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E-grāmata: Behavioural Modelling and Simulation of Bicycle Traffic

(South China University of Technology, School of Civil Engineering and Transportation, China), (Tsinghua University, School of Civil Engineering, China)
  • Formāts: PDF+DRM
  • Sērija : Transportation
  • Izdošanas datums: 07-Sep-2021
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781785619526
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Behavioural Modelling and Simulation of Bicycle TrafficPalielināt
  • Formāts: PDF+DRM
  • Sērija : Transportation
  • Izdošanas datums: 07-Sep-2021
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781785619526
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Offering systematic analysis of the movements and behaviours of bicycles and their riders, this book examines data collection and evaluation approaches and then goes on to develop a framework for the theory and modelling of bike traffic. Coverage includes verification techniques, and a chapter on riding characteristics for context.



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.

About the authors xi
1 Section bicycle individual riding characteristics 1(14)
1.1 Concepts of bicycle traffic
1(1)
1.2 Overview of bicycle microscopic riding characteristics
2(2)
1.2.1 Instability
2(1)
1.2.2 Lateral pressure
2(1)
1.2.3 Short travel distance
3(1)
1.2.4 Quick start at intersection
3(1)
1.2.5 Clustering
3(1)
1.2.6 Agility
3(1)
1.3 Overview of bicycle traffic in various countries
4(2)
1.3.1 China
4(1)
1.3.2 The Netherlands
4(1)
1.3.3 The United States
5(1)
1.3.4 Germany
5(1)
1.4 Bicycle traffic features in comparison to other transport means
6(7)
1.4.1 Economize on energy consumption
6(1)
1.4.2 Saving traffic land consumption
7(2)
1.4.3 Effective reduction of urban traffic emission pollution
9(1)
1.4.4 A healthy traffic mode
10(1)
1.4.5 Low travel cost
10(1)
1.4.6 Lower internal and external transport costs
10(3)
1.5 The main content of this book
13(1)
References
13(2)
2 Microscopic bicycle microscopic riding characteristics 15(16)
2.1 Cyclists' psychological characteristics
15(3)
2.1.1 Cyclists' psychological process
15(2)
2.1.2 Cyclists' psychological characteristics
17(1)
2.2 Static and dynamic measurements
18(2)
2.2.1 Static measurements
18(1)
2.2.2 Dynamic measurements
18(1)
2.2.3 Running area
19(1)
2.3 S-shaped trajectory
20(1)
2.4 Speed characteristics
21(4)
2.4.1 Free riding speed
21(1)
2.4.2 Riding speeds
21(1)
2.4.3 Influencing factors of bicycle riding speeds
22(3)
2.5 Braking characteristics
25(2)
2.6 Turning characteristics
27(1)
References
28(3)
3 Bicycle microscopic behavioral characteristics at signalized intersections 31(50)
3.1 Field data collection
32(5)
3.1.1 Data acquisition scheme based on video
32(1)
3.1.2 Basic information of data acquisition process
33(4)
3.2 Micro behavior data extraction
37(7)
3.2.1 Basic processing of video data
37(3)
3.2.2 Specific extraction method of basic behavior data
40(4)
3.3 Micro behavioral data analysis basis
44(4)
3.3.1 Basic concepts
45(1)
3.3.2 Distribution of random variables and parameter estimation
45(2)
3.3.3 Hypothesis testing of parameters and distribution functions
47(1)
3.4 Analysis of speed data
48(8)
3.4.1 Basic steps of analysis
48(1)
3.4.2 Sample overall analysis
48(2)
3.4.3 Data analysis of two-wheeled bicycle
50(5)
3.4.4 Tricycles speed data analysis
55(1)
3.5 Time-related behavior data
56(13)
3.5.1 Basic steps of analysis
56(1)
3.5.2 Judgment of overall distribution
56(1)
3.5.3 Sample grouping
57(1)
3.5.4 Bicycle-accepted gaps
57(8)
3.5.5 Bicycle-accepted lags
65(4)
3.6 Acceleration data analysis
