Logic-Driven Traffic Big Data Analytics: Methodology and Applications for Planning

Chapter 1 Logic driven traffic big data analytics: An introduction (New Material)Part I: MethodologyChapter 2 Built environment and travel behavior (New Material)Chapter 3 Data description and preparation (New Material)Chapter 4 Statistical models and methods (New Material)Chapter 5 Big data analytics and machine learning methods (New Material)Part II: ApplicationsTravel Demand AnalysisChapter 6 Spatial-temporal distribution model for travel origin-destination based on multi-source data (American Society of Civil Engineers, ASCE)Chapter 7 Spatiotemporal evolution of ride-sourcing markets under the new restriction policy: A case study in Shanghai (ELSEVIER)Traffic Congestion and Travel Time/SpeedChapter 8 Exploring spatially varying relationships between urban built environment and road travel time (ASCE)Chapter 9 Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data (World Society for Transport and Land Use Research, WSTLUR)Traffic Safety and Environmental AnalysisChapter 10 Analysis of the spatial-temporal distribution of traffic incidents based on urban built environment attributes and microblog data (New Material)Chapter 11 Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data (ELSEVIER)Policy and OptimizationChapter 12 Evidence on the impact of exclusive bus lane on the average speed of bus and car (New Material)Chapter 13 Optimization of traffic signal timing based on computer vision and reinforcement learning (New Material)Travel Pattern AnalysisChapter 14 Taxi driver speeding: Who, when, where, and how? A comparative study between Shanghai and New York City (Taylor & Francis)Chapter 15 A ride-sourcing group prediction model based on convolutional neural network (New Material)