Managed information gathering and fusion for transient transport problems

This paper deals with vehicular traffic management by communication technologies from Traffic Control Center point of view in road networks. The global goal is to manage the urban traffic by road traffic operations, controlling and interventional possibilities in order to minimize the traffic delays and stops and to improve traffic safety on the roads. This paper focuses on transient transport, when the controlling management is crucial. The aim was to detect the beginning time of the transient traffic on the roads, to gather the most appropriate data and to get reliable information for interventional suggestions. More reliable information can be created by information fusion, several fusion techniques are expounded in this paper. A half-automatic solution with Decision Support System has been developed to help with engineers in suggestions of interventions based on real time traffic data. The information fusion has benefits for Decision Support System: the complementary sensors may fill the gaps of one another, the system is able to detect the changing of the percentage of different vehicle types in traffic. An example of detection and interventional suggestion about transient traffic on transport networks of a little town is presented at the end of the paper. The novelty of this paper is the gathering of information - triggered by the state changing from stationer to transient - from ad hoc channels and combining them with information from developed regular channels. --information gathering,information fusion,Kalman filter,transient traffic,Decision Support System

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Computational Intelligence in Highway Management: A Review

Highway management systems are used to improve safety and driving comfort on highways by using control strategies and providing information and warnings to drivers. They use several strategies starting from speed and lane management, through incident detection and warning systems, ramp metering, weather information up to, for example, informing drivers about alternative roads. This paper provides a review of the existing approaches to highway management systems, particularly speed harmonization and ramp metering. It is focused only on modern and advanced approaches, such as soft computing, multi-agent methods and their interconnection. Its objective is to provide guidance in the wide field of highway management and to point out the most relevant recent activities which demonstrate that development in the field of highway management is still important and that the existing research exhibits potential for further enhancement

WATI: Warning of Traffic Incidents for Fuel Saving

Traffic incidents (heavy traffic, adverse weather conditions, and traffic accidents) cause an increase in the frequency and intensity of the acceleration and deceleration. The result is a very significant increase in fuel consumption. In this paper, we propose a solution to reduce the impact of such events on energy consumption. The solution detects the traffic incidents based on measured telemetry data from vehicles and the different driver profiles. The proposal takes into account the rolling resistance coefficient, the road slope angle, and the vehicles speeds, from vehicles which are on the scene of the traffic incident, in order to estimate the optimal deceleration profile. Adapted advice and feedback are provided to the drivers in order to appropriately and timely release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a dataset of 150 tests using 15 different drivers. The main contribution of this paper is the proposal of a system to detect traffic incidents and provide an optimal deceleration pattern for the driver to follow without requiring sensors on the road. The results show an improvement on the fuel consumption of up to 13.47%

Development of an Integrated Incident and Transit Priority Management Control System

The aim of this thesis is to develop a distributed adaptive control system which can work standalone for a single intersection to handle various boundary conditions of recurrent, non-recurrent congestion, transit signal priority and downstream blockage to improve the overall network in terms of productivity and efficiency. The control system uses link detectors’ data to determine the boundary conditions of all incoming and exit links. Four processes or modules are deployed. The traffic regime state module estimates the congestion status of the link. The incident status module determines the likelihood of an incident on the link. The transit priority module estimates if the link is flagged for transit priority based on the transit vehicle location and type. Finally, the downstream blockage module scans all downstream links and determines their recurrent blockage conditions. Three different urban incident detection models (General Regression Model, Neuro-Fuzzy Model and Binary Logit Model) were developed in order to be adopted for the incident status module. Among these, the Binary Logit Model was selected and integrated with the signal control logic. The developed Binary Logit Model is relatively stable and performs effectively under various traffic conditions, as compared to other algorithms reported in the literature. The developed signal control logic has been interfaced with CORSIM micro-simulation for rigorous evaluations with different types of signal phase settings. The proposed system operates in a manner similar to a typical pre-timed signal (with split or protected phase settings) or a fully actuated signal (with splitphase arrangement, protected phase, or dual ring phase settings). The control decisions of this developed control logic produced significant enhancement to productivity (in terms of Person Trips and Vehicle Trips) compared with the existing signal control systems in medium to heavily congested traffic demand conditions for different types of networks. Also, more efficient outcomes (in terms of Average Trip Time/Person and delay in seconds/vehicle) is achieved for relatively low to heavy traffic demand conditions with this control logic (using Split Pre-timed). The newly developed signal control logic yields greater productivity than the existing signal control systems in a typical congested urban network or closely spaced intersections, where traffic demand could be similarly high on both sides at peak periods. It is promising to see how well this signal control logic performs in a network with a high number of junctions. Such performance was rarely reported in the existing literature. The best performing phase settings of the newly developed signal control were thoroughly investigated. The signal control logic has also been extended with the logic of pre-timed styled signal phase settings for the possibility of enhancing productivity in heavily congested scenarios under a closely spaced urban network. The performance of the developed pre-timed signal control signal is quite impressive. The activation of the incident status module under the signal control logic yields an acceptable performance in most of the experimental cases, yet the control logic itself works better without the incident status module with the Split Pre-timed and Dual Actuated phase settings. The Protected Pre-timed phase setting exhibits benefits by activating the incident status module in some medium congested demand

An incident detection method considering meteorological factor with fuzzy logic

To improve the performance of automatic incident detection algorithm under extreme weather conditions, this paper introduces an innovative method to quantify the relationship between multiple weather parameters and the occurrence of traffic incident as the meteorological influencing factor, and combines the factor with traffic parameters to improve the effect of detection. The new algorithm consists of two modules: meteorological influencing factor module and incident detection module. The meteorological influencing factor module based on fuzzy logic is designed to determine the factor. On the basis of learning vector quantization (LVQ) neural network, the new incident detection module uses the factor and traffic parameters to detect incidents. The algorithm is tested with data collected from a typical freeway in Chongqing, China. Also, the performance of the algorithm is evaluated by the common criteria of detection rate (DR), false alarm rate (FAR) and mean time to detection (MTTD). The experiments conducted on the field data study the influence of different algorithm architectures exerted on the detection performance. In addition, comparative experiments are performed. The experimental results have demonstrated that the proposed algorithm has higher DR, lower FAR than the contrast algorithms, and the proposed algorithm has better potential for the application of freeway automatic incident detection