to BLOS studies in trip-end facilities such as bicycle parking facilities. The importance of these facilities has been highlighted instead within research related to comfort. Second, the advantages of separated bike facilities have been proven in many studies; however, scarce research has addressed the challenges associated with them (e.g., the heterogeneity within those facilities due to the presence of electric bikes and electric scooters). This issue is clearly noticeable within the research regarding flow studies. Furthermore, network evaluation (in comparison to segment and node indices) has been studied to a lesser extent, whereas issues such as connectivity can be evaluated mainly through a holistic approach to the system. This study takes one step toward demonstrating the importance of the integration of similar research domains in the BLOS field to eliminate the aforementioned shortcomings.

‘The bike breaks down. What are they going to do?’ Actor-networks and the Bicycles for Development movement

This article reports on an empirical study of ‘Bicycles for Development’ (BFD) – a nascent movement whereby used bicycles are collected (often in the global North) and distributed in development contexts (often in the global South) with the aim of achieving a range of positive social outcomes (e.g. access to education). Drawing from interviews ( n = 32) with participants from 19 BFD organizations, and informed by actor-network theory (ANT), the analysis presented herein specifically highlights three key factors that facilitate and/or hinder BFD work: (a) government regulations that potentially stem or ease the flow of bicycles into development contexts; (b) the bicycle’s material constitution, and specifically its sturdiness (or lack thereof); and (c) environmental conditions that impact how bicycles are made, distributed and used. A key theme that cuts across these findings is the potential for non-humans to cause ‘frictions’ that potentially disrupt, divert, but also help in realizing the programmes of action of BFD organizations. We consider the relevance of these findings for both the BFD movement in particular and the wider Sport for Development and Peace (SDP) movement in general. Based on study findings, we argue for ANT as a useful framework for achieving a widened analytical focus and thus for delivering more robust accounts of development contexts under study.

Constructing sub-Saharan African mobilities through the flow of second-hand objects: Scripting bicycles for Namibian users

This article examines how civil society operates to configure mobilities for development contexts and subjects. In order to understand the politics and processes of constructing mobilities and the potential for decolonising mobility design, the paper traces the flow of second-hand bicycles from the global North to Namibia using a framework of Script Analysis. The issues of power in constructing social and technical meanings attributed to the bicycles are examined as they are re-valued for humanitarian use. The paper investigates how Namibian users subscribe to, reject and adapt the bicycles' meanings and physical properties in order suit their diverse needs, which are often disparate from techno-rational understandings of transport solutions for economic development. The analysis finds that the bicycle is prescribed singularly as an object that intends to technologically modernise utilitarian subjects in a universal development imaginary. Simultaneously, alternative framings of mobility, for example the way in which bicycles are appropriated to perform modern and global identities, are discouraged in hegemonic mechanisms of development.

A methodology for the assessment of pedestrians-cyclists shared space level of service

The limited available public space and the intention to promote active mobility, enhance the importance and the interest about pedestrians-cyclists shared spaces. This paper proposes a new methodology for determining the level of service (LOS) in shared spaces, based on the hindrance concept. In this context: a) the types of events between the users were identified, b) regression tree models for predicting the events’ frequency were developed, c) weights were assigned to the various events, based on their negative impact on users’ perceived comfort and safety. The assignment of weights consists a novelty of the proposed methodology since there is no other study attempting to identify their negative impact. Eventually, a mathematical formula suitable for optimizing the design and management of pedestrians-cyclists shared spaces, by minimizing users’ hindrance, is being produced. The objective of the methodology is to be a reliable and easy to use tool for planners and decision-makers. From the application of the methodology in the city of Thessaloniki, Greece, it is identified that: a) the pedestrian and bicycle flow rates play a decisive role in the frequency of experiencing events, b) “delayed passing” is the most disturbing situation for both pedestrians and cyclists, c) “passings” have similar negative impact on users’ perceptions with “meetings”.

