Haalbaarheid van dynamisch verkeersmanagement gebaseerd op gegevensuitwisseling tussen een verkeerscentrale en voertuigen

In theory data originating from vehicles can be applied to support dynamic traffic management. This concept is called Floating Car Data (FCD), and has been extensively studied for the past decade. The main advantage of an FCD system is that it allows coverage of an extensive area in a short amount of time. This is in contradiction with the approach of extending the classical sensing infrastructure based on inductive loops and cameras. The downside is that the FCD technology is less mature then the classical infrastructure. It is not clear if in reality a FCD system will be able to effectively provide the same services with the same quality as existing systems. The final cost and organizational approach of an FCD roll-out is also uncertain. Hence policy makers face difficulties when deciding rather to invest in the further expansion of the classical infrastructure based on inductive loops and cameras, or to invest in the roll-out of an FCD system. The goal of this report is therefore to provide well-founded insights in the feasibility of using FCD in the context of dynamic traffic management. In contradiction to many existing studies we will adopt a top down approach instead of a bottom up approach. Within this research context we aim to answer the following questions:  What are the requirements for the FCD data? What is the required penetration rate? Which data should be part of the FCD samples? What is the sampling and transmission interval?  Which functionality can be provided using this data? Are they different for the different types of roads (highway, arterial road, urban environment)?  Are there reliability issues? What is the impact of the FCD system on the supporting mobile data network in case of high traffic concentrations?  How much will the roll-out of an FCD system cost?  How can the roll-out of an FCD system be organized best? To define an answer to these questions the report starts with an extensive literature study. From this study estimations can be derived regarding the first two groups of questions. However, they are not accurate enough. To further refine them a specially developed platform is utilized. This platform is based on microscopic traffic simulation. Concerning the last three groups of questions (impact on the mobile data network, cost, organization), no existing studies could be found in literature. To further research these questions several techniques are applied: an adjusted model for the determination of the load on a mobile data network that was developed in previous work, a specially developed cost model and a specially developed organizational model (so-called value networks). Based on the obtained results it can be concluded that it is best to aim for a FCD configuration with a penetration rate of 1% and a sampling rate of 10 seconds. Samples are first stored locally and contain accurate information regarding position and speed of the vehicle, and exact moment of sampling. Every 30 seconds an aggregate of 3 samples is then sent to the FCD server. During connection setup a security optimization is applied: the so-called SSL restart handshake. An FCD system as described above will be able to make accurate speed estimations in a highway environment. In this environment it will also be able to accurately determine the location of an incident and the tail of a traffic jam. On arterial roads and in urban

[1]  Xu Li,et al.  Performance Evaluation of Vehicle-Based Mobile Sensor Networks for Traffic Monitoring , 2009, IEEE Transactions on Vehicular Technology.

[2]  Hillel Bar-Gera,et al.  Evaluation of a Cellular Phone-Based System for Measurements of Traffic Speeds and Travel Times: A Case Study from Israel , 2007 .

[3]  Zhongya Wei,et al.  Spatial and Temporal Analysis of Probe Vehicle-based Sampling for Real-time Traffic Information System , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[4]  Gaetano Valenti,et al.  Traffic Estimation And Prediction Based On Real Time Floating Car Data , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[5]  Jeffery Archer,et al.  Simulation-Based ITS Test Platform for Traffic Management and Control System Research and Development , 2009 .

[6]  P. Demeester,et al.  Impact of introducing road charging on supporting mobile data networks , 2009, 2009 9th International Conference on Intelligent Transport Systems Telecommunications, (ITST).

[7]  Hidekatsu Hamaoka,et al.  Characteristics Based on the Slippery Road Information System by Utilizing the Taxi Probe Data , 2009 .

[8]  Ofer Avni Lessons Learned from Implementation of Cellphone Probe Data Collection Systemes , 2009 .

[9]  Sebastian Naumann,et al.  Floating Car Observer Development Succeeded , 2009 .

[10]  B.S. Kerner,et al.  Traffic state detection with floating car data in road networks , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[11]  Matthias Korner Traffic Conditions Determination Based on Floating Car Data with Short Capturing Intervals , 2009 .

[12]  Xiaoxu Bei,et al.  An Evaluation Method for Floating Car Data (FCD) System , 2009 .

[13]  Hironori Suzuki,et al.  DYNAMIC ESTIMATION OF TRAFFIC STATES ON A FREEWAY USING PROBE VEHICLE DATA , 2003 .

[14]  Johan De Mol,et al.  Verhoogde verkeersveiligheid op autosnelwegen dankzij ITS , 2010 .

[15]  Per-Olof Sjolander SRIS -- Slippery Road Information System , 2009 .

[16]  Zhang Wei,et al.  Sampling and Transmitting Intervals Optimization Based on GPS Equipped Floating Car , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[17]  Der-Horng Lee,et al.  Probe Vehicle Population and Sample Size for Arterial Speed Estimation , 2002 .

[18]  Tingting Zhao,et al.  Evaluating the Performance of Link Travel Time Estimation Based on Floating Car Data , 2010, 2010 International Conference on Optoelectronics and Image Processing.

[19]  S. Lorkowski,et al.  Travel Time Measurements using GSM and GPS Probe Data , 2009 .

[20]  Haris N. Koutsopoulos,et al.  Requirements and potential of GPS-based floating car data for traffic management: Stockholm case study , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[21]  W Huber,et al.  EXTENDED FLOATING-CAR DATA FOR THE ACQUISITION OF TRAFFIC INFORMATION , 1999 .

[22]  Naphtali Rishe,et al.  Communication Reduction for Floating Car Data-Based Traffic Information Systems , 2010, 2010 Second International Conference on Advanced Geographic Information Systems, Applications, and Services.

[23]  Géza Gordos,et al.  Benchmarking of Floating Car Data Sources , 2009 .

[24]  Timo Smura,et al.  Value Network Configurations in wireless local area access , 2010, 2010 9th Conference of Telecommunication, Media and Internet.

[25]  Longhui Gang,et al.  Impact of Probe Vehicles Sample Size on Link Travel Time Estimation , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[26]  David Fernández Llorca,et al.  Traffic Data Collection for Floating Car Data Enhancement in V2I Networks , 2010, EURASIP J. Adv. Signal Process..

[27]  Young Cho,et al.  Estimating Velocity Fields on a Freeway From Low-Resolution Videos , 2006, IEEE Transactions on Intelligent Transportation Systems.

[28]  Ali Haghani,et al.  Using Bluetooth Technology for Validating Vehicle Probe Data , 2009 .

[29]  M Neuherz,et al.  TRAFFIC INFORMATION POTENTIAL AND NECESSARY PENETRATION RATES , 2004 .