Traffic Data Collection and its Standardization

Traffic engineers are involved in transport modeling, traffic simulation, operation optimization, and the development of methods to control and analyze traffic itself. New developments of individual traffic, public transport as well as pedestrian movements are the hope to ensure mobility and accessibility in urban areas to secure mobility in the less profitable countryside, to increase safety, and to limit the effects on the environment caused by transportation. Around the globe, governments declare goals in each of the mentioned fields, mostly under the umbrella of intelligent transport systems (ITS) to develop a sustainable transportation for everyone.

The Transport Data Centre (TDC) within the New South Wales Ministry of Transport is the premier source of transport data for NSW. TDC’s role is to assist those involved in transport and land use planning to make informed decisions by collecting, analysing, processing, and providing reliable and up-to-date information on current and future travel patterns and employment and population trends. This information is used by Government and private sector clients for the evaluation of all major transport infrastructure developments, and strategic and service planning in NSW.

More than ever, traffic congestion is plaguing our heavily populated metropolitan areas. Transportation professionals have recognized that we cannot build our way out of this ever-increasing congestion. The challenge over the next decade is to get more out of the existing transportation system by improving its productivity. To address this challenge, we must evolve into “system managers”: agencies and individuals who manage the system through operational strategies, complemented by targeted expansion investments. The concept of system management has been embraced by many agencies at both state and federal levels. For example, the California Department of Transportation (Caltrans) and most of its stakeholders adopted the concept of the System Management pyramid, as depicted in Fig. 3.1. The foundation of system management is “System Monitoring and Evaluation”. This foundation provides support for a variety of informed investment decisions.

Gathering real life data, for whatever type of use, is a time consuming job. A lot of data is measured and stored in several places and different formats around the world. While a lot of it is not used, other institutions gather similar data on different locations or, worse, on the same ones. In this way, a lot of money and time is spent unnecessarily (Miska and Lint 2006). Thus, the aim of the International Traffic Database (ITDb) project is to provide traffic data to various groups (researchers, practitioners, public entities) in a format according to their particular needs, ranging from raw measurement data to statistical analysis.

Data mining has attracted considerable attention as a method that can be used to discover certain characteristics from large amounts of data. In traffic flow analysis, a large amount of traffic flow data is continuously collected and stored over several years.

Traffic flows are the result of movements of people and goods. They are modeled with the help of behavioral patterns that are supposed to remain relatively constant over time. In traditional transport modeling, some of these patterns are described by trip distribution functions, which represent the propensity to make trips with certain costs. The distribution functions (DF) are used to estimate a priori origin destination (OD) matrices.

For most Origin–Destination (OD) matrix estimation methods, a priori information in the form of a matrix (so-called a priori matrix) is necessary as an initial guess. In the estimation process, this matrix is updated with traffic counts until a final estimated matrix has been found. The more this a priori matrix matches the real matrix, the better the final outcome of the estimation will be.

Probe Vehicle Data (PVD) is becoming more and more common for the collection of information about the traffic state. In most cases, the information that can be obtained from a probe vehicle refers to the position, the speed and the direction of movement at certain time intervals. Especially in urban networks, the raw GPS data needs a cleaning process to map the measured position to the road network. The cleaned information about positions in the network at fixed moments where GPS signals are collected can be used to derive travel time along certain routes.

The management of urban traffic systems demands information for the real-time control of traffic flows as well as for strategic traffic management. In this context, state-of-the-art traffic information systems are mainly used to control varying traffic flows and to provide collective and individual information about the current traffic situation. However, the provision of information for strategic traffic management as well as for traffic demand dependent planning activities (e.g., in city logistics) is still a potential field of research due to the former lack of reliable city-wide traffic information. Recently, historical traffic data arising from telematics-based data sources provided information for time-dependent route planning, for the improvement of traffic flow models as well as for spatial and time-dependent forecasts. In this chapter, we focus on the analysis of historical traffic data, which serves as a background for sophisticated real-time applications.

Limited road capacities and an increasing traffic volume are, or will become a serious problem for urban mobility in many regions of the world such as Europe, China, Japan, or the USA. To ensure an acceptable level of traffic quality, local authorities need reliable traffic state information which can be used for the optimization of traffic management, e.g., for improvements in the control of traffic lights.

In the last 10 years, the tasks of motorway operators increased from tendering the construction, operation, and maintenance to motorway operation with a strong link to traffic management and provision of traffic information to the drivers of the single vehicles. This information was in the beginning roadside information available through Variable Message Signs (VMS) and broadcast information, e.g., by traffic status messages in the radio. In the last years, digital information has been transmitted to the single driver via the Traffic Message Channel (TMC), which informs the driver directly within the vehicle about the road- and traffic status.

Driving behavior observed at traffic networks varies considerably depending on the type of road section. At signalized junctions, drivers are taught to moderate their speed, and to comply with the priority rules set by the traffic light. Therefore, vehicles stop and queue up during the red phase, and they leave the junction during the green and amber phases. During these operations, vehicle driving patterns vary significantly. The way they decelerate, stop at the back of the queue and accelerate changes from driver to driver. Aggressive drivers may show to operate stronger accelerations and to respond more quickly to the right-of-way signal. During the amber phase, some aggressive drivers accelerate to clear the intersection faster, while risk-averse drivers may decide to decelerate earlier, or even brake hard to avoid passing the stop-sign after the start of the red phase. Moreover, the observed trajectories will depend on whether a queue is actually present and on its length, as well as on the road characteristics, e.g., how clear is the view upstream of the junction, whether one or more lanes are dedicated to a traffic stream, etc. Therefore, individual vehicle trajectories are found to be highly variable at signalized intersections, as individual speeds and speed variations have strong dynamic and stochastic patterns. In applications that require accurate estimates of vehicle driving modes, like when estimating concentration levels of emissions, it is fundamental to provide realistic estimates of these trajectories, and, more importantly, to derive, from these trajectories, realistic speeds and speed variations.