Traffic Information and Control

Cover
Contents
About the editors
1 Introduction
	1.1 Motivation
	1.2 Purpose
	1.3 Scope
	1.4 Book structure
Part I: Modern traffic information technology
	2 Traffic analytics with online web data
		2.1 Introduction
		2.2 Literature review
		2.3 Methodology
			2.3.1 System overview
				2.3.1.1 Data collection
				2.3.1.2 Data preprocessing
				2.3.1.3 Modeling and mining
				2.3.1.4 Applications
			2.3.2 Main algorithms and models
				2.3.2.1 Latent Dirichlet allocation
				2.3.2.2 Word embedding
				2.3.2.3 Bayesian network
				2.3.2.4 Deep learning
		2.4 Some results
			2.4.1 Traffic sentiment analysis and monitoring system
			2.4.2 Traffic event detection
			2.4.3 Traffic status prediction
			2.4.4 Semantic reasoning for traffic congestion
		2.5 Conclusion
		References
	3 Macroscopic traffic performance indicators based on floating car data: formation, pattern analysis, and deduction
		3.1 Introduction
		3.2 A macroscopic traffic performance indicator: network-level trip speed
			3.2.1 The mathematical form of the NLT speed
			3.2.2 The empirical data for analyses
			3.2.3 Descriptive analyses of influential factors
			3.2.4 Correlative relationships between variables
		3.3 Methods of time series analysis
			3.3.1 The concept and basic features of the time series
			3.3.2 The exponential smoothing method
			3.3.3 The ARIMA method
			3.3.4 The support vector machine (SVM) method
		3.4 Analyses of the NLT speed time series
			3.4.1 Evaluation criteria of the modeling performance
			3.4.2 The decomposition of the NLT speed time series
			3.4.3 The analysis based on exponential smoothing methods
			3.4.4 The analysis based on ARIMA models
			3.4.5 The analysis based on a hybrid ARIMA–SVM Model
		3.5 Conclusions
		References
	4 Short-term travel-time prediction by deep learning: a comparison of different LSTM-DNN models
		4.1 Introduction
		4.2 Traffic time series estimation with deep learning
			4.2.1 Recurrent neural network
			4.2.2 Convolutional neural networks
			4.2.3 Generative adversarial networks
		4.3 The LSTM-DNN models
		4.4 Experiments
			4.4.1 Datasets
			4.4.2 Evaluation metrics
			4.4.3 Hyperparameter settings for LSTM-DNN models
			4.4.4 Comparison between LSTM-DNN models and benchmarks
		4.5 Conclusion and future work
		References
	5 Short-term traffic prediction under disruptions using deep learning
		5.1 Introduction
		5.2 Literature review
			5.2.1 Traffic prediction under normal conditions
			5.2.2 Traffic prediction under disrupted conditions
				5.2.2.1 Traffic characteristics under disrupted conditions
				5.2.2.2 Traffic prediction under disrupted conditions
				5.2.2.3 Summary
			5.2.3 Review of traffic prediction using deep learning techniques
				5.2.3.1 Introduction
				5.2.3.2 Data representation in traffic prediction using deep learning
				5.2.3.3 Spatio-temporal features in traffic prediction using deep learning
			5.2.4 Summary
		5.3 Methodology
			5.3.1 Traffic network representation on a graph
			5.3.2 Problem formulation
			5.3.3 Model structure
				5.3.3.1 Temporal dependencies
				5.3.3.2 Spatial dependencies
				5.3.3.3 Attention mechanism
				5.3.3.4 Loss function and parameter optimisation
			5.3.4 Quantification of prediction accuracy
		5.4 Short-term traffic data prediction using real-world data in London
			5.4.1 Traffic speed data
			5.4.2 Preparation for the prediction model
				5.4.2.1 Traffic speed data preprocessing
				5.4.2.2 Graph representation
				5.4.2.3 Baseline methods for comparison
			5.4.3 Short-term traffic speed prediction under non-incident conditions
				5.4.3.1 Model setups
				5.4.3.2 Prediction results under non-incident conditions
			5.4.4 Short-term traffic data prediction under incidents
				5.4.4.1 Traffic incident data
				5.4.4.2 Prediction results during disruptions
		5.5 Conclusions and future research
		References
	6 Real-time demand-based traffic diversion
		6.1 Model of path choice behavior of driver under guidance information
			6.1.1 Discrete probability selection model
			6.1.2 Prospect theory model
			6.1.3 Fuzzy logic model
			6.1.4 Other models
		6.2 Optimization of traffic diversion strategy
			6.2.1 Responsive strategy
			6.2.2 Iterative strategy
		6.3 Research on dynamic O–D estimation
			6.3.1 Intersection model
			6.3.2 Expressway model
			6.3.3 Network model
		6.4 Dynamic traffic diversion model based on dynamic traffic demand estimation and prediction
			6.4.1 DODE model of urban expressway
				6.4.1.1 The module of METANET model
				6.4.1.2 The module of DODE model
			6.4.2 Traffic diversion model of urban expressway
				6.4.2.1 Simulation of driver’s diversion behavior
				6.4.2.2 Influence of diversion on the traffic flow of exit ramp
				6.4.2.3 Evaluation index of road network performance
			6.4.3 Dynamic traffic diversion model based on DODE
			6.4.4 Model solution
			6.4.5 Case study
				6.4.5.1 Experimental design
				6.4.5.2 Experimental analysis and results of traffic diversion
