Data-Driven Solutions to Transportation Problems

Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.

Synthesizes the newest developments in data-driven transportation scienceIncludes case studies and examples in each chapter that illustrate the application of methodologies and technologies employedUseful for both theoretical and technically-oriented researchers

1. Overview of Data-driven Transportation Science
2. Data-driven Energy Efficient Driving Control in Connected Vehicle Environment
3. Machine Learning and Computer Vision-Enabled Traffic Sensing Data Analysis and Quality Enhancement
4. Data Driven Approaches for Estimating Travel Time Reliability
5. Urban Travel Behavior Study Based on Data Fusion Model
6. Urban Travel Mobility Exploring with Large-Scale Trajectory Data
7. Public Transportation Big Data Mining and Analysis
8. Data Driven Gating Control for Network Based on Macroscopic Fundamental Diagram
9. Simulation-Based Optimization for Network Modeling with Heterogeneous Data
10. Network Modeling and Resilience Analysis of Air Transportation: A Data-Driven, Open-Source Approach