The Computer System Architecture of our first real-time real-world experiment of adaptive traffic signals with "connected" vehicles

Abstract Connected vehicles can transmit real-time information to traffic control management systems. Despite the recent technical advances of telecommunication networks and mobile computing there have been no real-time adaptive traffic signal control experiments with connected vehicles. Most of the research in this field has been carried out only with simulations. In this work we present the computer system that was adopted to regulate traffic signals in real-time with "smartphone-connected" vehicles as the only source of information. We introduce the description of the computer system architecture that was deployed in an experiment of a Floating Car Data (FCD)-based adaptive traffic signal in which a traffic signal has been regulated in real-time with 100% "smartphone-connected" vehicles. The description of the system based on commonly-used technologies could help others to develop and deploy new traffic signal management systems in new "connected" intersections

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

A review of traffic signal control methods and experiments based on Floating Car Data (FCD)

Abstract This paper intends to give a short review of the state of the art on the use of floating car data concerning the management of traffic flow at signalized intersections. New technologies such as connected and autonomous vehicles and Co-operative Intelligent Transportation Systems (C-ITS) are going to change the future of traffic control and management. Traffic signal control systems can be reorganized by using Floating Car Data (FCD), yet the concept of floating car data (FCD) has been mainly studied to gain traffic information and/or signal information. Only recent works have been focalizing on the potential application of FCD for traffic signal real-time control. This paper aims to evidence the most important concepts that can be extracted from the literature on this important topic

Travel Time in Macroscopic Traffic Models for Origin-Destination Estimation

Transportation macroscopic modeling is a tool for analyzing and prioritizing future transportation improvements. Transportation modeling techniques continue to evolve with improvements to computer processing speeds and traffic data collection. These improvements allow transportation models to be calibrated to real life traffic conditions. The transportation models rely on an origin-destination (OD) matrix, which describes the quantity and distribution of trips in a transportation network. The trips defined by the OD matrix are assigned to the network through the process of traffic assignment. Traffic assignment relies on the travel time (cost) of roadways to replicate route choice of trips between OD trip pairs. Travel time is calculated both along the roadway and from delay at the intersections. Actuated traffic signals, one form of signalized intersections, have not been explicitly modeled in macroscopic transportation models. One of the objectives of this thesis is to implement actuated signals in the macroscopic modeling framework, in order to improve traffic assignment by more accurately representing delay at intersections. An actuated traffic signal module was implemented into QRS II, a transportation macroscopic model, using a framework from the 2010 Highway Capacity Manual. Results from actuated intersections analyzed with QRS II indicate the green time for each phase was reasonably distributed and sensitive to lane group volume and input parameters. Private vendor travel time data from companies such as Navteq and INRIX, have extensive travel time coverage on freeways and arterials. Their extensive travel time coverage has the potential to be useful in estimating OD matrices. The second objective of this thesis is to use travel time in the OD estimation framework. The presented OD estimation method uses travel time to determine directional split factors for bi-directional traffic counts. These directional split factors update target volumes during the OD estimation procedure. The OD estimation technique using travel time from floating car runs was tested using a mid-sized network in Milwaukee, WI. The analysis indicates applicability of using travel time in OD estimation