Transportation: GIS for intelligent transport system

Transportation: GIS for intelligent transport system


Apart from constructing impeccable transport facilities, governments across the globe are utilising GIS in their quest for smarter and safer transportation networks

The focus of most transportation professionals in the urban areas of the world has shifted from the construction of new highway facilities to maximising the utility of existing infrastructure, and the development of new public transport facilities and capabilities. At the same time, a renewed focus has emerged on using technology effectively to make urban centers more efficient, livable and sustainable. These trends have produced two overlapping initiatives: intelligent transportation systems (ITS) and the concept of smart cities. It is clear that GIS technology will play an ever increasing role in both initiatives. As a platform to integrate and fuse vast amounts of information, GIS has already come to play a significant role in a large number of ITS systems which are briefly reviewed here.

Traveller information systems
These systems were one of the first applications in ITS, and were designed to provide the public with current information on traffic conditions and alternative travel options. Such systems brought together information from various sensors and automated systems to provide a single source of up-to-the-minute travel information and choices. One of the early examples, and still one of the best, is the system implemented for the San Francisco Bay region ( ). It combines information on real-time traffic speeds along with travel options (and a journey planner) for public transport, car sharing, bicycling and parking availability into a single application which is widely used by locals and tourists alike. The current traffic site fuses four separate services in real time to deliver information to the public: traffic speeds from embedded loop detectors, streaming video from CCTV cameras, incident information from the California Highway Patrol, and current construction activity from the California Department of Transportation, all organised and presented through GIS. In fact all of the information from the various model choices is managed by GIS in the background, including the stop, route and fare information from over 50 different public transport providers.

Similar systems have been developed throughout Europe and Asia, with Transport Direct and Madrid’s EMT system being some of the better examples.

Traffic management systems
Much of the same hardware installed for Traveller Information Systems was also designed to provide the information for urban traffic management systems. Initially, these technologies were often stand-alone systems, with the result that early traffic control centers were often an assemblage of large banks of CCTV monitors, and characterised by a number of non-integrated technologies for traffic management. As these systems matured, integration of these disparate technologies became a primary goal, along with the desire to achieve inter-agency coordination to facilitate better traffic management, incident management and emergency response. It was largely as agencies began to focus on inter-agency coordination, and ways to present a more effective “common operating picture” of their urban transportation systems that GIS came to the forefront.

A number of the leading traffic management solutions now incorporate GIS as a way of better understanding (and managing) urban traffic. Siemen’s Stratos solution, along with other leading traffic management solution providers Transcore, Iteris and Kapsch, among others, have all integrated GIS components into their traffic management solutions.

The goal of such systems is to dynamically adjust traffic flow through urban centers responding to incidents and traffic congestion in real time. As various traffic data and sensors become more ubiquitous, there is a greater effort to develop automated algorithms to make these adjustments with less human intervention. In addition, such information can be fed back into the planning efforts of these transportation agencies to assist them in better designing new roadway improvements, and in better managing their existing traffic flows.

Incident management
The Los Angeles County currently experiences over 3,000 incidents a day, all of which impact the levels of congestion on the freeway system and urban arterials. As such, great effort has gone into the coordination of the allied agencies responsible for dealing with (and clearing) such incidents. A GIS-based system helps identify the location of each incident as the telephone call comes in to the dispatch centre, and each of the allied agencies (highway patrol, tow truck operators, emergency response units) is notified through a central GIS-based system. In this way, inter-agency coordination is facilitated, with the result of a more efficient response to such incidents, and lower traffic congestion.

As more sources of real time traffic information become available, this will begin to influence other sectors of emergency response as well, with ambulances, fire trucks, police cars, among others, making greater use of real time and predictive traffic data. Existing research shows that ambulances responding to a traffic accident at the same location but at different times of the day should transport patients to different hospitals based on the relative congestion levels, with time differences on the order of 15 minutes, crucial time with an injured patient.

Safety management
One of the major promises of ITS is greater safety on our roadways. Considerable research has gone into new technologies designed to be incorporated into the vehicle, as well as to utilise emerging technologies to better understand causes of traffic accidents. Both depend on the use of GIS, and are advancing rapidly.

In the first instance, there are a range of in-vehicle technologies which are designed to improve road safety and assist driver behaviour. Known as Advanced Driver Assistance Systems (ADAS), these constitute technologies designed for collision avoidance, lane departure warning systems, intelligent speed adaptation, and pedestrian warning systems, among others. Each is designed to warn the driver of unsafe driving, and potential hazards unknown to the driver.

If a driver is approaching an upcoming turn at too high speed, ADAS technology would first issue a warning, and if not acted upon, the car would assist in braking without the driver’s active involvement. Similar warnings would alert the driver of pedestrians in an upcoming intersection, or of any unsafe roadway conditions. Central to such systems is a highly accurate road network with high-precision roadway geometrics, all managed in a GIS database. Many of the major commercial street network vendors have turned their attention to collecting such roadway characteristics, often utilising high-precision LiDAR data collection methods. In time, such efforts will lead to evermore automated guidance systems in our vehicles.

The same LiDAR data collection techniques are being used to extract roadway geometrics for use in crash analysis. Extracting such characteristics as bank, slope and roadway curvature, these variables can now be added to other roadway and driver characteristics, for more sophisticated crash analysis. In addition, the rapidly evolving field of geospatial statistics is being applied to these analyses, all in an effort to better identify problematic locations of high crash frequency. A coordinated multi-agency research effort between the Utah Department of Transportation, Esri and the AAA Foundation is looking to apply such techniques to develop the next generation of crash analysis methodologies.

In each of these areas of ITS, GIS has become an essential technology for fusing together vast amounts of data, all with the goal of making our roadways safer and less congested, and ultimately our cities more livable and sustainable.