Application of Cluster Analysis with Unsupervised Learning to Dockless Shared Bicycle Flow Control and Dispatching

Application of Cluster Analysis with Unsupervised Learning to Dockless Shared Bicycle Flow Control and Dispatching

Development of Bicycle Safety Index Models for Safety of Bicycle Flow at 3-Legged Junctions on Urban Roads under Mixed Traffic Conditions

In Urban areas of developing countries like India, there have been a rapid increase in the bicycle volumes and traffic-bicycle conflicts during the last few decades. To enhance bicycle safety under mixed traffic conditions, there is a need to improve the bicycle facilities on the urban roads at un-signalized 3-legged junctions. The present study aims to develop bicycle safety index models using variables like bicycle volumes, bicycle speed (km/min), bicycle markings and bicycle safety ratings. The data was collected at locations of different 3-legged junctions in the central business district area of Srinagar City in India. At these selected locations, bicycle volume count and bicycle average speeds (km/hr) were observed during peak hours. Bicycle flows were categorized based on different age groups, gender, bicycle speed and direction. The bicycle safety index models were calibrated and validated using the collected data. The model results confirm estimation of correct bicycle safety levels at un-signalized 3-legged junctions. The study will also be helpful to improve the existing bicycle facilities, bicycle flows (bikes/hour) and also, to provide bicycle safety measures at un-signalized 3-legged junctions on urban roads in mixed traffic environment.

Bicycle Traffic Volume Estimation Based on GPS Data

All the analytic methods for assessing the safety or comfort of bicyclists in urban area have as a common factor the number of bicycles that enter the system in a certain time interval or an estimate of that. The estimation of the average bicycle volume based on manual and automatic measurements is time-consuming and often require the use of expensive technology. The paper presents a method of estimation based on GPS data from a bike sharing system as a low-cost option for data collection. The analysis was made for the city of Krakow (Poland), using the daily volume of bicycles from 5 automatic counter loops and GPS data from a bike sharing system called Wavelo. Based on the two-factor analysis of variance (ANOVA) and the Tukey post-hoc test, the influence of "localization" and "day of the week" factors on the share of Wavelo bicycles in the entire bicycle flow was estimated. It was shown that examined share is not significantly different between individual days of the week, but changes significantly between analyzed locations. Developed models are characterized by high R2 coefficients (exceeding 0.90) and the average error of estimation up to 11.5%. The results of the studies show that bicycle volume can be estimated based on GPS data from bike sharing system. However, it is necessary to carry out control measurements to verify developed models and their possible application in other locations.

Using road class as a replacement for predicted motorized traffic flow in spatial network models of cycling

Recent years have seen renewed policy interest in urban cycling due to the negative impacts of motorized traffic, obesity and emissions. Simulating bicycle mode share and flows can help decide where to build new infrastructure for maximum impact, though modelling budgets are limited. The four step model used for vehicles is not typically used for this task as, aside from the expense of use, it is designed around too-large zone sizes and a simplified network. Alternative approaches are based on aggregate statistics or spatial network analysis, the latter being necessary to create a model sufficiently sensitive to infrastructure location, although still requiring considerable modelling effort due to the need to simulate motor vehicle flows in order to account for the effect of motorized traffic in disincentivising cycling. The model presented uses an existing spatial network analysis methodology on an unsimplified network, but simplifies the analysis by substituting explicit prediction of motorized traffic flow with an alternative based on road classification. The method offers a large reduction in modelling effort, but nonetheless gives model correlation with actual cycling flows (R2 = 0.85) broadly comparable to a previous model with motorized traffic fully simulated (R2 = 0.78).

The landscape returns the gaze

AbstractThis paper looks at bikescapes – and particularly dockless share bikes – with a focus on their rapid proliferation and subsequent partial demise in Sydney. Four principal themes emerged from this study: first, bikes are an important part of the cityscape, and studies of urban semiotics need to take greater account of modes of transport. Second, the rise of docked and dockless share bikes has changed the ways the city is felt and perceived: as bikes circulate within the city, these shifting bikescapes make visible changes to the physical city environment. The ebb and flow of dockless bikes – from neat alignments to dispersed arrangements – provide an insight into changing patterns of work, leisure, and mobility, and present entropic rather than ordered city processes. Third, these bikes became significant discourse markers, material artefacts where discourses of consumption, convenience, contamination, and co-operation intersect. Dockless share bikes sit at the hub of a tussle over public and private ownership of space and information, in terms both of their physical incursion into public space and as syphons of personal information. Finally, they suggest not only that aspects of the cityscape may play an active role in semiotic networks, but that the semiotic landscape may be returning our gaze.

