Data-Driven Traffic Engineering: Understanding of Traffic and Applications Based on Three-Phase Traffic Theory 9780128191385, 0128191384

Table of contents :
Front-Matter_2021_Data-Driven-Traffic-Engineering
Front Matter
Copyright_2021_Data-Driven-Traffic-Engineering
Copyright
Aim-of-the-book_2021_Data-Driven-Traffic-Engineering
Aim of the book
Expected-readers_2021_Data-Driven-Traffic-Engineering
Expected readers
Scope-and-outline-of-the-book_2021_Data-Driven-Traffic-Engineering
Scope and outline of the book
Chapter-1---Introduction_2021_Data-Driven-Traffic-Engineering
Introduction
References
Chapter-2---How-traffic-data-are-measure_2021_Data-Driven-Traffic-Engineerin
How traffic data are measured
Loop detector data
Probe vehicle data
Propagating congestion
Localized congestion
Localized and propagating congestion: A general traffic pattern
Localized congestion: Complete freeway blockage (``Megajam´´)
Induced congestion: Propagating structures
Similar and heterogeneous congested traffic patterns
Complex empirical traffic patterns
Traffic patterns in urban areas
Camera-based microscopic measurements
References
Chapter-3---Analysis-of-congested-traffic-pattern-fea_2021_Data-Driven-Traff
Analysis of congested traffic pattern features on freeways: A historical overview
About empirical studies of traffic congestion
A brief history of Kerner's three-phase traffic theory
The beginning
Kerner's synchronized flow as a new traffic phase
Paradigm shift in traffic science caused by the discovery of the empirical nucleation nature of a traffic breakdown
Kerner's indifferent zone in car following
Some milestones of related projects in the past
The situation today
Summary of some main hypotheses of Kerner's three-phase traffic theory
Main types of spatiotemporal congested traffic patterns
Detection of congested traffic patterns based on probe vehicles
Phase transition points
Examples of applications of probe vehicle data for reconstruction of traffic in space and time
Prediction of upstream fronts of traffic phases (in particular jam front warning) based on probe vehicle data
References
Chapter-4---Congested-traffic-patterns-in-urb_2021_Data-Driven-Traffic-Engin
Congested traffic patterns in urban areas
Synchronized flow patterns at a traffic signal
Classification of urban traffic patterns
Probability of speed breakdown
Detection of urban traffic patterns based on camera observations
Traffic flow optimization by change of vehicle behavior
References
Chapter-5---Applications-of-traffic-in-transpor_2021_Data-Driven-Traffic-Eng
Applications of traffic in transportation science
Introduction
Reconstruction of freeway congested traffic patterns based on measured detector data
FOTO and ASDA models for stationary loop data
FOTO and ASDA models for probe vehicle data
The impact of severe weather on freeway traffic characteristics
Description of weather database
ASDA/FOTO congested pattern examples in fair weather conditions
ASDA/FOTO congested pattern examples in severe weather conditions
Analysis and spatial-temporal weather and traffic radar
Mobility parameters
Route choice behaviour in networks
Jam tail warning
Identifying traffic states from empirical microscopic data
Generating jam fronts from state transitions
Evaluation
Fuel consumption in road networks
Empirical microscopic fuel consumption data
Different applications of using energy matrices
Automated driving
NEDC versus WLTP
Traffic simulation
Cumulated acceleration and energy efficiency of vehicles
Automated driving: The problem of merging
Traffic information for in-vehicle control units
Traffic services for navigation systems
Traffic service protocols TMC and TPEG
Traffic services in case of global pandemic
Estimation of arrival time
References
Chapter-6---Future-directions_2021_Data-Driven-Traffic-Engineering
Future directions
References
Index_2021_Data-Driven-Traffic-Engineering
Index
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