VEhicle COntrol and Navigation) An After Seismic Disaster Application VECON (VEhicle COntrol and Navigation) An After Seismic Disaster Application Laboratory of Geodesy Aristotle University of Thessaloniki, Department of Civil Engineering Prof. P. Savvaidis - Ass. Prof. I. Ifadis - Dr. K. Lakakis

* Continuous Updating about which vehicles are available and what are their positions. * Knowledge about the Street Network of service area. * Knowledge about the Travel Cost of street segments. * Optimum combination of the previous three demands to achieve the optimum choice of the Responding Vehicle. * Ability of Continuous Communication among Operation Center and vehicles. * Ability of Alternative Traffic Paths when some streets are closed - Real Time Procedure. Emergency Vehicle Fleets “major demands”

* Continuous information of available vehicles and their positions. Ability to scout changes. * Knowledge about the Street Network of service area and Ability to scout changes. Ability of a very quick adaptation to new conditions of Traffic Cost of street segments. * Ability of a very quick adaptation to new conditions of Traffic Cost of street segments. * Optimum combination of the three previous demands to achieve the optimum choice of the Responding Vehicle. * Ability of Continuous Communication among Operation Center and vehicles. Ability of Alternative Traffic Paths when some streets are closed - Real Time Procedure. * Ability of Alternative Traffic Paths when some streets are closed - Real Time Procedure. In the Case of a Seismic Disaster Some Some major demands become more emergent

In this paper we work on the previous “red” In this paper we work on the previous “red” major demands éHéHéHéHow can we scout the traffic changes as soon as possible ? éWéWéWéWhat kind of traffic data we ‘ll use ? Use of : Regression and time series models. Regression and time series models. DGPS positioning and travel times. DGPS positioning and travel times. The main questions are: The answers are:

Vecon Vecon is a : traffic scouting & predicting system, real-time, which provides precise urban navigation

Vecon Basic Elements (Real Time) Kinematic Differential G.P.S G.I.S - Graph Theory Position and Traffic Data(Travel Times) Regression and Time Series Models

Continuous GPS Reference Station Controlled Moving Units Telecommunication System (UHF) GIS Unit CONTROL STATION VEHICLES GPS antenna GPS receiver Prime Unit of GPS Differential Corrections GPS antenna GPS receiver RTCM ready Antennas Transceivers Modems Spatial and Time Digital Maps (x,y,t) Regression, Exponential smoothing, Autoregression, Adaptive Filters, etc. Graph Algorithms. Shortest path, Best delivery route, etc. User Interface USER USER (ΝΜΕΑ format) (RTCM format) Vecon Framework

Vecon Basic Flowchart Kinematic DGPS observations in everyday Traffic Conditions Spatial and Time data (x, y, t) Regression and Time Series Models Spatial and Time Digital Maps of street Network Dynamic Time Models for Travel Costs Efficient Land Navigation

Linear Regression Υ = α + βX + ε Vecon Vecon spatial analysis model Map matching prediction zone reliability zone

Experimental Documentation of Spatial Digital Map Development Method  The regression models errors approached very well the normal distribution, when we had statistical sample (n>28).  The regression models Υ to Χ and Χ to Υ were same in practical level.  On 90% of the occasions the predictive accuracy ( prediction zone) of regression models was about 1.5m. On the others 10% we had an accuracy about 5.5m.  A one pass linear regression model was more reliable and accurate, than a cumulative (use the sum of passes) one.  We suggest to choose the first one-pass model, that has the better predictive accuracy (smaller prediction zone), than the predictive accuracy of the cumulative model, at the same time.

Simple Exponential Smoothing Υ t+1 = α t X t + (1-α t ) Υ t Vecon Vecon time series models Autoregressive Model Y t = φ 1 Υ i-1 + φ 2 Υ i-2 + ………. + φ ρ Y i-ρ + e t

Experimental Documentation of Time Digital Map Development Method  The high variances, which observed to street segment (arc) travel- times, decreased substantially when we examined the variances of entire route travel-times.  After the examination of different values of factor a, the simple (one factor) expontetial model proved not suitable.  The autoregressive model gave us encouraging results. Although we had high standard deviations, the rate of adjusted R square, the Durbin Watson values and the examination of autocorrelation charts suggest the use of the autoregressive model, with the note to use samples as big as possible.  When we have small (but statistically enough) samples, the time series model that we suggest is moving average model, not because of its better performances, but because of its simplicity.

CONCLUSIONS VECON is an autonomous VEhicle COntrol and Navigation system, which can create its necessary Spatial and Time data.VECON is an autonomous VEhicle COntrol and Navigation system, which can create its necessary Spatial and Time data. VECON is a Dynamic System, which updates and optimizes its capabilities, every time we will decide to.VECON is a Dynamic System, which updates and optimizes its capabilities, every time we will decide to. VECON system becomes much more useful, after a major urban disaster - like a Seismic Disaster - because of its Real- Time Mobile Mapping and Timing capacities.VECON system becomes much more useful, after a major urban disaster - like a Seismic Disaster - because of its Real- Time Mobile Mapping and Timing capacities. VECON system uses only three types of data (x,y,t), one observation resource (RTK-DGPS) and simple on-line processes. So we can say that it is as «real-time» as possible.VECON system uses only three types of data (x,y,t), one observation resource (RTK-DGPS) and simple on-line processes. So we can say that it is as «real-time» as possible. VECON’ s expanding (to more vehicles) is an easy and not expensive procedure.VECON’ s expanding (to more vehicles) is an easy and not expensive procedure.