69(8)
3.6.1 Bicycle deceleration
69(4)
3.6.2 Bicycle starting acceleration
73(4)
3.7 Density data analysis
77(3)
3.7.1 Dynamic group density
77(2)
3.7.2 Static group density
79(1)
3.8 Summary
80(1)
Reference
80(1)
4 Cyclists' crossing behavior model at signalized intersection 81(28)
4.1 Introduction
81(2)
4.1.1 Distribution of accepted gaps
81(1)
4.1.2 Cyclists' gap acceptance behavior model
82(1)
4.2 Gap acceptance choice behavior model
83(18)
4.2.1 Model construction and basic formula
83(4)
4.2.2 Model calibration and optimization
87(9)
4.2.3 Model result analysis
96(5)
4.3 Lag acceptance choice behavior model
101(6)
4.3.1 Model construction and basic formula
101(1)
4.3.2 Model calibration and optimization
102(2)
4.3.3 Model result analysis
104(3)
4.4 Summary
107(1)
References
107(2)
5 Bicycle microscopic behavior analysis patterns 109(32)
5.1 General pattern of behavior analysis
109(2)
5.2 Human behavior characteristics
111(1)
5.3 Behavior analysis patterns in psychology and sociology
112(12)
5.3.1 Individual behavior differences and common characteristics
112(4)
5.3.2 Classification of behaviors
116(6)
5.3.3 Major behavior patterns
122(2)
5.4 Behavior analysis patterns in economics
124(10)
5.4.1 Consumer choice behavior research
124(3)
5.4.2 Decision-making behavior
127(2)
5.4.3 Gaming behavior
129(5)
5.5 Behavior analysis patterns in traffic engineering
134(5)
5.5.1 Overview
134(3)
5.5.2 Car-following behavior model
137(2)
5.6 Summary
139(1)
References
139(2)
6 Analysis of bicycle microscopic behavior at un-signalized intersections 141(42)
6.1 Description and definition of the problem
141(4)
6.1.1 Problem description
141(1)
6.1.2 Problem definition
142(2)
6.1.3 Problem scope
144(1)
6.2 Building a theoretical analysis framework
145(5)
6.2.1 Behavioral characteristics of cyclist's crossing un-signalized intersections
145(1)
6.2.2 Analysis framework structure
146(1)
6.2.3 Support theory for the cyclist's analysis framework
147(3)
6.3 Tactical-level model support theory-expected utility theory of decision analysis
150(22)
6.3.1 Basic concepts
150(3)
6.3.2 Basic concept of utility function
153(2)
6.3.3 Savage theorem (expected utility theorem)
155(2)
6.3.4 Mathematical model of utility function
157(6)
6.3.5 Multi-attribute utility theory
163(9)
6.4 Operational-level model support theory-Social Force model
172(8)
6.4.1 Basic concepts
172(2)
6.4.2 Social field dynamics
174(3)
6.4.3 Behavioral mathematical model of social field theory
177(3)
6.5 Summary
180(1)
References
180(3)
7 Microscopic behavior model of bicycle crossing un-signalized intersections 183(48)
7.1 Normative cyclist behavior theory and model
183(25)
7.1.1 Theoretical assumptions
183(2)
7.1.2 Theoretical framework of NCB model
185(9)
7.1.3 Features of the NCB theoretical framework
194(2)
7.1.4 NCB tactical-level behavioral theory model
196(4)
7.1.5 NCB operational-level behavioral theory model
200(8)
7.2 Microscopic behavior modeling of bicycle crossing un-signalized intersections
208(21)
7.2.1 The cyclist's path planning behavior model (tactical level)
208(11)
7.2.2 The cyclist's dynamic riding behavior model (operational level)
219(10)
7.3 Summary
229(1)
References
230(1)
8 Empirical analysis of bicycle microscopic behavior model 231(64)
8.1 Field data collection
231(12)
8.1.1 Purpose and significance of data collection
231(1)
8.1.2 Data/information required by the model
232(1)
8.1.3 Data collection scheme
233(5)
8.1.4 Situations of field data collection
238(5)
8.2 Data collection and preprocessing
243(4)
8.2.1 The SP data and RP data
243(2)
8.2.2 Accuracy analysis of the RP data
245(2)
8.3 Data reliability analysis
247(4)