Finding communities in bicycle sharing system

The bicycle sharing system (BSS) provides a more sustainable transport paradigm in big cities. The recorded cycling trajectories can be used to detect human movement patterns. Community detection methods have been used to study BSS from a complex network perspective. However, the previous used modularity-based methods not only ignored the interdependencies of bicycle flows in the system, but also suffered from the problem of resolution limit. The in-depth analysis of detection results is also lacked. In this paper, we propose an interactive visual analytics system to detect the cycling communities of bicycle sharing system. Different kinds of community detection algorithms are adopted for finding station clusters; multiple inter-linked views are designed to visualize properties of the detected substructures from different perspectives. The real bicycle sharing dataset in Hangzhou is used for analysis, which demonstrates the effectiveness of our method. By using the system, analyzers can compare the cluster results generated by different algorithms, investigate the reason of the partition results based on different metrics, and find the relationship among human activity communities and the city subregional structures. This study provides insights into using bicycle sharing data to reveal human travel pattern and BSS usage pattern, which potentially aids in developing urban planning policies.

Speeding behavior and speed limits for heterogeneous bicycle flow

Objective: Speed limits for motorized vehicles are widely used to reduce noise and promote road safety. Unfortunately, there is little research on speed limits for bicycles, especially for electric bicycles. This article aims to analyze the speeding behavior and propose the suggested speed limit values for electric bicycles.Methods: Field speed data were collected from 11 segments of separated bicycle lanes in Hangzhou, China. The speeding behavior of different types of bicycles was analyzed under different speed limits. Mixed logistic models are proposed to model speeding behavior and its influencing factors. Eighty-fifth percentile speeds under different conditions were calculated as speed limits. Stepwise regression models are proposed to model the relationships between the 85th percentile speeds and their influencing factors, and speed limits for different types of bicycles were determined.Results: It is recommended that the speed limits for pure electric bicycle traffic be set as 25–30 km/h and 30–35 km/h for bicycle lanes with widths less than 3 m and greater than 3 m, respectively. The recommended speed limit for heterogeneous bicycle flows is 25 km/h.Conclusions: The suggested speed limit values for different bicycle traffic flow can help to improve speed limit laws and policies for bicycle traffic.

Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone

This research describes numerical methods to analyze the absolute transport demand of cyclists and to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle counts and map-matched GPS traces. The methods are demonstrated with data from the city of Bologna, Italy: approximately 27,500 GPS traces from cyclists were recorded over a period of one month on a volunteer basis using a smartphone application. One method estimates absolute, city-wide bicycle flows by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts at the main bikeways of the city. As there is a fairly high correlation between the two sources of flow data, the absolute bike-flows of the entire network have been correctly estimated. Another method describes a novel, total deviation metric per link which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route. The deviations are accepted by cyclists either to avoid unpleasant road attributes along the shortest route or to experience more favorable road attributes along the chosen route. The total deviation metric indicates to the planner which road links are contributing most to the total deviation of all cyclists. In this way, repellant and attractive road attributes for cyclists can be identified. This is why the total deviation metric is of practical help to prioritize bike infrastructure construction on individual road network links. Finally, the map-matched traces allow the calibration of a discrete choice model between two route alternatives, considering distance, share of exclusive bikeway, and share of low-priority roads.

Analysis of Bicycle Headway Distribution, Saturation Flow and Capacity at a Signalized Intersection using Empirical Trajectory Data

Predicting the bicycle flow capacity at signalized intersections of various characteristics is crucial for urban infrastructure design and traffic management. However, it is also a difficult task because of the large heterogeneity in cycling behavior and several limitations of traditional capacity estimation methods. This paper proposes several methodological improvements, illustrates them using high-resolution trajectory data collected at a busy signalized intersection in the Netherlands, and investigates the influence of key variables of capacity estimation. More specifically, it shows that the (virtual) sublane width has a significant effect on the shape of the headway distribution at the stop line. Furthermore, a new method is proposed to calculate the saturation headway (a key variable determining capacity), which excludes the cyclists initially located close to the stop line using a distance-based rule instead of a fixed number (as is usually done in practice). It is also shown that the saturation headway is quite sensitive to the sublane width. Moreover, a new, empirically based method is proposed to identify the number of sublanes that can be accommodated in a given cycle path, which is another key influencing variable. This method yields considerably lower estimates of the number of sublanes than traditional methods, which rely solely on the (available) cycle path width. Finally, the authors show that methodological choices such as the sublane width and the method used to estimate the number of sublanes have a considerable effect on capacity estimates. Therefore, this paper highlights the need to define a sound methodology to estimate bicycle flow capacity at signalized intersections and proposes some steps to move toward that direction.

