Cycling is an important part of the urban transport system and short-distance travel in many modern cities around the world. With no emissions and occupying much less road space than cars, bikes are clean and sustainable. Bicycle traffic needs to be tracked and analysed in order to generate reliable predictions and make correct decisions when adapting and building traffic infrastructure, to account for bikes in road traffic systems, and to model and plan interactions between bikes and autonomous vehicles. Offering a systematic analysis of the movements and behaviours of bicycles and their riders, this book discusses data collection and evaluation approaches, and the development of a framework for the theory and modelling of bike traffic followed by model verification techniques and riding characteristics for context. This book contains valuable information for researchers involved with intelligent transportation systems, traffic modelling and simulation, and particularly those with an especial interest in bicycle traffic. The book will also be of interest to advanced students in these and related fields, and transportation policymakers.
Inspec keywords: bicycles; roads; road traffic; traffic engineering computing; behavioural sciences; data acquisition; computer simulation
Other keywords: bike traffic modelling; riding characteristics; data collection; short-distance travel; bicycle traffic simulation; cycling; model verification techniques; behavioural modelling; autonomous vehicles; urban transport system; traffic modelling; road traffic systems
Subjects: Data handling techniques; Systems theory applications in transportation; Traffic engineering computing; General and management topics
Bicycles, also known as a bike or a cycle, are usually small two-wheeled land vehicles. After people get on the bicycle, they pedal as the power. Thus, a bicycle is a green, healthy and environment-friendly means of transportation. As the microscopic dynamic characteristics of bicycles are quite different from those of motor vehicles, we introduce some unique bicycle microscopic riding characteristics.The main aim is to analyze and model the microcosmic behavior of bicycle traffic on roads, signalized intersections and un-signalized intersections, so as to lay a theoretical and data foundation for bicycle facilities planning, design and the relevant traffic management measures. At the same time, we also analyze and model the cyclists' microscopic behaviors on various facilities, which can lay a foundation for the microscopic simulation model for urban mixed traffic flow.
Bicycle traffic is not only very different from motor vehicle traffic in terms of trip length, resource occupation and environmental conservations, but also it has its own riding characteristics in terms of microscopic motion. Compared with other motor vehicles, a bicycle has its own structural characteristics, such as the unstable two-wheel structure, relatively small size, flexibility, powered by human, etc. These characteristics make its microscopic riding motion, quite different from the dynamic motions of motor vehicles. The article focuses on the psychological characteristics of cyclists and the microscopic riding characteristics of cyclist-bicycle unit (also known as bicycle individual). We first analyze the psychological process and main psychological characteristics of bicycle riders are elaborated, and then the riding characteristics of individual bicycles, such as dynamic and static traffic characteristics, serpentine trajectory, speeds distribution, factors affecting speed, braking and turning characteristics.
Road intersections are the main manifestations of urban traffic congestions. Therefore, the key to making full use of road resources is the utilization of intersection resources. It is an effective and economic method to improve the capacity of road network and alleviate the urban traffic congestion, by improving the capacity of intersections. Therefore, we analyzed cyclists' microscopic behaviors at intersections (signalized and un-signalized ones). The microcosmic behavior characteristics of bicycles at signalized intersections mainly include the speed of bicycles crossing intersections, the acceptance gap between bicycles and conflicting vehicle flows, the deceleration of encountering red lights, the acceleration of starting and the statistical characteristics of density. By observing and analyzing the bicycles' behavioral characteristics at signalized intersections, as well as their interactions between motor vehicles, bicycles and pedestrians, we can provide theoretical and behavioral basis for improving the management measures of urban signalized intersections, planning and design of bicycle-related traffic facilities.