				6.4.5.3 Experimental analysis and results of DODE
		6.5 Conclusion
		References
	7 Game theoretic lane change strategy for cooperative vehicles under perfect information
		7.1 Introduction
		7.2 Problem formulation
		7.3 Highway traffic system dynamics
			7.3.1 Longitudinal dynamics
			7.3.2 Lateral dynamics
			7.3.3 Lane change and dynamic communication topology
			7.3.4 Closed-loop dynamics
		7.4 Game theoretic formulation of the lane change decision problem
			7.4.1 Dynamic lane change game formulation
			7.4.2 Existence of equilibrium
			7.4.3 Properties of the lane change dynamic game
		7.5 Numerical examples
			7.5.1 Experimental setting
			7.5.2 Scenario 1: delayed merge
			7.5.3 Scenario 2: courtesy lane change
		7.6 Conclusion
		References
	8 Cooperative driving and a lane change-free road transportation system
		8.1 Introduction
		8.2 Cooperative driving strategies at intersections
			8.2.1 Safety driving pattern-based strategy
			8.2.2 Reservation-based strategy
			8.2.3 Trajectory optimization-based strategy
		8.3 Cooperative driving strategies at on-ramps
			8.3.1 Virtual vehicle mapping strategy
			8.3.2 Slot-based strategy
		8.4 Lane change-free road transportation system
			8.4.1 Lane change-free road transportation system: an illustration
			8.4.2 System design
				8.4.2.1 Overall approaching process
				8.4.2.2 Conflict avoidance-based cooperative driving strategy
			8.4.3 Simulation test
		8.5 Conclusion and future direction
		Acknowledgements
		References
Part II: Modern traffic signal control
	9 Urban traffic control systems: architecture, methods and development
		9.1 Introduction
			9.1.1 Brief description
				9.1.1.1 Europe
				9.1.1.2 The United States
				9.1.1.3 Australia
			9.1.2 Classification
			9.1.3 Level of traffic control system
		9.2 SCOOT
			9.2.1 Overview
			9.2.2 Basic principles
			9.2.3 System architecture
			9.2.4 Optimization process
				9.2.4.1 Demand detection
				9.2.4.2 Queue prediction
				9.2.4.3 Congestion prediction
				9.2.4.4 Performance prediction
				9.2.4.5 Signal optimization
			9.2.5 Additional features
				9.2.5.1 Gating
				9.2.5.2 Bus priority
		9.3 SCATS
			9.3.1 Overview
			9.3.2 Basic principles
			9.3.3 System architecture
			9.3.4 Optimization process
				9.3.4.1 Demand detection
				9.3.4.2 Cycle determination
				9.3.4.3 Split determination
				9.3.4.4 Offset determination
		9.4 Summaries and limitation analysis
		9.5 Future analysis of urban traffic control system
			9.5.1 Changes in system environments
				9.5.1.1 Traffic data
				9.5.1.2 Traffic control objects and variables
				9.5.1.3 The demand of computational power
			9.5.2 Standardization
			9.5.3 Summary
		References
	10 Algorithms and models for signal coordination
		10.1 Introduction
		10.2 Basic MAXBAND approach
		10.3 Extended MAXBAND approach
			10.3.1 Variable bandwidth method
			10.3.2 Multimode band method
			10.3.3 Path-based method
		10.4 MAXBAND for network system
		10.5 Discussion and open issues
		10.6 Conclusion
		References
	11 Emerging technologies to enhance traffic signal coordination practices
		11.1 Coordination timing development and optimization
			11.1.1 Developing cycle length and splits using controller event data
			11.1.2 Optimizing offsets and phasing sequences based on travel-run trajectories
		11.2 Field implementation and timing diagnosis
		11.3 Performance measures for assessing the quality of signal coordination
		11.4 Signal timing documentation
		11.5 Summary
		References
	12 Traffic signal control for short-distance intersections with dynamic reversible lanes
		12.1 Introduction
		12.2 Application of dynamic reversible lane
		12.3 Model of signal timing
			12.3.1 Signal phase and sequence
			12.3.2 Signal timing model
		12.4 Calibration and validation
			12.4.1 Simulation scenarios
			12.4.2 Validation of the proposed plan
		12.5 Adaptability analysis
			12.5.1 Road conditions
			12.5.2 Left-turning traffic proportion
		12.6 Conclusion
		References
	13 Multiday evaluation of adaptive traffic signal system based on license plate recognition detector data
		13.1 Introduction
		13.2 Methodology
			13.2.1 Travel time delay
			13.2.2 Travel time-based measurements
				13.2.2.1 Scatter diagram of travel time (delay)
				13.2.2.2 Cumulative frequency diagram of travel time
			13.2.3 PCD and related indexes
			13.2.4 Travel time reliability indexes
				13.2.4.1 The 95th percentile travel time
				13.2.4.2 Buffer index
		13.3 Case description and dataset
			13.3.1 Case description
			13.3.2 Dataset
		13.4 Results
			13.4.1 Evaluation of travel time delay
				13.4.1.1 Scatter diagram analysis of travel time delay
				13.4.1.2 Quantitative analysis of travel time delay improvement
			13.4.2 Cumulative frequency diagram of travel time
		13.5 Evaluation of the PCD
			13.5.1 Travel time reliability evaluation
				13.5.1.1 The 95th percentile travel time
				13.5.1.2 Buffer index
		13.6 Conclusion
		Acknowledgments
		References
	14 Conclusion
Index
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