8.3.1 SP data reliability analysis
247(4)
8.3.2 RP data reliability analysis
251(1)
8.4 Path planning model parameter identification
251(18)
8.4.1 SP data analysis
251(13)
8.4.2 Parameter identification
264(3)
8.4.3 Parameter identification results
267(2)
8.5 Dynamic riding model parameter identification
269(18)
8.5.1 RP data analysis
269(11)
8.5.2 Model parameter identification
280(3)
8.5.3 Parameter identification and analysis results
283(4)
8.6 Summary
287(1)
Appendix A: Questionnaire group analysis statistics
288(6)
A.1 Comparison of investigator sample groups
288(1)
A.2 Group analysis of the questionnaire influencing factors
289(6)
A.2.1 Group analysis by gender
289(1)
A.2.2 Group analysis by ages
290(2)
A.2.3 Group analysis by trip purpose
292(1)
A.2.4 Group analysis by commute traffic mode
293(1)
References
294(1)
9 Confirmation of validity of bicycle microscopic behavior model 295(22)
9.1 Model validation method
295(7)
9.1.1 Basic concepts
295(1)
9.1.2 Common methods for model validation
296(2)
9.1.3 Validation of bicycle micro-behavior model
298(4)
9.2 Model validity analysis
302(12)
9.2.1 The sub-model validations
302(6)
9.2.2 Comprehensive model validation
308(1)
9.2.3 Macroscopic model validity analysis
309(1)
9.2.4 Conclusions of model validity analysis
309(5)
9.3 Summary
314(2)
Reference
316(1)
10 Neural network-based bicycle collision avoidance behavioral model at un-signalized intersections 317(34)
10.1 Introduction to artificial neural networks (ANNs)
317(4)
10.1.1 Artificial neuron model
317(1)
10.1.2 Transfer function
318(2)
10.1.3 Learning method and learning rules
320(1)
10.2 How BP neural network works
321(3)
10.2.1 Structure of BP neural network
321(1)
10.2.2 BPNN standard learning process
321(2)
10.2.3 Problems and improvement of BP algorithm
323(1)
10.3 Modeling of bicycle conflict-avoidance behavior based on NN
324(8)
10.3.1 Determine inputs and outputs
324(2)
10.3.2 Data normalization
326(1)
10.3.3 Learning sample division
327(1)
10.3.4 Neural network structure
327(2)
10.3.5 Training algorithm and parameter selection
329(3)
10.4 The bicycle conflict avoidance model based on BPNN
332(6)
10.4.1 BPNN-based bicycle conflict avoidance model in B-C conflict situations
333(2)
10.4.2 BPNN-based bicycle conflict avoidance model in B-B conflict situations
335(1)
10.4.3 BPNN-based bicycle conflict avoidance model in B-P conflict situations
336(1)
10.4.4 NN-based bicycle conflict avoidance model considering gender and type of conflict object
336(2)
10.5 Model simulation and verification
338(11)
10.5.1 Model simulation
338(2)
10.5.2 Model verification
340(9)
10.6 Summary
349(1)
References
350(1)
Index 351
Ling Huang, PhD is an associate professor in the School of Civil Engineering and Transportation at South China University of Technology, China. She is also a member of Traffic Modeling and Simulation Professional Committee of China. Her research interests include cyclist behaviour, bicycle sharing system, traffic simulation, behaviour analysis, driving behaviour, pedestrian, computer vision and traffic simulation applications.



Jianping Wu, PhD is a professor in the School of Civil Engineering at Tsinghua University, China, and director of the Future Transport Research Centre of Tsinghua University, the University of Cambridge, Massachusetts Institute of Technology. He is associate editor of IET Intelligent Transport Systems, a fellow of the IET, member of the Environment and Engineering Committee (CEE) of the WFEO, executive director of the China Association of Simulation, and Cheung Kong Scholar Professor of the Ministry Education of China.
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