The article mainly focuses on modeling the acceptance gap behavior of bicycles when they encounter the conflict motor traffic flow at signalized intersections. There are often some conflicts between bicycles and motor vehicles at intersections, so bicycles have to cross the conflict motor traffic flow or vice versa. The time headway between two successive vehicles in the motor traffic flow (priority traffic flow) is called a gap. It is generally believed that critical gap is the minimum gap duration that a traffic individual (including motor vehicles, bicycles and pedestrians) will accept in a specific situation. The value of this critical gap is subjective, so the numerical value varies from person to person, but some of its statistical percentile values can be used for analysis and design. Usually, the distribution center of the observed data of the accepted gap is regarded as the critical gap in the design.
This chapter aims to use the overall concept of the system, introduce multiple research methods, and draw on the achievements of various disciplines, especially basic theories such as psychology, behavioral science, microeconomics, operations research, to explore and develop suitable for mixed traffic flow. To explore a suitable behavior analysis pattern for bicycles at un-signalized intersections under mixed traffic flow situations.
In Chapter 5, we discussed the analysis of human behavior patterns in various disciplines. Drawing on the methods and research results of behavior analysis in various disciplines, this chapter attempts to construct a theoretical framework for cyclist's microscopic behavior analysis of crossing un-signalized intersections. We first describe and define the problem that is a detailed description of the behavior of bicycles crossing un-signalized intersections. At the same time, we analyze the psychological and behavioral processes of the cyclist's crossing behavior, and what types of behavior patterns are included. Then, based on the results of behavioral research in various subject areas (psychology, behavioral science, microeconomics, traffic engineering, etc.), we propose a two-layer hierarchical microscopic behavior analysis framework structure for cyclist's crossing un-signalized intersection behavior. The basic structure and input and output relationship of the framework are introduced. In the last two sections, we discuss and compare the methods and supporting theories used in the two-layer behavior analysis framework, determine to choose the subjective expectation theory of decision analysis as the tactical-level model support theory, and the psychological field/social field theory as the support theory of the operational-level model.
Based on the framework of the cyclist's microscopic behavior analysis at unsignalized intersections in Chapter 6, in this chapter, we specifically model the microscopic behavior model of bicycle crossing un-signalized intersections. In Section 7.1, we propose a normative cyclist behavior (NCB) theory and model and introduce the basic premise assumptions of the model, then we introduce the overall framework of the theoretical model, expound and discuss the basic concepts of the theory, and summarize its innovation points and characteristics. In Section 7.2, we model the tactical and operational-level behavior modules theoretically. We specifically model the general formula of the theoretical model for the microscopic behavior characteristics of bicycles at un-signalized intersections.
In this article we introduce the field data, data collection schemes and various field data collection and collation processes required for bicycle microscopic behavior modeling at the un-signalized intersection. Accuracy analysis of acquired vehicle dynamic data from video files was performed and data reliability analysis was performed on the actual collected stated preference (SP) and RP data. Then the parameter identification of the tactical level model and operational level model of the bicycle crossing behavior model at the un-signalized intersection were introduced. Finally, the model parameter identification results and related analysis conclusions are given.
Whether a bicycle microscopic behaviour model at the mixed traffic flow un-signalized intersections can be truly applied in mixed traffic flow simulation models, traffic safety, traffic management and other fields, reflecting its practical value, depends on the validity and reliability of the model. With the continuous development of simulation technology, traffic flow simulation is more and more widely used in various fields of traffic engineering. In this article we first introduce the commonly used methods to confirm the validity of the model, and then use the actual field data to analyze the validity of the microscopic cyclist behaviour model at un-signalized intersections and finally draw conclusions.
In this article, we use the neural network (NN) to study bicycle's conflict avoidance behaviors when crossing the un-signalized intersections under mixed traffic flow conditions. We introduce the basic concepts and learning rules of NNs, the structure and algorithm principle of back propagation (BP) NN, and modeling ideas and steps of applying NN to construct the bicycle conflict-avoidance (BCA) behavior model. We will then construct three types of BPNN-based BCA behavior models at un-signalized intersections. Finally we will verify the NN-based bicycle's conflict avoidance behavior model at un-signalized intersections by field data.