Capacity, Capacity Drop, and Relation of Capacity to the Path Width in Bicycle Traffic

Bicycle usage is encouraged in many cities because of its health and environmental benefits. As a result, bicycle traffic increases which leads to questions on the requirements of bicycle infrastructure. Design guidelines are available but the scientific substantiation is limited. This research contributes to understanding bicycle traffic flow by studying the aggregated movements of cyclists before and after the onset of congestion within the setting of a controlled bottleneck flow experiment. The paper quantitatively describes the relation between capacity and path width, provides a qualitative explanation of this relation by analyzing the cyclist configuration for different path widths, and studies the existence of a capacity drop in bicycle flow. Using slanted cumulative curves and regression analysis, the capacity of a bicycle path is found to increase linearly with increasing path width. A steady drop in flow rate is observed after the onset of congestion, indicating that the capacity drop phenomenon is observed in bicycle traffic. The results presented in this paper can help city planners to create bicycle infrastructure that can handle high cyclist demand.

Large-Scale Bicycle Flow Experiment: Setup and Implementation

Cycling research at the operational behavioral level is limited, mainly because of the lack of empirical data. To overcome this data shortage, we performed a controlled, large-scale cycling experiment in the Netherlands. In this paper we describe the methodology for setting up and implementing such an experiment, from the motivation of its design using a conceptual model describing cyclist behavior to adjustments that were required during the experiment. The main contribution of this paper is, therefore, to be used as a guide in future experimental data collections. Moreover, we present the characteristics of the participants and their bicycles, and provide a qualitative description of phenomena observed during the experiment. Finally, we elaborate on the potential that the collected dataset holds for future research into understanding and modeling operational cycling behavior.

Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction

Urban traffic passenger flows prediction is practically important to facilitate many real applications including transportation management and public safety. Recently, deep learning based approaches are proposed to learn the spatio-temporal characteristics of the traffic passenger flows. However, it is still very challenging to handle some complex factors such as hybrid transportation lines, mixed traffic, transfer stations, and some extreme weathers. Considering the multi-channel and irregularity properties of urban traffic passenger flows in different transportation lines, a more efficient and fine-grained deep spatio-temporal feature learning model is necessary. In this paper, we propose a deep irregular convolutional residual LSTM network model called DST-ICRL for urban traffic passenger flows prediction. We first model the passenger flows among different traffic lines in a transportation network into multi-channel matrices analogous to the RGB pixel matrices of an image. Then, we propose a deep learning framework that integrates irregular convolutional residential network and LSTM units to learn the spatial-temporal feature representations. To fully utilize the historical passenger flows, we sample both the short-term and long-term historical traffic data, which can capture the periodicity and trend of the traffic passenger flows. In addition, we also fuse other external factors further to facilitate a real-time prediction. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing as well as bike flows in New York. The results show that the proposed DST-ICRL significantly outperforms both traditional and deep learning based urban traffic passenger flows prediction methods.

Measuring immediate impacts of a new mass transit system on an existing bike-share system in China

Mass transit systems as metros thrive nowadays in China – the largest bike-share market all over the world. Hence, it is meaningful to study how a new metro system impact on an existing bike-share system (BS) and understand how they interact to ensure they inter-enhance rather than inter-replace each other. By using trip-pattern and land use to create control and treatment groups based on trip data from Suzhou Public Bicycle System (SPBS), a ‘‘before-and-after’’ study is initiated to measure changes of an existing BS during a period when a new metro system (Here, Metro line 4: M4) opened. Normalized bike flow data (NF) is introduced to cluster and indicate trip-pattern in this study. The result indicates that most SPBS ridership and users within the metro’s catchment (the treatment group) have largely increased since the introduction of the M4, while non-commuting cycling increased more than commuting cycling. The exception happens in metros’ hub, where SPBS ridership slightly decreased after M4. As for SPBS trip-pattern, before M4 was introduced, trip-pattern in CBD areas in Gusu District is found to be partially imbalanced (high returning in the morning and high renting in the evening), while balanced pattern (similar returning and renting in morning and evening) is more often seen in other areas. After M4 was introduced, a general trip-pattern change from the balanced to the imbalanced is observed regardless of land use, except for educational land use existing in Wuzhong District. This further indicates that after new metros was introduced, SPBS tends to serve “first-mile” interchange (origins to metros) rather than “last-mile” interchange (metros to destinations) in the morning and vice versa in the evening regardless of land use type. This unique pattern could lead to different policy and management in enhancing interchanging service between BS and mass transit.

