A dynamic traffic assignment model for highly congested urban networks

The management of severe congestion in complex urban networks calls for dynamic traffic assignment (DTA) models that can replicate real traffic situations with long queues and spillbacks. DynaMIT-P, a mesoscopic traffic simulation system, was enhanced and calibrated to capture the traffic characteristics in a sub-area of Beijing, China. The network had 1698 nodes and 3180 directed links in an area of around 18 square miles. There were 2927 non-zero origin–destination (OD) pairs and around 630,000 vehicles were simulated over 4 h of the morning peak. All demand and supply parameters were calibrated simultaneously using sensor counts and floating car travel time data. Successful calibration was achieved with the Path-size Logit route choice model, which accounted for overlapping routes. Furthermore, explicit representations of lane groups were required to properly model traffic delays and queues. A modified treatment of acceptance capacity was required to model the large number of short links in the transportation network (close to the length of one vehicle). In addition, even though bicycles and pedestrians were not explicitly modeled, their impacts on auto traffic were captured by dynamic road segment capacities.Beijing Transportation Research Cente

Examining the potential of floating car data for dynamic traffic management

Traditional traffic monitoring systems are mostly based on road side equipment (RSE) measuring traffic conditions throughout the day. With more and more GPS-enabled connected devices, floating car data (FCD) has become an interesting source of traffic information, requiring only a fraction of the RSE infrastructure investment. While FCD is commonly used to derive historic travel times on individual roads and to evaluate other traffic data and algorithms, it could also be used in traffic management systems directly. However, as live systems only capture a small percentage of all traffic, its use in live operating systems needs to be examined. Here, the authors investigate the potential of FCD to be used as input data for live automated traffic management systems. The FCD in this study is collected by a live country-wide FCD system in the Netherlands covering 6-8% of all vehicles. The (anonymised) data is first compared to available road side measurements to show the current quality of FCD. It is then used in a dynamic speed management system and compared to the installed system on the studied highway. Results indicate the FCD set-up can approximate the installed system, showing the feasibility of a live system

New ITS applications for metropolitan areas based on Floating Car Data

The paper describes a couple of FCD based vehicular traffic applications and services. This new method is especially beneficial for regions with a poor traffic monitoring infrastructure because the necessary monetary effort to establish such a system is very small in comparison to conventional systems and it is flexible and easily adaptable to other regions. Particularly, emerging markets like China with a fast-changing road network and a high penetration of lat-est information technologies on one side but with serious foreseeable traffic related problems on the other side can surely profit from this approach. The new data collection and analysing methods result in better performance of the services enhance the scope of the services and hopefully enlarge user acceptance. All of the proposed solutions are prototypes and not all of them have been extensively tested up to now. Certainly, specific data processing methods need further research, some refinements and calibrations. Additionally, some applications still suffer from insufficient data penetration. Nevertheless, the approach is very general and it is very likely that FCD availability will sharply increase in near future and will enhance the quality of services

Feasibility of expanding traffic monitoring systems with floating car data technology

Trajectory information reported by certain vehicles (Floating Car Data or FCD) can be applied to monitor the road network. Policy makers face difficulties when deciding to invest in the expansion of their infrastructure based on inductive loops and cameras, or to invest in a FCD system. This paper targets this decision. The provided FCD functionality is investigated, minimum requirements are determined and reliability issues are researched. The communication cost is derived and combined with other elements to assess the total costs for different scenarios. The outcome is to target a penetration rate of 1%, a sample interval of 10 seconds and a transmission interval of 30 seconds. Such a deployment can accurately determine the locations of incidents and traffic jams. It can also estimate travel times accurately for highways, for urban roads this is limited to a binary categorization into normal or congested traffic. No reliability issues are expected. The most cost efficient scenario when deploying a new FCD system is to launch a smartphone application. For Belgium, this costs 13 million EUR for 10 years. However, it is estimated that purchasing data from companies already acquiring FCD data through their own product could reduce costs with a factor 10

Reconstructing the Traffic State by Fusion of Heterogeneous Data

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on stationary detector data as typically collected by traffic control centres, and may be augmented by floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: 1. compensation for gaps in data caused by detector failure; 2. separation of noise from dynamic traffic information; and 3. the fusion of floating car data with stationary detector data.Comment: For more information see http://www.mtreiber.de or http://www.akesting.d