Multichannel queueing behaviour in urban bicycle traffic


 
 
 The objective of this paper is to propose a method to analyse and describe cyclists’ behaviour at signalized intersections with specific focus on the multichannel (multi-lane) queue phenomenon. As we observed, cyclists form queues without a fixed-lane and FIFO discipline, for which the classical, car-oriented analytical approach becomes insufficient. Cyclists’ multichannel queueing behaviour is common and characterized by substantial degree of variability, especially in case of shorter queues which emerge regularly at cycle crossings. Although cyclist behaviour has been widely studied by transportation research community, their queueing behaviour picture is still incomplete. Namely, there is no method addressed to analyse the full scope of these phenomena and to quantify their impact on the cyclist queue performance.
 To bridge this gap, we introduce the technique to observe multichannel queues and report relevant observations, which we then complement with a methodological framework to analyse obtained results and provide a complete multichannel queue description. We video-record cyclists as they enqueue to one of multiple channels, form the queue and smoothly merge into a single lane again as the queue discharges. We apply the method to analyse results from a pilot study of 160 cyclists forming 50 queues in the city of Krakow, Poland. The proposed method allows us to analyse and quantify the observed queue performance and its characteristics: the number of channels, their emergence process, channel and queue lengths, discharge process with FIFO violations, starting and discharging times. Findings from pilot study reveal that both queue length and discharge times strongly depend on queue formation process.
 The contribution of this paper is the method to describe multichannel cyclist queueing behaviour, enriching current picture of bicycle flow and cyclists’ behaviour. Since the method has been developed on relatively short queues (up to 10 cyclists), findings included in this paper primarily refer to such queue sizes. Nonetheless, the method is formulated in a generic way, applicable also for longer bicycle queues. Possible practical implications are new estimates for queue lengths and discharge times - useful for bicycle infrastructure design and traffic engineering purposes.
 
 

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

The Bike Sharing System is a sustainable urban transport solution that consists of a fleet of bikes placed in various stations. Users will be satisfied if they find available bikes at their departure station and free docks at the destination. Despite the regulation operations of the system provider (i.e., redistribution of bikes by truck) deeper modifications (bike fleet size or station capacity) are often necessary to ensure a satisfactory service rate. In this paper, we model a sub-graph of a Bike Sharing System using the closed queuing network with a Repetitive-Service-Random-Destination blocking mechanism. This model is solved using the Maximum Entropy Method. This model faithfully reproduces the system dynamics considering the limited capacity of stations. We analyze the performance, particularly, via an overall performance indicator of the system. The various control and monitoring decisions (fleet-size, capacity of stations, incoming and outgoing flow of bikes) are applied to find out the best performance levels. The results demonstrate that the overall performance is robust enough regarding the fleet size changes but it degrades with the increase of the stations’ capacity. Finally, the arrival and departure flows control is an efficient and powerful operational leverage.

Empirical Analysis of the Macroscopic Characteristics of Bicycle Flow during the Queue Discharge Process at a Signalized Intersection

Signalized intersections are one of the most common types of bottleneck in urban cycling networks. Gaining knowledge on the macroscopic characteristics of bicycle flow during the queue discharge process is crucial for developing ways to reduce the delay experienced by cyclists at intersections. This paper aims to determine these characteristics (including jam density, shockwave speed, and discharge flow), and to unveil possible relationships between them, particularly whether and to what extent discharge flow is correlated with jam density and shockwave speed (which is of high relevance from a traffic management viewpoint). To this end, the study analyzes high-resolution bicycle trajectories derived from video footage on a one-direction cycle path leading to an intersection in Amsterdam (the Netherlands). Linear regression analysis is used to investigate the relationships between macroscopic variables. The results indicate that jam density, shockwave speed, and discharge flow vary considerably across traffic-signal cycles, which highlights the stochastic nature of bicycle flow. Furthermore, the results show that discharge flow is strongly positively correlated with jam density and shockwave speed. It is hypothesized that there is a causal relationship between these variables, which would imply that traffic engineers can increase discharge flows (thus reducing delay) at signalized intersections if they find effective ways to increase jam densities and shockwave speeds.